Open access peer-reviewed chapter

Genetic Diversity in Small Populations

Written By

Arne Nils Linløkken

Submitted: 19 November 2017 Reviewed: 01 April 2018 Published: 05 November 2018

DOI: 10.5772/intechopen.76923

From the Edited Volume

Genetic Diversity and Disease Susceptibility

Edited by Yamin Liu

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The chapter focuses on animal populations of low genetic diversity, among which some have low population size and are, or have been, threatened by extinction. Genetic diversity is regarded as a must for a species to be able to adapt to environmental challenges, but despite this, several species, also among advanced animal groups like birds and mammals, seem to thrive well with low genetic diversity. Some species are assumed to have done so for thousands of years. Other species have low genetic diversity resulting from heavy bottleneck events, in some cases very close to extinction, caused by human activities. Although some species live with surprisingly low genetic diversity, being prone to further loss of genetic variation, this may be retarded due to sexual selection and fitness superiority of heterozygotes. Simulations with population size N = 25 showed that a homozygote fitness of 0.75 compared to fitness = 1.0 of the heterozygote resulted in exclusion of a p = 0.10 frequency allele in <10% of 50 simulation over 50 generations, whereas fitness 1.0 of all genotypes resulted in exclusion of the p = 0.10 allele in 78% of 50 simulations.


  • allele exclusion
  • genotypes
  • heterozygote fitness
  • population size
  • selection

1. Introduction

Genetic diversity is a crucial characteristic of any population or species, as genes coding for causative traits are tools by which the populations are equipped to adapt to environmental challenges [1, 2, 3]. High genetic diversity, therefore, is assumed an advantage or even a must, for species survival when environmental factors are changing, like climate change, new species appear, and among those new parasites and diseases. The development at present, with steadily decreasing number of species, often caused by human activity [4] imposes a responsibility on human society to take countermeasures. This is necessary also for our own welfare and prosperity.

In the conservation of populations and species, the preservation of natural habitats, for example wooden areas, of sufficient size should always be the first priority, though it is not always possible, and it may already be too late. One could claim, in a conservation perspective, that the population or the species, first of all is a gene pool, and that the preservation of a population handles about preservation of the gene pool as the most inalienable. The genetic diversity should therefore be explored and described as soon as possible in any population, but primarily for those already known to be threatened. Alleles may go extinct, especially low frequency alleles in small populations, and new alleles are added by a certain mutation rate, not necessarily keeping up with the loss rate.

Several molecular biological methods are available, and the choice of method is a matter of discussion and depends on the purpose. Amplified fragment length polymorphism (AFLP) [5] randomly amplified polymorphic DNA (RAPD) [6], restriction fragment length polymorphism (RFLP) [6], microsatellite analysis (MS), also denoted simple sequence repeat (SSR) [7] and single nucleotide polymorphism (SNP) [8] are conducted on selected marker loci. The latter two are favorites, and MS loci are polymorphic, that is, one locus may exhibit several different alleles, commonly 3–15, whereas SNP loci, like RFLPs, are biallelic. MS analysis includes usually 10–20 marker loci, sometimes more, whereas SNP analysis includes several thousand loci. Microsatellite loci are noncoding and therefore neutral, though the loci may be linked to coding loci and apparently be under selection, if the linked locus is under selection. SNPs may be located in coding loci and consequently be under selection. The SNP assays have an advantage due to being easier to standardize across detection platforms and laboratories than the MS method.

To describe the genetic variation of a population and relatedness, or lack of such, between individuals and populations, the MS method is well suited due to its high variability. Allele frequencies may be compared between populations and genetic structure within groups of populations, for example, in metapopulations, may be explored. Number of alleles (allele richness, when adjusted for sample size) per analyzed locus is an important index together with the fraction of heterozygote genotypes, that is, observed (HO) and expected (assuming Hardy Weinberg equilibrium) heterozygosity (HE), often referred to as genetic diversity, the inbreeding (FIS) and outbreeding (FST) coefficient. Estimates of effective population size Ne [9] may also be conducted based on linkage disequilibrium, heterozygote excess, and others [10, 11]. FST is one of several indices of genetic differentiation between populations. Microsatellites are well suited for that kind of studies due to the high variability.

