Skip to contents

simple niche model

Source: R/snd_niche.R
snd_niche.Rd

Defines niche model based in the "Probability of collection" model by Holland and Patzkowsky (1999). The collection probability follows the shape of a bell curve across a gradient, where opt determines the peak (mean) of the bell curve, and tol the standard deviation. "snd" stands for "scaled normal distribution", as the collection probability has the shape of the probability density of the normal distribution.

Usage

snd_niche(opt, tol, prob_modifier = 1, cutoff_val = NULL)

Arguments

opt

optimum value, gradient value where collection probability is highest

tol

tolerance to changes in gradient. For large values, collection probability drops off slower away from opt

prob_modifier

collection probability modifier, collection probability at opt.

cutoff_val

NULL or a number. If a number, all collection probabilities at gradient values below cutoff_value are set to 0. This can for example be used to model exclusively marine species when the gradient is water depth (see examples).

Value

a function for usage with apply_niche.

References

  • Holland, Steven M. and Patzkowsky, Mark E. 1999. "Models for simulating the fossil record." Geology. https://doi.org/10.1130/0091-7613(1999)027%3C0491:MFSTFR%3E2.3.CO;2

See also

  • apply_niche() for usage of the returned function

  • bounded_niche() for another niche model

  • vignette("advenced_functionality") for details on how to create user defined niche models

Examples


# using water depth as niche
wd = seq(-3, 40, by = 0.5)
f = snd_niche(opt = 10, tol = 5)

plot(wd, f(wd), xlab = "Water depth", ylab = "Prob. of collection")

# set cutoff value at to 0 to model non-terrestrial species.
f = snd_niche(opt = 10, tol = 5, cutoff_val = 0)
plot(wd, f(wd), xlab = "Water depth", ylab = "Prob. of collection")


# see also
#vignette("event_data")
#for examples how to use it for niche modeling