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The effective population size (N_e) is the size of an idealised population that would experience the same rate of genetic drift as the real population. The effective population size is normally smaller than the census population size N, partly because chance events prevent some individuals from breeding, and partly due to background selection and genetic hitchhiking. Idealised populations are based on unrealistic but convenient assumptions including random mating, rarity of natural selection such that each gene evolves independently, and constant population size.

The same real population could have a different effective population size for different properties of interest, such as genetic drift over one generation vs. over many generations, and a different effective population size at different sites in the genome. The effective population size is most commonly measured with respect to the coalescence time. In an idealised diploid population with no selection at any locus, the expectation of the coalescence time in generations is equal to twice the census population size. The effective population size is measured from genetic data as within-species genetic diversity divided by four times the mutation rate ${\displaystyle \mu }$, because in such an idealised population, the heterozygosity is equal to ${\displaystyle 4N\mu }$. In a population with selection at many loci and abundant linkage disequilibrium, the coalescent effective population size may not reflect the census population size at all, or may reflect its logarithm.

The concept of effective population size was introduced in the field of population genetics in 1931 by the American geneticist Sewall Wright. Some versions of the effective population size are used in wildlife conservation.

Overview: Types of effective population size

Depending on the quantity of interest, effective population size can be defined in several ways. Ronald Fisher and Sewall Wright originally defined it as "the number of breeding individuals in an idealised population that would show the same amount of dispersion of allele frequencies under random genetic drift or the same amount of inbreeding as the population under consideration". More generally, an effective population size may be defined as the number of individuals in an idealised population that has a value of any given population genetic quantity that is equal to the value of that quantity in the population of interest. The two population genetic quantities identified by Wright were the one-generation increase in variance across replicate populations (variance effective population size) and the one-generation change in the inbreeding coefficient (inbreeding effective population size). These two are closely linked, and derived from F-statistics, but they are not identical.

Today, the effective population size is usually estimated empirically with respect to the sojourn or coalescence time, estimated as the within-species genetic diversity divided by the mutation rate, yielding a coalescent effective population size. Another important effective population size is the selection effective population size 1/s_critical, where s_critical is the critical value of the selection coefficient at which selection becomes more important than genetic drift.

Empirical measurements

In Drosophila populations of census size 16, the variance effective population size has been measured as equal to 11.5. This measurement was achieved through studying changes in the frequency of a neutral allele from one generation to another in over 100 replicate populations.

For coalescent effective population sizes, a survey of publications on 102 mostly wildlife animal and plant species yielded 192 N_e/N ratios. Seven different estimation methods were used in the surveyed studies. Accordingly, the ratios ranged widely from 10 for Pacific oysters to 0.994 for humans, with an average of 0.34 across the examined species. Based on these data they subsequently estimated more comprehensive ratios, accounting for fluctuations in population size, variance in family size and unequal sex-ratio. These ratios average to only 0.10-0.11.

A genealogical analysis of human hunter-gatherers (Eskimos) determined the effective-to-census population size ratio for haploid (mitochondrial DNA, Y chromosomal DNA), and diploid (autosomal DNA) loci separately: the ratio of the effective to the census population size was estimated as 0.6–0.7 for autosomal and X-chromosomal DNA, 0.7–0.9 for mitochondrial DNA and 0.5 for Y-chromosomal DNA.

Variance effective size

In the Wright-Fisher idealized population model, the conditional variance of the allele frequency ${\displaystyle p'}$, given the allele frequency ${\displaystyle p}$ in the previous generation, is

${\displaystyle \operatorname {var} (p'\mid p)={p(1-p) \over 2N}.}$

Let ${\displaystyle {\widehat {\operatorname {var} }}(p'\mid p)}$ denote the same, typically larger, variance in the actual population under consideration. The variance effective population size ${\displaystyle N_{e}^{(v)}}$ is defined as the size of an idealized population with the same variance. This is found by substituting ${\displaystyle {\widehat {\operatorname {var} }}(p'\mid p)}$ for ${\displaystyle \operatorname {var} (p'\mid p)}$ and solving for ${\displaystyle N}$ which gives

${\displaystyle N_{e}^{(v)}={p(1-p) \over 2{\widehat {\operatorname {var} }}(p)}.}$

Theoretical examples

In the following examples, one or more of the assumptions of a strictly idealised population are relaxed, while other assumptions are retained. The variance effective population size of the more relaxed population model is then calculated with respect to the strict model.