The value of such indices depends on the markers, that is, marker set chosen, so the comparison between populations should be based on the same marker set. The same applies to SNP analysis, but SNPs are advantageous when the aim is to focus on important traits to explore selectivity and fitness among individuals and populations [12, 13, 14]. In a breeding context, for improved growth and survival of economical important species, to secure survival and fertility of populations and species, wild or domestic, the preservation of certain alleles or combination of such, can be monitored. There is a potential for selection of mates when animals are bred in captivity by conducting genetic screening of parental generation before fertilization to strengthen or weaken specific traits [15].


2. A short review

2.1. Low genetic diversity, but still successful

Though genetic diversity is assumed to be a prerequisite of success, there are several known examples of viable and apparently successful species with low genetic variability, like the African Cheetah (Acinonyx jubatus), with expected heterozygosity HE < 0.0153, showing no characters of inbreeding, like reduced fertility, survival or fluctuating asymmetry, in the wild [16]. There are problems with reproduction in captivity, that is, in zoos [17], but this may be due to management as reproduction of cheetahs in North America was improved by changed husbandry [18, 19, 20], though this could potentially be due to limited adaptability as a consequence of low genetic diversity.

Mauritius kestrel (Falco punctatus) of the Mauritius Islands was characterized as one of the rarest bird species in the world when only one pair was left in 1974, after deforestation and invading species. After careful breeding, by picking naturally laid eggs in nest in the wild, for hatching and breeding chics for stocking, the endemic species now counts several hundred pairs [21, 22]. The population appears viable, though the genetic variability is low with heterozygosity H = 0.10, as compared with historical H = 0.20 (from up to 170 years old museum skins) and H = 0.59–0.70 in continental kestrel species [21].

Another example of successful species with low genetic diversity is two species of albatross, the wandering albatross (Diomedea exulans) with a circumpolar distribution in the Southern Sea, breeding on six islands in numbers of tens of thousands, and the Amsterdam albatross (Diomedea amsterdamensis) breeding on the Amsterdam Islands in the Indian sea. The Amsterdam albatross was down in only five breeding pairs due to introduction of cattle, cats and ship rats [23]. The two species are supposed to have developed from a common root 840,000 years ago, and this time span includes repeated glaciations, and the low genetic diversity with H ≤ 0.08 may have existed before the deviation [24]. Both seem successful in their natural environment, though, the question is what will happen if the species encounter a new environment? Nevertheless, it is questioned whether their low genetic diversity has ever been a potential problem?

In Australia, with its distinctive fauna, the duck-billed platypus (Ornithorhynchus anatinus), representing the primitive mammal order Monotremata, is one of the most special. If any species deserves special attention, this is one of them. The distribution is limited to South and East Australia, and the populations are small. Reserves are established and platypuses have also been stocked to establish new populations [25], the last mean of conservation action, next to breeding in captivity. Two island populations are described by Furlan et al. [25]: one natural occurring population on King Island and a stocked population on Kangaroo Island. The King Island population has low genetic diversity due to low population number, whereas the stocked population has quite high genetic diversity due to admixture of specimens from different populations. Though the genetic diversity generally is low, HO = 0.026–0.55, in platypus populations, they survive.

In North America, the black-footed ferret (Mustela nigripes) has been present from pleistocen (> 11,700 years ago) when they immigrated from Asia over the Bering strait [26]. The species was extinct in the wild, after the close to extinction of its main prey the prairie dog (Cynomys sp), followed by plague, when a breeding program started in 1985, based on 18 individuals, of which seven reproduced in captivity [27]. The expected heterozygosity dropped to HE ≤ 0.11 in some populations after bottleneck events in the 1970s, but the populations now seem to reproduce without noticeable effects of inbreeding.

