plot.adaptInv: Create Contour Plot of Pairwise Invasibility Analysis Results

In adapt3: Adaptive Dynamics and Community Matrix Model Projections

Create Contour Plot of Pairwise Invasibility Analysis Results

Description

Function plot.adaptInv plots pairwise invasibility contour plots, diagnostic population plots, and elasticity plot. This function is based on code derived from Roff's Modeling Evolution: An Introduction to Numerical Methods (2010, Oxford University Press).

Usage

## S3 method for class 'adaptInv'
plot(
  x,
  res_variant = 1,
  inv_variant = 2,
  repl = 1,
  pip = TRUE,
  elast = FALSE,
  run = 1,
  filled = TRUE,
  plot.title,
  plot.axes,
  axes = TRUE,
  frame.plot = TRUE,
  auto_ylim = TRUE,
  auto_col = TRUE,
  auto_lty = TRUE,
  auto_title = FALSE,
  auto_axis = FALSE,
  auto_axis_ticks = 5,
  ...
)

Arguments

x	An adaptInv object, created with function invade3().
res_variant	The number of the variant representing the resident subpopulation.
inv_variant	The number of the variant representing the mutant subpopulation.
repl	The replicate number to plot, in the fitness data frame within the adaptInv object entered in argument x.
pip	A logical value indicating whether to produce a pairwise invasibility plot. If FALSE, then will produce a diagnostic population size plot. Defaults to TRUE.
elast	A logical value indicating whether to produce an elasticity plot. Such plots can only be produced when trait optimization is performed during invasibility analysis. Defaults to FALSE.
run	An integer giving the run to plot if pip = FALSE.
filled	A logical value indicating whether to produce a filled contour plot, or a standard contour plot. Defaults to TRUE, but reverts if invader fitness is consistently positive, or consistently negative, relative to the resident.
plot.title	A title for the plot.
plot.axes	A generic parameter providing axis information for pairwise invasibility plots.
axes	A logical value indicating whether to include axis lines. Defaults to TRUE.
frame.plot	A logical value indicating whether to frame the plot.
auto_ylim	A logical value indicating whether the maximum of the y axis should be determined automatically. Defaults to TRUE, but reverts to FALSE if any setting for ylim is given. Used only if pip = FALSE.
auto_col	A logical value indicating whether to shift the color of lines associated with each patch automatically. Defaults to TRUE, but reverts to FALSE if any setting for col is given. Used only if pip = FALSE.
auto_lty	A logical value indicating whether to shift the line type associated with each replicate automatically. Defaults to TRUE, but reverts to FALSE if any setting for lty is given. Used only if pip = FALSE.
auto_title	A logical value indicating whether to add a title to each plot. The plot is composed of the concatenated population and patch names. Defaults to FALSE. Used only if pip = FALSE.
auto_axis	A logical value indicating if the axis labels should be set as the first variable variable in the input trait axis in the given adaptInv object for PIPs. Defaults to FALSE.
auto_axis_ticks	A single integer value giving the number of ticks to include on both axes of a PIP or on the x axis of an elasticity plot, if auto_axis = TRUE. Defaults to 5, or to the number of variants in the used trait axis, whichever is smaller.
...	Other parameters used by functions plot.default().

Value

A contour plot showing the overall fitness dynamics of the invader variant, assuming a pairwise invasibility analysis.

Notes

By default, function plot.adaptInv produces a filled contour plot in which grey regions show where the invader has positive fitness relative to the resident, and white regions show where the invader has negative fitness relative to the resident. Fitness here refers to the Lyapunov coefficient of the invader relative to the resident, calculated over the final fitness_times in the original call to function invade3().

Examples

library(lefko3)
data(cypdata)

sizevector <- c(0, 0, 0, 0, 1, 2.5, 4.5, 8, 17.5)
stagevector <- c("SD", "P1", "SL", "D", "XSm", "Sm", "Md", "Lg", "XLg")
repvector <- c(0, 0, 0, 0, 1, 1, 1, 1, 1)
obsvector <- c(0, 0, 0, 0, 1, 1, 1, 1, 1)
matvector <- c(0, 0, 0, 1, 1, 1, 1, 1, 1)
immvector <- c(0, 1, 1, 0, 0, 0, 0, 0, 0)
propvector <- c(1, 0, 0, 0, 0, 0, 0, 0, 0)
indataset <- c(0, 0, 0, 1, 1, 1, 1, 1, 1)
binvec <- c(0, 0, 0, 0.5, 0.5, 1, 1, 2.5, 7)

cypframe_raw <- sf_create(sizes = sizevector, stagenames = stagevector,
  repstatus = repvector, obsstatus = obsvector, matstatus = matvector,
  propstatus = propvector, immstatus = immvector, indataset = indataset,
  binhalfwidth = binvec)

cypraw_v1 <- verticalize3(data = cypdata, noyears = 6, firstyear = 2004,
  patchidcol = "patch", individcol = "plantid", blocksize = 4,
  sizeacol = "Inf2.04", sizebcol = "Inf.04", sizeccol = "Veg.04",
  repstracol = "Inf.04", repstrbcol = "Inf2.04", fecacol = "Pod.04",
  stageassign = cypframe_raw, stagesize = "sizeadded", NAas0 = TRUE,
  NRasRep = TRUE)

cypsupp2r <- supplemental(stage3 = c("SD", "P1", "SL", "D", 
    "XSm", "Sm", "SD", "P1"),
  stage2 = c("SD", "SD", "P1", "SL", "SL", "SL", "rep",
    "rep"),
  eststage3 = c(NA, NA, NA, "D", "XSm", "Sm", NA, NA),
  eststage2 = c(NA, NA, NA, "XSm", "XSm", "XSm", NA, NA),
  givenrate = c(0.10, 0.40, 0.25, NA, NA, NA, NA, NA),
  multiplier = c(NA, NA, NA, NA, NA, NA, 1000, 1000),
  type =c(1, 1, 1, 1, 1, 1, 3, 3),
  stageframe = cypframe_raw, historical = FALSE)

cypmatrix2r <- rlefko2(data = cypraw_v1, stageframe = cypframe_raw, 
  year = "all", patch = "all", stages = c("stage3", "stage2", "stage1"),
  size = c("size3added", "size2added"), supplement = cypsupp2r,
  yearcol = "year2", patchcol = "patchid", indivcol = "individ")
cypmean <- lmean(cypmatrix2r)

cyp_start <- start_input(cypmean, stage2 = c("SD", "P1", "D"),
  value = c(1000, 200, 4))

c2d_4 <- density_input(cypmean, stage3 = c("P1", "P1"), stage2= c("SD", "rep"),
  style = 2, time_delay = 1, alpha = 0.005, beta = 0.000005, type = c(2, 2))

# A simple projection allows us to find a combination of density dependence
# and running time that produces a stable quasi-equilibrium
cyp_proj <- projection3(cypmean, times = 250, start_frame = cyp_start,
  density = c2d_4, integeronly = TRUE)
plot(cyp_proj)

cyp_ta <- trait_axis(stageframe = cypframe_raw,
  stage3 = rep("P1", 15),
  stage2 = rep("rep", 15),
  multiplier = seq(from = 0.1, to = 10.0, length.out = 15),
  type = rep(2, 15))

cyp_inv <- invade3(axis = cyp_ta, mpm = cypmean, density = c2d_4, times = 350,
  starts = cyp_start, entry_time = c(0, 250), fitness_times = 30,
  var_per_run = 2)
plot(cyp_inv)

adapt3 documentation built on June 7, 2026, 9:08 a.m.