simNpC {AHMbook}

Generate counts from a single population observed over T years under a binomial observation process

Description

Generates counts from a single population observed over T years and which can be observed with or without imperfect detection. The goal is to focus on what happens with relative-abundance inference when temporal patterns in abundance are confounded with temporal patterns in detection probability. Hence, we can simulate a stable population or one with linear increase or decrease with specified start and end points, and around which there is Poisson noise. The observed counts are Binomial outcomes with a detection probability which can similarly be chosen to be constant or change linearly over time.

Usage

simNpC(T = 20, expN = c(100, 75), dp = c(0.5, 0.5), show.plot = TRUE)

Arguments

T

The length of the time series.

expN

The expected abundance at start and end of period, linear trend.

dp

The detection probability at start and end of period, linear trend.

show.plot

Choose whether to show plots or not. Set to FALSE when using function in simulations.

Value

A list with the values of the arguments entered and the following additional elements:

lambda

T vector, expected abundance for each year.

p

T vector, detection probability (dp) for each year.

N

T vector, realized abundance.

C

T vector, observed counts.

Author(s)

Marc Kéry & Andy Royle

References

Kéry, M. & Royle, J.A. (2021) Applied Hierarchical Modeling in Ecology AHM2 - 1.2.

Examples

# Run with the default arguments and look at the structure of the output:
set.seed(123)
tmp <- simNpC()
str(tmp)
tmp$C