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Evaluation of methodology for detection of genetic bottlenecks: inferences from temporally replicated lake trout populations
[Évaluation d'une méthode génétique de détection des goulots d'étranglement par une approche diachronique de populations de truites]
Comptes Rendus. Biologies, Volume 326 (2003) no. S1, pp. 61-67.

Résumés

Statistical methods have been proposed to detect recent bottlenecks on the basis of genetic characterizations of natural populations. In the absence of direct estimates of contemporary or historical population numbers, we tested the indirect M-ratio method based on microsatellite motif size frequency profiles using three historical and three contemporary Great Lakes populations of Salvelinus namaysuch for which severe reductions in population numbers are documented. Simulations employing plausible ranges of empirical population parameter values were used to explore bottleneck likelihood surfaces. We show that single values of the M-ratio are not sufficient to unambiguously infer a bottleneck without knowledge of mutation rates and effective population size (i.e., 4Neμ or Θ). Inferences of the degree of population bottleneck would be best supported if analyses were conducted across plausible ranges of Θ and by qualitative comparisons among population samples.

Des méthodes statistiques d'évaluation des goulots d'étranglement basées sur les caractéristiques génétiques des populations naturelles ont été proposées récemment. En absence de toute connaissance directe de la taille des populations actuelles et passées, des évaluations indirectes de tels événements évolutifs grâce aux données génétiques sont importantes pour mener à bien des plans de conservation et de gestion des espèces. Nous avons testé ici la méthode du rapport M (M-ratio) qui repose sur les profils observés de dispersion des allèles à des locus microsatellites en utilisant trois populations historiques et trois populations contemporaines de la truite des Grands Lacs (Salvelinus namaysuch) genotypées à cinq locus microsatellite. Le suivi historique des populations de cette espèce dans les Grands Lacs nord-américains montrent des réductions sévères des effectifs sur deux générations. Une gamme plausible de paramètres caractéristiques des populations naturelles a été utilisée pour explorer par simulation si chacune de ces populations avait été affectée ou non par un goulot d'étranglement. Nous montrons que les seules valeurs du rapport M ne sont pas suffisantes pour rendre compte efficacement d'un tel événement sans connaissance du taux de mutation (μ) et de la taille effective de la population (Ne) à travers le paramètre Θ(=4Neμ). Une meilleure preuve de l'existence d'un goulot d'étranglement récent sera apporté si les études employant cette méthode sont conduites sur une gamme plausible de valeurs de Θ, ainsi que par des comparaisons qualitatives entre échantillons.

Métadonnées
Publié le :
DOI : 10.1016/S1631-0691(03)00039-8
Keywords: bottleneck, M-ratio, mutation model, Salvelinus namaycush, microsatellite
Mot clés : goulot d'étranglement, rapport M, modèle de mutation, Salvelinus namaycush, microsatellite

Bruno Guinand 1 ; Kim T. Scribner 1

1 Department of Fisheries and Wildlife, Michigan State University, East Lansing, MI 48824-1222, USA
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     author = {Bruno Guinand and Kim T. Scribner},
     title = {Evaluation of methodology for detection of genetic bottlenecks: inferences from temporally replicated lake trout populations},
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Bruno Guinand; Kim T. Scribner. Evaluation of methodology for detection of genetic bottlenecks: inferences from temporally replicated lake trout populations. Comptes Rendus. Biologies, Volume 326 (2003) no. S1, pp. 61-67. doi : 10.1016/S1631-0691(03)00039-8. https://comptes-rendus.academie-sciences.fr/biologies/articles/10.1016/S1631-0691(03)00039-8/

Version originale du texte intégral

1 Introduction

Bottlenecks in population number are expected to reduce population levels of genetic variation [1,2]. Lower fitness could result as a consequence of decreased effective population size, and/or inbreeding [3]. There are current and pressing needs for methodologies that provide inferences regarding the occurrence of recent bottlenecks if demographic processes have led to demonstrable changes in population genetic characteristics, and concomitantly to reductions in population viability and evolutionary potential. Several methods testing for bottlenecks in molecular data have been recently proposed [4–8]. However, the availability of critical evaluations of new methods using empirical data is generally lacking [9–11].

