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The logistic growth model

AP.CALC:
FUN‑7 (EU)
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FUN‑7.H (LO)
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FUN‑7.H.1 (EK)
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FUN‑7.H.2 (EK)
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FUN‑7.H.3 (EK)
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FUN‑7.H.4 (EK)
The logistic differential equation dN/dt=rN(1-N/K) describes the situation where a population grows proportionally to its size, but stops growing when it reaches the size of K.

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  • orange juice squid orange style avatar for user Elijah Lovold
    At he said that, if the population starts at zero, it will stay zero forever. If that's the case how are humans here today because more than 5 billion years ago the population of everything was zero? XD
    (6 votes)
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    • hopper cool style avatar for user obiwan kenobi
      If you think about it, everything on Earth currently had a single common ancestor 5 billion years ago. There are many theories for the origin of this common ancestor, but we know that it must exist because if it didn't exist, there would be no organisms to reproduce and gradually mutate into every organism we see on Earth today. Hope this helps! Note that the single ancestor could be created by external factors but the creation of later life is purely through biological processes.
      (7 votes)
  • aqualine seedling style avatar for user Jay
    So I get the addition of a cap on population growth in order to account for carrying capacity. However, isn't there also a necessity to include some form of threshold. For example, if you only have one individual, population physically cannot grow (unless it's about plants). And even if you have two individuals, there's a huge change they won't come in contact, and thus breeding wouldn't occur. So how would a minimum threshold factor in?
    (7 votes)
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    • piceratops ultimate style avatar for user Miles Ford
      The logistic function itself can model more than just populations so in the general form you don't want it limited to only values above 2. To specify it for a specific function you've developed you just indicate "where No >= 2" (that No is meant to be N subscript o) or similar. As for the 'will they meet and breed' well that's a completely separate modelling situation. The logistic equation models growth given that it's happening.
      (3 votes)
  • blobby green style avatar for user Razia al-Din
    Won't K itself decay over time? I mean the resources of this planet are really finite.
    (5 votes)
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  • piceratops ultimate style avatar for user Toby Jeffcote
    Can a similar differential equation be used to demonstrate the opposite effect?
    For example the progressive decrease in an initial population to asymptote at a specified low level?
    Thanks
    (3 votes)
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  • purple pi purple style avatar for user Comett8
    How are you supposed to know to use (1-N/K)? For any modeling with differential equations, how do you just know how to set it up? (i.e. - From Sal's video on Newton's law of cooling, how were you supposed to know that dT/dt = -k(T-T(a))?)
    (3 votes)
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    • ohnoes default style avatar for user Tejas
      1-N/K was just an assumption that was made. You make these assumptions based on your intuition of what is happening. Then you check whether it actually fits reality. As for Newton's law of cooling, it became easier, because dT/dt = -k(T-T(a)) is an actual scientific law.
      (1 vote)
  • blobby green style avatar for user Roman Ka
    Wouldnt (K-N)/K also fulfill the needs of what want to have in the parenthesis?
    (1 vote)
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  • blobby green style avatar for user farouk.sanhadji
    why the Nature of K is defferent than the nature of N ie: N is the poulation in function of time and K is the limitation of the enviorement. they should be the same if they are part of a fraction?

    and what if K is dependant of N ie: in real life resources are produced at a certain level in proportion of the population?
    (4 votes)
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    • piceratops ultimate style avatar for user Miles Ford
      K is the limitation of the population within the environment. So, like N, its units are 'number of population'. N changes with time but at any specific time is just the population count. K is a constant population count, representing the highest number N could reach.

      If K is dependent on N, as in I'm interpreting you as meaning that for example as N increases then K increases, then it isn't a logistic function to start with. You could develop a function with a further dampening factor that expressed K as a function of N I guess.
      (1 vote)
  • female robot grace style avatar for user Maha Amin
    I am wondering in this type of differential equations, we only see the change of population (N) with respect to only one factor (time --> dt), but how to include other factors like amount of food , water , temperature, ...etc which also affects (N) ??
    (2 votes)
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    • starky ultimate style avatar for user Michele Franzoni
      You take all these factors into account when you assign a k value: the more you know about resources et cetera, the more sensible the estimate for k is going to be. Of course each "microvariable" will change over time but keep in mind these two things

      1) sometimes these changes are negligible with respect to the span of time you are considering for your population study

      2) you can't model exaclty a complex (and often chaotic) system
      (1 vote)
  • area 52 yellow style avatar for user Surya Raju
    How would we find the equation of the model where there is a dampening oscillation of the population around the cap as shown in the previous video?
    (2 votes)
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  • piceratops seedling style avatar for user miguel.ramosfonseca
    Would a model with K-N (instead of 1-N/K) also work?
    (2 votes)
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Video transcript