2.2. How to keep a small but diverse gene pool

The species described above, all with low genetic diversity in at least some populations, still seem viable, but a crucial question is whether the low diversity populations are sustainable. Can they meet environmental changes to come? The lower the diversity and population size N, the higher the risk of loss from genetic drift following bottleneck events, and after generations, fixation of the most frequent allele at a locus may be expected, when loss rate exceeds mutation rate. Experiments have demonstrated lower fitness of low diversity specimens of, for example an estuarine crustacean (Americamysis bahia) showed reduced fitness (fertility, survival) in populations with low genetic diversity compared to populations of high diversity, and this was most pronounced in stressful environments [28]. Closely related mates may lead to inbreeding depression with loss of low frequent alleles. Nevertheless, inbreeding in wild populations of moderate size is not necessarily harmful, as it may lead to exclusion of recessive harmful alleles, purging, and result in a population that is more adapted to its environment [29, 30]. The effect, or cost, of inbreeding in wild populations is difficult to observe, and unfit combinations may be excluded in all stages of life, from pre-zygotic to reproductive phase [31].

Salmonid fishes are commonly bred in fish farms for food production and for stocking in rivers and lakes to improve fishery. Major economic interests are involved, and considerable effort is spent on research. Lehnert et al. [32, 33] found that sperm competition and cryptic female choice (CFC) help to maintain allele richness in Chinook salmon (Oncorhynchus tshawytscha). An assessment of genetic variation within metapopulations of steelhead trout (Oncorhynchus mykiss) related to climate and landscape showed that climate variation induced genetic variation [34], and the genetics of river living salmonids is affected by dams as obstacles to migration [35]. Several studies have showed genetic differentiation between wild and hatchery stocks, though of common origin, indicating serious effects of breeding based on forced, artificial mating, avoiding natural sexual selection [36, 37].

Human interventions of different kinds affect populations and their genetic diversity and structure, and the effect within a given time span is impossible to predict. Nevertheless, the loss or exclusion of alleles from a population is in any circumstances worrisome when it is due to human action. Conservation of metapopulations, consisting of small and moderately sized (effective population size Ne < 50) populations with some possibility of admixing, is one way to secure allele preservation. To explore this, natural metapopulations may be studied. Linløkken et al. [38] found in a study of brown trout (Salmo trutta) in nine tributaries to Lake Mjøsa in central Norway, that effective population size was positively related to habitat length (size). A bit unexpected, the heterozygosity based on MSs, was not correlated with effective population size (mostly < 90) and was the highest in the middle-sized habitats. There was significant inbreeding coefficient FIS in some of them, and the observed heterozygosity was in most cases lower than the expected. The low observed heterozygosity indicated inbreeding, which may lead to allele loss, but the lack of correlation between heterozygosity and Ne may suggest that other mechanisms worked. It could be due to increased fitness of heterozygotes, compared with the homozygotes, acting as a mechanism to slow down allele exclusion in populations. A conflicting interpretation of observed heterozygote excess is that heterozygote excess may indicate a recent bottleneck event [39].

Experiments with fruit flies (Drosophila melanogaster) demonstrated excessive heterozygosity, and this was explained by associative overdominance, that is, though the markers are noncoding loci, they are linked to causative loci that are under selection. Higher fitness of heterozygotes compared with homozygotes at the linked loci will retain the allele exclusion [40]. Noncoding or neutral markers may also be linked to (hitchhiking with) causative loci where coding alleles are removed by selection, called purging, excluding harmful recessive alleles. The reduction of hitchhiking non-coding alleles is called background selection [40].