Using DNA obtained from collections of scales from lake trout (Salvelinus namaycush) in Lakes Huron and Superior of the North American Great Lakes basin, we investigate properties of one recently proposed “bottleneck test”; the M-ratio method developed by Garza & Williamson [8]. Lake trout populations experienced dramatic reductions in population number and across the Great Lakes due to overexploitation, introduction of predatory sea-lamprey (Petromyzon marinus), and pollution (review in [12]). Lake trout were totally extirpated from Lake Michigan in the 1950's and in most part of Lake Huron in the 1960's [12]. Populations from Lake Superior were nearly completely extirpated by 1960–1961, except for several remnant populations [13]. Extirpation occurred in all lakes over a period often years, or less than two lake trout generations. Hence, there is direct evidence of strong demographic bottlenecks [12], offering a good empirical model system to test indirect methodologies based on genetic data. Like other proposed methods [4–8], the M-ratio is considered as being a ‘single sample’ method allowing bottlenecks to be detected based on multilocus genetic patterns of a single population sample. Using simulations based on empirical data taken at five microsatellite loci for three time-replicated lake trout populations, we show that the M-ratio provides better support for bottlenecks when two samples of the same population (temporal replicates) are evaluated over a range of model parameter values as compared to estimates from a single sample.

2 Materials and methods

Samples from historical and contemporary lake trout populations were collected from Lake Huron (Parry Sound [PS]; sampled in 1958 and 1999) and Lake Superior (Isle Royale [IR] and Gull Island [GI]; both sampled in 1959 and 1995) (Fig. 1). A total of 303 individuals were analysed. Samples from historical populations were based on archival scale samples retained by management agencies. For archival data, sampling dates corresponded to times of minimum historical census sizes for Lake Huron (1958), and occurred two years before estimated historical minimum population size was reached for the Gull Island population (1961; [14]) (Fig. 1). Contemporary samples represented close spatial replicates of their historical counterparts (Fig. 1) and are considered as being remnant and historically self-sustained populations [14,15]. Those populations represent remnant populations that survived bottleneck [13].

Fig. 1

Location of the six lake trout samples (three archival, three contemporary) from upper Laurentian Great Lakes used in this study. IR – Iale Royale, GI – Gull Island, and PS – Parry Sound. Numbers in location names indicate years of sampling (e.g. 58 – 1958). Sample sizes (n) of each population are given onto the figure.

We examined patterns of genetic variation at five dinucleotide microsatellite loci developed for other salmonids (Ogo1a [16]; Oneμ10 [17]; Scoμ19 [18]; Sfo1 [19]; and Ssa85 [20]). Microsatellite loci were selected for analysis due to their easy scoring in past and present populations (guidelines in [21]). Protocols are fully described elsewhere as well as care taken to avoid scoring error and artefacts in archival samples (e.g., allelic drop-out) [13].

