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Population dynamics

From Wikipedia, the free encyclopedia

Population dynamics is the type of mathematics used to model and study the size and age composition of populations as dynamical systems.

History

Population dynamics has traditionally been the dominant branch of mathematical biology, which has a history of more than 220 years,[1] 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.[2]:18 This principle provided the basis for the subsequent predictive theories, such as the demographic studies such as the work of Benjamin Gompertz[3] and Pierre François Verhulst in the early 19th century, who refined and adjusted the Malthusian demographic model.[4]

A more general model formulation was proposed by F. J. Richards in 1959,[5] 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,[6][7][8][9][10][11][12][13] as well as the alternative Arditi–Ginzburg equations.[14][15]

Logistic function

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."[16] 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).[2][13][17]

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.[13][17]

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.[18]

Epidemiology

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.

Geometric populations

Operophtera brumata populations are geometric.[19]
Operophtera brumata populations are geometric.[19]

The mathematical formula below can used to model geometric populations. Geometric populations grow in discreet 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.[20]

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

Therefore:

Nt+1 = λtNt

Term Definition
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

Doubling time

G. stearothermophilus has a shorter doubling time (td) than E. coli and N. meningitidis. Growth rates of 2 bacterial species will differ by unexpected orders of magnitude if the doubling times of the 2 species differ by even as little as 10 minutes. In eukaryotes such as animals, fungi, plants, and protists, doubling times are much longer than in bacteria. This reduces the growth rates of eukaryotes in comparison to Bacteria. G. stearothermophilus, E. coli, and N. meningitidis have 20 minute,[21] 30 minute,[22] and 40 minute[23] doubling times under optimal conditions respectively. If bacterial populations could grow indefinitely (which they do not) then the number of bacteria in each species would approach infinity (∞). However, the percentage of G. stearothermophilus bacteria out of all the bacteria would approach 100% whilst the percentage of E. coli and N. meningitidis combined out of all the bacteria would approach 0%. This graph is a simulation of this hypothetical scenario. In reality, bacterial populations do not grow indefinitely in size and the 3 species require different optimal conditions to bring their doubling times to minima.    Time in minutes % that is G. stearothermophilus   30 44.4%   60 53.3%   90 64.9%   120 72.7%   →∞ 100%    Time in minutes % that is E. coli   30 29.6%   60 26.7%   90 21.6%   120 18.2%   →∞ 0.00%    Time in minutes % that is N. meningitidis   30 25.9%   60 20.0%   90 13.5%   120 9.10%   →∞ 0.00%  Disclaimer: Bacterial populations are logistic instead of geometric. Nevertheless, doubling times are applicable to both types of populations.
G. stearothermophilus has a shorter doubling time (td) than E. coli and N. meningitidis. Growth rates of 2 bacterial species will differ by unexpected orders of magnitude if the doubling times of the 2 species differ by even as little as 10 minutes. In eukaryotes such as animals, fungi, plants, and protists, doubling times are much longer than in bacteria. This reduces the growth rates of eukaryotes in comparison to Bacteria. G. stearothermophilus, E. coli, and N. meningitidis have 20 minute,[21] 30 minute,[22] and 40 minute[23] doubling times under optimal conditions respectively. If bacterial populations could grow indefinitely (which they do not) then the number of bacteria in each species would approach infinity (). However, the percentage of G. stearothermophilus bacteria out of all the bacteria would approach 100% whilst the percentage of E. coli and N. meningitidis combined out of all the bacteria would approach 0%. This graph is a simulation of this hypothetical scenario. In reality, bacterial populations do not grow indefinitely in size and the 3 species require different optimal conditions to bring their doubling times to minima.
Time in minutes % that is G. stearothermophilus
30 44.4%
60 53.3%
90 64.9%
120 72.7%
→∞ 100%
Time in minutes % that is E. coli
30 29.6%
60 26.7%
90 21.6%
120 18.2%
→∞ 0.00%
Time in minutes % that is N. meningitidis
30 25.9%
60 20.0%
90 13.5%
120 9.10%
→∞ 0.00%
Disclaimer: Bacterial populations are logistic instead of geometric. Nevertheless, doubling times are applicable to both types of populations.