Variations in population size

Population size varies over time. Suppose there are t non-overlapping generations, then effective population size is given by the harmonic mean of the population sizes:

${\displaystyle {1 \over N_{e}}={1 \over t}\sum _{i=1}^{t}{1 \over N_{i}}}$

For example, say the population size was N = 10, 100, 50, 80, 20, 500 for six generations (t = 6). Then the effective population size is the harmonic mean of these, giving:

${\displaystyle {1 \over N_{e}}}$	${\displaystyle ={{\begin{matrix}{\frac {1}{10}}\end{matrix}}+{\begin{matrix}{\frac {1}{100}}\end{matrix}}+{\begin{matrix}{\frac {1}{50}}\end{matrix}}+{\begin{matrix}{\frac {1}{80}}\end{matrix}}+{\begin{matrix}{\frac {1}{20}}\end{matrix}}+{\begin{matrix}{\frac {1}{500}}\end{matrix}} \over 6}}$
	${\displaystyle ={0.1945 \over 6}}$
	${\displaystyle =0.032416667}$
${\displaystyle N_{e}}$	${\displaystyle =30.8}$

Note this is less than the arithmetic mean of the population size, which in this example is 126.7. The harmonic mean tends to be dominated by the smallest bottleneck that the population goes through.

Dioeciousness

If a population is dioecious, i.e. there is no self-fertilisation then

${\displaystyle N_{e}=N+{\begin{matrix}{\frac {1}{2}}\end{matrix}}}$

or more generally,

${\displaystyle N_{e}=N+{\begin{matrix}{\frac {D}{2}}\end{matrix}}}$

where D represents dioeciousness and may take the value 0 (for not dioecious) or 1 for dioecious.

When N is large, N_e approximately equals N, so this is usually trivial and often ignored:

${\displaystyle N_{e}=N+{\begin{matrix}{\frac {1}{2}}\approx N\end{matrix}}}$

Variance in reproductive success

If population size is to remain constant, each individual must contribute on average two gametes to the next generation. An idealized population assumes that this follows a Poisson distribution so that the variance of the number of gametes contributed, k is equal to the mean number contributed, i.e. 2:

${\displaystyle \operatorname {var} (k)={\bar {k}}=2.}$

However, in natural populations the variance is often larger than this. The vast majority of individuals may have no offspring, and the next generation stems only from a small number of individuals, so

${\displaystyle \operatorname {var} (k)>2.}$

The effective population size is then smaller, and given by:

${\displaystyle N_{e}^{(v)}={4N-2D \over 2+\operatorname {var} (k)}}$

Note that if the variance of k is less than 2, N_e is greater than N. In the extreme case of a population experiencing no variation in family size, in a laboratory population in which the number of offspring is artificially controlled, V_k = 0 and N_e = 2N.

Non-Fisherian sex-ratios

When the sex ratio of a population varies from the Fisherian 1:1 ratio, effective population size is given by:

${\displaystyle N_{e}^{(v)}=N_{e}^{(F)}={4N_{m}N_{f} \over N_{m}+N_{f}}}$

Where N_m is the number of males and N_f the number of females. For example, with 80 males and 20 females (an absolute population size of 100):

${\displaystyle N_{e}}$	${\displaystyle ={4\times 80\times 20 \over 80+20}}$
	${\displaystyle ={6400 \over 100}}$
	${\displaystyle =64}$

Again, this results in N_e being less than N.

Inbreeding effective size

Alternatively, the effective population size may be defined by noting how the average inbreeding coefficient changes from one generation to the next, and then defining N_e as the size of the idealized population that has the same change in average inbreeding coefficient as the population under consideration. The presentation follows Kempthorne (1957).

For the idealized population, the inbreeding coefficients follow the recurrence equation

${\displaystyle F_{t}={\frac {1}{N}}\left({\frac {1+F_{t-2}}{2}}\right)+\left(1-{\frac {1}{N}}\right)F_{t-1}.}$

Using Panmictic Index (1 − F) instead of inbreeding coefficient, we get the approximate recurrence equation

${\displaystyle 1-F_{t}=P_{t}=P_{0}\left(1-{\frac {1}{2N}}\right)^{t}.}$

The difference per generation is

${\displaystyle {\frac {P_{t+1}}{P_{t}}}=1-{\frac {1}{2N}}.}$

The inbreeding effective size can be found by solving

${\displaystyle {\frac {P_{t+1}}{P_{t}}}=1-{\frac {1}{2N_{e}^{(F)}}}.}$

This is

${\displaystyle N_{e}^{(F)}={\frac {1}{2\left(1-{\frac {P_{t+1}}{P_{t}}}\right)}}}$

although researchers rarely use this equation directly.

