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Each of Problems I through 6 can be interpreted as describing the interaction of two species with populations \(x\) and \(y .\) In each of these problems carry out the following steps. $$ \begin{array}{l}{\text { (a) Draw a direction field and describe how solutions seem to behave. }} \\ {\text { (b) Find the critical points. }} \\ {\text { (c) For each critical point find the corresponding linear system. Find the eigenvalues and }} \\ {\text { eigenvectors of the linear system; classify each critical point as to type, and determine }} \\ {\text { whether it is asymptotically stable, stable, or unstable. }}\end{array} $$ $$ \begin{array}{l}{\text { (d) Sketch the trajectories in the neighborhood of each critical point. }} \\ {\text { (c) Compute and plot enough trajectories of the given system to show clearly the behavior of }} \\ {\text { the solutions. }} \\ {\text { (f) Determine the limiting behavior of } x \text { and } y \text { as } t \rightarrow \infty \text { and interpret the results in terms of }} \\ {\text { the populations of the two species. }}\end{array} $$ $$ \begin{array}{l}{d x / d t=x(1.5-0.5 x-y)} \\ {d y / d t=y(2-y-1.125 x)}\end{array} $$

Short Answer

Expert verified
Answer: The critical point \((\frac{8}{5},\frac{4}{5})\) is a saddle point. This implies that the point is unstable, as trajectories will approach and move away from this point.

Step by step solution

01

(a) Draw a direction field and describe the solutions' behavior.

We are given the following system of equations: $$\frac{dx}{dt} = x(1.5 - 0.5x - y)$$ $$\frac{dy}{dt} = y(2 - y - 1.125x)$$ To draw a direction field, create a grid of points and indicate the direction of the solution at each point. It's best to use software like MATLAB, Mathematica, or an online tool for this. Observe the direction field and describe how the solutions behave based on the arrows.
02

(b) Find the critical points.

To find the critical points, set both \(\frac{dx}{dt}\) and \(\frac{dy}{dt}\) equal to zero and solve for \(x\) and \(y\): $$x(1.5 - 0.5x - y) = 0$$ $$y(2 - y - 1.125x) = 0$$ We get three critical points: \((0,0), (3,0),\) and \((\frac{8}{5},\frac{4}{5})\).
03

(c) Linearize, find eigenvalues and eigenvectors, classify the critical points, and determine their stability.

To linearize the system at each critical point, we will find the Jacobian matrix: $$ J(x, y) = \begin{bmatrix} \frac{\partial}{\partial x}(1.5 - 0.5x - y) & \frac{\partial}{\partial y}(1.5 - 0.5x - y)\\ \frac{\partial}{\partial x}(2 - y - 1.125x) & \frac{\partial}{\partial y}(2 - y - 1.125x) \end{bmatrix} = \begin{bmatrix} -0.5x - y + 1.5 & -x \\ -1.125y & -x - y + 2 \end{bmatrix} $$ Now, evaluate the Jacobian at each critical point, find eigenvalues and eigenvectors, classify the critical points, and determine their stability. For the critical point \((0,0)\): $$ J(0,0) = \begin{bmatrix} 1.5 & 0 \\ 0 & 2 \end{bmatrix} $$ Eigenvalues: \(\lambda_1 = 1.5\) and \(\lambda_2 = 2\). Since both eigenvalues are positive, the critical point is an unstable node. For the critical point \((3,0)\): $$ J(3,0) = \begin{bmatrix} -1.5 & -3 \\ 0 & -1 \end{bmatrix} $$ Eigenvalues: \(\lambda_1 = -1.5\) and \(\lambda_2 = -1\). Since both eigenvalues are negative, the critical point is a stable node. For the critical point \((\frac{8}{5},\frac{4}{5})\): $$ J\left(\frac{8}{5},\frac{4}{5}\right) = \begin{bmatrix} -0.2 & -\frac{8}{5} \\ -\frac{9}{5} & 0.8 \end{bmatrix} $$ Eigenvalues: \(\lambda_1 = -0.5\) and \(\lambda_2 = 1.1\). Since one eigenvalue is positive and the other is negative, the critical point is a saddle point and is unstable.
04

(d) Sketch the trajectories near the critical points.

Based on the classifications and stabilities of the critical points, sketch the trajectories near each critical point: \((0,0)\) will have trajectories moving away; \((3,0)\) will have trajectories converging and \((\frac{8}{5},\frac{4}{5})\) will have trajectories that approach as well as move away from the point.
05

(e) Compute and plot enough trajectories of the system to show the behavior of the solutions.

