simPH: Generate counts under a variant of a 'phenomenological model'

In AHMbook: Functions and Data for the Book 'Applied Hierarchical Modeling in Ecology' Vols 1 and 2

Generate counts under a variant of a 'phenomenological model'

Description

Function generates (insect) counts under a variant of a 'phenomenological model' of Dennis et al. (2016). Interannual population model is exponential population growth, with Poisson initial abundance governed by initial.lambda and annually varying growth rate (or productivity parameter) gamma. Within-year dynamics is described by a Gaussian curve with date of mean flight period mu (site- and year-specific) and length of flight period sigma (only year-specific). Counts are made subject to a detection probability (p), which varies randomly according to a uniform distribution for every single count. Counts are plotted for up to 16 populations only (but can be simulated for any number).

Usage

simPH(npop = 18, nyears = 17, nreps = 10, date.range = 1:150, initial.lambda = 300,
  gamma.parms = c(0, 0.3), mu.range = c(50, 80),  sigma.range = c(10, 20),
  p.range = c(0.4, 0.6), show.plot = TRUE)

Arguments

npop	The number of populations.
nyears	The number of years (seasons).
nreps	The number of surveys per year.
date.range	The dates over which surveys may be conducted.
initial.lambda	The Poisson mean of initial relative population size.
gamma.parms	The mean and SD of lognormal interannual productivity.
mu.range	The range of date of peak flight period (varies by site and year).
sigma.range	The range of sigma of Gaussian phenology curve (varies by year only).
p.range	The range of detection probabilities (varies by site, year and visit).
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:

gamma	(nyears-1) vector, change in abundance for each interval between years.
n	site x year matrix, true relative abundance.
mu	site x year matrix, mean date of the flight period.
sigma	nyears vector, half-length of flight period.
date	site x year x reps array, dates of the surveys.
a	site x year x reps array, phenology term.
lambda	site x year x reps array, expected counts.
p	site x year x reps array, probability of detection.
C	site x year x reps array, simulated counts.

Author(s)

Marc Kéry & Andy Royle

References

Dennis, E.B., et al (2016) Dynamic models for longitudinal butterfly data, Journal of Agricultural, Biological and Environmental Statistics, 21, 1-21.

Kéry & Royle (2021) Applied Hierarchical Modeling in Ecology AHM2 - 1.8.1.

Examples

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

AHMbook documentation built on Sept. 12, 2024, 6:37 a.m.