Simulation layers in bSims

Peter Solymos

Introductory stats books begin with the coin flip to introduce the binomial distribution. In R we can easily simulate an outcome from such a random variable \(Y \sim Binomial(1, p)\) doing something like this:

p <- 0.5

Y <- rbinom(1, size = 1, prob = p)

But a coin flip in reality is a lot more complicated: we might consider the initial force, the height of the toss, the spin, and the weight of the coin.

Bird behavior combined with the observation process presents a more complicated system, that is often treated as a mixture of a count distribution and a detection/nondetection process, e.g.:

D <- 2 # individuals / unit area
A <- 1 # area
p <- 0.8 # probability of availability given presence
q <- 0.5 # probability of detection given availability

N <- rpois(1, lambda = A * D)
Y <- rbinom(1, size = N, prob = p * q)

This looks not too complicated, corresponding to the true abundance being a random variables \(N \sim Poisson(DA)\), while the observed count being \(Y \sim Binomial(N, pq)\). This is the exact simulation that we need when we want to make sure that an estimator can estimate the model parameters (lambda and prob here). But such probabilistic simulations are not very useful when we are interested how well the model captures important aspects of reality.

Going back to the Poisson–Binomial example, N would be a result of all the factors influencing bird abundance, such as geographical location, season, habitat suitability, number of conspecifics, competitors, or predators. Y however would largely depend on how the birds behave depending on timing, or how an observer might detect or miss the different individuals, or count the same individual twice, etc.

Therefore the package has layers, that by default are conditionally independent of each other. This design decision is meant to facilitate the comparison of certain settings while keeping all the underlying realizations identical, thus helping to pinpoint effects without the extra variability introduced by all the other effects.

The conditionally independent layers of a bSims realization are the following, with the corresponding function:

  1. landscape (bsims_init),
  2. population (bsims_populate),
  3. behavior with movement and vocalization events (bsims_animate),
  4. the physical side of the observation process (bsims_detect), and
  5. the human aspect of the observation process (bsims_transcribe).

See this example as a sneak peek that we’ll explain in the following subsections:


phi <- 0.5                 # singing rate
tau <- 1:3                 # detection distances by strata
tbr <- c(3, 5, 10)         # time intervals
rbr <- c(0.5, 1, 1.5)      # count radii

l <- bsims_init(extent=10, # landscape
  road=0.25, edge=0.5)
p <- bsims_populate(l,     # population
  density=c(1, 1, 0))
e <- bsims_animate(p,      # events
  move_rate=1, movement=0.2)
d <- bsims_detect(e,       # detections
x <- bsims_transcribe(d,   # transcription
  tint=tbr, rint=rbr)

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

op <- par(mfrow=c(2,3), cex.main=2)
plot(l, main="Initialize")
plot(p, main="Populate")
plot(e, main="Animate")
plot(d, main="Detect")
plot(x, main="Transcribe")


The bsims_ini function sets up the geometry of a local landscape. The extent of the landscape determines the edge lengths of a square shaped area. With no argument values passed, the function assumes a homogeneous habitat (H) in a 10 units x 10 units landscape, 1 unit is 100 meters. Having units this way allows easier conversion to ha as area unit that is often used in the North American bird literature. As a result, our landscape has an area of 1 km\(^2\).

The road argument defines the half-width of the road that is placed in a vertical position. The edge argument defines the width of the edge stratum on both sides of the road. Habitat (H), edge (E), and road (R) defines the 3 strata that we refer to by their initials (H for no stratification, HER for all 3 strata present).

The origin of the Cartesian coordinate system inside the landscape is centered at the middle of the square. The offset argument allows the road and edge strata to be shifted to the left (negative values) or to the right (positive values) of the horizontal axis. This makes it possible to create landscapes with only two strata. The bsims_init function returns a landscape object (with class ‘bsims_landscape’).

(l1 <- bsims_init(extent = 10, road = 0, edge = 0, offset = 0))
#> bSims landscape
#>   1 km x 1 km
#>   stratification: H
(l2 <- bsims_init(extent = 10, road = 1, edge = 0, offset = 0))
#> bSims landscape
#>   1 km x 1 km
#>   stratification: HR
(l3 <- bsims_init(extent = 10, road = 0.5, edge = 1, offset = 2))
#> bSims landscape
#>   1 km x 1 km
#>   stratification: HER
(l4 <- bsims_init(extent = 10, road = 0, edge = 5, offset = 5))
#> bSims landscape
#>   1 km x 1 km
#>   stratification: HE

op <- par(mfrow = c(2, 2))
plot(l1, main = "Habitat")
points(0, 0, pch=3)
plot(l2, main = "Habitat & road")
lines(c(0, 0), c(-5, 5), lty=2)
plot(l3, main = "Habitat, edge, road + offset")
arrows(0, 0, 2, 0, 0.1, 20)
lines(c(2, 2), c(-5, 5), lty=2)
points(0, 0, pch=3)
plot(l4, main = "2 habitats")
arrows(0, 0, 5, 0, 0.1, 20)
lines(c(5, 5), c(-5, 5), lty=2)
points(0, 0, pch=3)



