anm.geo.growth {asbio} R Documentation

## Animated depictions of population growth

### Description

Animated depictions of geometric, exponential, and logistic growth.

### Usage


anm.geo.growth(n0, lambda, time = seq(0, 20), ylab = "Abundance",
xlab = "Time", interval = 0.1, ...)

anm.exp.growth(n, rmax, time = seq(0, 20), ylab = "Abundance",
xlab = "Time", interval = 0.1, ...)

anm.log.growth(n, rmax, K, time = seq(0, 60), ylab = "Abundance",
xlab = "Time", interval = 0.1, ...)

anm.geo.growth.tck()

anm.exp.growth.tck()

anm.log.growth.tck()


### Arguments

 n0 Population size at time zero for geometric population growth. lambda Geometric growth rate. time A time sequence, i.e. a vector of integers which must include 0. ylab Y-axis label. xlab X-axis label interval Animation interval in seconds per frame. ... Additional arguments to plot n Initial population numbers for exponential and logistic growth rmax The maximum intrinsic rate of increase K The carrying capacity

### Details

Presented here are three famous population growth models from ecology. Geometric, exponential and logistic growth. The first two model growth in the presence of unlimited resources. Geometric growth is exponential growth assuming non-overlapping generations, and is computed as:

N_t = N_{0}\lambda^t,

where N_t is the number of individuals at time y, \lambda is the geometric growth rate, and t is time.

Exponential growth allows simultaneous existence of multiple generations, and is computed as:

\frac{dN}{dt}=r_{max}N,

where r_{max} is the maximum intrinsic rate of increase, i.e. max(birth rate - death rate), and N is the population size. With logistic growth, exponential growth is slowed as N approaches the carrying capacity. It is computed as:

\frac{dN}{dt}=r_{max}N\frac{K-N}{K},

where r_{max} is the maximum rate of intrinsic increase, N is the population size, and K is the carrying capacity

Package tcltk allows implementation of all three models using GUIs.

### Author(s)

Ken Aho

anm.LVexp, anm.LVcomp
## Not run: