bSims: Bird Point Count Simulator

A highly scientific and utterly addictive bird point count simulator to test statistical assumptions and to aid survey design.

“I’ve yet to see any problem, however complicated, which when you looked at it the right way didn’t become still more complicated.” – Poul Anderson, Call Me Joe

The goal of the package is to:

• test statistical assumptions,
• aid survey design,
• and have fun while doing it!

Design objectives:

• small (point count) scale implementation,
• habitat is considered homogeneous except for edge effects,
• realistic but efficient implementation of biological mechanisms and observation process,
• defaults chosen to reflect common practice and assumptions,
• extensible (PRs are welcome).

See the package in action in the QPAD Book.

Install

CRAN version:

install.packages("bSims")

Development version:

remotes::install_github("psolymos/bSims")

See what is new in the NEWS file.

License

GPL-2

Contributing

Feedback and contributions are welcome:

• submit feature request or report issues here,
• fork the project and submit pull request, see CoC.

Examples

Command line

library(bSims)

phi <- 0.5
tau <- 1:3
dur <- 10
rbr <- c(0.5, 1, 1.5, Inf)
tbr <- c(3, 5, 10)

l <- bsims_init(10, 0.5, 1)
p <- bsims_populate(l, 1)
a <- bsims_animate(p, vocal_rate=phi, duration=dur)
o <- bsims_detect(a, tau=tau)

x <- bsims_transcribe(o, tint=tbr, rint=rbr)

get_table(x)
#>          0-3min 3-5min 5-10min
#> 0-50m         1      0       1
#> 50-100m       2      0       0
#> 100-150m      5      0       0
#> 150+m         5      3       1

head(get_events(a))
#>            x         y          t v  i
#> 1 -3.6616422 -1.676053 0.01126843 1 12
#> 2  4.6607856  4.537327 0.02661606 1 96
#> 3 -0.2867919  2.155661 0.05207233 1 47
#> 4  2.6507206 -1.110949 0.06329550 1 69
#> 5  2.1330323 -2.167675 0.11365119 1 72
#> 6  0.4926841 -3.517884 0.12323517 1 45

head(get_detections(o))
#>             x           y          t v         d  i  j
#> 3  -0.2867919  2.15566066 0.05207233 1 2.1746546 47 47
#> 10  0.7075451  1.01541218 0.26632984 1 1.2376114 58 58
#> 14  0.5770644 -0.47429169 0.35091111 1 0.7469645 62 62
#> 16  0.4761707 -0.04406422 0.35179595 1 0.4782052 52 52
#> 18  1.0957120 -2.41834073 0.45279692 1 2.6549871 60 60
#> 33  1.0111698  1.82788079 0.87025627 1 2.0889262 57 57

Shiny apps

A few Shiny apps come with the package. These can be used to interactively explore the effects of different settings.

Compare distance functions:

run_app("distfunH")
run_app("distfunHER")

Compare simulation settings for single landscape:

run_app("bsimsH")
run_app("bsimsHER")

Replicating simulations

Interactive sessions can be used to explore different settings. Settings can be copied from the Shiny apps and replicated using the bsims_all function:

b <- bsims_all(extent=5, road=1, density=c(1,1,0))
b
#> bSims wrapper object with settings:
#>   extent : 5
#>   road   : 1
#>   density: 1, 1, 0

The object has handy methods:

b$settings()      # retrieve settings
b$new()           # replicate once
b$replicate(10)   # replicate 10x

The $replicate() function also runs on multiple cores:

library(parallel)
b <- bsims_all(density=0.5)
B <- 4  # number of runs
nc <- 2 # number of cores

## sequential
system.time(bb <- b$replicate(B, cl=NULL))
#>    user  system elapsed 
#>   0.789   0.014   0.829

## parallel clusters
cl <- makeCluster(nc)
## note: loading the package is optional
system.time(clusterEvalQ(cl, library(bSims)))
#>    user  system elapsed 
#>   0.001   0.001   1.347
system.time(bb <- b$replicate(B, cl=cl))
#>    user  system elapsed 
#>   0.012   0.002   0.544
stopCluster(cl)

## parallel forking
if (.Platform$OS.type != "windows") {
  system.time(bb <- b$replicate(B, cl=nc))
}
#>    user  system elapsed 
#>   0.449   0.117   0.583