Bottleneck testing was performed for each population using the M-ratio (hereafter M) method introduced by Garza & Williamson [8]. M represents the ratio of the number of alleles to total range in allele size. Computations were made for all loci, and average M computed across loci in each population. As other methods [4–7], this method allows for bottleneck testing based on a single sample. Hence, each population can be evaluated individually. Sampling at this level allows greater understanding of the evolution of M-ratios in temporarily replicated lake trout populations (Table 1), and if dramatic reductions in lake trout numbers that occurred in each lake impacted populations genetically over a period of 30–40 years (4–6 generations). Based on comparisons of genetics data used to compute M and known history of a given number of species, Garza & Williamson [8] reported that values of M lower than 0.7 indicated evidence of a bottleneck, whereas values greater than 0.8 indicated species with no bottleneck history. Values of M depended on several parameters such as the percentage of one-step mutation Δg(g=1; i.e., each mutation changes allele size by only one repeat unit; SMM model [22]), the size of alternate mutations Δg that are not one-step (g=2,3,…,n; two-phase model [23]), and Θ defined as four times the product of effective population size (Ne) and mutation rate (μ). These parameters are difficult to estimate in empirical samples, yet strongly influence the equilibrium value of the M-ratio. Hence, we tested each estimate of M under different evolutionary scenarios including (i) a two-phase model of mutations with proportions of non one-step mutations being 0.00 (SMM model), 0.05, 0.10, and 0.20; (ii) Δg varying from 2 to 4 (this latter value suggested by results obtained by Garza & Williamson [8] in simulations); and (iii) Θ being equal to 0.1, 1, 4, 10, 100 (a range that encompasses many realistic scenarios for natural populations). For each lake trout sample, a population at equilibrium is simulated 10 000 times for each combination of these parameters and simulated values of M(M sim ) were computed. The empirical estimate of M across polymorphic locus was then compared to the distribution of Msim values to access significance (95% criterion). This procedure was replicated ten times and results report the average level of significance. For each population and for each combination of parameters, we obtained an average likelihood surface facilitating estimation of the extent to which populations had possibly been affected by a genetic bottleneck. Other tests attempting to evaluate existence of bottlenecks were not carried out on our data. For instance, the approach of Luikart et al. [5] requires more loci than available here, leading to inaccurate detection of reduction in population size. Beaumont's [6] method assumes a strict SMM that is not likely correct for the lake trout data.

Table 1

Observed values of the M-ratio averaged over the number of polymorphic microsatellite loci for each archival and contemporary sample considered in this study (Fig. 1). Individual tables report the average likelihood surface that a bottleneck occurred in each sample based on ten replicates. For each individual replicate, a population at equilibrium for each parameter combination (Θ, δ, and the percentage range of larger mutations [8]) considered in this study is simulated 10 000 times and a p-value that a bottleneck occurred computed. Hence, for each likelihood surface, reported numbers represent p-values of each parameter combination averaged over ten replicates. For agreement, cells in roman correspond to the range of parameter values showing evidence of bottleneck (average p-value<0.05); cells in bold – the range of parameter values showing no evidence of bottlenecks (average p-value>0.05), respectively

