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
“Love the simulation we're dreaming in” - Dua Lipa, Physical
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.
Check out the QPAD workshop.
Install
CRAN version:
install.packages("bSims")
Development version:
remotes::install_github("psolymos/bSims")
See what is new in the NEWS file.
License
Contributing
Feedback and contributions are welcome:
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
#> 0.790 0.013 0.830
## parallel clusters
cl <- makeCluster(nc)
## note: loading the package is optional
system.time(clusterEvalQ(cl, library(bSims)))
#> user system elapsed
#> 0.001 0.000 1.289
system.time(bb <- b$replicate(B, cl=cl))
#> user system elapsed
#> 0.013 0.002 0.655
stopCluster(cl)
## parallel forking
if (.Platform$OS.type != "windows") {
system.time(bb <- b$replicate(B, cl=nc))
}
#> user system elapsed
#> 0.413 0.108 0.544