issj.sim {AHMbook}R Documentation

Simulate open distance sampling data for the Island Scrub Jays

Description

Function to simulate open distance sampling data for the Island Scrub Jays, based on Sollmann et al (2015).

To recreate the data sets used in the book with R 3.6.0 or later, include sample.kind="Rounding" in the call to set.seed. This should only be used for reproduction of old results.

Usage

issj.sim(B, db, lam, sigma, phi, gamma, npoints, nyrs, nbsize = -1.02)

Arguments

B

Radius of the circle sampled; a site is a circle of radius B around a point.

db

Distance categories; a vector of cut points from 0 to B inclusive.

lam

Expected abundance per site, a vector of length nsites or an nsites x 1 matrix.

sigma

Scale parameter of the half-normal detection function at each site, a vector of length nsites or an nsites x 1 matrix.

phi

Survival probability

gamma

Recruitment rate

npoints

Number of sites where point counts are conducted.

nyrs

Number of years

nbsize

Size parameter for the negative binomial distribution used to generate individual counts per site for year 1.

Value

A list with the following elements:

NcList

A list with one element per year, with distances of all animals from the point.

detList

A list with one element per year, a npoints x nbands matrix with the number of animals detected in each distance category for each point.

N

The (true) number of animals at each point for each year, a nsites x nyears matrix.

cell

The site IDs where point counts are conducted.

y

npoints x nyears matrix of total detections

dclass

a vector with the distance class for each animal detected

site

a corresponding vector with the site for each animal detected

nsite

the number of sites in the study area

lam, phi, gamma, sigma

the values of the corresponding arguments

Author(s)

Marc Kéry & Andy Royle, based on Sollmann et al (2015)

References

Sollmann, R., Gardner, B., Chandler, R.B., Royle, J.A., Sillett, T.S. (2015) An open population hierarchical distance sampling model. Ecology 96, 325-331.

Kéry, M. & Royle, J.A. (2016) Applied Hierarchical Modeling in Ecology AHM1 - 9.7.1.

Examples

# A toy example with just 20 sites
set.seed(2015)
tmp <- issj.sim(B = 300,
    db = c(0,50, 100, 150, 200, 250, 300),
    lam = c(3.01, 7.42, 20.51, 1.60, 0.42, 3.42, 8.24, 0.66, 0.32, 0.39, 0.46, 0.52,
      0.63, 0.36, 4.93, 0.47, 2.07, 0.42, 0.48, 0.47),
    sigma = c(110, 91, 70, 114, 135, 101, 88, 130, 133, 134, 134, 135, 131, 135, 100,
      135, 110, 135, 134, 135),
    phi = 0.6, gamma = 0.35,
    npoints = 15, nyrs = 4)
str(tmp)
# Compare the number detected with the true numbers present
with(tmp, cbind(y, N[cell, ]))

[Package AHMbook version 0.2.3 Index]