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Predator-Prey Model (Lotka-Volterra)

Recall

As long as algebra is taught in school, there will be prayer in school, Cokie Roberts

Don’t panic, The Hitchhiker’s Guide to the Galaxy

Non Linear System

Differential equations

An algebraic equation is a mathematical statement that declares or asserts the equality of two algebraic expressions. These expressions are constructed using:

  1. Dependent and independent variables. Variables represent unknown quantities. The independent variable is chosen freely, while the dependent variable changes in response to the independent variable.
  2. Constants. Fixed numerical values that do not change.
  3. Algebraic operations. Operations such as addition, subtraction, multiplication, division, exponentiation, and root extraction.

Definition. A differential equation is an equation that involves one or more dependent variables, their derivatives with respect to one or more independent variables, and the independent variables themselves, e.g., $\frac{dy}{dx} = 3x +5y, y’ + y = 4xcos(2x), \frac{dy}{dx} = x^2y+y, etc.$

It involves (e.g., $\frac{dy}{dx} = 3x +5y$):

The Existence and Uniqueness Theorem provides crucial insight into the behavior of solutions to first-order differential equations ODEs. It states that if:

Then the differential equation y' = f(x, y) has a unique solution to the initial value problem through the point (x0, y0) .

A first-order linear differential equation (ODE) has the general form: a(x)y' + b(x)y = c(x) where y′ is the derivative of y with respect to x, and a(x), b(x), and c(x) are functions of x. If c(x) = 0, the equation is called homogeneous, i.e., a(x)y’ + b(x)y = 0.

The equation can also be written in the standard linear form as: y’ + p(x)y = q(x) where $p(x)=\frac{b(x)}{a(x)}\text{ and }q(x) = \frac{c(x)}{a(x)}$

A second-order linear homogeneous differential equation ODE with constant coefficients is a differential equation of the form: y'' + Ay' + By = 0 where:

This equation is homogeneous, meaning that there are no external forcing terms (like a function of t) on the right-hand side.

Non-linear Systems

Predator-Prey Model (Lotka-Volterra)

In ecological modeling, predator-prey interactions are crucial for understanding population dynamics within ecosystems. One classic model that describes these interactions is the Lotka-Volterra equations. This model captures the cyclical nature of predator and prey populations, such as sharks (predators) and fish (prey).

Let:

Predator and Prey dynamics

Predator and Prey dynamics

In the absence of prey, predators (sharks) die out due to lack of food: x’ = -ax, where a > 0 is the natural rate of predators. Growth due to consuming prey: x’ -ax + bxy, where b > 0 represents the rate at which predators consume prey.

Without predators, prey reproduce and their population grows exponentially: y′ = cy where c > 0 is the natural growth rate of prey. Prey population decreases as they are consumed by predators: y′= cy − dxy where d > 0 represents the rate at which prey are consumed.

The system of equations modeling the interaction is: $\begin{cases} x’ = -ax + bxy \\ y’ = cy -dxy \end{cases}$

Critical points occur where the population sizes do not change over time (x′= 0 and y′= 0).

Setting the Equations to Zero: $\begin{cases} x(-a + by) = 0 ~(i)\\ y(c-dx) = 0 ~(ii)\end{cases}$

If x = 0 (no predators) ⇒[(ii)] yc = 0 ⇒[c is a positive constant] y = 0 (no fish, both populations are extinct).

Otherwise, by (i) -a + by = 0 ⇒ y = ab > 0 (a and b are positive constants). By (ii) c -dx = 0 ⇒ x = cd. It only produces two critical points: (0, 0) and (cd, ab).

Critical point: (cd, ab), both populations are stable and non-zero.

Linearizing the System Near the Origin (0, 0)

To understand the system’s behavior near the origin, we linearize it by ignoring the non-linear terms (bxy and −dxy). At (0, 0), the terms bxy and dxy as negligible since x and y are near zero.

Linearized System $\begin{cases} x’ = -ax \\ y’ = cy \end{cases}$

Matrix Representation $(\begin{smallmatrix}x’\\ y’\end{smallmatrix})=(\begin{smallmatrix}-a & 0\\ 0 & c\end{smallmatrix})(\begin{smallmatrix}x\\ y\end{smallmatrix})$

If we have a diagonal matrix, the eigenvalues are the diagonal elements, λ1 = -a < 0, λ2 = c > 0.

For complementary sake, det(A -λI) = 0 ↭ $(\begin{smallmatrix}-a-λ & 0\\ 0 & c-λ\end{smallmatrix}) = (-a -λ)(c-λ) = 0$. Hence, λ = -a, c. Given that a > 0 and c > 0, λ1 = -a is a negative eigenvalue, and λ2 = c is a positive eigenvalue. Detailed Analysis of the Matrix with Two distinct real Eigenvalues , general solution: x(t) = x0e-at, y(t) = y0ect

Since one eigenvalue is negative and the other is positive, the critical point (0,0) is a saddle point. Saddle points are unstable because trajectories move toward the critical point along the stable direction (x-axis, predator population) and away from it along the unstable direction (y-axis, prey population). (Refer to Figure ii for a visual representation and aid in understanding it).

The Trace-Determinat Plane

Simplifying the Model

For simplicity, let’s assume that a = b = c = d = 1 (set all parameters to 1). The system becomes:

$\begin{cases} x’ = -x + xy \\ y’ = y -xy \end{cases}$

Critical points with simplified parameters: (0, 0) and (1, 1).

Linearizing at the Non-Zero Critical Point (1, 1)

In other to linearize the system at the critical point (cd, ab) = (1, 1), we are going to use the Jacobian matrix:

$J = (\begin{smallmatrix}\frac{∂x’}{∂x} & \frac{∂x’}{∂y}\\ \frac{∂y’}{∂x} & \frac{∂y’}{∂y}\end{smallmatrix}) = (\begin{smallmatrix}-1+y & x\\ -y & 1-x\end{smallmatrix})$

Evaluate at (1, 1) $(\begin{smallmatrix}-1+y & x\\ -y & 1-x\end{smallmatrix})\bigg|_{(1, 1)} = (\begin{smallmatrix}0 & 1\\ -1 & 0\end{smallmatrix})$. The Linearized system is:

$\begin{cases} x’ = y \\ y’ = -x \end{cases}$.

The characteristic equation is obtained from det(J -λI) = $(\begin{smallmatrix}-λ & 1\\ -1 & -λ\end{smallmatrix}) = λ^2+1 = 0$. Eigenvalues are λ = ±i (purely imaginary).

Detailed Analysis of the Matrix with Complex Eigenvalues , The general solution to this system (λ = α±βi) is $x(t) = e^{αt}[C_1(\begin{smallmatrix}cos(βt)\\ -sin(βt)\end{smallmatrix})+ C_2(\begin{smallmatrix}sin(βt)\\ cos(βt)\end{smallmatrix})] = [λ = ±i, α = 0, β = 1] C_1(\begin{smallmatrix}cos(t)\\ -sin(t)\end{smallmatrix})+ C_2(\begin{smallmatrix}sin(t)\\ cos(t)\end{smallmatrix})$ where c1 and c2 are constant determined by initial conditions.

Both x(t) and y(t) involve sine and cosine functions. There is no exponential growth or decay factor. The solutions are periodic with a period of 2π. The amplitude of oscillations remains constant over time. Plotting x(t) versus y(t) yields closed curves (concentric circles or ellipses centered at the origin). This is characteristic of center behavior in dynamical systems.

Since there is no exponential term (no real part in eigenvalues), trajectories neither spiral inward nor outward. The system remains in perpetual oscillation around the critical point. At (1, 1), the trajectories represent oscillations of predator and prey populations around their equilibrium values. Populations return to their starting values after each cycle.

Deriving the Second-Order ODE

Starting from the linearized system:

$\begin{cases} x’ = y \\ y’ = -x \end{cases}$.

