Skip to contents

Ecological drift simulation under the Unified Neutral Theory of Biodiversity

untb.Rd

Simulates ecological drift under the UNTB. Function untb() carries out the simulation; function select() carries out a single generational step.

Usage

untb(start, prob=0, D=1, gens=150, keep=FALSE, meta=NULL)
select(a, D=length(a), prob=0, meta=NULL)
select.mutate(a, D=length(a), prob.of.mutate=0)
select.immigrate(a, D=length(a), prob.of.immigrate=0, meta)

Arguments

a, start

Starting ecosystem; coerced to class census. Usually, pass an object of class count; see examples. To start with a monoculture of size 10, use start=rep(1,10) and to use start=1:10.

prob, prob.of.immigrate, prob.of.mutate

Probability of “new” organism not being a descendent of an existing individual

D

Number of organisms that die in each timestep

gens

Number of generations to simulate

keep

In function untb() Boolean with default FALSE meaning to return the system at the end of the simulation and TRUE meaning to return a matrix whose rows are the ecosystem at successive times

meta

In function untb(), the metacommunity; coerced to a count object. Default of NULL means to use a “greedy” system in which every mutation gives rise to a new, previously unencountered species. This would correspond to an infinitely large, infinitely diverse, Hubbellian ecosystem (which is not too ridiculous an assumption for a small island near a large diverse mainland).

In function select.immigrate(), a simplified representation of a metacommunity.

Details

Functions select.immigrate() and select.mutate() are not really intended for the end user; they use computationally efficient (and opaque) integer arithmetic.

References

S. P. Hubbell 2001. “The Unified Neutral Theory of Biodiversity”. Princeton University Press.

Author

Robin K. S. Hankin

Examples


data(butterflies)
untb(start=butterflies, prob=0, gens=100)
#>  1  3  2  4  5  7  6  8  9 10 11 18 12 15 16 13 14 19 25 29 37 20 21 24 27 32 
#> 61 50 48 37 23 19 18 15 15  8  6  6  5  5  5  4  4  4  4  4  4  3  3  3  3  3 
#> 17 22 23 26 34 28 30 31 33 35 36 
#>  2  2  2  2  2  1  1  1  1  1  1 

a <- untb(start=1:10,prob=0.005, gens=1000,keep=TRUE)
plot(species.count(a),type="b")

matplot(species.table(a),type="l",lty=1)