Archival
Θ
%a 0.1 1 4 10 50
IR59, M=0.723
δ=2 0 0.0000 0.0004 0.0065 0.0445 0.633
0.05 0.0000 0.0012 0.0116 0.0698 0.735
0.1 0.0004 0.0035 0.0221 0.1042 0.817
0.2 0.0019 0.0126 0.0546 0.1970 0.913
δ=3 0 0.0000 0.0001 0.0059 0.0426 0.6391
0.05 0.0013 0.0095 0.0559 0.2043 0.8973
0.1 0.0095 0.0327 0.1597 0.4011 0.9737
0.2 0.0421 0.1391 0.4146 0.7102 0.9986
δ=4 0 0.0000 0.0003 0.0047 0.0445 0.6454
0.05 0.0045 0.0257 0.1582 0.4110 0.9728
0.1 0.0200 0.0867 0.3754 0.7150 1.0000
0.2 0.0826 0.2887 0.7396 0.9421 1.0000
GI59, M=0.698
δ=2 0 0.0000 0.0000 0.0001 0.0004 0.0130
0.05 0.0000 0.0001 0.0003 0.0007 0.0192
0.1 0.0000 0.0003 0.0007 0.0012 0.0291
0.2 0.0001 0.0008 0.0011 0.0036 0.0477
δ=3 0 0.0000 0.0000 0.0000 0.0003 0.0116
0.05 0.0005 0.0001 0.0039 0.0106 0.0652
0.1 0.0029 0.0093 0.0251 0.0415 0.1612
0.2 0.0229 0.0594 0.1058 0.1396 0.4626
δ=4 0 0.0000 0.0000 0.0001 0.0003 0.0120
0.05 0.0019 0.0085 0.0355 0.0815 0.3743
0.1 0.0098 0.0462 0.1707 0.2796 0.7782
0.2 0.5510 0.2106 0.4991 0.6286 0.9235
PS58, M=0.713
δ=2 0 0.0000 0.0003 0.0007 0.0026 0.0265
0.05 0.0001 0.0004 0.0009 0.0039 0.0398
0.1 0.0005 0.0080 0.0018 0.0064 0.0547
0.2 0.0041 0.0044 0.0056 0.0120 0.0904
δ=3 0 0.0000 0.0000 0.0008 0.0027 0.0274
0.05 0.0028 0.0061 0.0164 0.0271 0.1131
0.1 0.0115 0.0271 0.0475 0.0752 0.2256
0.2 0.0455 0.1048 0.1619 0.2078 0.6689
δ=4 0 0.0000 0.0000 0.0006 0.0023 0.0274
0.05 0.0006 0.0024 0.0755 0.1289 0.3341
0.1 0.0254 0.0829 0.2322 0.3413 0.7751
0.2 0.0968 0.2729 0.5386 0.6674 0.9428
Contemporary
Θ
%a 0.1 1 4 10 50
IR59, M=0.630
δ=2 0 0.0000 0.0000 0.0000 0.0001 0.0004
0.05 0.0000 0.0000 0.0000 0.0001 0.0008
0.1 0.0000 0.0000 0.0000 0.0001 0.0012
0.2 0.0000 0.0000 0.0000 0.0001 0.0024
δ=3 0 0.0000 0.0000 0.0000 0.0000 0.0001
0.05 0.0001 0.0001 0.0030 0.0005 0.0021
0.1 0.0004 0.0019 0.0021 0.0032 0.0119
0.2 0.0070 0.0146 0.0174 0.0149 0.0556
δ=4 0 0.0000 0.0000 0.0000 0.0000 0.0030
0.05 0.0006 0.0013 0.0077 0.0137 0.0280
0.1 0.0028 0.0113 0.0514 0.0705 0.0965
0.2 0.0178 0.0960 0.2230 0.2465 1.0000
GI95, M=0.667
δ=2 0 0.0000 0.0000 0.0000 0.0000 0.0011
0.05 0.0000 0.0000 0.0000 0.0001 0.0023
0.1 0.0000 0.0000 0.0001 0.0003 0.0024
0.2 0.0005 0.0002 0.0001 0.0005 0.0073
δ=3 0 0.0000 0.0000 0.0000 0.0001 0.0013
0.05 0.0004 0.0006 0.0009 0.0026 0.0096
0.1 0.0025 0.0039 0.0067 0.0097 0.0302
0.2 0.0119 0.0180 0.0397 0.0439 0.2393
δ=4 0 0.0000 0.0000 0.0000 0.0001 0.0006
0.05 0.0008 0.0043 0.0179 0.0029 0.0694
0.1 0.0045 0.0310 0.0929 0.1317 0.4687
0.2 0.0363 0.1445 0.3382 0.3907 0.7856
PS91, M=0.698
δ=2 0 0.0000 0.0000 0.0004 0.0009 0.0139
0.05 0.0000 0.0001 0.0004 0.0024 0.0172
0.1 0.0001 0.0003 0.0010 0.0029 0.0292
0.2 0.0023 0.0018 0.0029 0.0066 0.0475
δ=3 0 0.0000 0.0001 0.0006 0.0007 0.0137
0.05 0.0019 0.0038 0.0098 0.0152 0.0621
0.1 0.0082 0.0160 0.0369 0.0447 0.1388
0.2 0.0363 0.0863 0.1177 0.1413 0.4812
δ=4 0 0.0000 0.0001 0.0001 0.0013 0.0136
0.05 0.0059 0.0017 0.0601 0.0993 0.2265
0.1 0.0222 0.0600 0.1856 0.2682 0.6054
0.2 0.0742 0.2303 0.4861 0.5861 0.8802

a % larger mutations.

3 Results and discussion

Multilocus Hardy–Weinberg equilibrium was supported in each sample (results not shown). Significance of M-ratios as postulated based on previous literature [24], (i.e., M<0.7) indicate that four of six lake trout samples, including all contemporary samples, showed evidence of bottlenecks (Table 1). If the 0.7 threshold proposed in [8] was not reached for historical populations, estimates are very close to 0.7 indicating that low historical population sizes or other evolutionary events (e.g., coancestry and variance of reproductive success also affecting effective population size) had already demonstrably affected genetic diversity. No significant relationship between sample size and M was observed (r=0.14; p>0.1, 4 df).