The bsims_populate function populates the landscape we created by the bsims_init function, which is the first argument we have to pass to bsims_populate. The function returns a population object (with class ‘bsims_population’). The most important argument that controls how many individuals will inhabit our landscape is density that defines the expected value of individuals per unit area (1 ha). By default, density = 1 (\(D=1\)) and we have 100 ha in the landscape (\(A=100\)) which translates into 100 individuals on average (\(E[N]=\lambda=AD\)). The actual number of individuals in the landscape might deviate from this expectation, because \(N\) is a random variable (\(N \sim f(\lambda)\)). The abund_fun argument controls this relationship between the expected (\(\lambda\)) and realized abundance (\(N\)). The default is a Poisson distribution:

#> bSims population
#>   1 km x 1 km
#>   stratification: H
#>   total abundance: 97

Changing abund_fun can be useful to make abundance constant or allow under or overdispersion, e.g.:

summary(rpois(100, 100)) # Poisson variation
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    72.0    94.0   100.0   100.7   107.0   130.0
summary(MASS::rnegbin(100, 100, 0.8)) # NegBin variation
#>    Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
#>    0.00   15.75   69.00  106.59  147.25  563.00
negbin <- function(lambda, ...) MASS::rnegbin(1, lambda, ...)
bsims_populate(l1, abund_fun = negbin, theta = 0.8)
#> bSims population
#>   1 km x 1 km
#>   stratification: H
#>   total abundance: 109
## constant abundance
bsims_populate(l1, abund_fun = function(lambda, ...) lambda)
#> bSims population
#>   1 km x 1 km
#>   stratification: H
#>   total abundance: 100

Once we determine how many individuals will populate the landscape, we have control over the spatial arrangement of the nest location for each individual. The default is a homogeneous Poisson point process (complete spatial randomness). Deviations from this can be controlled by the xy_fun. This function takes distance as its only argument and returns a numeric value between 0 and 1. A function function(d) reurn(1) would be equivalent with the Poisson process, meaning that every new random location is accepted with probability 1 irrespective of the distance between the new location and the previously generated point locations in the landscape. When this function varies with distance, it leads to a non-homogeneous point process via this accept-reject algorithm. The other arguments (margin, maxit, fail) are passed to the underlying accepreject function to remove edge effects and handle high rejection rates.

In the next example, we fix the abundance to be constant (i.e. not a random variable, \(N=\lambda\)) and different spatial point processes:

D <- 0.5
f_abund <- function(lambda, ...) lambda

## systematic
f_syst <- function(d)
  (1-exp(-d^2/1^2) + dlnorm(d, 2)/dlnorm(exp(2-1),2)) / 2
## clustered
f_clust <- function(d)
  exp(-d^2/1^2) + 0.5*(1-exp(-d^2/4^2))

p1 <- bsims_populate(l1, density = D, abund_fun = f_abund)
p2 <- bsims_populate(l1, density = D, abund_fun = f_abund, xy_fun = f_syst)
p3 <- bsims_populate(l1, density = D, abund_fun = f_abund, xy_fun = f_clust)

distance <- seq(0,10,0.01)
op <- par(mfrow = c(3, 2))
plot(distance, rep(1, length(distance)), type="l", ylim = c(0, 1), 
  main = "random", ylab=expression(f(d)), col=2)

plot(distance, f_syst(distance), type="l", ylim = c(0, 1), 
  main = "systematic", ylab=expression(f(d)), col=2)

plot(distance, f_clust(distance), type="l", ylim = c(0, 1), 
  main = "clustered", ylab=expression(f(d)), col=2)


The get_nests function extracts the nest locations. get_abundance and get_density gives the total abundance (\(N\)) and density (\(D=N/A\), where \(A\) is extent^2) in the landscape, respectively.

If the landscape is stratified, that has no effect on density unless we specify different values through the density argument as a vector of length 3 referring to the HER strata:

D <- c(H = 2, E = 0.5, R = 0)

op <- par(mfrow = c(2, 2))
plot(bsims_populate(l1, density = D), main = "Habitat")
plot(bsims_populate(l2, density = D), main = "Habitat & road")
plot(bsims_populate(l3, density = D), main = "Habitat, edge, road + offset")
plot(bsims_populate(l4, density = D), main = "2 habitats")