Description Usage Arguments Details Value Author(s) References See Also Examples

Functions to simulate population dynamic models.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ```
popExp(N0, lamb, tmax, intt = 1)
estEnv(N0, lamb, tmax, varr, npop = 1, ext = FALSE)
BDM(tmax, nmax = 10000, b, d, migr = 0, N0, barpr = FALSE)
simpleBD(tmax = 10, nmax = 10000, b = 0.2, d = 0.2, N0 = 10,
cycles = 1000, barpr = FALSE)
estDem(N0 = 10, tmax = 10, nmax = 10000, b = 0.2, d = 0.2, migr = 0,
nsim = 20, cycles = 1000, type = c("simpleBD", "BDM"), barpr = FALSE)
popLog(N0, tmax, r, K, ext = FALSE)
popStr(tmax, p.sj, p.jj, p.ja, p.aa, fec, ns, nj, na, rw, cl)
logDiscr(N0, tmax, rd, K)
bifAttr(N0, K, tmax, nrd, maxrd = 3, minrd = 1)
``` |

`N0` |
number of individuals at start time. |

`lamb` |
finite rate of population growth. |

`tmax` |
maximum simulation time. |

`intt` |
interval time size. |

`varr` |
variance. |

`npop` |
number of simulated populations. |

`ext` |
extinction. |

`nmax` |
maximum population size. |

`b` |
birth rate. |

`d` |
death rate. |

`migr` |
migration. logical. |

`barpr` |
show progress bar. |

`cycles` |
number of cycles in simulation. |

`nsim` |
number of simulated populations. |

`type` |
type of stochastic algorithm. |

`r` |
intrinsic growth rate. |

`K` |
carrying capacity. |

`p.sj` |
probability of seed survival. |

`p.jj` |
probability of juvenile survival. |

`p.ja` |
probability of transition from juvenile to adult phase. |

`p.aa` |
probability of adult survival. |

`fec` |
mean number of propagules per adult each cycle. |

`ns` |
number of seeds at initial time. |

`nj` |
number of juveniles at initial time. |

`na` |
number of adults at initial time. |

`rw` |
number of rows for the simulated scene. |

`cl` |
number of columns for the simulated scene. |

`rd` |
discrete growth rate. |

`nrd` |
number of discrete population growth rate to simulate. |

`maxrd` |
maximum discrete population growth rate. |

`minrd` |
minimum discrete population growth rate. |

popExp simulates discrete and continuous exponential population growth.

estEnv simulates a geometric population growth with environmental stochasticity.

BDM simulates simple stochastic birth death and immigration dynamics of a population (Renshaw 1991). simpleBD another algorithm for simple birth dead dynamics. This is usually more efficient than BDM but not implemented migration.

estDem creates a graphic output based on BDM simulations.

Stochastic models uses lambda values taken from a normal distribution with mean lambda and variance varr.

popLog simulates a logistic growth for continuous and discrete models.

popStr simulates a structured population dynamics, with Lefkovitch matrices.

In popStr the number of patches in the simulated scene is defined by rw*cl.

logDiscr simulates a discrete logistic growth model.

bifAttr creates a bifurcation graphic for logistic discrete models.

The functions return graphics with the simulation results, and a matrix with the population size for deterministic and stochastic models.

Alexandre Adalardo de Oliveira and Paulo Inacio Prado ecovirtualpackage@gmail.com

Gotelli, N.J. 2008. A primer of Ecology. 4th ed. Sinauer Associates, 291pp. Renshaw, E. 1991. Modelling biological populations in space and time Cambridge University Press. Stevens, M.H.H. 2009. A primer in ecology with R. New York, Springer.

`metaComp`

, http://ecovirtual.ib.usp.br

1 2 3 4 |

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.