In the last video, we took a stab at modeling population as a function of time. And we said, okay, well maybe the rate of change of population with respect to time is going to be proportional to the population itself. That the rate will increase as the population increases. And when you actually try to solve this differential equation, you try to find an N of T that satisfies this, we found that an exponential would work. An exponential satisfies this differential equation. And it would look like this visually. It would look like this, it would look like this visually. Where you're starting at a population of N not, this is the time axis, this is the population axis. And as time increases, population increases exponentially. Now we said there's an issue there. What if Thomas Malthus is right? That the environment can't support... let's say that the environment can't support... let me do this in a new color. Let's say that the environment really can't support more than K, more than a population of K. Then clearly the population can't just go and go right through the ceiling. They're not going to be able to have food, or water or resources or whatever it might be. They might generate too much pollution. Who knows what it might be. And so this first stab at modeling population doesn't quite do the trick. Especially if you are kind of in Malthus's camp. And that's where PF, and once again I'm sure I'm mispronouncing the name, Verhulst is going to come into the picture. Because he read Malthus's work, and said, "Well yeah, I think I can do a pretty good job "of modeling the type of behavior "that Malthus is talking about." And he says what we really want is something... Let me write it, so the rate... let's try to model. Let's set up another differential equation. And now let's say okay, instead of, if N is substantially smaller than what the environment can support, then yeah, that makes sense to have exponential growth. But maybe we can dampen this, or maybe we can bring this growth to zero as N approaches K. And so how can we actually modify this? Maybe we can multiply it by something that for when N is small, when N is much smaller than K, this term right over here is going to be close to one and when N is close to K, this term is close to zero. So let me write that. So these are our goals for this term, right over here. When N is much smaller than K, so now the population is not constrained at all, people can have babies and those babies can be fed and then they can have babies, et cetera, et cetera, then this thing should be close to one. And so then you have essentially our old model. But then as N approaches K, then this thing should approach, then this term or this expression should approach zero. And what that does is as N approaches the natural limit, the ceiling to population, then no matter what this is doing, if this thing is approaching zero, that's going to make the actual rate of growth approach zero. So food is going to be more scarce, it's going to be harder to find things. And so what can I construct here, dealing with N and K that will have these properties? And for fun, you might actually want to pause the video and see if you can construct a fairly simple algebraic statement using N and K and maybe the number one if you find the need, for an expression that has these properties. Well, let's see. What if we start with a one and we subtract N over K? Does this have those properties? Well yeah, sure it does. When N is really small, or I should say when it's a small fraction of K, this is going to be a small fraction, then this whole thing is going to be pretty close to one. It's going to be a little bit less than one. And when N approaches K, as N gets closer and closer and closer to K, then this thing right over here is going to approach one, which means this whole expression is going to approach zero. Which is exactly what we wanted. And this thing right over here is actually, and this is used in tons of applications, not just in population modeling but that's kind of one of its first reasons or motivations. This differential equation right over here is actually quite famous. It's called the logistic differential equation. And in the next video, we're actually going to solve this. And it's actually, this is a separable differential equation. You can actually solve it just using standard techniques of integration. it's a little bit hairier than this one, so we're going to work through it together. And we're going to look at the solution. The solution to the logistic differential equation is the logistic function, which once again essentially models population in this way. But before we actually solve for it, let's just try to interpret this differential equation and think about what the shape of this function might look like. And to do that, let me draw some axes here. Let me draw some axes here. So that's my time axis, that's my population axis, let me scroll up a little bit because sometimes the subtitles show up around here and then people can't see what's going on. So let's think about a couple of permutations, a couple situations. So if our initial, if our N at time equals zero, remember N is a function of T. If at time equals zero N is equal to zero, then this term is going to be zero and then your rate of change is going to be zero, and so you're not going to add any population. And that's good. Because if your population is zero, how are you going to actually be able to add population? There's no one there to have children. So there's actually one constant solution to this differential equation, which is just N of T is equal to zero. And this satisfies the logistic differential equation. Hey, if your population starts at zero if N sub not is zero, then you're just going to be at zero forever. Well that's actually what would happen in real life if there's no one there to have kids. Now let's think about another situation. What if our population, what if N not is equal to K? What happens if N not is equal to, so that's K right over there, what happens if at time equals zero, this is our population. Well if N is equal to K, then this is one minus one, then this thing is zero, and so our rate of population change is going to be zero. So essentially if my population is zero then after a little bit of time, my population is still the same K. If my rate of change of population is zero, that means my population is staying constant. And so my population's just going to stay there at K. And that's actually believable. Malthus would actually probably say that you're gonna have, maybe it grows a little bit beyond the capacity of the environment and then you have some flood, or some hurricane or some famine and it goes around. But for our purposes, you can never model anything perfectly. For our purposes, that's pretty good. You're at the limit of what the environment can handle, so you just kind of stay there. That's actually another constant solution. that N of T, if it starts, and now you can kind of appreciate why initial conditions are important. If you start at zero you're going to stay at zero, if you start at K you're going to stay at K. So that is N of T just stays K. But now let's think of a more interesting scenario. Let's assume an initial population that's someplace between zero and K. So this is going to be... I'm going to assume initial population that is someplace, it's greater than zero, so there are people to actually have children, and it is less than K, so we aren't fully maxing out the environment. Or the land or the food or the water or whatever it might be. So what's going to happen? So, and once again, I'm just going to kind of sketch it, and then we're going to solve it in the next video. So when N is a lot less than K, it's a small fraction of K, this term is going to be the main one that's influencing it. Because this is a small fraction of K, I mean even the way I drew it, it looks like it's about a sixth or a seventh or an eighth of K, so it's one minus 1/8. So it's gonna be 7/8 times this. So really, this is what's going to dictate what our rate of growth is. And if this is kind of dictating it, we're kind of looking more of a... well, let's just think of it this way. As the population grows, the rate of change is going to grow. So it's going to look something like this. As our population gets larger, our slope is getting higher. It's getting steeper and steeper. But then as N approaches K, then this thing is gonna become, this is gonna be close to one minus... close to one. And so this is going to become a very small number, it's going to make this whole thing approach zero. So as N approaches K, the whole thing, the rate of change is going to flatten out. And we're gonna asymptote towards K. And so the solution to the logistic differential equation should look something like this. Depending on what your initial conditions are. If your initial condition is here, maybe it does something like this. If your initial condition's here, maybe it does something like this. And once again, this is what's fun about doing differential equations. Before even doing the fancy math, you can kind of get an intuition, just by thinking through the differential equation of what it is likely to be. Down here, or when N is much smaller than K its rate of increase is increasing as N increases, and over here as N gets close to K, its rate of increase is decreasing. Now let's actually in the next video, actually solve for what N, a solution to this and see if that confirms what our intuition is.