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Liebig's law states that growth only occurs at the rate permitted by the most limiting factor. [ 2 ] For instance, in the equation below, the growth of population O {\displaystyle O} is a function of the minimum of three Michaelis-Menten terms representing limitation by factors I {\displaystyle I} , N {\displaystyle N} and P {\displaystyle P} .
Carrying capacity is a commonly used concept for biologists when trying to better understand biological populations and the factors which affect them. [1] When addressing biological populations, carrying capacity can be seen as a stable dynamic equilibrium, taking into account extinction and colonization rates. [ 16 ]
The standard logistic function is the logistic function with parameters =, =, =, which yields = + = + = / / + /.In practice, due to the nature of the exponential function, it is often sufficient to compute the standard logistic function for over a small range of real numbers, such as a range contained in [−6, +6], as it quickly converges very close to its saturation values of 0 and 1.
The identification of a factor as limiting is possible only in distinction to one or more other factors that are non-limiting. Disciplines differ in their use of the term as to whether they allow the simultaneous existence of more than one limiting factor (which may then be called "co-limiting"), but they all require the existence of at least one non-limiting factor when the terms are used.
However, as the population reaches its maximum (the carrying capacity), intraspecific competition becomes fiercer and the per capita growth rate slows until the population reaches a stable size. At the carrying capacity, the rate of change of population density is zero because the population is as large as possible based on the resources ...
If a constraint's throughput capacity is elevated to the point where it is no longer the system's limiting factor, this is said to "break" the constraint. The limiting factor is now some other part of the system, or may be external to the system (an external constraint). This is not to be confused with a breakdown.
The factors range from 0 for no growth to 1 for maximum growth. Actual growth is calculated by the total multiplication of each growth factor. For example, if three factors had a value of 0.5, the actual growth would be: 0.5 × 0.5 × 0.5 = 0.125, which is 12.5% of optimum. If each of the three factors had a value of 0.9 the actual growth would be:
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