3. On population size and heterozygosity of brown trout

3.1. Heterozygote excess in small populations of brown trout

A small tributary to the Lake Savalen in Central Norway serves as spawning area for brown trout of the lake. The number of breeders (effective population size of one cohort, Nb), based on linkage disequilibrium in 10 MS loci, was estimated to Nb = 38 for young of the year (0+) in autumn, and Nb = 35 for 1-year (1+) old fish in June the subsequent year (i.e., of the same cohort) [41]. The observed heterozygosity based on the same MSs, was HO = 0.69 for 0+, and increased to HO = 0.78 for 1+, and both were significantly higher than the expected heterozygosity (HE = 0.67–0.72). This corresponded to HO = 0.333 for both 0+ and 1+ and HE = 0.323 and 0.325, respectively, based on SNPs. For both marker types, the deviation from Hardy–Weinberg equilibrium was significant, and this excess of heterozygotes is interesting. When comparing wild 0+ and 1+ and a group of hatchery brown trout, all of the same cohort, Linløkken et al. [41] found that allele frequencies were changed from October to June in the subsequent year and was even more differentiated in the hatchery group.

By analyzing biallelic markers, that is, with two possible homozygotes and one heterozygote, like in SNPs, this is simpler to explore than in cases of the poly-allelic microsatellites. Outlier FST analysis of 3871 SNP loci detected 421 (10.8%) loci as candidates of selection, and among those, 34 loci showed significant mean length differences between genotypes in the 1+ wild fish group. In 30 of these loci, the largest genotype was significantly more frequent in the 1+ than in the 0+ group, indicating positive selection of large specimens, and 19 (63%) of these large genotypes were heterozygotes. This indicated that the differentiation between fry and the yearlings was in part due to size selective mortality, disfavoring the smallest specimens of fry through increased autumn to spring mortality. At five loci, only one of the homozygotes was recorded in the 0+ group (Figure 1). The heterozygote was significantly more frequent in the 1+ than in the 0+ group (Fisher exact test, P < 0.05) and was larger than the homozygote, different from in the 0+ group (Figure 2) (t-test, P < 0.05).

Figure 1.

The distribution of genotypes of five SNP loci (numbers refer to Linløkken et al. [36]) in young of the year (W.0+, N = 48) and 1-year-old (W.1+, N = 47) brown trout of the same cohort and population. P denotes the observed frequency of the low frequency allele at the five loci.

Figure 2.

Mean lengths of young of the year (+) and 1-year-old brown trout (□) of different genotypes at loci at which mean length of heterozygotes were larger than that of homozygotes, and heterozygotes were more frequent in the 1-year-old group (W.1+) than in the young of the year group (W.0+) (Figure 1). Vertical lines show 95% confidence limits.

3.2. Simulating the fate of a low frequency allele at biallelic loci

The low allele frequency of Figure 1 (p = approximately 0.10) was used to simulate allele exclusion by means of the Allele Simulator software (available on the web:, choosing population size N = 25, 50, and 100, and performing 50 replicates of 50 simulations over 50 generations (corresponding to 150–250 years with maturation at 3–5 years of age). To explore the effect of allele frequency on exclusion rate, 50 simulations with N = 25 and allele frequency p = 0.01, 0.05, 0.10, 0.25, and 0.50 were conducted. Fitness was set to 1.00 for all genotypes, and the proportion of exclusion showed a curved decrease by increasing p and resulted in 97% exclusion with p = 0.01, being reduced to 90% with p = 0.05, further to 78% with p = 0.10 and to 23% with p = 0.50 (Figure 3). This suggests that with N = 25, the probability of retaining a p = 0.01 allele in 50 generations, without any heterozygote superiority, is close to null.

Figure 3.

The proportion of 50 simulations that led to extinction during 50 generations in a population of N = 25 as a function of the initial frequency of the allele.

The initial allele frequency was then set to p = 0.10, and 50 simulations were run with fitness = 1.00 of all genotypes and N = 25, 50, 100, 200, and 400. The proportion of exclusion decreased exponentially by increasing N, and less than 50% of the simulations ended in exclusion when N > 77, and 62% of the simulations ended with exclusion with N = 50 (calculated from the regression, Figure 4). With N = 400, only 4% of the simulations resulted in exclusion.

Figure 4.

The proportion of 50 simulations that led to extinction during 50 generations of an allele with initial frequency p = 0.10 as a function of population size N = 25, 50, 100, 200, and 400.