Differentiate equation (i) with respect to t: x’’ = y’. Substitute y′ from equation (ii): x’’ = -x. We get a second-Order ODE: x’’ + x = 0.

Characteristic Equation: r2 + 1 = 0 ⇒λ = ±i. General Solution: x(t) = Acos(t) + Bsin(t). Since y = x’ (i), y(t) = -Asin(t) + Bcos(t).

Trace-Determinant Analysis

Characteristic Equation: λ2-Tλ + D = 0 where T is the trace Trace (sum of diagonal elements) and D is the determinant of the Jacobian matrix.

At (1, 1): Jacobian Matrix: $(\begin{smallmatrix}0 & 1\\ -1 & 0\end{smallmatrix})$, T = 0, D = 1, Δ = T2-4D = 02-4·1 = -4.

Interpretation

The Trace-Determinat Plane

Centers are across the line T = 0. A small change of the parameters, I might get a sink or a spiral source. In other words, you cannot predict the behaviour when you change a little its parameters (The behavior near (1, 1) is sensitive to changes in the parameters a, b, c, d). The non-linear system at (1, 1) might exhibit center behaviour, a spiral sink, or a spiral source depending on parameter values.

$\begin{cases} x’ = -ax + bxy ~(i)\\ y’ = cy -dxy ~(ii)\end{cases}$

Another approach: Eliminating time to find trajectories

Given

$\begin{cases} x’ = -x + xy ~(i)\\ y’ = y -xy ~(ii)\end{cases}$

$\begin{cases} x’ = x(-1 + y) ~(i)\\ y’ = y(1 -x) ~(ii)\end{cases}$

To find the trajectories (relationship between x and y), eliminate time t. Divide (ii) and (i): $\frac{dy}{dx} = \frac{y’}{x’} = \frac{y(1-x)}{x(-1+y)} = \frac{y(1-x)}{x(y-1)}$

Separate variables: $\frac{y-1}{y}dy = \frac{1-x}{x}dx ↭[\text{Simplify}] (1-\frac{1}{y})dy = (\frac{1}{x}-1)dx$

Integrating Both Sides: $\int (1-\frac{1}{y})dy = \int (\frac{1}{x}-1)dx ⇒ y -ln(y) = ln(x) -x + c_1$

Let’s exponentiate: $e^{y -ln(y)} = \frac{e^y}{e^{ln(y)}} = \frac{e^{ln(x)}}{e^x}·e^{c_1} ⇒\frac{e^y}{y} = \frac{x}{e^x}c_2 ⇒ \frac{x}{e^x}·\frac{y}{e^y} = c$ The integral curves (trajectories of the system) are graphs of this equation. These are contour curves of $xe^{-x}·ye^{-y} = h(x, y)$ (Refer to Figure i for a visual representation and aid in understanding it)

Relation Between Non-linear Systems and First-order ODE’s

$f(x) = xe^{-x}$ has a maximum at 1 (f(1) = 1e), $ye^{-y}$ has a maximum at 1, too. h(x, y) is the product of both functions, h(x, y) = f(x)·f(y), hence h(x, y) has a maximum at (1, 1) [$h(1, 1) = (\frac{1}{e})^2$], that is, as a figure of speech, the peak of the mountain.

As x or y approach 0 or infinity, h(x,y) approaches 0. The contours h(x, y) = C are closed curves around (1, 1). These are concentric closed curves that move clockwise around (1, 1). These represent the closed orbits in the phase plane, confirming the oscillatory behavior of the system (populations oscillate around the equilibrium point (1, 1)).

Population dynamic.

  1. High Predator, Low Prey: Predators consume prey rapidly. Prey population declines sharply. Predator population eventually declines due to lack of food.
  2. Low Predator, High Prey: Reduced predation allows prey population to grow. Predator population begins to recover due to increased food availability.
  3. This leads to an endless cycle of population rises and falls. The system reflects natural oscillations observed in predator-prey relationships.

