Show positivity and boundedness of a non-linear system

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This discussion focuses on demonstrating the boundedness of a nonlinear system defined by three differential equations involving populations N (prey), P (predator), and K (resource). The equations are analyzed using Lyapunov functions and bounding techniques. The analysis confirms that N, P, and K are all bounded above, with specific conditions established for each variable. The findings indicate that if P exceeds a certain threshold, it will decrease, ensuring boundedness.

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I want to show the boundedness of the nonlinear system below. Assume, ##N, P, K, t > 0## and all other parameters to be positive.

$$\frac{{d}N}{{d}t} ~=~ rN\left(1 - \frac{N}{K}\right) - \frac{a(1 - m)NP}{1 + \gamma(1 - m)N}$$

$$\frac{{d}P}{{d}t} ~=~ \frac{b(1 - m)NP}{1 + \gamma(1 - m)N} - cP$$

$$\frac{{d}K}{{d}t} ~=~ \frac{(K - \alpha)(p - K)}{q + K}$$

My attempt is this:

We show that the system is bounded by showing each equation is bounded.

For, ##\frac{{d}K}{{d}t} = \frac{(K - \alpha)(p - K)}{q + K}##, the

denominator ##q+K>0## ensures it is well-defined. Now if ##\alpha<p## then ##K##

is bounded between ##\alpha## and ##p## and if ##\alpha>p##, then ##K## is bounded between

##p## and ##\alpha##. Therefore, ##\min(\alpha,p)\le K \le \max(\alpha,p)##.

Now for, ##\frac{{d}N}{{d}t} = rN\left(1 - \frac{N}{K}\right) - \frac{a(1 - m)NP}{1 + \gamma(1 - m)N} \implies \frac{{d}N}{{d}t} \le rN\left(1 - \frac{N}{K}\right)##. Now since ##K## is bounded, we choose the upper and lower bounds to be ##K_{max}## and ##K_{min}## respectively. Then

##K\le K_{max} \implies - \frac{N}{K} \le \frac{N}{K_{max}}##. Therefore, for any

##\epsilon >0##, we choose ##N_{upper} = K_{max} + \epsilon##. So we have,

##\frac{{d}N}{{d}t} \le rN\left(1 - \frac{N}{K_{max}}\right) < -rN\frac{\epsilon}{K_{max}} < 0##. This implies it is always bounded above.

Now for, ##\frac{{d}P}{{d}t} ~=~ \frac{b(1 - m)NP}{1 + \gamma(1 - m)N} - cP##,

we consider the Lyapunov function ##V(t)## defined as:

$$V(t) = N(t) + \frac{a}{b}P(t)$$

Since ##N(t) \ge 0## and ##P(t) \ge 0##, it follows that ##V(t) \ge 0##.

Let's compute the derivative of $$V(t)$$ with respect to time:

$$ \frac{dV}{dt} = \frac{dN}{dt} + \frac{a}{b}\frac{dP}{dt} $$

Substitute the expressions for $$\frac{dN}{dt}$$ and $$\frac{dP}{dt}$$ from equations (1) and (2):

$$\begin{align*}

\frac{dV}{dt} &= \left[ rN\left(1 - \frac{N}{K}\right) - \frac{a(1 - m)NP}{1 + \gamma(1 - m)N} \right] + \frac{a}{b}\left[ \frac{b(1 - m)NP}{1 + \gamma(1 - m)N} - cP \right] \\

\frac{dV}{dt} &= rN\left(1 - \frac{N}{K}\right) - \frac{a(1 - m)NP}{1 + \gamma(1 - m)N} + \frac{ab(1 - m)NP}{b(1 + \gamma(1 - m)N)} - \frac{ac}{b}P \\

\frac{dV}{dt} &= rN\left(1 - \frac{N}{K}\right) - \frac{a(1 - m)NP}{1 + \gamma(1 - m)N} + \frac{a(1 - m)NP}{1 + \gamma(1 - m)N} - \frac{ac}{b}P

\end{align*}$$

The terms cancel out:

$$ \frac{dV}{dt} = rN\left(1 - \frac{N}{K}\right) - \frac{ac}{b}P $$

We know that ##K(t) \ge K_{min}## and ##N(t) \ge 0##. The function ##f(N) = rN\left(1 - \frac{N}{K}\right)## is a parabola opening downwards with roots at ##N=0## and ##N=K##. Its maximum value is achieved at ##N = K/2##, with the value ##r(K/2)(1 - 1/2) = rK/4##.

Since ##K(t) \le K_{max}##, the maximum value of ##rN\left(1 - \frac{N}{K}\right)## for all possible ##K(t)## is bounded by ##rK_{max}/4##.

Therefore, we can write:

$$ \frac{dV}{dt} \le \frac{rK_{max}}{4} - \frac{ac}{b}P $$

Let $$M_V = \frac{rK_{max}}{4}$$, which is a positive constant. Then:

$$ \frac{dV}{dt} \le M_V - \frac{ac}{b}P $$

Now, consider the case where ##P(t)## becomes sufficiently large. If ##P(t) > \frac{M_V b}{ac}##, then:

$$ M_V - \frac{ac}{b}P < M_V - \frac{ac}{b}\left(\frac{M_V b}{ac}\right) = M_V - M_V = 0 $$

So, if ##P(t) > \frac{M_V b}{ac}##, then ##\frac{dV}{dt} < 0##. This implies that if ##P(t)## exceeds this threshold, the value of ##V(t)## will start to decrease. Since ##V(t) = N(t) + \frac{a}{b}P(t)## and ##N(t) \ge 0##, a decrease in ##V(t)## when ##P(t)## is large means that ##P(t)## must also decrease. Thus, ##P(t)## cannot grow indefinitely.

Therefore, ##P(t)## is ultimately bounded above. Since ##P(t) \ge 0##, it is also bounded below. So, there exists a constant ##P_{upper} > 0## such that ##0 \le P(t) \le P_{upper}## for all sufficiently large ##t##.
 
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The first equation is, when N>>1
\frac{dN}{dt} \approx -\frac{r}{K}N^2 &lt; 0
So N seems to be upper bounded.

The third equation is, when K>>1
\frac{dK}{dt} \approx -K &lt; 0
So K seems to be upper bounded.

The second equation is
\frac{dP}{dt} =-D P
where
D=c-\frac{b(1-m)N}{1+\gamma(1-m)N}
Can we expect D>0 in your system so that P is upper bounded ?
 
Last edited:
Can you find a Liapunov function for the system?
 

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