Modeling the populations of foxes and rabbits given a baseline

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Homework Help Overview

The discussion revolves around modeling the populations of foxes and rabbits using a system of differential equations. The original poster presents a mathematical approach involving eigenvalues and eigenvectors to solve the initial value problem defined by the equations governing the populations.

Discussion Character

  • Mathematical reasoning, Problem interpretation, Assumption checking

Approaches and Questions Raised

  • The original poster attempts to solve the characteristic equations and derive the general solution using one eigenvalue/eigenvector pair. Some participants question whether this approach captures the full general solution and suggest considering the other eigenvalue/eigenvector pair. Others discuss the implications of using complex solutions in the context of population modeling.

Discussion Status

Participants are actively exploring the implications of their mathematical approaches, with some providing guidance on how to incorporate both eigenvalue/eigenvector pairs into the general solution. There is recognition of the need to apply initial conditions to determine constants in the solution, although there is no explicit consensus on the final form of the solution.

Contextual Notes

The problem is constrained by the requirement to model populations, which cannot be imaginary numbers, leading to discussions about the interpretation of complex solutions in this context. Initial conditions are provided, but the application of these conditions to derive constants remains a point of exploration.

JessicaHelena
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Homework Statement
The population of foxes and rabbits on Nantucket Island has been studied by biologists. They measure the populations relative to a baseline, in hundreds of animals. (So ##x(2)=5## means that there are 500 more foxes than the baseline value, and ##y(2)=−5## means that there are 500 fewer rabbits than the baseline value.)

The biologists have established the following relationship between ##x(t)## (foxes' population) and ##y(t)## (rabbits' population): ##x' = 0.5x + y## ##y' = -2.25x + 0.5y##

Suppose that at ##t=0## there are ##100## more foxes than the baseline: ##x(0) = 1##; the rabbit population is at the baseline value, ##y(0) = 0##. What is the solution to this initial value problem?
Relevant Equations
Characteristic Equation
lambda^2 - (trA) lambda + det A
From solving the characteristic equations, I got that ##\lambda = 0.5 \pm 1.5i##. Since using either value yields the same answer, let ##\lambda = 0.5 - 1.5i##. Then from solving the system for the eigenvector, I get that the eigenvector is ##{i}\choose{1.5}##. Hence the complex solution is ##{i}\choose{1.5}## ##e^{(0.5 - 1.5i)t}##.

Using the Euler's formula ##e^{iwt} = \cos(\omega t) + i\sin(\omega t)##, I get the real parts of ##x## and ##y## is given by

##{x}\choose{y}## = ##e^{0.5t}## ##{0}\choose{1.5}## ##\cos(1.5t)## + ##e^{0.5t}## ##{1}\choose{0}## ##\sin(1.5t)##

And given that $x(0) = 1$ and $y(0) = 0$, I arrived at:
##x(t) = \sin(1.5t) e^{0.5t} + e^{0.5t}##
##y(t) = 1.5\cos(1.5t) e^{0.5t} - 1.5e^{0.5t}##

However, these equations turned out to be the wrong model.
Where might I have gone wrong? Any help would really be appreciated!
 
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JessicaHelena said:
Homework Statement:: The population of foxes and rabbits on Nantucket Island has been studied by biologists. They measure the populations relative to a baseline, in hundreds of animals. (So ##x(2)=5## means that there are 500 more foxes than the baseline value, and ##y(2)=−5## means that there are 500 fewer rabbits than the baseline value.)

The biologists have established the following relationship between ##x(t)## (foxes' population) and ##y(t)## (rabbits' population): ##x' = 0.5x + y## ##y' = -2.25x + 0.5y##

Suppose that at ##t=0## there are ##100## more foxes than the baseline: ##x(0) = 1##; the rabbit population is at the baseline value, ##y(0) = 0##. What is the solution to this initial value problem?
Relevant Equations:: Characteristic Equation
lambda^2 - (trA) lambda + det A
JessicaHelena said:
From solving the characteristic equations, I got that ##\lambda = 0.5 \pm 1.5i##. Since using either value yields the same answer, let ##\lambda = 0.5 - 1.5i##. Then from solving the system for the eigenvector, I get that the eigenvector is ##{i}\choose{1.5}##. Hence the complex solution is ##{i}\choose{1.5}## ##e^{(0.5 - 1.5i)t}##.

