Population dynamics has traditionally been the dominant branch of mathematical biology, which has a history of more than 220 years, although over the last century the scope of mathematical biology has greatly expanded.
The beginning of population dynamics is widely regarded as the work of Malthus, formulated as the Malthusian growth model. According to Malthus, assuming that the conditions (the environment) remain constant (ceteris paribus), a population will grow (or decline) exponentially.: 18 This principle provided the basis for the subsequent predictive theories, such as the demographic studies such as the work of Benjamin Gompertz and Pierre François Verhulst in the early 19th century, who refined and adjusted the Malthusian demographic model.
A more general model formulation was proposed by F. J. Richards in 1959, further expanded by Simon Hopkins, in which the models of Gompertz, Verhulst and also Ludwig von Bertalanffy are covered as special cases of the general formulation. The Lotka–Volterra predator-prey equations are another famous example, as well as the alternative Arditi–Ginzburg equations.
Simplified population models usually start with four key variables (four demographic processes) including death, birth, immigration, and emigration. Mathematical models used to calculate changes in population demographics and evolution hold the assumption ('null hypothesis') of no external influence. Models can be more mathematically complex where "...several competing hypotheses are simultaneously confronted with the data." For example, in a closed system where immigration and emigration does not take place, the rate of change in the number of individuals in a population can be described as:
where N is the total number of individuals in the specific experimental population being studied, B is the number of births and D is the number of deaths per individual in a particular experiment or model. The algebraic symbols b, d and r stand for the rates of birth, death, and the rate of change per individual in the general population, the intrinsic rate of increase. This formula can be read as the rate of change in the population (dN/dT) is equal to births minus deaths (B - D).
Using these techniques, Malthus' population principle of growth was later transformed into a mathematical model known as the logistic equation:
where N is the biomass density, a is the maximum per-capita rate of change, and K is the carrying capacity of the population. The formula can be read as follows: the rate of change in the population (dN/dT) is equal to growth (aN) that is limited by carrying capacity (1-N/K). From these basic mathematical principles the discipline of population ecology expands into a field of investigation that queries the demographics of real populations and tests these results against the statistical models. The field of population ecology often uses data on life history and matrix algebra to develop projection matrices on fecundity and survivorship. This information is used for managing wildlife stocks and setting harvest quotas.
Intrinsic rate of increase
The rate at which a population increases in size if there are no density-dependent forces regulating the population is known as the intrinsic rate of increase. It is
where the derivative is the rate of increase of the population, N is the population size, and r is the intrinsic rate of increase. Thus r is the maximum theoretical rate of increase of a population per individual – that is, the maximum population growth rate. The concept is commonly used in insect population ecology or management to determine how environmental factors affect the rate at which pest populations increase. See also exponential population growth and logistic population growth.
Population dynamics overlap with another active area of research in mathematical biology: mathematical epidemiology, the study of infectious disease affecting populations. Various models of viral spread have been proposed and analysed, and provide important results that may be applied to health policy decisions.
The mathematical formula below can used to model geometric populations. Geometric populations grow in discrete reproductive periods between intervals of abstinence, as opposed to populations which grow without designated periods for reproduction. Say that N denotes the number of individuals in each generation of a population that will reproduce.
Where: Nt is the population size in generation t, and Nt+1 is the population size in the generation directly after Nt; Bt is the sum of births in the population between generations t and t+1 (i.e. the birth rate); It is the sum of immigrants added to the population between generations; Dt is the sum of deaths between generations (death rate); and Et is the sum of emigrants moving out of the population between generations.
When there is no migration to or from the population,
Assuming in this case that the birth and death rates are constants, then the birth rate minus the death rate equals R, the geometric rate of increase.
Nt+1 = Nt + RNt
Nt+1 = (Nt + RNt)
Take the term Nt out of the brackets again.
Nt+1 = (1 + R)Nt
1 + R = λ, where λ is the finite rate of increase.