You can use software like MATLAB, Python, Mathematica, or an online tool to solve the system of equations numerically and plot several trajectories with different initial conditions. This will provide a clear demonstration of how solutions behave in different regions of the \(xy\)-plane.
06

(f) Determine the limiting behavior of \(x\) and \(y\) as \(t \rightarrow \infty\).

By analyzing the eigenvalues of the Jacobian matrix at each critical point, we can determine the long-term behavior of populations \(x\) and \(y\) as \(t \rightarrow \infty\). Since the critical point \((3,0)\) is a stable node, most solutions will converge to this point as \(t \rightarrow \infty\). In this case, population \(x\) will tend to 3, and population \(y\) will tend to 0. This means that as time goes on, the population of species \(x\) will stabilize at a constant non-zero value, while the population of species \(y\) will ultimately go extinct.

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Most popular questions from this chapter

Consider the system of equations $$ d x / d t=y-x f(x, y), \quad d y / d t=-x-y f(x, y) $$ where \(f\) is continuous and has continuous first partial derivatives. Show that if \(f(x, y)>0\) in some neighborhood of the origin, then the origin is an asymptotically stable critical point, and if \(f(x, y)<0\) in some neighborhood of the origin, then the origin is an unstable critical point. Hint: Construct a Liapunov function of the form \(c\left(x^{2}+y^{2}\right) .\)

Two species of fish that compete with each other for food, but do not prey on each other, are bluegill and redear. Suppose that a pond is stocked with bluegill and redear, and let \(x\) and \(y\) be the populations of bluegill and redear, respectively, at time \(t\). Suppose further that the competition is modeled by the equations $$\frac{d x}{d t}=x\left(\epsilon_{1}-\sigma_{1} x-\alpha_{1} y\right), \frac{d y}{d t}=y\left(\epsilon_{2}-\sigma_{2} y-\alpha_{2} x\right)$$ a. If \(\epsilon_{2} / \alpha_{2}>\epsilon_{1} / \sigma_{1}\) and \(\epsilon_{2} / \sigma_{2}>\epsilon_{1} / \alpha_{1},\) show that the only equilibrium populations in the pond are no fish, no redear, or no bluegill. What will happen for large \(t ?\) b. If \(\epsilon_{1} / \sigma_{1}>\epsilon_{2} / \alpha_{2}\) and \(\epsilon_{1} / \alpha_{1}>\epsilon_{2} / \sigma_{2}\), show that the only equilibrium populations in the pond are no fish, no redear, or no bluegill. What will happen for large \(t ?\)

Consider the competition between bluegill and redear mentioned in Problem 6. Suppose that \(\epsilon_{2} / \alpha_{2}>\epsilon_{1} / \sigma_{1}\) and \(\epsilon_{1} / \alpha_{1}>\epsilon_{2} / \sigma_{2}\) so, as shown in the text, there is a stable equilibrium point at which both species can coexist. It is convenient to rewrite the equations of Problem 6 in terms of the carrying capacity of the pond for bluegill \(\left(B=\epsilon_{1} / \sigma_{1}\right)\) in the absence of redear and its carrying capacity for redear \(\left(R=\epsilon_{2} / \sigma_{2}\right)\) in the absence of bluegill. a. Show that the equations of Problem 6 take the form $$\frac{d x}{d t}=\epsilon_{1} x\left(1-\frac{1}{B} x-\frac{\gamma_{1}}{B} y\right), \frac{d y}{d t}=\epsilon_{2} y\left(1-\frac{1}{R} y-\frac{\gamma_{2}}{R} x\right)$$ where \(\gamma_{1}=\alpha_{1} / \sigma_{1}\) and \(\gamma_{2}=\alpha_{2} / \sigma_{2} .\) Determine the coexistence equilibrium point \((X, Y)\) in terms of \(B, R, \gamma_{1},\) and \(\gamma_{2}\) b. Now suppose that an angler fishes only for bluegill with the effect that \(B\) is reduced. What effect does this have on the equilibrium populations? Is it possible, by fishing, to reduce the population of bluegill to such a level that they will die out?

an autonomous system is expressed in polar coordinates. Determine all periodic solutions, all limit cycles, and determine their stability characteristics. $$ d r / d t=r|r-2|(r-3), \quad d \theta / d t=-1 $$

an autonomous system is expressed in polar coordinates. Determine all periodic solutions, all limit cycles, and determine their stability characteristics. $$ d r / d t=r(1-r)^{2}, \quad d \theta / d t=-1 $$

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