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ODE and Population Models
2008 REU ODE and Population Models
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Differential Equations!
Intro Often know how populations change over time (e.g. birth rates, predation, etc.), as opposed to knowing a ‘population function’ Differential Equations! Knowing how population evolves over time w/ initial population population function
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Example – Hypothetical rabbit colony lives in a field, no predators.
Let x(t) be population at time t; Want to write equation for dx/dt Q: What is the biggest factor that affects dx/dt? A: x(t) itself! more bunnies more baby bunnies
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1st Model—exponential, Malthusian Solution:
x(t)=x(0)exp(at)
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Critique Unbounded growth Non integer number of rabbits
Unbounded growth even w/ 1 rabbit! Let’s fix the unbounded growth issue dx/dt = ????
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Logistic Model dx/dt = ax(1-x/K) K-carrying capacity
we can change variables (time) to get dx/dt = x(1-x/K) Can actually solve this DE Example: dx/dt = x(1-x/7)
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Solutions: Critique: Still non-integer rabbits
Still get rabbits with x(0)=.02
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Fixed Points (equilibria)
In Previous example: x=0 and x=7 are fixed points Fixed Point: dx/dt = 0 (so it’s fixed!) Stability: stable – near solutions tend to fixed point unstable = not stable
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Stability Note: near x=7 d/dx ( du/dt) <0 (stable)
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Stability Note: near x=0 d/dx ( du/dt) > 0 (unstable)
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Taylor series at x* dx/dt=f(x) (no dependence on t)
dx/dt = f(x)= c0+c1(x-x*)+c2(x-x*)^2+ …. (c0 = 0) If c1≠0, we can tell stability.
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Moral: If dx/dt = f(x) and f(x*)=0
1) d/dx( f(x)) <0 at x* then x* is stable. 2) d/dx( f(x) ) >0 at x* then x* is unstable.
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x’ versus x For first order autonomous equations, plotting x’ versus x encapsulates all this info x’ positive (unstable) x’ negative (stable)
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Reality check Find and classify all equilibria of dx/dt = sin (x(t))
Firefly example (tomorrow)
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Rabbit vs. Deer
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compete for the same food source.
Let x(t) rabbits and y(t) deer compete for the same food source. dx/dt = dy/dt = Ax(1-x/K) -Cxy By(1-y/W) -Dxy Or…. (after changes of coordinates…) dx/dt = x(1-x-ay) dy/dt = y(b-by-cx)
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Analysis of one case dx/dt = x(1-x-2y) dy/dt = y(2-2y-5x)
Equlibria/Fixed Points: (0,0) , (0,1), (1,0), (1/4,3/8) Q: How do we know if these are stable or unstable? A: Linear approximation (derivative)
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Linear Systems dx/dt= Ax (given by matrix mult) Fixed Point(s)?
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What’s an eigenvalue again?
Ax = λx (λ,x) are eigenvalue eigenvector pair Who cares? Think about: x(t) = exp (λt)x (Handout/Maple)
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Other Tools Trapping regions Poincare Bendixson Nullclines
Series solutions ,etc. Invariant Sets Bifurcations
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Suppose we have 2 species; one predator y(t) (e. g
Suppose we have 2 species; one predator y(t) (e.g. wolf) and one its prey x(t) (e.g. hare)
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Actual Data
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Model Want a DE to describe this situation
dx/dt= ax-bxy = x(a-by) dy/dt=-cy+dxy = y(-c+dx) Let’s look at: dx/dt= x(1-y) dy/dt=y(-1+x)
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Called Lotka-Volterra Equation, Lotka & Volterra independently studied this post WW I.
Fixed points: (0,0), (c/d,a/b) (in example (1,1)).
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Phase portrait y (1,1) x
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A typical portrait: a ln y – b y + c lnx – dx=C
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Solution vs time
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Critiques Nicely captures periodic nature of data
Orbits are all bounded, so we do not need a logistic term to bound x. Periodic cycles not seen in nature
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Previte’s Population Projects
3-species chains REU 3 Competing Species 2002/3 REU 4-species chains /5 REUs Adding a scavenger /7 REUs (other interactions possible!)
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3-species model (REU 2000) 3 species food chain!
x = worms; y= robins; z= eagles dx/dt = ax-bxy =x(a-by) dy/dt= -cy+dxy-eyz =y(-c+dx-ez) dz/dt= -fz+gyz =z(-f+gy)
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Critical analysis of 3-species chain
ag > bf → unbounded orbits ag < bf → species z goes extinct ag = bf → periodicity
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ag ≠ bf ag=bf
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2000 REU and paper
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Tools used in analysis Linearization Trapping regions Invariant sets
Liapunov functions (“energy” functions)
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One open conjecture ag>bf y tends to a limit as time increases
all numerical evidence shows this, but no analytic proof.
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4-species model dw/dt = aw-bxw =w(a-bx) dx/dt= -cx+dwx-exy =x(-c+dw-ey) dy/dt= -fy+gxy - hyz =y(-f+gx-hz) dz/dt= -iz+jyz =z(-i+jy)
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2004/5 REU did analysis Orbits bounded again as in n=2
Quasi periodicity (next slide) ag<bf gives death to top 2 ag=bf gives death to top species ag>bf gives quasi-periodicity
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Quasi-periodicity
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Previte’s doughnut conjecture (ag>bf)
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This is wide open Project never finished
Proof seems too hard, may involve deep topics such as KAM theory, Hamiltonian systems
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Simple Scavenger Model
lynx beetle hare
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Semi-Simple scavenger– Ben Nolting 2005
Know (x,y) -> (c, 1-bc) use this to see fc+gc+h=e every solution is periodic fc+gc+h<e implies z goes extinct fc+gc+h>e implies z to a periodic on the cylinder
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Ben Nolting and his poster in San Antonio, TX
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Scavenger Model with feedback (Malorie Winters & James Greene 2006/7)
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Biological Example (crowding prey)
crayfish Predator of mayfly nymph Scavenger of trout carcasses Rainbow Trout (predator) Mayfly nymph (Prey) Crayfish are scavenger & predator
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Analysis (Malorie Winters)
Regions of periodic behavior and Hopf bifurcations and stable coexistence. Regions with multi stability and dependence on initial conditions
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Malorie Winters, and in New Orleans, LA
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REU 2007 James Greene finds a model that exhibits chaos
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2007 scavenger system dx/dt=x(1-bx-y-z) b, c, e, f, g, β > 0
dy/dt=y(-c+x) dz/dt=z(-e+fx+gy-βz)
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Period Doubling cascade and attractor
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TO DO Finish up the analysis from 2007
Including Hopf Bifurcation analysis, boundedness of orbits, and compare onset of chaos with other models Crowd the predator
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