To explore the effects of relative heterozygote fitness, the fitness of the heterozygote was set to 1.0, whereas the fitness of the two homozygotes was set equal, varying from 0.75 to 1.0, that is, the heterozygote fitness was similar or higher than that of the homozygotes. The simulations (Figure 5) showed that when fitness was equal for all genotypes, exclusion of the p = 0.10 allele decreased from 78% with N = 25 to 65% of the simulations with N = 50 and further to 40% with N = 100 (Figure 6). With fitness 0.90 of the homozygotes, less than 50% of the simulations ended with exclusion with N = 25, corresponding to less than 20% with N = 50, and less than 5% ended in exclusion with N = 100. Less than 10% of the simulations led to exclusion with homozygote fitness = 0.75 and N = 25, less than 1% led to exclusion with N = 50, and null simulations ended with exclusion with N = 100.

Figure 5.

Proportion of 50 simulations of the frequency of an allele with initially p = 0.10 during 50 generations, population size N = 50 with fitness = 1 for all genotypes (upper panel), and with fitness = 1.0 of the heterozygote and fitness = 0.80 for both the homozygote (lower panel).

Figure 6.

Proportion of 50 simulations of the frequency of an allele with initial frequency p = 1.0 that led to extinction within 50 generation with fitness = 1 of the heterozygote and fitness 0.75–1.0 of the two homozygotes and population size N = 25, 50, and 100.


4. Conclusion

Many animal species, among them representatives of advanced groups like birds and mammals, thrive well despite low genetic diversity, that is, apparently with a limited toolbox for evolutionary adaptation to new environments. Nevertheless, when genetic diversity is low, it is important to retain the alleles that still exist to avoid fixation at all loci. In small populations, like N = 25, the exclusion rate is quite high for alleles of frequency p = 0.10, and it increased inversely with the allele frequency and population size, according to the simulation experiments. This will, to some extent, be compensated for by mutations and introgression from migrants. The exclusion rate was reduced when heterozygote fitness exceeded that of the homozygotes, as was expected, and the increased heterozygote fitness helps effectively to retard the exclusion rate of alleles. As an example, young of the year and 1-year-old brown trout suggested positive selection of heterozygotes during the first winter, possibly due to faster growth and increased survival of large specimens.



This work was funded by the Inland Norway University of Applied Sciences, as a part of the author’s work as associated professor at this institution.


Conflict of interest

There is no conflict of interest.


