Answer:Limiting Population: Solving Logistic Equation with P_0

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In summary: QED.In summary, the question asks to show that the limiting population, M, is equal to (B_0*P_0)/D_0 when given the logistic equation dP/dt = aP-bP^2, where B = aP is the time rate at which births occur and D = bP^2 is the rate at which deaths occur. The initial population is P(0) = P_0, and B_0 births per month and D_0 deaths per month are occurring at time t=0. The steps to solve this problem involve setting up the logistic equation and rearranging it to get (1/(P(a-bP)))dP=dt. From there, it
  • #1
highlander2k5
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I'm stuck on what to do here. The question reads Consider a population P(t) satisfying the logistic equation dP/dt = aP-bP^2, where B = aP is the time rate at which births occur and D = bP^2 is the rate at which deaths occer. If the intial population is P(0) = P_0 (supposed to be P sub not), and B_0 births per month and D_0 deaths per month are occurring at time t=0, show that the limiting population is M = (B_0*P_0)/D_0.

My question is am I setting this up right? Where do I go from my last spot to get it to look like M = (B_0*P_0)/D_0?

Here's what I got:
1) dP/dt=(aP-bP^2)P
2) dP/dt=(a-bP)P^2
3) integral((1/P^2)+bP)dp = integral(a)dt
4) ? Please Help ?
 
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  • #2
highlander2k5 said:
I'm stuck on what to do here. The question reads Consider a population P(t) satisfying the logistic equation dP/dt = aP-bP^2, where B = aP is the time rate at which births occur and D = bP^2 is the rate at which deaths occer. If the intial population is P(0) = P_0 (supposed to be P sub not), and B_0 births per month and D_0 deaths per month are occurring at time t=0, show that the limiting population is M = (B_0*P_0)/D_0.

My question is am I setting this up right? Where do I go from my last spot to get it to look like M = (B_0*P_0)/D_0?

Here's what I got:
1) dP/dt=(aP-bP^2)P
Since you were told that dP/dt= (aP- bP^2) where did you get that additional P?

2) dP/dt=(a-bP)P^2
3) integral((1/P^2)+bP)dp = integral(a)dt
4) ? Please Help ?
I have no idea where you got (1/P^2 + bP)dP!

From dP/dt= (a-bP)P, you get dP/((a-bP)P)= dt and can integrate the left side using "partial fractions".
 
  • #3
I think you are making it a little more complicated than it needs to be. If you take "limiting population" to mean "steady-state population", what condition does that place on the time derivative? Figuring this out will easily allow you to find M. Then to get the answer in the required form, you will need to figure out what a and b are in terms of P(0), B(0), and D(0).
 
  • #4
highlander2k5 said:
I'm stuck on what to do here. The question reads Consider a population P(t) satisfying the logistic equation dP/dt = aP-bP^2, where B = aP is the time rate at which births occur and D = bP^2 is the rate at which deaths occer. If the intial population is P(0) = P_0 (supposed to be P sub not), and B_0 births per month and D_0 deaths per month are occurring at time t=0, show that the limiting population is M = (B_0*P_0)/D_0.

My question is am I setting this up right? Where do I go from my last spot to get it to look like M = (B_0*P_0)/D_0?

Here's what I got:
1) dP/dt=(aP-bP^2)P
2) dP/dt=(a-bP)P^2
3) integral((1/P^2)+bP)dp = integral(a)dt
4) ? Please Help ?

I am having the same problem (I have the same book, hoping bumping this will answer it). The error you had was you should START with dP/dt=aP-bP^2.

I started by separating the eqn into:
(1/aP-bP^2)dp=dt --> (1/(P(a-bP)))dP=dt
From there I tried partial fractions, but the answer seems way to complex.
Anyone know how to answer the initial question the other user typed?
 
  • #5
I think I solved it. You don't integrate. M is known to be the limiting population, and you just need to prove B_o*P_o/D_o which if you substitute is (a/b)

Take P(a-bP) --> (b/b)(P(a-bP), which will can be simplified to bP(a/b-P)
If you compare to kP(M-P), they are equal. a/b is M, which is limiting capacity.
 

What is the logistic equation and how does it relate to limiting population?

The logistic equation is a mathematical model that describes the growth of a population over time, taking into account limiting factors such as resources and competition. It is often used to study population dynamics and predict the maximum population size that an environment can sustain, also known as the carrying capacity.

What is the significance of P_0 in the logistic equation?

P_0, also known as the initial population size, is a key parameter in the logistic equation. It represents the starting point for the population growth and has a significant impact on the entire trajectory of the population's growth and eventual stabilization. Without a proper estimation of P_0, the predictions of the logistic equation may not accurately reflect the real population dynamics.

How is the logistic equation solved to determine the limiting population?

The logistic equation can be solved using calculus or numerical methods to determine the population size at any given time. To find the limiting population, the equation needs to be solved for the point where the population growth rate is equal to zero, also known as the inflection point. This represents the point at which the population growth slows down and eventually reaches its maximum sustainable size.

What are some real-world applications of the logistic equation?

The logistic equation has many applications in fields such as biology, ecology, and economics. It can be used to model the growth of animal and plant populations, predict the spread of diseases, and analyze the dynamics of human populations. In economics, it is used to study market trends and predict the growth of industries and businesses.

Are there any limitations to using the logistic equation for predicting limiting population?

While the logistic equation is a useful tool for studying population dynamics, it has some limitations. It assumes that the environment remains constant and does not take into account external factors such as natural disasters or human intervention. It also assumes that all individuals in a population have equal access to resources, which may not always be the case. Therefore, the predictions of the logistic equation should be interpreted with caution and validated with other data and models.

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