Simulate abundances in habitats linked by isodars

Simulates abundances at n sites along a chain of isodars: habitat 1 is drawn uniformly between 0 and hab1_max, and each following habitat \(k + 1\) = intercepts[k] + slopes[k] x habitat \(k\) + noise. Values are rounded to integers and negative values set to 0. The default is three habitats, set by slope1, slope2, int1 and int2; the sequence of random numbers is the same as in version 1.0.0.

Usage

simulate_isodars(
  slope1 = 1,
  slope2 = 1,
  int1 = 0,
  int2 = 0,
  noise = 1,
  n = 30,
  hab1_max = 100/(slopes[1]^1.2) - intercepts[1],
  slopes = c(slope1, slope2),
  intercepts = c(int1, int2),
  p_missing = 0,
  min_habitats = 2,
  format = c("wide", "long")
)

Arguments

slope1, int1

Isodar of habitat 2 on habitat 1.

slope2, int2

Isodar of habitat 3 on habitat 2.

noise

Standard deviation of the Gaussian noise.

n

Number of sites.

hab1_max

Upper bound of abundance in habitat 1. The default, 100 / slopes[1]^1.2 - intercepts[1], keeps total abundances in a comparable range across parameter values.

slopes, intercepts

Any number of habitats: vectors of length \(H - 1\) giving the isodar of each habitat on the previous one (default: c(slope1, slope2) and c(int1, int2)).

p_missing

Probability that a habitat is missing (not sampled) at a site; at least min_habitats habitats are kept at every site.

min_habitats

Minimum number of habitats recorded per site.

format

"wide" (one column per habitat, NA where missing) or "long" (columns site, habitat, abundance; missing habitats have no row).

Value

Wide: a tibble with columns hab1, ..., habH and total_abundance. Long: a tibble with site, habitat and abundance.

Examples

set.seed(1)
simulate_isodars(1, 1.1, 1, 0, noise = 2, n = 10)
#> # A tibble: 10 × 4
#>     hab1  hab2  hab3 total_abundance
#>    <dbl> <dbl> <dbl>           <dbl>
#>  1    26    26    28              80
#>  2    37    39    43             119
#>  3    57    59    67             183
#>  4    90    92   103             285
#>  5    20    20    24              64
#>  6    89    93   104             286
#>  7    94    95   106             295
#>  8    65    65    72             202
#>  9    62    59    61             182
#> 10     6     9    12              27
simulate_isodars(slopes = c(1.5, 1, 0.8), intercepts = c(5, 0, 2), n = 5,
                 p_missing = 0.3)
#> # A tibble: 5 × 5
#>    hab1  hab2  hab3  hab4 total_abundance
#>   <dbl> <dbl> <dbl> <dbl>           <dbl>
#> 1    NA    NA    44    38              82
#> 2    49    NA    79    65             193
#> 3    NA    42    43    NA              85
#> 4    NA    25    26    24              75
#> 5    NA    11    10    10              31