Quasi-extinction risk is defined as the probability of a population falling below a critical density. It is an important tool for assessing the future viabilty of threatened animal and plant populations. Quasi-extinction is traditionally computed using a Monte Carlo simulation of a discrete stochastic population growth model. For the continuous geometric Brownian motion model there is an analytical formula for computing quasi-extinction risk. The formula was originally derived by Ginzburg, et al. (1982; Risk Analysis 2:385--399).
In order to compute the quasi-extinction probability, I must be able to compute the cumulative distribution function for the normal distribution function. The normal distribution with a mean of zero and a standard deviation of 1 is:
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The cumulative distribution is computed by integrating the normal distribution function from -infinity to y.
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The formula below is for quasi-extinction defined as the first crossing time. The following parameters are defined:
r is the mean rate of growth,
sigma is the standard deviation of the growth rate,
theta is the critical threshold which is expressed as a fraction of the original abundance, and
thorizon is the time horizon.
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What is the affect of the variation in the growth rate on the quasi-extinction risk? Intuitively, it is expected that as the population growth rate becomes more variable then the quasi-extinction probability should also increase.
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Using the Plot3D function the mapping of theta and sigma to quasi-extinction risk can be explored Note this computation takes some time to compute. This possibly could be optimized by using Mathematica's built-in functions for probability distributions in the Statistics library.
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| Created by Mathematica (July 5, 2005) |