  1. 1. Franklin IR, Allendorf FW. The 50/500 rule is still valid - reply to Frankham et al. Letter to Editor. Biological Conservation. 2014;176:284-285
  2. 2. Lande R. Genetics and demography in biological conservation. Science. 1988;241:1455-1460
  3. 3. Nevo E. Evolution of genome–phenome diversity under environmental stress. Proceedings of the National Academy of Sciences of the United States of America. 2001;98:6233-6240
  4. 4. Ceballos G, Ehrlich PR, Barnosky AD, García A, Pringle RM, Palmer TM. Accelerated modern human–induced species losses: Entering the sixth mass extinction. Science Advances. 2015;1:e1400253. DOI: 10.1126/sciadv.1400253
  5. 5. Vos P, Hogers R, Bleeker M, Reijans M, van de Lee T, Hornes M, Frijters A, Pot J, Peleman J, Kuiper M. AFLP: A new technique for DNA fingerprinting. Nucleic Acids Research. 1995;23:4407-4414
  6. 6. Williams JG, Kubelik AR, Livak KJ, Rafalski JA, Tingey SV. DNA polymorphisms amplified by arbitrary primers are useful as genetic markers. Nucleic Acids Research. 1990;18:6531-6535
  7. 7. Balloux F, Lugon-Moulin N. The estimation of population differentiation with microsatellite markers. Molecular Ecology. 2002;11:155-165
  8. 8. Thomas PD, Kejariwal A. Coding single-nucleotide polymorphisms associated with complex vs. Mendelian disease: Evolutionary evidence for differences in molecular effects. Proceedings of the National Academy of Sciences of the United States of America. 2004;101:15398-15403
  9. 9. Wright S. Evolution in Mendelian populations. Genetics. 1931;16:97-159
  10. 10. Wang J. Estimation of effective population sizes from data on genetic markers. Philosophical Transactions of the Royal Society B: Biological Sciences. 2005;360:1395-1409
  11. 11. Waples RS, England PR. Estimating contemporary effective population size on the basis of linkage disequilibrium in the face of migration. Genetics. 2011;189:633-644
  12. 12. Brooks SA, Gabreski N, Miller D, Brisbin A, Brown HE, Streeter C, Mezey J, Cook D, Antczak DF. Whole-genome SNP association in the horse: Identification of a deletion in myosin Va responsible for lavender foal syndrome. PLoS Genetics. 2010;6:e1000909
  13. 13. Davoli R, Fontanesi L, Cagnazzo M, Scotti E, Buttazzoni L, Yerle M, Russo V. Identification of SNPs, mapping and analysis of allele frequencies in two candidate genes for meat production traits: The porcine myosin heavy chain 2B (MYH4) and the skeletal muscle myosin regulatory light chain 2 (HUMMLC2B). Animal Genetics. 2003;34:221-225
  14. 14. Kolbehdari D, Wang Z, Grant JR, Murdoch B, Prasad A, Xiu Z, Marques E, Stothard P, Moore SS. A whole-genome scan to map quantitative trait loci for conformation and functional traits in Canadian Holstein bulls. Journal of Dairy Science. 2008;91:2844-2856
  15. 15. Matsumoto Y, Goto T, Nishino J, Nakaoka H, Tanave A, Takano-Shimizu T, Mott RF, Koide T. Selective breeding and selection mapping using a novel wild-derived heterogeneous stock of mice revealed two closely-linked loci for tameness. Scientific Reports. 2017;7:4607
  16. 16. Merola M. A reassessment of homozygosity and the case for inbreeding depression in the cheetah, Acinonyx jubatus: Implications for conservation. Una reevaluación de la homosigocidad y un argumento Para la depresión de endocría en el cheetah Acinonyx jubatus: Implicaciones Para la conservación. Conservation Biology. 1994;8:961-971
  17. 17. O'Brien SJ, Roelke ME, Marker LL, Newman A, Winkler CA, Meltzer D, Colly L, Everman JF, Bush M, Wildt DE. Genetic basis of species vulnerability in the cheetah. Science. 1985;227:1428-1434
  18. 18. Widt DE, Brown JL, Bush M, Barone MA, Cooper KA, Grisham J, Howard JG. Reproductive status of cheetahs Acinonyx jubatus in north American zoos: The benefits of physiological surveys for strategic planning. Zoo Biology. 1993;12:45-80
  19. 19. Marker-Kraus L, Grisham J. Captive breeding of cheetahs in North America zoos. Zoo Biology. 1993;12:5-18
  20. 20. Marker-Kraus L. History of the cheetah: Acinonyx jubatus in zoos 1829-1994. International Zoo Yearbook. 1997;35:27-43