Nt+1 = λNt
|At t+1||Nt+1 = λNt|
|At t+2||Nt+2 = λNt+1 = λλNt = λ2Nt|
|At t+3||Nt+3 = λNt+2 = λλNt+1 = λλλNt = λ3Nt|
Nt = λtN0
|λt||Finite rate of increase raised to the power of the number of generations (e.g. for t+2 [two generations] → λ2 , for t+1 [one generation] → λ1 = λ, and for t [before any generations - at time zero] → λ0 = 1|
The doubling time (td) of a population is the time required for the population to grow to twice its size. We can calculate the doubling time of a geometric population using the equation: Nt = λtN0 by exploiting our knowledge of the fact that the population (N) is twice its size (2N) after the doubling time.
Ntd = λtd × N0
2N0 = λtd × N0
λtd = 2N0 / N0
λtd = 2
The doubling time can be found by taking logarithms. For instance:
td × log2(λ) = log2(2)
log2(2) = 1
td × log2(λ) = 1
td = 1 / log2(λ)
td × ln(λ) = ln(2)
td = ln(2) / ln(λ)
td = 0.693... / ln(λ)
td = 1 / log2(λ) = 0.693... / ln(λ)
Half-life of geometric populations
The half-life of a population is the time taken for the population to decline to half its size. We can calculate the half-life of a geometric population using the equation: Nt = λtN0 by exploiting our knowledge of the fact that the population (N) is half its size (0.5N) after a half-life.
Nt1/2 = λt1/2 × N0
0.5N0 = λt1/2 × N0
λt1/2 = 0.5N0 / N0
λt1/2 = 0.5
The half-life can be calculated by taking logarithms (see above).
t1/2 = 1 / log0.5(λ) = ln(0.5) / ln(λ)
Geometric (R) growth constant
R = b - d
Nt+1 = Nt + RNt
Nt+1 - Nt = RNt
Nt+1 - Nt = ΔN
|ΔN||Change in population size between two generations (between generation t+1 and t).|
ΔN = RNt
ΔN/Nt = R
Finite (λ) growth constant
1 + R = λ
Nt+1 = λNt
λ = Nt+1 / Nt
Mathematical relationship between geometric and logistic populations
In geometric populations, R and λ represent growth constants (see 2 and 2.3). In logistic populations however, the intrinsic growth rate, also known as intrinsic rate of increase (r) is the relevant growth constant. Since generations of reproduction in a geometric population do not overlap (e.g. reproduce once a year) but do in an exponential population, geometric and exponential populations are usually considered to be mutually exclusive. However, both sets of constants share the mathematical relationship below.
The growth equation for exponential populations is
Nt = N0ert
|e||Euler's number - A universal constant often applicable in logistic equations.|
|r||intrinsic growth rate|
Assumption: Nt (of a geometric population) = Nt (of a logistic population).
N0ert = N0λt
N0 cancels on both sides.
N0ert / N0 = λt
ert = λt
Take the natural logarithms of the equation. Using natural logarithms instead of base 10 or base 2 logarithms simplifies the final equation as ln(e) = 1.
rt × ln(e) = t × ln(λ)
|ln||natural logarithm - in other words ln(y) = loge(y) = x = the power (x) that e needs to be raised to (ex) to give the answer y.
In this case, e1 = e therefore ln(e) = 1.
rt × 1 = t × ln(λ)
rt = t × ln(λ)
t cancels on both sides.
rt / t = ln(λ)
r = ln(λ)
er = λ
Evolutionary game theory
Evolutionary game theory was first developed by Ronald Fisher in his 1930 article The Genetic Theory of Natural Selection. In 1973 John Maynard Smith formalised a central concept, the evolutionarily stable strategy.
Population dynamics have been used in several control theory applications. Evolutionary game theory can be used in different industrial or other contexts. Industrially, it is mostly used in multiple-input-multiple-output (MIMO) systems, although it can be adapted for use in single-input-single-output (SISO) systems. Some other examples of applications are military campaigns, water distribution, dispatch of distributed generators, lab experiments, transport problems, communication problems, among others.
- Delayed density dependence
- Lotka-Volterra equations
- Minimum viable population
- Maximum sustainable yield
- Nicholson–Bailey model
- Pest insect population dynamics
- Population cycle
- Population dynamics of fisheries
- Population ecology
- Population genetics
- Population modeling
- Ricker model
- r/K selection theory
- System dynamics
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