plot.density.sample.3d: 3D visualization of population density surface and animal...

In dill/wisp: Wildlife Simulation Package (wisp)

Description

Function that makes use of the RGL library to create an interactive 3D representation of the density surface created by Wisp (dictating the dispersion of animals within the study area) as well as the distribution of animals across the study area (this is best visualized by rotating the surface so it is viewed from directly overhead).

Usage

plot.density.sample.3d(density, sampled = NULL, scale.fact = 0.5)

Arguments

density	density object (created by generate.density,
sampled	sample object resulting from capture-recapture, line transect, or point transect sampling. If this argument is omitted, then only the population density surface is produced.
scale.fact	A scaling factor to adjust the size of the spheres representing individual animals.

Details

The RGL window can be maximized, moved, or otherwise resized to make visibility easier. Side-effect of calling this function is that an RGL window is opened and not closed. So if this function is called repeatedly, there can be a proliferation of RGL windows on the user's computer.

Value

A 3D object created in an RGL window able to be rotated and viewed interactively.

Note

If a sample object is provided along with a density object, then the individual animals placed onto the density surface are both uniquely sized, and differentially coloured. The size of an individual's sphere is proportional to the animal's exposure (the larger the sphere, the easier the animal is to catch/detect).

The colour scheme is slightly different dependent on whether the sampling was from capture-recapture or distance sampling. With distance sampling designs, animal spheres are coloured as follows:

blue

animal is outside the covered region, hence undetectable

red

animal is inside covered region, and detected

black

animal is inside covered region, and not detected

With mark-recapture designs, the sphere colours represent:

red

animal was captured on more than 75% of capture occasions

green

animal was captured on fewer than 75% but more than 50% of capture occasions

blue

animal was captured on fewer than 50% of capture occasions

black

animal was never captured on any capture occasion

Author(s)

Eric Rexstad, RUWPA [e-mail]

References

Borchers, Buckland, and Zucchini (2002), Estimating animal abundance: closed populations. Chapter 5 http://www.ruwpa.st-and.ac.uk/estimating.abundance

See Also

plot.density.population, add.hotspot, and set.stripe

Examples

myreg <- generate.region(x.length =100, y.width = 50)
mydens <- generate.density(myreg,nint.x = 100, nint.y = 50, southwest = 1, southeast = 10, northwest = 20)
mydens<-add.hotspot(mydens, 20,10, 25,20)
mydens <- add.hotspot(mydens, 40,40,50,5)
mydens <- add.hotspot(mydens, 70,10,30,15)
mydens <- set.stripe(mydens, 40,0,70,50,value=0, width=10)
mydens <- set.stripe(mydens, 60,25,100,40,value=0, width=7)
plot.density.sample.3d(mydens)
#  Generate sample created by cr sampling
cr.poppars<-setpars.population(density.pop = mydens, number.groups = 100, size.method = "poisson",
                              size.min = 1, size.max = 5, size.mean = 1, exposure.method = "beta",
                              exposure.min = 2, exposure.max = 10, exposure.mean = 3,
                              exposure.shape = 0.5)
cr.pop<-generate.population(cr.poppars)

cr.des<-generate.design.cr(myreg, n.occ = 6)
cr.survpars<-setpars.survey.cr(cr.pop, cr.des, pmin.unmarked=0.00001, pmax.unmarked=0.5, improvement=0.01)

cr.samp<-generate.sample.cr(cr.survpars)
summary(cr.samp)
plot.density.sample.3d(mydens,cr.samp)
#  Generate and view line transect sample from same underlying density
lt.poppars<-setpars.population(density.pop = mydens, number.groups = 200,
                              size.method = "poisson", size.min = 1, size.max = 30,
                              size.mean = 10, exposure.method = "beta", exposure.min = 0,
                              exposure.max = 1, exposure.mean = 0.4, exposure.shape = 0.5)
lt.pop<-generate.population(lt.poppars)
lt.despars<-setpars.design.lt(myreg, n.transects=10, n.units=10, visual.range=3, percent.on.effort=1)
lt.des<-generate.design.lt(lt.despars, seed=3)
lt.survpars<-setpars.survey.lt(lt.pop, lt.des, disthalf.min=1.5, disthalf.max=3)
lt.samp<-generate.sample.lt(lt.survpars)
summary(lt.samp)
plot.density.sample.3d(mydens,lt.samp, scale.fact=1.5)
#  Generate and view point transect sample from same underlying density
pt.poppars<-setpars.population(density.pop = mydens, number.groups = 200, size.method = "poisson",
                               size.min = 1, size.max = 30, size.mean = 10, exposure.method = "beta",
                               exposure.min = 0, exposure.max = 1, exposure.mean = 0.4,
                               exposure.shape = 0.5)
pt.pop<-generate.population(pt.poppars)

pt.despars<-setpars.design.pt(myreg, n.transects=8, n.units=32, visual.range=3.5)
pt.des<-generate.design.pt(pt.despars)

pt.survpars<-setpars.survey.pt(pt.pop, pt.des, disthalf.min=2, disthalf.max=4)
pt.samp<-generate.sample.pt(pt.survpars)
summary(pt.samp)
plot.density.sample.3d(mydens,pt.samp, scale.fact=1.5)

dill/wisp documentation built on May 15, 2019, 8:31 a.m.