  21. 21. Groombridge JJ, Jones CG, Bruford MW, NRA. Conservation biology: ‘Ghost’ alleles of the Mauritius kestrel. Nature. 2000;403:616-616
  22. 22. Jones CG, Heck W, LR E, Mungroo Y, Slade G, Cade T. Resauration of the Mauritius kestrel Falco punctatus population. Ibis. 1995;137:S173-S180
  23. 23. Weimerskirch H, Jouventin P. Population dynamics of the wandering albatross, Diomedea exulans, of the Crozet Islands: Causes and consequences of the population decline. Oikos. 1987;49:315-322
  24. 24. Milot E, Weimerskirch H, Duchesne P, Bernatchez L. Surviving with low genetic diversity: The case of albatrosses. Proceedings of the Royal Society B: Biological Sciences. 2007;274:779-787
  25. 25. Furlan E, Stoklosa J, Griffiths J, Gust N, Ellis R, Huggins RM, Weeks AR. Small population size and extremely low levels of genetic diversity in island populations of the platypus, Ornithorhynchus anatinus. Ecology and Evolution. 2012;2:844-857
  26. 26. Hillman CN, Clark TW. Mammalian species. Mustela nigripes. The American Society of Mammalogists. 1980;126:1-3
  27. 27. Wisely SM, Buskirk SW, Fleming MA, McDonald DB, Ostrander EA. Genetic diversity and fitness in black-footed ferrets before and during a bottleneck. Journal of Heredity. 2002;93:231-237
  28. 28. Markert JA, Champlin DM, Gutjahr-Gobell R, Grear JS, Kuhn A, McGreevy TJ, Roth A, Bagley MJ, Nacci DE. Population genetic diversity and fitness in multiple environments. BMC Evolutionary Biology. 2010;10:205
  29. 29. López-Cortegano E, Vilas A, Caballero A, García-Dorado A. Estimation of genetic purging under competitive conditions. Evolution. 2016;70:1856-1870
  30. 30. Caballero A, Bravo I, Wang J. Inbreeding load and purging: Implications for the short-term survival and the conservation management of small populations. Heredity. 2017;118:177-185
  31. 31. Crnokrak P, Roff DA. Inbreeding depression in the wild. Heredity. 1999;83:260-270
  32. 32. Lehnert SJ, Heath DD, Devlin RH, Pitcher TE. Post-spawning sexual selection in red and white Chinook salmon (Oncorhynchus tshawytscha). Behavioral Ecology. 2017;28:1-10
  33. 33. Lehnert SJ, Pitcher TE, Devlin RH, Heath DD. Red and white Chinook salmon: Genetic divergence and mate choice. Molecular Ecology. 2016;25:1259-1274
  34. 34. Hand BK, Muhlfeld CC, Wade AA, Kovach RP, Whited DC, Narum SR, Matala AP, Ackerman MW, Garner BA, Kimball JS, Stanford JA, Luikart G. Climate variables explain neutral and adaptive variation within salmonid metapopulations: The importance of replication in landscape genetics. Molecular Ecology. 2016;25:689-705
  35. 35. Heggenes J, Qvenild T, Stamford MD, Taylor EB. Genetic structure in relation to movements in wild European grayling (Thymallus thymallus) in three Norwegian rivers. Canadian Journal of Fisheries and Aquatic Sciences. 2006;63:1309-1319
  36. 36. Linløkken AN, Haugen TO, Kent MP, Lien S. Genetic differences between wild and hatchery-bred brown trout (Salmo trutta L.) in single nucleotide polymorphisms linked to selective traits. Ecology and Evolution. 2017;7:4963-4972
  37. 37. Liu F, Sun F, Xia JH, Li J, Fu GH, Lin G, Tu RJ, Wan ZY, Quek D, Yue GH. A genome scan revealed significant associations of growth traits with a major QTL and GHR2 in tilapia. Scientific Reports. 2014;4:7256
  38. 38. Linløkken AN, Johansen W, Wilson R. Genetic structure of brown trout, Salmo trutta, populations from differently sized tributaries of Lake Mjøsa in south-East Norway. Fisheries Management and Ecology. 2014;21:515-525
  39. 39. Cornuet JM, Luikart G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics. 1996;144:2001-2014
  40. 40. Schou MF, Loeschcke V, Bechsgaard J, Schlotterer C, Kristensen TN. Unexpected high genetic diversity in small populations suggests maintenance by associative overdominance. Molecular Ecology. 2017;26:6510-6523
  41. 41. Linløkken AN, Haugen TO, Mathew PK, Johansen W, Lien S. Comparing estimates of number of breeders Nb based on microsatellites and single nucleotide polymorphism of three groups of brown trout (Salmo trutta L.). Fisheries Management and Ecology. 2016;23:152-160

Written By

Arne Nils Linløkken

Submitted: 19 November 2017 Reviewed: 01 April 2018 Published: 05 November 2018