chesson: Two-species model of the storage effect In primer: Functions and data for A Primer of Ecology with R

Description

Simulates a fluctuating environment over time, and two species' responses to the environment, after Chesson (1994).

Usage

 ```1 2 3``` ```chesson(alpha=c(1.1*1e-5, 1e-5), d=.1, years=10, N0=c(1e3,1e5), w=c(.6, 1), env.var=1, specialization=1, spread=0.67, type=1) ```

Arguments

 `alpha` a vector of length 2; the negative effects of all individuals (of both species) on each population – typically different among species. `d` disturbance rate; the proportion of all individuals killed at each time step. `years` numbers of time steps `N0` vector of length 2; initial abundances. `w` vector of length 2; average fitnesses for each species. `env.var` degree of environmental variability. `specialization` determines the narrowness of each species fitness response. `spread` determines how far apart the peak fitness responses are. `type` determines the form of C, the negative effect of competition.

Details

The argument `type` controls the value of e^C, the effect of competition on reproduction, where the annual finite rate of increase is R=e^(E-C). `type = 1` causes e^C = alpha[i] N[J,i], that is, a species-specific fixed fraction of juveniles that depends on each species response to competition. This is illustrated in a for-loop in Stevens (2009, Ch. 9, Storage Effect, Simulating Dynamics). Any other value for `type` results in the same negative effect on both species that depends on the number of juveniles and the disturbance rate.

Value

Returns a list of length one, for use with `ode` in the `deSolve` package.

 `Component 1 ` vector of the state variables, y.

Author(s)

Hank Stevens <[email protected]>

References

Chesson, P.L. (1994) Multispecies competition in variable environments. Theoretical Population Biology, 45, 227–276.

Stevens. M.H.H. (2009) A Primer of Ecology with R. Use R! Series. Springer.

`succniche`
 ``` 1 2 3 4 5 6 7 8 9 10``` ```out <- chesson(years=50) layout(matrix(1:4, nc=2)) matplot(out[["time"]], out[["Ns"]], type='l', lty=c(1:3), xlab="Time", ylab="N", log="y") plot(out[["time"]][-1], out[["env"]], type='l', xlab="Time", ylab="Environment") matplot(out[["env"]], out[["Es"]], xlab="Environment", ylab="Density-independent reproduction") matplot(out[["env"]], out[["Rs"]], xlab="Environment", ylab="Annual growth rate") ```