simIDS {AHMbook}

Simulate data for an integrated distance sampling, point count and occupancy study

Description

Generates hierarchical distance sampling data, point count data, and occupancy (detection/nondetection) data for an integrated distance sampling (IDS) model with shared density and availability processes, but possibly different detection/perceptability processes (i.e., different detection functions).

The function calls simHDSpoint to generate hierarchical distance sampling (HDS) data under a point transect protocol with a half-normal detection function. Density is modeled as a log-linear regression on a "habitat" covariate, with coefficients mean.density and beta.density.

Point count and occupancy data are also generated by calls to simHDSpoint, assuming that the underlying detection process involves reduced probability of detection with distance from the observer. The distances from the observer are discarded, and only the counts (for PC data) or species detection information (for occupancy) retained.

Availability is modeled according to Sólymos et al. (2013), where the probability of availability depends on the duration of the observation.

Usage

simIDS(mean.density = 1, beta.density = 1, mean.phi = 0.14, beta.phi = 0,
    nsites_HDS = 1000, sigHDS = 100, maxDist_HDS = 200, nbins = 4,
    range.dur.HDS = c(5, 5),
    nsites_PC = 10000, sigPC = 70, maxDist_PC = 500, range.dur.PC = c(3, 30),
    nsites_OC = 5000, sigOC = sigPC, maxDist_OC = maxDist_PC,
    range.dur.OC = range.dur.PC,
    show.plots = TRUE)

Arguments

mean.density

the expected value of density (animals per hectare) when the habitat covariate = 0; the intercept of the log-linear regression for density is log(mean.density).

beta.density

the slope of the log-linear regression for density on a habitat covariate.

mean.phi

the expected value of the availability parameter.

beta.phi

the slope of the log-linear regression for availability on a covariate - not yet implemented.

nsites_HDS

the number of sites (point transects) for distance sampling.

sigHDS

the value of the scale parameter of the half-normal detection function for distance sampling.

maxDist_HDS

the truncation distance for distance sampling (in meters); any observations beyond this distance are discarded.

nbins

the number of distance bins for grouping distance sampling data.

range.dur.HDS

the range of durations for distance sampling; durations for each site are simulated from a uniform distribution with this range.

nsites_PC

the number of sites for point counts; set to 0 to suppress generation of point counts.

sigPC

the value of the scale parameter of the half-normal detection function for point counts.

maxDist_PC

the maximum distance from the observer for detection of animals when conducting point counts (m).

range.dur.PC

the range of durations for point counts; durations for each site are simulated from a uniform distribution with this range.

nsites_OC

the number of sites for occupancy surveys; set to 0 to suppress generation of occupancy data.

sigOC

the value of the scale parameter of the half-normal detection function for occupancy surveys.

maxDist_OC

the maximum distance from the observer for detection of animals when conducting occupancy surveys (m).

range.dur.OC

the range of durations for point counts; durations for each site are simulated from a uniform distribution with this range.

show.plots

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:

dds

simulated durations for distance sampling: a vector length nsites_HDS with a value for each site

umf_hds0

an unmarkedFrameDS [package "unmarked"] with the simulated distance sampling data BEFORE allowing for availability

umf_hds1

an unmarkedFrameDS [package "unmarked"] with the simulated distance sampling data AFTER allowing for availability

dpc

simulated durations for point counts: a vector length nsites_PC with a value for each site

umf_pc0

an unmarkedFramePCount [package "unmarked"] with the simulated point count data BEFORE allowing for availability

umf_pc1

an unmarkedFramePCount [package "unmarked"] with the simulated point count data AFTER allowing for availability

doc

simulated durations for occupancy surveys: a vector length nsites_OC with a value for each site

umf_oc0

an unmarkedFrameOccu [package "unmarked"] with the simulated occupancy data BEFORE allowing for availability

umf_oc1

an unmarkedFrameOccu [package "unmarked"] with the simulated occupancy data AFTER allowing for availability

Note

A function to analyze these data, IDS, will appear in a future version of unmarked. In the meantime, a devel version can be installed with remotes::install_github("kenkellner/unmarked", ref="IDS").

Author(s)

Ken Kellner, Marc Kéry, Mike Meredith

References

Kéry, M. & Royle, J.A. (2016) Applied Hierarchical Modeling in Ecology AHM1 - 8.5.1.

Sólymos, P. et al. (2013) Calibrating indices of avian density from non-standardized survey data: making the most of a messy situation. Methods in Ecology and Evolution 4, 1047-1058.

Kéry, M. et al. (2022) Integrated distance sampling models for simple point counts. (Submitted to Ecology)

Examples

# Simulate a data set with the default arguments and look at the structure of the output
tmp <- simIDS()
str(tmp)