The doubling time (td) of a population is the time required for the population to grow to twice its size.[24] We can calculate the doubling time of a geometric population using the equation: Nt+1 = λtNt by exploiting our knowledge of the fact that the population (N) is twice its size (2N) after the doubling time.[20]

2Ntd = λtd × Nt

λtd = 2Ntd / Nt

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)

Or:

td × ln(λ) = ln(2)

td = ln(2) / ln(λ)

td = 0.693... / ln(λ)

Therefore:

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+1 = λtNt by exploiting our knowledge of the fact that the population (N) is half its size (0.5N) after a half-life.[20]

0.5Nt1/2 = λt1/2 × Nt

Term Definition
t1/2 Half-life.

λt1/2 = 0.5Nt1/2 / Nt

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

Term Definition
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.[25] However, both sets of constants share the mathematical relationship below.[20]

The growth equation for exponential populations is

Nt = N0ert

Term Definition
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).

Therefore:

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)

Term Definition
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)

The results:

r = ln(λ)

and

er = λ

Evolutionary game theory

Evolutionary game theory was first developed by Ronald Fisher in his 1930 article The Genetic Theory of Natural Selection.[26] In 1973 John Maynard Smith formalised a central concept, the evolutionarily stable strategy.[27]

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.

Trivia

The computer game SimCity, Sim Earth and the MMORPG Ultima Online, among others, tried to simulate some of these population dynamics.

