Modeling the populations of foxes and rabbits given a baseline

In summary, the biologists have established a relationship between the populations of foxes and rabbits on Nantucket Island, measured relative to a baseline. By solving the characteristic equations, it was found that the eigenvalues for this system are ##0.5 \pm 1.5i##. After choosing one of the eigenvalues as ##0.5 - 1.5i## and solving for the corresponding eigenvector, a complex solution was obtained as ##{i}\choose{1.5}## ##e^{(0.5 - 1.5i)t}##. Using the Euler's formula, the real parts of the solutions for ##x(t)## and ##y(t)## were found to
  • #1
JessicaHelena
188
3
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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  • #2
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.
 
  • #3
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?
 
  • #4
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.
 
  • #5
@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.
 
  • #6
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##.
 
  • #7
@Mark44 Oh so x(t) and y(t) can be imaginary as well?
 
  • #8
@Mark44
Thank you—I did get the answers right! Could you tell me how you got the c_1,c_2 values?
 
Last edited:
  • #9
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).
 

1. What is the purpose of modeling the populations of foxes and rabbits?

The purpose of modeling the populations of foxes and rabbits is to better understand the dynamics between these two species and how their populations may change over time. This can provide insights into the natural balance of ecosystems and potential impacts of factors such as climate change or human interventions.

2. What factors are typically included in a model of fox and rabbit populations?

Factors that may be included in a model of fox and rabbit populations include birth and death rates, predation rates, availability of resources, and competition for resources. Other factors such as weather conditions and disease outbreaks may also be considered.

3. How is the baseline for the model determined?

The baseline for the model is typically determined by gathering data on the current population sizes of foxes and rabbits in a specific area. This data can be collected through field observations, surveys, or other methods. The baseline is then used as a starting point for the model, which can be adjusted over time as new data becomes available.

4. What are some potential limitations of modeling fox and rabbit populations?

Some potential limitations of modeling fox and rabbit populations include the complexity of natural ecosystems, as well as the difficulty in accurately predicting and accounting for all the variables that may impact populations. Models may also be affected by biases or errors in data collection or assumptions made by the scientist.

5. How can modeling fox and rabbit populations be useful for conservation efforts?

Modeling fox and rabbit populations can be useful for conservation efforts by providing insights into how changes in the environment or other factors may impact these species. This can help conservationists make informed decisions about how to manage and protect these populations, such as implementing measures to reduce human impact or monitoring for potential disease outbreaks.

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