Abstract
Genetic drift is a random process that can lead to the fixation of alleles, but at the cost of losing alleles, especially those with low frequencies, and to increased homozygosity in the population. This process is described for an idealised population and its effect illustrated on different population sizes. The founder effect, a similar random process, is also described. The conditions of the idealised population are rarely met in real populations. Therefore, in order to estimate genetic drift it is necessary to make adjustments, for example by estimating effective population size (N e ). Methods to estimate N e in populations with unequal sex–ratio in breeders, e.g. herd species, and with unequal family sizes, are provided. Problems in estimating genetic drift due to generation overlap are also discussed. Effective population size can also be estimated from pedigrees. This method is illustrated with the Nepalese red panda as example. Computer programs that simulate Mendelian segregation in pedigrees (gene dropping) estimate the expected genetic drift and are not hindered by unequal sex–ratio, unequal family size or generation overlap. The general methodology of gene dropping is described. Software implementations can differ in complexity, e.g. from single locus (two alleles) to multiple loci (multiple alleles) on chromosomes and crossing over. The effects of simple and complex models on genetic loss in the wolverine population were evaluated in the context of model selection. Effective population sizes are estimated in Nepalese red pandas from expected genetic drift between census intervals.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Allendorf FW, Luikart G, Aitken SN (2013) Conservation and the genetics of populations, 2nd edn. Wiley–Blackwell, Chichester
Ballou JD, Foose TJ (1996) Demographic and genetic management of captive populations. In: Kleiman DG, Allen ME, Thompson KV, Lumpkin S (eds) Wild mammals in captivity: principles and techniques, 1st edn. The University of Chicago Press, Chicago, pp 263–283
Ballou JD, Lees C, Faust LJ, Long S, Lynch C, Bingaman Lackey L, Foose TJ (2010) Demographic and genetic management of captive populations. In: Kleiman DG, Thompson KV, Kirk Baer C (eds) Wild mammals in captivity: principles and techniques for zoo management, 2nd edn. The University of Chicago Press, Chicago, pp 219–252
Ballou JD, Lacy RC, Pollak JP (2011) PMx: software for demographic and genetic analysis and management of pedigreed populations (version 1.0). Technical report, Chicago Zoological Society, Brookfield
Crow JF, Kimura M (1970) An introduction to population genetics theory. Harper and Row, New York
Engen S, Lande R, Saether BE (2005) Effective size of a fluctuating age–structured population. Genetics 170(2):941–954
Felsenstein J (1971) Inbreeding and variance effective numbers in populations with overlapping generations. Genetics 681(4):581–597
Frankham R, Ballou JD, Briscoe DA (2010) Introduction to conservation genetics, 2nd edn. Cambridge University Press, Cambridge
Gilbert JP, Hammel EA (1966) Computer simulation and analysis of problems in kinship and social structure. Am Anthropol 68(1):71–93
Haldane JBS (1919) The combination of linkage values, and the calculation of distances between the loci of linked factors. J Genet 8:299–309
Kimura M, Crow JF (1963) The measurement of effective population number. Evolution 17(3):279–288
Lacy RC (1993) Genes: a computer program for the analysis of pedigrees and genetic management of populations. Software, Chicago Zoological Society, Brookfield
Lande R, Barrowclough G (1987) Effective population size and genetic variation. In: Soulé ME (ed) Viable populations for conservation. Cambridge University Press, Cambridge, pp 87–123
MacCluer JW (1967) Monte Carlo methods in human population genetics: a computer model incorporating age-specific birth and death rates. Am J Hum Genet 19(3):303–312
MacCluer JW, VandeBerg JL, Read B, Ryder OA (1986) Pedigree analysis by computer simulation. Zoo Biol 5:147–160
Princée FPG (1988) Genetic variation in the zoo population of the red panda subspecies Ailurus fulgens fulgens. Zoo Biol 7(3):219–231
Princée FPG (1989) Preservation of genetic variation in the red panda population. In: Glatston AR (ed) Red panda biology. SPB Academic Publishing, The Hague, pp 171–182
Princée FPG (1998) Genetic management of small animal populations in zoos and wildlife reserves: the use of genome models in the estimation of genetic variation and the effects of social structures. PhD thesis, University of Groningen, Groningen
R Core Team (2015) R: a language and environment for statistical computing. R foundation for statistical computing, Vienna, Austria. https://www.R-project.org/
Scobie P, Flesness NR (1989) Single Population Animal Record Keeping System (SPARKS), software and manual. International Species Information System, Apple Valley
Stam P (1979) Interference in genetic crossing over and chromosome mapping. Genetics 92(2):573–594
Starfield AM, Bleloch A (1991) Building models for conservation and wildlife management, 2nd edn. Burgess International Group, Edina
Wright S (1922) Coefficients of inbreeding and relationship. Am Nat 56:330–338
Wright S (1931) Evolution in Mendelian populations. Genetics 16(2):97–159
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing AG
About this chapter
Cite this chapter
Princée, F.P.G. (2016). Genetic Drift and Simulations. In: Exploring Studbooks for Wildlife Management and Conservation. Topics in Biodiversity and Conservation, vol 17. Springer, Cham. https://doi.org/10.1007/978-3-319-50032-4_14
Download citation
DOI: https://doi.org/10.1007/978-3-319-50032-4_14
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-50031-7
Online ISBN: 978-3-319-50032-4
eBook Packages: Biomedical and Life SciencesBiomedical and Life Sciences (R0)