dynPop {EcoVirtual}

Population Dynamic Models

Description

Functions to simulate population dynamic models.

Usage

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)

Arguments

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.

Details

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.

Value

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

Author(s)

Alexandre Adalardo de Oliveira and Paulo Inacio Prado [e-mail]

References

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.

See Also

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

Examples

## Not run: 
popStr(p.sj=0.4, p.jj=0.6, p.ja=0.2, p.aa=0.9, fec=0.8, ns=100,nj=40,na=20, rw=30, cl=30, tmax=20)

## End(Not run)