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==== II.Theta Logistic model ==== | ==== II.Theta Logistic model ==== | ||
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| + | We’ve already seen that even though logistic model does a better job on fitting the data of population than the exponential model does, the basic logistic model is still not the best model. | ||
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| + | In order to fit better model and address the limitations set by the logistic model, Gilpin and Ayala(1973) presented a new version of the logistic model (as cited in Clark et al., 2010) called “theta logistic model”. | ||
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| + | As a large population size continue to grow, individual growth rate should slow down which is not included in the classical model. The new theta-logistic model, however, take this into account. (Clark et al., 2010) | ||
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| + | A new term θ is added to the classic logistic model. The linear density dependence held by the | ||
| + | classical logistic model can be altered to become curvilinear. It is this new term θ that provides additional generality and flexibility to explain the impact by change of individual growth rate parameter r_a with respect to population density N. (Salisbury,2011) | ||
| + | |||
| + | How does θ work mathematically in this model? With the variation of θ, it can reflect relation between intraspecific competition and population density. In the classic logistic model, we have | ||
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==== III.Logistic Model with Allee’s Effect ==== | ==== III.Logistic Model with Allee’s Effect ==== | ||
Revision as of 17:23, 2 December 2018
Contents
Logistic Models
I. Classic Logistic Models
After realizing limitations of the exponential model, we may wonder: can we do better? We know that the resources are not unlimited. Therefore, at some point, the growth rate must slow down and eventually the population should reach the place where the environment cannot hold more. Based on the above description, it is reasonable to come up to a graph like this:
Caption1
Caption2
Figure 1 An explanation of logistic growth model Adapted from Lumen Boundless Biology 2013
Mathematically, we have a model, which is called Logistic equation.
Figure 2 An explanation about the growth rate changes over time Adapted from Lumen Boundless Biology
For the moment, let’s just ignore the equation f(x) on the right. We’ll cover the mathematical part in the later section. Right now, we’re focusing on the actual meaning of this graph. Let’s check together whether this graph has all the features we discussed before.
By taking the tangent line of each pair of points along the x-axles, we find that the slope is indeed getting smaller and smaller. And when x is sufficiently large, the slope is close to 0. It means, the growth rate of population decreases in each period of years. And after a long time, the number of populations becomes stable. It makes sense intuitively. Please be aware that the decline of growth rate does not indicate the drop of population. On the contrary, the total population is still increasing as shown in the graph. For more advanced readers, the growth rate is represented by the derivative of the function. And the carrying capacity is the limit of the function when x approaches infinity.
The actual formula was first proposed by the Belgium mathematician Pierre-François Verhulst in 1835, who stated “The virtual increase of population is limited by the size and the fertility of the country. As a result, the population gets closer and closer to a steady state” in his Note on the law of population growth. (1838 as cited in Bacaër, 2011,)
Here N, K, r represent population, carrying capacity, and a growth rate parameter respectively. Noted the formula is given in the differentiation form.
This model assumes that the growth rate r is linear and it is decreasing in terms of N. In other words, r_a=F(N). For a more complete model, we will introduce r_m as the max growth rate. But it is beyond our text slope.
Interestingly, if N(t) is much smaller than K, this ordinary equation is approximately as our previous exponential model. Here comes another question. Can we write N(t) explicitly just like the exponential one?
The exciting answer is yes! But understanding how to get it requires some math background in an introductory level ordinary differential equation class. We strongly encourage interested readers take MA366 or MA 266 held by Purdue math department.
Let’s do some math!
Figure 3 Merma, 2004 P80 The logistic equation Adapted from Northwestern University website
And we’ll see in later section that even the complicated logistic equation cannot predict the population accurately.
II.Theta Logistic model
We’ve already seen that even though logistic model does a better job on fitting the data of population than the exponential model does, the basic logistic model is still not the best model.
In order to fit better model and address the limitations set by the logistic model, Gilpin and Ayala(1973) presented a new version of the logistic model (as cited in Clark et al., 2010) called “theta logistic model”.
As a large population size continue to grow, individual growth rate should slow down which is not included in the classical model. The new theta-logistic model, however, take this into account. (Clark et al., 2010)
A new term θ is added to the classic logistic model. The linear density dependence held by the classical logistic model can be altered to become curvilinear. It is this new term θ that provides additional generality and flexibility to explain the impact by change of individual growth rate parameter r_a with respect to population density N. (Salisbury,2011)
How does θ work mathematically in this model? With the variation of θ, it can reflect relation between intraspecific competition and population density. In the classic logistic model, we have