Interpretations of M values for each population sample were not straightforward. Tests employing combinations of parameters (Θ), and different mutation models indicated that hypotheses of genetic bottlenecks were not universally supported across all parameter combination (cells in bold; Table 1). Without further information of effective population size allowing estimates of plausible ranges for Θ, or of information on mutational properties of the loci employed, (knowledge of δ and of the percentage of larger mutations; Table 1), it is difficult to know if each population was bottlenecked. Like for many species, any estimate of Θ is readily available for lake trout. Based on estimation made by Bernatchez & Wilson [25] on lake trout mitochondrial DNA (mtDNA) data, effective population of females was estimated to 42×103 in Great Lakes. This estimate is rather low according to the values reported by those authors for a total of 42 freshwater and anadromous fish species (range 7×102-−7×106; scored 14th on 42 species). Assuming a mutation rate of 0.02×10−6 [25], Θ could be estimated as roughly 33.6×10−2. This estimate clearly ranged among values of Θ chosen for simulations (Table 1). For mutation models parameters encompassing δ=2 and δ=3, and for a percentage of larger mutations often up to 10% [i.e., for the majority of simulations (Table 1)], such values indicate that lake trout populations have very likely been influenced genetically by a bottleneck.

While plausible ranges of population parameters are consistent with occurrence of a bottleneck, it should be kept in mind that results reported in [25] are based on mtDNA data and cannot likely be transferred to microsatellite data. Because of maternal inheritance, mtDNA is more sensitive to bottleneck than other markers inherited from both parents (i.e., greater likelihood of drift owing to a four-fold lower expected evolutionary effective population size). A shift in the estimation of Θ due to bi-parentally inherited markers and to mutation rates more realistic for microsatellite loci is then possible. For instance, assuming a mutation rate of 10−4 classically used for microsatellite loci, and no change in other parameters leads to Θ=16.8. For such an estimate and any knowledge of mutation model, inference of bottleneck is not straightforward, particularly for δ=4 and for higher percentages of larger mutations (e.g., 0.1–0.2) (Table 1).

Without clear knowledge of the mutational patterns affecting loci, values of M may not be able to accurately infer population bottlenecks. In the lake trout case, better support for bottleneck can be made by comparing the likelihood surface across different population parameter values (for Θ) and mutation models. We found that bottlenecks were consistently indicated across a wider range of parameter values in contemporary samples than in archival historical samples, as indicated by the larger area of cells in bold in archival samples than in contemporary samples for a given mutation model (Table 1). Inferences were qualitative and not purely quantitative based on simple interpretation of M-values as proposed in [8]. This qualitative result indicates that bottlenecks can be inferred from genetics data using the M-ratio. However, inferences are best made using archival data as previously advocated in other fish studies, e.g., [26–29]. The value of the M-ratio method as a “single sample” method to detect bottleneck is likely to be limited without knowledge of mutation models, percentage of larger mutations, and a rough estimate of Θ. Number of loci probably plays a role [8], but the present study considered a number of loci that is very classically used in most empirical data. For example, archival population with higher M-values (IR59, PS58, M>0.7; Table 1) showed likelihood surfaces indicating that no bottleneck was evidence over a wide range of parameter combinations. The reverse was true for contemporary Lake Superior samples (IR95, GI95) that have the lowest M-ratios (Table 1). Similar explorations of likelihood surfaces should be produced for careful interpretation of M-values, particularly for species showing evidence of less stringent bottleneck than lake trout.

Acknowledgements

Study supported by the Great Lakes Fishery Trust, the Great Lakes Protection Fund, and by the Partnership for Ecosystem Research and Management (PERM) cooperative agreement between the Michigan DNR and Michigan State University. We thank K.S. Page, M. Burnham-Curtis, and especially J.C. Garza for providing programs to compute the M-ratio.


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