Theoretical example: overlapping generations and age-structured populations

When organisms live longer than one breeding season, effective population sizes have to take into account the life tables for the species.

Haploid

Assume a haploid population with discrete age structure. An example might be an organism that can survive several discrete breeding seasons. Further, define the following age structure characteristics:

${\displaystyle v_{i}=}$ Fisher's reproductive value for age ${\displaystyle i}$,

${\displaystyle \ell _{i}=}$ The chance an individual will survive to age ${\displaystyle i}$, and

${\displaystyle N_{0}=}$ The number of newborn individuals per breeding season.

The generation time is calculated as

${\displaystyle T=\sum _{i=0}^{\infty }\ell _{i}v_{i}=}$ average age of a reproducing individual

Then, the inbreeding effective population size is

${\displaystyle N_{e}^{(F)}={\frac {N_{0}T}{1+\sum _{i}\ell _{i+1}^{2}v_{i+1}^{2}({\frac {1}{\ell _{i+1}}}-{\frac {1}{\ell _{i}}})}}.}$

Diploid

Similarly, the inbreeding effective number can be calculated for a diploid population with discrete age structure. This was first given by Johnson, but the notation more closely resembles Emigh and Pollak.

Assume the same basic parameters for the life table as given for the haploid case, but distinguishing between male and female, such as N₀ and N₀ for the number of newborn females and males, respectively (notice lower case ƒ for females, compared to upper case F for inbreeding).

The inbreeding effective number is

${\displaystyle {\begin{aligned}{\frac {1}{N_{e}^{(F)}}}={\frac {1}{4T}}\left\{{\frac {1}{N_{0}^{f}}}+{\frac {1}{N_{0}^{m}}}+\sum _{i}\left(\ell _{i+1}^{f}\right)^{2}\left(v_{i+1}^{f}\right)^{2}\left({\frac {1}{\ell _{i+1}^{f}}}-{\frac {1}{\ell _{i}^{f}}}\right)\right.\,\,\,\,\,\,\,\,&\\\left.{}+\sum _{i}\left(\ell _{i+1}^{m}\right)^{2}\left(v_{i+1}^{m}\right)^{2}\left({\frac {1}{\ell _{i+1}^{m}}}-{\frac {1}{\ell _{i}^{m}}}\right)\right\}.&\end{aligned}}}$

Coalescent effective size

According to the neutral theory of molecular evolution, a neutral allele remains in a population for Ne generations, where Ne is the effective population size. An idealised diploid population will have a pairwise nucleotide diversity equal to 4${\displaystyle \mu }$Ne, where ${\displaystyle \mu }$ is the mutation rate. The sojourn effective population size can therefore be estimated empirically by dividing the nucleotide diversity by the mutation rate.

The coalescent effective size may have little relationship to the number of individuals physically present in a population. Measured coalescent effective population sizes vary between genes in the same population, being low in genome areas of low recombination and high in genome areas of high recombination. Sojourn times are proportional to N in neutral theory, but for alleles under selection, sojourn times are proportional to log(N). Genetic hitchhiking can cause neutral mutations to have sojourn times proportional to log(N): this may explain the relationship between measured effective population size and the local recombination rate.

Selection effective size

In an idealised Wright-Fisher model, the fate of an allele, beginning at an intermediate frequency, is largely determined by selection if the selection coefficient s ≫ 1/N, and largely determined by neutral genetic drift if s ≪ 1/N. In real populations, the cutoff value of s may depend instead on local recombination rates. This limit to selection in a real population may be captured in a toy Wright-Fisher simulation through the appropriate choice of Ne. Populations with different selection effective population sizes are predicted to evolve profoundly different genome architectures.

