turnover {secr} | R Documentation

## Specifying a Dynamic Population

### Description

`sim.popn` can simulate a multi-session population with known between-session survival, recruitment and movement probabilities. The parameter settings to achieve this are passed to `sim.popn` in its ‘details’ argument. Components of ‘details’ that are relevant to turnover are described below; see `sim.popn` for others.

Multi-session populations are generated in `sim.popn` whenever its argument ‘nsessions’ is greater than 1. If `details$lambda` remains NULL (the default) then the population for each successive session is generated de novo from the given density model (model2D, D etc.). If a value is specified for `details$lambda` then only the first population is generated de novo; remaining populations are generated iteratively with probabilistic mortality, recruitment and movement as described here.

### Turnover components of `sim.popn` details argument

Component | Description | Default
`phi` | per capita survival rate φ | 0.7
`survmodel` | probability model for number of survivors | ``binomial"
`lambda` | finite rate of increase λ = N_{t+1} / N_t | none
`recrmodel` | probability model for number of recruits | ``poisson"
`superN` | optional superpopulation size for multinomial recruitment model | see below
`movemodel` | ``static", ``uncorrelated", ``normal", ``exponential" or ``t2D" | ``static"
`move.a` | first parameter of movement kernel (replacing sigma.m) | 0
`move.b` | second parameter of movement kernel | 1
`edgemethod` | treatment of animals that cross the boundary | ``wrap"
`sigma.m` | deprecated in 3.2.1; use move.a | 0
`wrap` | deprecated in 3.1.6; use edgemethod | TRUE i.e. edgemethod = ``wrap"

### Survival

Survival is usually thought of as a Bernoulli process (outcome 0 or 1 for each individual) so the number of survivors S is a binomial variable (survmodel = "binomial"). Another approach is to fix the proportion surviving, but this can be done exactly only when φ N is an integer. A (slightly ad hoc) solution is to randomly choose between the two nearest integers with probability designed in the long term (over many sessions) to give the required φ (survmodel = "discrete").

### Population growth and recruitment

Per capita recruitment (f) is the difference between lambda and phi (f = λ - φ), which must be non-negative (phi > lambda causes an error). The number of recruits B is a random variable whose probability distribution is controlled by details$recrmodel:

Value | Probability model
"constantN" | Exact replacement of animals that die (B = N_t - S)
"binomial" | Binomial number of recruits (B ~ bin(N_t, f)
"poisson" | Poisson number of recruits (B ~ pois(f N_t))
"discrete" | Minimum-variance number of recruits (see Survival)
"multinomial" | The POPAN model, conditioning on superpopulation size (e.g., Schwarz and Arnason 1996))

In the case of binomial recruitment there is a maximum of one recruit per existing individual, so lambda <= (phi+1). Multinomial recruitment requires a value for the superpopulation size. This may be provided as the details component "superN". If not specified directly, a value is inferred by projecting a trial initial (simulated) population using the specified phi and lambda.

### Movement

Individuals may shift their home range centre between sessions. Movement probability is governed by a circular kernel specified by ‘movemodel’ and the parameter values ‘move.a’ and ‘move.b’ (optional). By default there is no movement between sessions (movemodel = "static"). Other options are

``uncorrelated" | individuals are randomly assigned a new, independent location within the buffered area
``normal" | bivariate normal (Gaussian) kernel with parameter move.a (previously called sigma.m)
``exponential" | negative exponential (Laplace) kernel with parameter move.a
``t2D" | 2-dimensional t-distribution with scale parameter move.a and shape parameter move.b (2Dt of Clark et al. 1999).

The package openCR >=1.4.0 provides functions for constructing and plotting these kernels and summarising their properties (`make.kernel`; `plot` and `summary` methods for kernel objects).

In secr <3.2.1 sigma.m was also used to indicate two special cases; these continue to work but may be discontinued in the future:

sigma.m = 0 corresponds to movemodel = ‘static’

sigma.m < 0 corresponds to movemodel = ‘uncorrelated’

If movement takes an animal across the boundary of the arena (buffered area) in `sim.popn` the component "edgemethod" comes into play. By default, locations are toroidally wrapped i.e. the animal re-joins the population on the opposing edge. Other options are “clip” (discard), “stop” (stop just inside the boundary), “reflect” (bounce off edges to the limit of the dispersal) and “none" (allow centres outside the buffered area).

### References

Clark, J. S, Silman, M., Kern, R., Macklin, E. and HilleRisLambers, J. (1999) Seed dispersal near and far: patterns across temperate and tropical forests. Ecology 80, 1475–1494.

Nathan , R., Klein, E., Robledo-Arnuncio, J. J. and Revilla, E. (2012) Dispersal kernels: a review. In: J Clobert et al. Dispersal Ecology and Evolution. Oxford University Press. Pp 187–210.

### See Also

`sim.popn`

### Examples

    par (mfrow = c(2,3), mar = c(1,1,1,1))

    ## birth and death only
    grid <- make.grid(nx = 7, ny = 4, detector = 'proximity', spacing = 10)
    pop <- sim.popn (Nbuffer = 100, core = grid, nsessions = 6,
        details = list(lambda = 0.8, phi = 0.6))
    sapply(pop, nrow)  ## how many individuals?
    plot(pop)

    ## movement only
    pop2 <- sim.popn (Nbuffer = 100, core = grid, nsessions = 6,
        details = list(lambda = 1, phi = 1, movemodel = 'normal',
        move.a = 10, edgemethod = "wrap"))
    pop3 <- sim.popn (Nbuffer = 100, core = grid, nsessions = 6,
        details = list(lambda = 1, phi = 1, movemodel = 'normal',
        move.a = 10, edgemethod = "clip"))
    pop4 <- sim.popn (Nbuffer = 100, core = grid, nsessions = 10,
        details = list(lambda = 1, phi = 1, movemodel = 'normal',
        move.a = 10, edgemethod = "stop"))
    sapply(pop2, nrow)  ## how many individuals?
    plot(pop2)

    ## show effect of edgemethod --
    ## first session blue, last session red
    cols <- c('blue',rep('white',4),'red')
    par (mfrow=c(1,2))
    plot(pop2, collapse = TRUE, seqcol = cols)
    plot(pop3, collapse = TRUE, seqcol = cols)

[Package secr version 3.2.1 Index]