Using the Euler's formula ##e^{iwt} = \cos(\omega t) + i\sin(\omega t)##, I get the real parts of ##x## and ##y## is given by

##{x}\choose{y}## = ##e^{0.5t}## ##{0}\choose{1.5}## ##\cos(1.5t)## + ##e^{0.5t}## ##{1}\choose{0}## ##\sin(1.5t)##

And given that $x(0) = 1$ and $y(0) = 0$, I arrived at:
##x(t) = \sin(1.5t) e^{0.5t} + e^{0.5t}##
##y(t) = 1.5\cos(1.5t) e^{0.5t} - 1.5e^{0.5t}##

However, these equations turned out to be the wrong model.
Where might I have gone wrong? Any help would really be appreciated!
I don't see anything wrong in your work, except that your solution uses only one of the eigenvalue/eigenvector pairs, so you're not getting the full general solution. Before substituting x(0) and y(0), expand your equations for ##{x(t)}\choose{y(t)}## with the other eigenvalue/eigenvector pair.
 
Mark44 said:
you're not getting the full general solution

@Mark44 I had thought that just using one eigenvalue would yield the full general solution.
When you say
Mark44 said:
expand your equations for (x(t)y(t))(x(t)y(t)){x(t)}\choose{y(t)} with the other eigenvalue/eigenvector pair
does that mean I need to work out the x(t) and y(t) for with the other eigenvalue/eigenvector pair, and add the two x(t) s for the general x(t) solution and add the two y(t) s for the general y(t) solution?
 
JessicaHelena said:
does that mean I need to work out the x(t) and y(t) for with the other eigenvalue/eigenvector pair, and add the two x(t) s for the general x(t) solution and add the two y(t) s for the general y(t) solution?
Yes, and I believe this is why you aren't getting the right solution.

You already have this:
Hence the complex solution is ##{i}\choose{1.5}## ##e^{(0.5 - 1.5i)t}##.
This is the basic solution for one eigenvalue/eigenvector. You know the other eigenvalue (1/2 + 3i/2), so it shouldn't take much work to find its associated eigenvalue to get the other basic solution.
The general solution will be a linear combination of these two basic solutions. Use that solution with your initial conditions.
 
@Mark44

Hmm okay, so the first complex solution I get is ##{i}\choose{1.5}## ##e^{(0.5-1.5i)t}## and the other complex solution I get is ##{i}\choose{-1.5}## ##e^{(0.5+1.5i)t}##.

The general solution to this system can be given by
##{x}\choose{y}## = ##c_1 e^{\lambda_1} v_1 + c_2 e^{\lambda_2} v_2##
where lambdas are eigenvalues and v's are eigenvectors.

Since we're modelling populations, we cannot have imaginary #'s. Hence I just take the real parts only, and get that

##{x}\choose{y}## = ##e^{0.5t} (c_1-c_2)## ##{\sin(1.5t)}\choose{1.5\cos(1.5t)}##

And I'm also given that x(0) = 1 and y(0) = 0.
However, I'm not really sure what to do with the c_1 and c_2, or how to use the information above (initial conditions) in general to arrive at my solution. Somehow this part is harder.
 
JessicaHelena said:
Hence I just take the real parts only
This might be where your problem lies.
I get this, based on your work:
##\begin{bmatrix} x(t) \\ y(t) \end{bmatrix} = e^{0.5t} \begin{bmatrix}c_1 i e^{-1.5it} + c_2ie^{1.5it} \\ c_1 1.5 e^{-1.5it} -c_2 1.5 e^{1.5it}\end{bmatrix}##
Now substitute your initial conditions. For what it's worth, I get ##c_1 = c_2 = \frac{-i}2##.
 
@Mark44 Oh so x(t) and y(t) can be imaginary as well?
 
@Mark44
Thank you—I did get the answers right! Could you tell me how you got the c_1,c_2 values?
 
Last edited:
JessicaHelena said:
Oh so x(t) and y(t) can be imaginary as well?
Turns out they aren't.

Here are the equations:
I get this, based on your work:
##\begin{bmatrix} x(t) \\ y(t) \end{bmatrix} = e^{0.5t} \begin{bmatrix}c_1 i e^{-1.5it} + c_2ie^{1.5it} \\ c_1 1.5 e^{-1.5it} -c_2 1.5 e^{1.5it}\end{bmatrix}##
Using x(0) = 1 and y(0) = 0, the system above becomes
##x(0) = c_1 \cdot 1 + c_2 \cdot 1 = 1##
##y(0) = 3/2 c_1 \cdot 1 - 3/2 c_2 = 0##
Solve this system for ##c_1## and ##c_2##.

I used the values for these constants and got ##x(t) = e^{t/2}\cos(3t/2)##. I stopped there and didn't work out y(t).
 

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