Modeling with First Order DE’s — Question 8

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Question 8

An isolated laboratory population is modeled by a per-capita growth rate that decreases linearly with population size: P′=P(a−bP),a>0,b>0.P'=P(a-bP),\qquad a>0,\ b>0. Treat PP as a continuous number of individuals, with time in days. Under unchanged laboratory conditions, the instantaneous total growth rate is 2020 individuals/day both when P=100P=100 and when P=200P=200. A new culture begins with P(0)=100P(0)=100.

Tasks

  1. Identify aa and bb, including units, from the two instantaneous rate observations.

  2. Solve the new culture’s IVP and verify the result. State the assumptions supporting use of the fitted parameters in this culture.

  3. Find when the population first reaches 250250 individuals and explain why this is a unique future time.

  4. An exponential model is fitted only to the initial population and its initial growth rate. Compare its rate prediction at P=200P=200 with the second observation, and explain what that discrepancy says about the model.

Original worksheet page 1: question and worked solution for 2-7-008
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Question 8 – Solution

Strategy. Convert total growth observations to per-capita rates before estimating the crowding coefficient.

Step 1: Calibrate the rate law. The observations give a−100b=0.20,a−200b=0.10.a-100b=0.20,\qquad a-200b=0.10. Subtracting yields a=0.30 day−1,b=0.001 individual−1day−1.\boxed{a=0.30\text{ day}^{-1},\qquad b=0.001\text{ individual}^{-1}\text{day}^{-1}}. The model becomes P′=0.30P(1−P/300)P'=0.30P(1-P/300). Both specified populations give total rate 2020 individuals/day.

Step 2: Solve the IVP. Separation for the branch 0<P<3000<P<300 gives ln⁡P300−P=0.30t+C.\ln\frac{P}{300-P}=0.30t+C. Using P(0)=100P(0)=100 gives C=−ln⁡2C=-\ln 2 and P(t)=3001+2e−0.30t,t≥0.\boxed{P(t)=\frac{300}{1+2e^{-0.30t}}},\qquad t\ge 0. If z=2e−0.30tz=2e^{-0.30t}, then P′=90z/(1+z)2=0.30P(1−P/300)P'=90z/(1+z)^2=0.30P(1-P/300), and the initial value is 100100. Applying the calibration assumes the same fixed resources and crowding law, no migration, and a population scale for which the continuous approximation is appropriate.

Step 3: Locate the target. Setting P=250P=250 yields e−0.30t=1/10e^{-0.30t}=1/10, so t=ln⁡100.30≈7.675 days.\boxed{t=\frac{\ln 10}{0.30}\approx 7.675\text{ days}}. The formula increases strictly from 100100 toward 300300 for t≥0t\ge 0, so 250250 is attained exactly once. Its denominator remains positive throughout this physical time range.

Step 4: Test the competing exponential model. For P′=rPP'=rP, the initial observation forces r=20/100=0.20r=20/100=0.20 day−1^{-1}. At P=200P=200, that model predicts 4040 individuals/day, double the observed rate. Matching one population and one instantaneous slope does not validate a constant per-capita growth law.

The second observation distinguishes the two proposed laws. It supports the calibrated crowding model over this exponential alternative under the stated exact-data assumptions, but two rate observations alone do not establish that the model remains accurate at every population size.

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