Models niches by removing events (fossil occurrences) when they are outside of their niche using the function thin.
Combines the functions niche_def and gc ("gradient change") to determine how the taxons' collection probability changes with time/position. This is done by composing niche_def and gc. The result is then used as a thinning on the events x.
Arguments
- x
events, e.g. times/ages of fossil occurrences or their stratigraphic position.
- niche_def
function, specifying the niche along a gradient. Should return 0 when taxon is outside of niche, and 1 when inside niche. Values between 0 and 1 are interpreted as collection probabilities.
- gc
function, stands for "gradient change". Specifies how the gradient changes, e.g. with time
Value
numeric vector, timing/location of events (e.g. fossil ages/locations) preserved after the niche model is applied
See also
apply_taphonomy() to model taphonomic effects based on the same principle, thin() for the underlying mathematical procedure. Basic niche models available are bounded_niche() and snd_niche()
Examples
## setup
# using water depth as gradient
t = scenarioA$t_myr
wd = scenarioA$wd_m[,"8km"]
gc = approxfun(t, wd)
plot(t, gc(t), type = "l", xlab = "Time", ylab = "water depth [m]",
main = "gradient change with time")
# define niche
# preferred wd 10 m, tolerant to intermediate wd changes (standard deviation 10 m), non-terrestrial
niche_def = snd_niche(opt = 10, tol = 10, cutoff_val = 0)
plot(seq(-1, 50, by = 0.5), niche_def(seq(-1, 50, by = 0.5)), type = "l",
xlab = "water depth", ylab = "collection probability", main = "Niche def")
# niche pref with time
plot(t, niche_def(gc(t)), type = "l", xlab = "time",
ylab = "collection probability", main = "collection probability with time")
## simulate fossil occurrences
foss_occ = p3(rate = 100, from = 0, to = max(t))
# foss occ without niche pref
hist(foss_occ, xlab = "time")
foss_occ_niche = apply_niche(foss_occ, niche_def, gc)
# fossil occurrences with niche preference
hist(foss_occ_niche, xlab = "time")
# see also
#vignette("event_data")
# for a detailed example on niche modeling