See also

• Minimum viable population
• Small population size

References

1. "Effective population size". Blackwell Publishing. Retrieved 4 March 2018.
2. Wright S (1931). "Evolution in Mendelian populations" (PDF). Genetics. 16 (2): 97–159. doi:10.1093/genetics/16.2.97. PMC 1201091. PMID 17246615.
3. Wright S (1938). "Size of population and breeding structure in relation to evolution". Science. 87 (2263): 430–431. doi:10.1126/science.87.2263.425-a.
4. James F. Crow (2010). "Wright and Fisher on Inbreeding and Random Drift". Genetics. 184 (3): 609–611. doi:10.1534/genetics.109.110023. PMC 2845331. PMID 20332416.
5. ^ Lynch, M.; Conery, J.S. (2003). "The origins of genome complexity". Science. 302 (5649): 1401–1404. Bibcode:2003Sci...302.1401L. CiteSeerX 10.1.1.135.974. doi:10.1126/science.1089370. PMID 14631042. S2CID 11246091.
6. ^ R.A. Neher; B.I. Shraiman (2011). "Genetic Draft and Quasi-Neutrality in Large Facultatively Sexual Populations". Genetics. 188 (4): 975–996. arXiv:1108.1635. doi:10.1534/genetics.111.128876. PMC 3176096. PMID 21625002.
7. Buri, P (1956). "Gene frequency in small populations of mutant Drosophila". Evolution. 10 (4): 367–402. doi:10.2307/2406998. JSTOR 2406998.
8. R. Frankham (1995). "Effective population size/adult population size ratios in wildlife: a review". Genetics Research. 66 (2): 95–107. doi:10.1017/S0016672300034455.
9. S. Matsumura; P. Forster (2008). "Generation time and effective population size in Polar Eskimos". Proc Biol Sci. 275 (1642): 1501–1508. doi:10.1098/rspb.2007.1724. PMC 2602656. PMID 18364314.
10. Karlin, Samuel (1968-09-01). "Rates of Approach to Homozygosity for Finite Stochastic Models with Variable Population Size". The American Naturalist. 102 (927): 443–455. doi:10.1086/282557. ISSN 0003-0147. S2CID 83824294.
11. Kempthorne O (1957). An Introduction to Genetic Statistics. Iowa State University Press.
12. Felsenstein J (1971). "Inbreeding and variance effective numbers in populations with overlapping generations". Genetics. 68 (4): 581–597. doi:10.1093/genetics/68.4.581. PMC 1212678. PMID 5166069.
13. Johnson DL (1977). "Inbreeding in populations with overlapping generations". Genetics. 87 (3): 581–591. doi:10.1093/genetics/87.3.581. PMC 1213763. PMID 17248780.
14. Emigh TH, Pollak E (1979). "Fixation probabilities and effective population numbers in diploid populations with overlapping generations". Theoretical Population Biology. 15 (1): 86–107. Bibcode:1979TPBio..15...86E. doi:10.1016/0040-5809(79)90028-5.
15. Gillespie, JH (2001). "Is the population size of a species relevant to its evolution?". Evolution. 55 (11): 2161–2169. doi:10.1111/j.0014-3820.2001.tb00732.x. PMID 11794777.
16. Hahn, Matthew W. (2008). "Toward a selection theory of molecular evolution". Evolution. 62 (2): 255–265. doi:10.1111/j.1558-5646.2007.00308.x. PMID 18302709.
17. Masel, Joanna (2012). "Rethinking Hardy–Weinberg and genetic drift in undergraduate biology". BioEssays. 34 (8): 701–10. doi:10.1002/bies.201100178. PMID 22576789. S2CID 28513167.
18. Daniel B. Weissman; Nicholas H. Barton (2012). "Limits to the Rate of Adaptive Substitution in Sexual Populations". PLOS Genetics. 8 (6): e1002740. doi:10.1371/journal.pgen.1002740. PMC 3369949. PMID 22685419.
19. Lynch, Michael (2007). The Origins of Genome Architecture. Sinauer Associates. ISBN 978-0-87893-484-3.
20. Rajon, E.; Masel, J. (2011). "Evolution of molecular error rates and the consequences for evolvability". PNAS. 108 (3): 1082–1087. Bibcode:2011PNAS..108.1082R. doi:10.1073/pnas.1012918108. PMC 3024668. PMID 21199946.

External links

• Holsinger, Kent (2008-08-26). "Effective Population Size". University of Connecticut. Archived from the original on 2005-05-24.
• Whitlock, Michael (2008). "The Effective Population Size". Biology 434: Population Genetics. The University of British Columbia. Archived from the original on 2009-07-23. Retrieved 2005-02-25.
• https://web.archive.org/web/20050524144622/http://www.kursus.kvl.dk/shares/vetgen/_Popgen/genetics/3/6.htm — on Københavns Universitet.

• Population genetics
• Population ecology
• Ecological metrics
• Quantitative genetics