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Ecosystem Modeling I Kate Hedstrom, ARSC November 2008.

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Presentation on theme: "Ecosystem Modeling I Kate Hedstrom, ARSC November 2008."— Presentation transcript:

1 Ecosystem Modeling I Kate Hedstrom, ARSC November 2008

2 Annual Phytoplankton Cycle Strong vertical mixing in winter, low sun angle keep phytoplankton numbers low Spring sun and reduced winds contribute to stratitification, lead to spring bloom Stratification prevents mixing from bringing up fresh nutrients, plants become nutrient limited, also zooplankton eat down the plants

3 Annual Cycle Continued In the fall, the grazing animals have declined or gone into winter dormancy, early storms bring in nutrients, get a smaller fall bloom Winter storms and reduced sun lead to reduced numbers of plants in spite of ample nutrients We want to model these processes to better understand them and their interannual variability

4 One Species The equation for one species, growing without bounds: The known solution to this ordinary differential equation:

5 Difference Equation Replacing the time derivative with a finite change: Solving for the new time as a function of the old time:

6 Sticking in Some Numbers Try b = 0.1, delta t = 1, initial N = 10 If the units are days, we have e times more critters after 10 days In the plot, green is the exact solution while blue is the approximate solution using a one- day timestep

7 Linear Plot

8 Log Plot

9 Sources of Errors Size of timestep relative to timescales in the problem Numerical scheme Roundoff errors How do you tell which is the trouble here? Since the numerical growth is also exponential, we can adjust (tune) the time constant to obtain the correct solution

10 Two Species First rate equations for two species (one prey, one predator) were written by Lotka and Volterra during the 1920’s and 1930’s: Coupled, nonlinear, differential equations N 1 is prey, N 2 is predator, b, d, K 1, K 2 are constants, t is time

11 Assumptions Prey will grow exponentially without limit if no predators Rate of prey being eaten is proportional to the number of prey and the number of predators New predators happen immediately after eating prey No other prey options

12 Steady Solution No change in time: Trivial solution is N 1 = N 2 = 0 Any solution satisfying the following is also steady:

13 Difference Equations We can make an approximation to the differential equations by assuming finite timesteps: These equations can be solved on a computer Need initial values for N 1, N 2, plus values for the constants

14 Numerical Solution One steady solution is given by N 1 =1000, N 2 =10, d=0.1, b=0.1, K 1 =0.01, K 2 =0.0001 Using delta t =1, we get the steady solution numerically Any perturbation from the initial values for N 1 and N 2 will lead to expanding oscillations.

15 Initial N 1 =999

16 Initial N 1 =980

17 Red is ten times predator, blue is prey Horizontal axis is time Uncontionally unstable

18 Why the instability? Invalid assumption in the equations –No limit to the number of prey supported –No alternate prey Unstable numerical scheme Did you code it right?

19 Limit on the Prey Modifying the growth term for an environment that supports up to M prey: This equation is nonlinear, harder to solve exactly Growth rate becomes negative if N 1 > M

20 One Species

21 Limiter in Lotka-Volterra Model

22 Lotka-Volterra PZ Model Same model as the original two component model: Different constants: P=N 1 =75, Z=N 2 =10, b=a=1, etc. Need a smaller timestep than one day for such large growth rates

23 dt=0.1 dt=0.001

24 Hmmm The author is obviously using dt=0.1 He then adds a Z cannibalism term to damp the oscillations: This acts very much like the damper on the prey species

25 My Results with Cannibalism

26 Planetary Orbits

27 Euler Timestep and Orbits

28 Leapfrog Step Second-order accurate Stable for orbits Even steps and odd steps are decoupled

29 Timestepping Schemes Euler –Unconditionally unstable for some classes of problems –Errors are linear in delta t (low order) Others –There are many, many other options –Some are higher order –Each has its own stability properties

30 Back to Lotka-Volterra Euler step, dt=0.01

31 Leapfrog-Trapezoidal Step Second-order accurate, more stable

32 Large Amplitude Cycles

33 Conclusions Lotka-Volterra is cyclic, not unstable Simple-minded numerical schemes can get us in trouble Putting in more terms can lead to realistic-looking results, such as the limits on exponential growth More complex ecosystem models still use Euler stepping with the damping terms

34 Questions Timestep vs. timestepping scheme Data - growth rate data, say, to improve or disprove a model Error bars

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