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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NormalD = 1/(Sqrt[2 Pi]) Exp[-x^2/2]

General :: spell1 : Possible spelling error: new symbol name \"NormalD\" is similar to existing symbol \"Normal\".  More…

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^(-x^2/2)/(2 π)^(1/2)

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Plot[NormalD, {x, -5, 5}]

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The cumulative distribution is computed by integrating the normal distribution function from -infinity to y.

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NormalDCDF[y_] := Integrate[NormalD, {x, -Infinity, y}]

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Plot[NormalDCDF[y], {y, -5, 5}]

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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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QuasiExtinction[r_, sigma_, theta_, thorizon_] := NormalDCDF[(-theta - r thorizon)/Sqrt[thorizon sigma^2]] + Exp[(-2 r theta)/sigma^2] NormalDCDF[(-theta + r thorizon)/Sqrt[thorizon sigma^2]]

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QuasiExtinction[0, 0.1, 0.5, 10]

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0.113846

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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Plot[QuasiExtinction[0, sigma, 0.5, 10], {sigma, 0.01, 1.}, AxesLabel  {σ_r, p}]

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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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Plot3D[QuasiExtinction[0, sigma, theta, 10], {theta, 0.01, 0.5}, {sigma, 0.01, 0.5}, AxesLabel {σ_r, θ, p}]

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