Effect of fishing with nets at a constant rate k

Modified System: $\begin{cases} x’ = -ax + bxy -kx ~(i)\\ y’ = cy -dxy -ky ~(ii)\end{cases}$ where the terms −kx and -ky represents additional deaths due to fishing.

Combining terms:

$\begin{cases} x’ = -(a+k)x + bxy ~(i)\\ y’ = (c-k)y -dxy ~(ii)\end{cases}$

New Critical Point:

  1. From x′ = 0: -(a+k)x + bxy = 0 ⇒x[−(a+k)+by]=0 ⇒ x = 0 or y = $\frac{a+k}{b}$
  2. From y’ = 0: cy -dxy -ky = 0 ⇒ y[(c−k)−dx]=0 ⇒ y = 0 or x = $\frac{c-k}{d}$

The old critical point was $(\frac{c}{d}, \frac{a}{b})$. Critical point shifts to: $(\frac{c-k}{d}, \frac{a+k}{b})$ (Refer to Figure ii for a visual representation and aid in understanding it).

Relation Between Non-linear Systems and First-order ODE’s

Effect on Populations:

The effect of fishing with nets is that reduces the predator (shark) population and increases the prey (fish) population.

Fishing removes prey and predators directly (-ky, -kx). Reduced predator numbers lead to fewer prey being eaten. The indirect positive effect outweighs the direct negative effect, resulting in a higher prey and lower predator population at equilibrium.

Statement of Volterra’s Principle: If both predator and prey populations are subjected to a proportional external mortality (e.g., fishing, pollution) that affects them equally, the prey population will increase on average, while the predator population will decrease on average.

The Lotka-Volterra equations provide valuable insights into predator-prey dynamics, demonstrating how interactions between species can lead to oscillatory behaviors. By analyzing the system’s critical points, linearizing near these points, and understanding the effects of external factors like fishing, we gain a deeper appreciation for the complex balance within ecosystems.

Analogy: Mosquito Plague and Pesticides

Suppose a fish pond experiences a mosquito infestation. What should we do? Let’s assume that the decision is to spray pesticides (e.g., DDT) to kill those pesky mosquitoes.

Mosquito Plague and Pesticides

Unintended Consequences. The result is quite the same as before because:

  1. Pesticides harm both mosquitoes and fish. When pesticides are applied, they do not discriminate between target and non-target species. Both mosquitoes and fish in the pond are affected, leading to unintended mortality in fish populations.
  2. Fish are natural predators of mosquitoes. Fish help control mosquito populations by preying on their larvae. Reducing the fish population diminishes this natural control mechanism.
  3. Reducing the fish population can lead to a mosquito population increase due to reduced predation. With fewer fish to consume mosquito larvae, mosquito numbers can rebound or even increase, exacerbating the original infestation problem.

In our analysis, the equilibrium (critical point) moves in the same direction, so the population of mosquitoes (prey) increases as the population of fish (predator) decreases, hence spraying pesticides may worsen the mosquito problem. This analysis highlights the complexity of ecological interventions.

Interventions like fishing or pesticide use can have profound and sometimes unintended effects. Careful analysis and consideration of ecological interactions are essential for sustainable management.

Bibliography

This content is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License and is based on MIT OpenCourseWare [18.01 Single Variable Calculus, Fall 2007].
  1. NPTEL-NOC IITM, Introduction to Galois Theory.
  2. Algebra, Second Edition, by Michael Artin.
  3. LibreTexts, Calculus and Calculus 3e (Apex). Abstract and Geometric Algebra, Abstract Algebra: Theory and Applications (Judson).
  4. Field and Galois Theory, by Patrick Morandi. Springer.
  5. Michael Penn, and MathMajor.
  6. Contemporary Abstract Algebra, Joseph, A. Gallian.
  7. YouTube’s Andrew Misseldine: Calculus. College Algebra and Abstract Algebra.
  8. MIT OpenCourseWare [18.03 Differential Equations, Spring 2006], YouTube by MIT OpenCourseWare.
  9. Calculus Early Transcendentals: Differential & Multi-Variable Calculus for Social Sciences.
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