simuCamtrap: Simulate animal movement within the range of the camera trap...

In Xinhai-Li/Rcameratrap: Estimating wildlife density within the camera trap grid

Simulate animal movement within the range of the camera trap grid and obtain the pseudo camera trap result

Description

Correlated random walk of the target animal is simulated within the range of camera trap grid, using the distributions of step length, turning angles, and size of home range from footprint chain data. The simulated movement of the default 1-10 individuals generate pseudo camera trap data, which are matched with the real data using the random forest algorithm, in order to find the best fit of animal abundance among the abundance from 1 to 10 taken by each camera for one species. Such simulation can be repeated for several times defined by number of iteration.

Usage

simuCamtrap(
  x,
  detect = 50,
  bearing = runif(camera.N, 0, 2 * pi),
  step.N = 5000,
  step.V = 2,
  step.L = 10,
  bias = 30/360 * 2 * pi,
  range = 4000,
  ind = 10,
  iteration = 3
)

Arguments

x	A data.frame with column names "Lon", "Lat", "Group_size", "Date", "Time"
detect	The detection radias (m) of a camera.
bearing	The bearing direction of a camera.
step.N	The number of steps the animal walks during the camera trapping.
step.V	The standard diviation of the step length
step.L	The mean step length (m) of the animal.
bias	The standard diviation of the changing angle (degree) between two steps
range	Maximum distance (m) the animal moves from the original site.
ind	The number of individuals that are simulated.
iteration	The number of simulations.

Value

A dataframe with the first column to be the number of individuals, and the rest columns are number of pictures (simulated) for each camera

Author(s)

Xinhai Li ([e-mail])

Examples

par(mfrow = c(1, 2))
# maximum number of individuals in the camera grid is 10 (ind=10)
sim.out = simuCamtrap(trapresult, ind = 10, iteration = 2) # more iterations are expected for higher prediction accuracy

Xinhai-Li/Rcameratrap documentation built on March 22, 2022, 9:57 a.m.