plot.capthist: Plot Detection Histories

Description

Display a plot of detection (capture) histories or telemetry data over a map of the detectors.

Usage

# S3 method for capthist
plot(x, rad = 5, hidetraps = FALSE, tracks = FALSE,
    title = TRUE, subtitle = TRUE, add = FALSE, varycol = TRUE, 
    icolours = NULL, randcol = FALSE, lab1cap = FALSE, laboffset = 4, ncap = FALSE, 
    splitocc = NULL, col2 = "green", type = c("petal", "n.per.detector", "n.per.cluster",
        "sightings", "centres", "telemetry", "nontarget"),
    cappar = list(cex = 1.3, pch = 16, col = "blue"), 
    trkpar = list(col = "blue", lwd = 1), 
    labpar = list(cex = 0.7, col = "black"), ...)
plotMCP(x, add = FALSE, col = "black", fill = NA, lab1cap = FALSE, laboffset = 4,
    ncap = FALSE, ...)

Value

For type = "petal", the number of detections in x. For type = "sightings", the number of sightings of unmarked animals in x. For type = "n.per.detector" or type = "n.per.cluster", a dataframe with data for a legend (see Examples).

plotMCP invisibly returns a list in which each component is a 2-column (x,y) dataframe of boundary coordinates for one individual.

Arguments

x: an object of class capthist
rad: radial displacement of dot indicating each capture event from the detector location (used to separate overlapping points)
hidetraps: logical indicating whether trap locations should be displayed
tracks: logical indicating whether consecutive locations of individual animals should be joined by a line
title: logical or character string for title
subtitle: logical or character string for subtitle
add: logical for whether to add to existing plot
varycol: logical for whether to distinguish individuals by colour
icolours: vector of individual colours (when varycol = TRUE), or colour scale (non-petal plots)
randcol: logical to use random colours (varycol = TRUE)
lab1cap: logical for whether to label the first capture of each animal
laboffset: distance by which to offset labels from points
ncap: logical to display the number of detections per trap per occasion
splitocc: optional occasion from which second colour is to be used
col2: second colour (used with splitocc)
type: character string ("petal", "n.per.detector" or "n.per.cluster")
cappar: list of named graphical parameters for detections (passed to par)
trkpar: list of named graphical parameters for tracks (passed to par)
labpar: list of named graphical parameters for labels (passed to par)
...: arguments to be passed to plot.traps
col: vector of line colour numbers or names (plotMCP only)
fill: vector of fill colour numbers or names (plotMCP only)

Details

By default, a `petal' plot is generated in the style of Density (Efford 2012) using eqscplot from the MASS library.

If type = "n.per.detector" or type = "n.per.cluster" the result is a colour-coded plot of the number of individuals at each unit, pooled over occasions.

If type = "sightings" the sightings of unmarked animals are displayed on a petal-like plot (requires mark-resight data) (see also sightingPlot).

If type = "centres" then a single point is plotted for each animal, jittered on each axis by a random amount (limits +/- rad/2).

If type = "telemetry" and the `telemetryxy' attribute is not NULL then the telemetry locations are plotted.

If type = "nontarget" and the `nontarget' attribute is not NULL then the nontarget captures or interference events are plotted.

If title = FALSE no title is displayed; if title = TRUE, the session identifer is used for the title.

If subtitle = FALSE no subtitle is displayed; if subtitle = TRUE, the subtitle gives the numbers of occasions, detections and individuals.

If x is a multi-session capthist object then a separate plot is produced for each session. Use par(mfrow = c(nr, nc)) to allow a grid of plots to be displayed simultaneously (nr rows x nc columns).

These arguments are used only for petal plots: rad, tracks, varycol, randcol, lab1cap, laboffset, ncap, splitocc, col2, trkpar, and labpar. Call occasionKey to add a key to the petals.

If icolours = NULL and varycol = TRUE then a vector of colours is generated automatically as topo.colors((nrow(x)+1) * 1.5). If there are too few values in icolours for the number of individuals then colours will be re-used.

plotMCP plots minimum convex polygons of individual location data over a base plot of detector locations. Usually the data are telemetry locations in the xylist attribute of the capthist object; if this is not present and x is a polygon search capthist then the individual xy data are plotted.

References

Efford, M. G. (2012) DENSITY 5.0: software for spatially explicit capture--recapture. Department of Mathematics and Statistics, University of Otago, Dunedin, New Zealand. https://www.otago.ac.nz/density/.

Examples

Run this code

demotrap <- make.grid()
tempcapt <- sim.capthist(demotrap, 
    popn = list(D = 5, buffer = 50), 
    detectpar = list(g0 = 0.15, sigma = 30))
plot(tempcapt, border = 10, rad = 3, tracks = TRUE, 
    lab1cap = TRUE, laboffset = 2.5)

## type = n.per.cluster

## generate some captures
testregion <- data.frame(x = c(0,2000,2000,0),
    y = c(0,0,2000,2000))
popn <- sim.popn (D = 10, core = testregion, buffer = 0,
    model2D = "hills", details = list(hills = c(-2,3)))
t1 <- make.grid(nx = 1, ny = 1)
t1.100 <- make.systematic (cluster = t1, spacing = 100,
    region = testregion)
capt <- sim.capthist(t1.100, popn = popn, noccasions = 1)

## now plot captures ...
temp <- plot(capt, title = "Individuals per cluster",
    type = "n.per.cluster", hidetraps = FALSE,
    gridlines = FALSE, cappar = list(cex = 1.5))

if (interactive()) {
    ## add legend; click on map to place top left corner
    legend (locator(1), pch = 21, pt.bg = temp$colour,
        pt.cex = 1.3, legend = temp$legend, cex = 0.8)
}

if (FALSE) {

## try varying individual colours - requires RColorBrewer
library(RColorBrewer)
plot(infraCH[[2]], icolours = brewer.pal(12, "Set3"), tracks = TRUE,
    bg = "black", cappar = list(cex = 2), border = 10, rad = 2,
    gridlines = FALSE)

## generate telemetry data
te <- make.telemetry()
tr <- make.grid(detector = "proximity")
totalpop <- sim.popn(tr, D = 20, buffer = 100)
tepop <- subset(totalpop, runif(nrow(totalpop)) < 0.05)
teCH <- sim.capthist(te, popn = tepop, renumber=FALSE, detectfn = "HHN",
    detectpar = list(lambda0 = 3, sigma = 25))
plot(teCH, type = 'telemetry', tracks = TRUE)

## simple "centres" example
## polygon data require 'hazard' detection function 14:19
CH <- sim.capthist(make.poly(), nocc = 20, detectfn = 'HHN', 
    detectpar = list(lambda0 = 1, sigma = 10))
plot(CH, cappar = list(col = 'orange'), varycol = FALSE, border = 10)
plot(CH, type = 'centres', add = TRUE, rad = 0)
}

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