fit.twocamera: Estimation of animal density from two-camera surveys.

In b-steve/palm: Fitting Point Process Models via the Palm Likelihood

Estimation of animal density from two-camera surveys.

Description

Estimates animal density (amongst other parameters) from two-camera aerial surveys. This conceptualises sighting locations as a Neyman-Scott point pattern.

Usage

fit.twocamera(
  points,
  cameras = NULL,
  d,
  w,
  b,
  l,
  tau,
  R,
  edge.correction = "pbc",
  start = NULL,
  bounds = NULL,
  trace = FALSE
)

Arguments

points	A vector (or single-column matrix) containing the distance along the transect that each detection was made.
cameras	An optional vector containing the camera ID (either 1 or 2) that made the corresponding detection in points.
d	The length of the transect flown (in km).
w	The distance from the transect to which detection of individuals on the surface is certain. This is equivalent to the half-width of the detection zone.
b	The distance from the transect to the edge of the area of interest. Conceptually, the distance between the transect and the furthest distance a whale could be on the passing of the first camera and plausibly move into the detection zone by the passing of the second camera.
l	The lag between cameras (in seconds).
tau	Mean dive-cycle duration (in seconds).
R	Truncation distance (see fit.ns).
edge.correction	The method used for the correction of edge effects. Either "pbc" for periodic boundary conditions, or "buffer" for a buffer-zone correction.
start	A named vector of starting values for the model parameters.
bounds	A list with named components. Each component should be a vector of length two, giving the upper and lower bounds for the named parameter.
trace	Logical; if TRUE, parameter values are printed to the screen for each iteration of the optimisation procedure.

Details

This function is simply a wrapper for fit.ns, and facilitates the fitting of the model proposed by Stevenson, Borchers, and Fewster (2019). This function presents the parameter D.2D (two-dimensional cetacean density in cetaceans per square km) rather than D for enhanced interpretability.

For further details on the cluster capture-recapture estimation approach, see Fewster, Stevenson and Borchers (2016).

Value

An R6 reference class object.

References

Fewster, R. M., Stevenson, B. C., and Borchers, D. L. (2016) Trace-contrast methods for capture-recapture without capture histories. Statistical Science, 31: 245–258.

Stevenson, B. C., Borchers, D. L., and Fewster, R. M. (2019) Cluster capture-recapture to account for identification uncertainty on aerial surveys of animal populations. Biometrics, 75: 326–336.

See Also

Use coef.palm to extract estimated parameters, and plot.palm to plot the estimated Palm intensity function. Use boot.palm to run a parametric bootstrap, allowing calculation of standard errors and confidence intervals.

See sim.twocamera to simulate sightings from a two-camera aerial survey.

Examples

## Fitting model.
fit <- fit.twocamera(points = example.twocamera$points, cameras = example.twocamera$cameras,
                     d = 500, w = 0.175, b = 0.5, l = 20, tau = 110, R = 1)
## Printing estimates.
coef(fit)
## Plotting the estimated Palm intensity.
plot(fit)

b-steve/palm documentation built on Sept. 22, 2023, 9:27 a.m.