1. Continuous Models Chapter 4

2. Bacteria Growth-Revisited Consider bacteria growing in a nutrient rich medium Variables –Time, t –N(t) = bacteria density at time t Dimension of N(t) is # cells/vol. Parameters –k = growth/reproduction rate per unit time Dimension of k is 1/time

3. Bacteria Growth Revisited Now suppose that bacteria densities are observed at two closely spaced time points, say t and t +  t If death is negligible, the following statement of balance holds: Bacteria Density @ t +  t Bacteria density @ time t New bacteria Produced in the Interval t +  t - t = + N(t+  t) = N(t) + kN(t)  t

4. Bacteria Growth Revisited Rearrange these terms Assumptions – N(t) is large--addition of one or several new cells is of little consequence –There is no new mass generated at distinct intervals of time, ie cell growth and reproduction is not correlated. Under these assumptions we can say that N(t) changes continuously

5. Bacteria Growth Revisited Upon taking the limit The continuous model becomes Its solution is

6. Properties of the Model Doubling Time/Half life: Steady state – N e = 0 Stability –N e = 0 is stable if k < 0 –N e = 0 is unstable if k > 0

7. Modified Model Now assume that growth and reproduction depends on the available nutrient concentration New Variable –C(t) = concentration of available nutrient at time t Dimensions of C are mass/vol

8. Modified Model New assumptions –Population growth rate increases linearly with nutrient concentration –  units of nutrient are consumed in producing one new unit of bacteria

9. Modified Model We now have two equations Upon integration we see So any initial nutrient concentration can only support a fixed amount of bacteria

10. The Logistic Growth Model Substitute to find where Intrinsic growth rate Environmental Carrying capacity

11. The Logistic Growth Model Model Solution Note: as N K, N/K 1 and 1-N/K 0 As the population size approaches K, the population growth rate approaches zero

12. Breakdown In general, single species population growth models can all be written in the following form where g(N) is the actual growth rate. Actual growth rate Actual birth rate Actual death rate =

13. Breakdown The logistic equations makes certain assumptions about the relationship between population size and the actual birth and death rates. The actual death rate of the population is assumed to increase linearly with population size Intrinsic death rate

14. Breakdown The actual birth rate of the population is assumed to decrease linearly with population size Intrinsic birth rate

15. Breakdown Rearrange to get:

16. Breakdown Now let Intrinsic growth rate Intrinsic birth rate Intrinsic death rate = Carrying capacity Sensitivity of birth and death rate to population size

17. Plot of Actual Birth and Death Rates   K

18. Assuming Linearity Linearity is the simplest way to model the relationship between population size and actual birth and death rates This may not be the most realistic assumption for many population A curve of some sort is more likely to be realistic, as the effect of adding individuals may not be felt until some critical threshold in resource per individual has been crossed

19. Solution Profiles

20. General Single Species Models Steady States –Solutions of f(N) = 0 N = 0 is always a steady state So must determine when g(N) = 0 for nontrivial steady states –Example Steady states are N = 0 and N = K, both always exist.

21. General Single Species Models Stability –How do small perturbations away from steady state behave? 1.Let N = N e + n where |n| << 1 2.Substitute into model equation 3.Expand RHS in a Taylor series and simplify 4.Drop all nonlinear terms

22. General Single Species Models Stability –Once steps 1 - 4 are preformed, you’ll arrive at an equation for the behavior of the small perturbations –n(t) grows if Therefore N = N e is unstable –N(t) decays if Therefore N = N e is stable

23. General Single Species Models Stability –Analysis shows that stability is completely determined by the slope of the growth function, f(N), evaluated at the steady state. Example unstable stable

24. General Single Species Models Stability N e = 0 is always unstable N e = K is always stable

25. Compare Continuous and Discrete Logistic Model DiscreteContinuous Solutions grow or decay -- possible oscillations Solutions grow or decay --no oscillations Solutions approach N = 0 or N = K or undergo period doubling bifurcations to chaos All solutions approach N = K

26. Nondimensionalization Definition: Nondimensionalization is an informed rescaling of the model equations that replaces dimensional model variables and parameters with nondimensional counterparts

27. Why Nondimensionalize? To reduce the number of parameters To allow for direct comparison of the magnitude of parameters To identify and exploit the presence of small/large parameters Note: Nondimensionalization is not unique!!

28. How to Nondimensionalize Perform a dimensional analysis Variables/DimensionParameters/Dimension Ndensity t time r1/time N0density

29. How to Nondimensionalize Introduce an arbitrary scaling of all variables Substitute into the model equation Original Model Scaled Model

30. How to Nondimensionalize Choose meaning scales Let Time is scaled by the intrinsic growth rate Population size is scaled by the initial size

31. How to Nondimensionalize Note: The parameters of the system are reduced from 2 to 0!! There are no changes in initial conditions or growth rate that can qualitatively change the behavior of the solutions-- ie no bifurcations!!

32. Nondimensionalize the Logistic Equation