See also

References

  1. ^ Malthus, Thomas Robert. An Essay on the Principle of Population: Library of Economics
  2. ^ a b Turchin, P. (2001). "Does Population Ecology Have General Laws?". Oikos. 94 (1): 17–26. doi:10.1034/j.1600-0706.2001.11310.x. S2CID 27090414.
  3. ^ Gompertz, Benjamin (1825). "On the Nature of the Function Expressive of the Law of Human Mortality, and on a New Mode of Determining the Value of Life Contingencies". Philosophical Transactions of the Royal Society of London. 115: 513–585. doi:10.1098/rstl.1825.0026.
  4. ^ Verhulst, P. H. (1838). "Notice sur la loi que la population poursuit dans son accroissement". Corresp. Mathématique et Physique. 10: 113–121.
  5. ^ Richards, F. J. (June 1959). "A Flexible Growth Function for Empirical Use". Journal of Experimental Botany. 10 (29): 290–300. Retrieved 16 November 2020.
  6. ^ Hoppensteadt, F. (2006). "Predator-prey model". Scholarpedia. 1 (10): 1563. Bibcode:2006SchpJ...1.1563H. doi:10.4249/scholarpedia.1563.
  7. ^ Lotka, A. J. (1910). "Contribution to the Theory of Periodic Reaction". J. Phys. Chem. 14 (3): 271–274. doi:10.1021/j150111a004.
  8. ^ Goel, N. S.; et al. (1971). On the Volterra and Other Non-Linear Models of Interacting Populations. Academic Press.
  9. ^ Lotka, A. J. (1925). Elements of Physical Biology. Williams and Wilkins.
  10. ^ Volterra, V. (1926). "Variazioni e fluttuazioni del numero d'individui in specie animali conviventi". Mem. Acad. Lincei Roma. 2: 31–113.
  11. ^ Volterra, V. (1931). "Variations and fluctuations of the number of individuals in animal species living together". In Chapman, R. N. (ed.). Animal Ecology. McGraw–Hill.
  12. ^ Kingsland, S. (1995). Modeling Nature: Episodes in the History of Population Ecology. University of Chicago Press. ISBN 978-0-226-43728-6.
  13. ^ a b c Berryman, A. A. (1992). "The Origins and Evolution of Predator-Prey Theory" (PDF). Ecology. 73 (5): 1530–1535. doi:10.2307/1940005. JSTOR 1940005. Archived from the original (PDF) on 2010-05-31.
  14. ^ Arditi, R.; Ginzburg, L. R. (1989). "Coupling in predator-prey dynamics: ratio dependence" (PDF). Journal of Theoretical Biology. 139 (3): 311–326. doi:10.1016/s0022-5193(89)80211-5.
  15. ^ Abrams, P. A.; Ginzburg, L. R. (2000). "The nature of predation: prey dependent, ratio dependent or neither?". Trends in Ecology & Evolution. 15 (8): 337–341. doi:10.1016/s0169-5347(00)01908-x. PMID 10884706.
  16. ^ Johnson, J. B.; Omland, K. S. (2004). "Model selection in ecology and evolution" (PDF). Trends in Ecology and Evolution. 19 (2): 101–108. CiteSeerX 10.1.1.401.777. doi:10.1016/j.tree.2003.10.013. PMID 16701236. Archived from the original (PDF) on 2011-06-11. Retrieved 2010-01-25.
  17. ^ a b Vandermeer, J. H.; Goldberg, D. E. (2003). Population ecology: First principles. Woodstock, Oxfordshire: Princeton University Press. ISBN 978-0-691-11440-8.
  18. ^ Jahn, Gary C.; Almazan, Liberty P.; Pacia, Jocelyn B. (2005). "Effect of Nitrogen Fertilizer on the Intrinsic Rate of Increase of Hysteroneura setariae (Thomas) (Homoptera: Aphididae) on Rice (Oryza sativa L.)". Environmental Entomology. 34 (4): 938–43. doi:10.1603/0046-225X-34.4.938.
  19. ^ Hassell, Michael P. (June 1980). "Foraging Strategies, Population Models and Biological Control: A Case Study". The Journal of Animal Ecology. 49 (2): 603–628. doi:10.2307/4267. JSTOR 4267.
  20. ^ a b c d "Geometric and Exponential Population Models" (PDF). Archived from the original (PDF) on 2015-04-21. Retrieved 2015-08-17.
  21. ^ "Bacillus stearothermophilus NEUF2011". Microbe wiki.
  22. ^ Chandler, M.; Bird, R.E.; Caro, L. (May 1975). "The replication time of the Escherichia coli K12 chromosome as a function of cell doubling time". Journal of Molecular Biology. 94 (1): 127–132. doi:10.1016/0022-2836(75)90410-6. PMID 1095767.
  23. ^ Tobiason, D. M.; Seifert, H. S. (19 February 2010). "Genomic Content of Neisseria Species". Journal of Bacteriology. 192 (8): 2160–2168. doi:10.1128/JB.01593-09. PMC 2849444. PMID 20172999.
  24. ^ Boucher, Lauren (24 March 2015). "What is Doubling Time and How is it Calculated?". Population Education.
  25. ^ "Population Growth" (PDF). University of Alberta.
  26. ^ "Evolutionary Game Theory". Stanford Encyclopedia of Philosophy. The Metaphysics Research Lab, Center for the Study of Language and Information (CSLI), Stanford University. 19 July 2009. ISSN 1095-5054. Retrieved 16 November 2020.
  27. ^ Nanjundiah, V. (2005). "John Maynard Smith (1920–2004)" (PDF). Resonance. 10 (11): 70–78. doi:10.1007/BF02837646. S2CID 82303195.

Further reading

  • Andrey Korotayev, Artemy Malkov, and Daria Khaltourina. Introduction to Social Macrodynamics: Compact Macromodels of the World System Growth. ISBN 5-484-00414-4
  • Turchin, P. 2003. Complex Population Dynamics: a Theoretical/Empirical Synthesis. Princeton, NJ: Princeton University Press.
  • Smith, Frederick E. (1952). "Experimental methods in population dynamics: a critique". Ecology. 33 (4): 441–450. doi:10.2307/1931519. JSTOR 1931519.

External links

This page was last edited on 23 November 2020, at 10:23
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