Why does the population go to extinction if the solution is real?

In summary, the population of a certain species subjected to a specific kind of predation is modeled by a difference equation. The steady states of this equation are 0 and a solution involving the parameters a and b. If the parameter a^2 is greater than 4b^2, it is possible for the population to be driven to extinction if it becomes less than a certain critical size. This is illustrated by considering the sequence of population values and showing that it decreases towards 0 if the ratio of population values is less than 1. The cob web plot also shows that the only steady state for this case is 0, indicating the possibility of a critical extinction point.
  • #1
Dustinsfl
2,281
5
The population of a certain species subjected to a specific kind of predation is modeled
by the difference equation

$$
u_{t+1}=\frac{au_t^2}{b^2+u_t^2}, \quad a>0.
$$

Determine the equilibria and show that if $a^2 > 4b^2$ it is possible for the populationto be driven to extinction if it becomes less than a critical size which you should find.

So the steady states are

$u_*=0$

$u_*=\frac{a\pm\sqrt{a^2-4b^2}}{2}$

So why if the solution is real, does the population go to extinction?
 
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  • #2
dwsmith said:
The population of a certain species subjected to a specific kind of predation is modeled
by the difference equation

$$
u_{t+1}=\frac{au_t^2}{b^2+u_t^2}, \quad a>0.
$$

Determine the equilibria and show that if $a^2 > 4b^2$ it is possible for the populationto be driven to extinction if it becomes less than a critical size which you should find.

So the steady states are

$u_*=0$

$u_*=\frac{a\pm\sqrt{a^2-4b^2}}{2}$

So why if the solution is real, does the population go to extinction?

The recursive relation can be written in the form...

$\displaystyle \Delta_{n}=u_{n+1}-u_{n}= - \frac{b^{2}\ u_{n} - a\ u^{2}_{n} - u^{3}_{n}}{b^{2}+u^{2}_{n}}= f(u_{n})\ ,\ a>0$ (1)

Under the hypothesis that is $a^{2}>4\ b^{2}$ and that is $u_{n} \ge 0\ \forall n$ the function f(x) has two 'attractive fixed points' in $x_{0}=0$ and $\displaystyle x_{+}= \frac{a+ \sqrt{a^{2}-4 b^{2}}}{2}$ and a 'repulsive fixed point' in $\displaystyle x_{-}= \frac{a- \sqrt{a^{2}-4 b^{2}}}{2}$. The solution of (1) depends from the 'initial state' $u_{0}$ and precisely...

a) if $0 \le u_{0} < x_{-}$ the sequence has limit $x_{0}=0$...

b) if $u_{0}=x_{-}$ the sequence has limit $x_{-}$...

c) if $u_{0}>x_{-}$ the sequence has limit $x_{+}$...

Kind regards

$\chi$ $\sigma$
 
  • #3
chisigma said:
The recursive relation can be written in the form...

$\displaystyle \Delta_{n}=u_{n+1}-u_{n}= - \frac{b^{2}\ u_{n} - a\ u^{2}_{n} - u^{3}_{n}}{b^{2}+u^{2}_{n}}= f(u_{n})\ ,\ a>0$ (1)

Under the hypothesis that is $a^{2}>4\ b^{2}$ and that is $u_{n} \ge 0\ \forall n$ the function f(x) has two 'attractive fixed points' in $x_{0}=0$ and $\displaystyle x_{+}= \frac{a+ \sqrt{a^{2}-4 b^{2}}}{2}$ and a 'repulsive fixed point' in $\displaystyle x_{-}= \frac{a- \sqrt{a^{2}-4 b^{2}}}{2}$. The solution of (1) depends from the 'initial state' $u_{0}$ and precisely...

a) if $0 \le u_{0} < x_{-}$ the sequence has limit $x_{0}=0$...

b) if $u_{0}=x_{-}$ the sequence has limit $x_{-}$...

c) if $u_{0}>x_{-}$ the sequence has limit $x_{+}$...

Kind regards

$\chi$ $\sigma$

Unfortunately, I just don't understand what you are doing to help in this post (or the others). I am not saying you aren't helping. I just don't understand your procedures and methods.
 
  • #4
dwsmith said:
Unfortunately, I just don't understand what you are doing to help in this post (or the others). I am not saying you aren't helping. I just don't understand your procedures and methods.

On MHF I wrote a tutorial section where the general procedure for solving this type of recursive relations was illustrated ... 'unfortunately' MHF collapsed and now, with the approval of Administrators, I can try to rewrite the same tutorial section on MHB...

Kind regards

$\chi$ $\sigma$
 
  • #5
dwsmith said:
The population of a certain species subjected to a specific kind of predation is modeled
by the difference equation

$$
u_{t+1}=\frac{au_t^2}{b^2+u_t^2}, \quad a>0.
$$

Determine the equilibria and show that if $a^2 > 4b^2$ it is possible for the populationto be driven to extinction if it becomes less than a critical size which you should find.

So the steady states are

$u_*=0$

$u_*=\frac{a\pm\sqrt{a^2-4b^2}}{2}$

So why if the solution is real, does the population go to extinction?

Consider:

\[ \frac{u_{t+1}}{u_t}=\frac{au_t}{b^2+u_t^2}\]

\( \{u_t\} \) is a decreasing sequence if the above is less than \(1\). So solve:

\[ \frac{au_t}{b^2+u_t^2}<1\]

and the result will follow (you will need to justify it going to zero rather than decreasing to a non-zero limit but that is easily done).

CB
 
  • #6
CaptainBlack said:
Consider:

\[ \frac{u_{t+1}}{u_t}=\frac{au_t}{b^2+u_t^2}\]

\( \{u_t\} \) is a decreasing sequence if the above is less than \(1\). So solve:

\[ \frac{au_t}{b^2+u_t^2}<1\]

and the result will follow (you will need to justify it going to zero rather than decreasing to a non-zero limit but that is easily done).

CB

I am not sure how or why the result follows? That leads me to the same quadratic to solve but with an inequality.

From the cob web plot, I see there is only the zero steady state when a^2<4b^2

How could there be a critical extinction point if the only steady state is 0?
 
Last edited:

FAQ: Why does the population go to extinction if the solution is real?

What is a discrete model extinction?

A discrete model extinction is a mathematical concept used to describe the disappearance of a species in a specific area or ecosystem due to various factors such as competition, predation, or environmental changes. It is a simplified representation of the complex interactions between species in an ecosystem.

How is a discrete model extinction different from a continuous model extinction?

A discrete model extinction considers the population of a species in a specific area at specific time intervals, while a continuous model extinction takes into account the continuous change in population over time. Discrete models are often used when there is limited data available or to simplify complex systems.

What are the main factors that can lead to a discrete model extinction?

The main factors that can lead to a discrete model extinction include competition for resources, predation, disease, and environmental changes such as climate change or habitat destruction. These factors can cause a decrease in population size or make the environment unsuitable for the species to survive.

How is a discrete model extinction useful in studying real-life ecosystems?

A discrete model extinction can provide insights into the potential effects of different factors on a species' population and can help predict the likelihood of extinction. It can also be used to compare and analyze different scenarios and determine the most effective conservation strategies for a species in a particular ecosystem.

What are some limitations of using a discrete model extinction?

One limitation of using a discrete model extinction is its simplicity, which may not accurately reflect the complexities of real-life ecosystems. It also relies on assumptions and may not account for all possible factors that can impact a species' survival. Additionally, it requires accurate data and assumptions about the species and its environment, which may not always be available.

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