Conserv Genet (2010) 11:1043–1049 DOI 10.1007/s10592-009-9949-2
RESEARCH ARTICLE
Detecting bottlenecks using BOTTLENECK 1.2.02 in wild populations: the importance of the microsatellite structure Romane Cristescu Æ William Bruce Sherwin Æ Kathrine Handasyde Æ Valma Cahill Æ Desmond W. Cooper
Received: 29 February 2008 / Accepted: 26 May 2009 / Published online: 19 June 2009 Ó Springer Science+Business Media B.V. 2009
Abstract Reduced, or bottlenecked, populations are more prone to adverse events. Thus, the detection of genetic bottleneck signatures in wildlife is an important issue for conservation. BOTTLENECK 1.2.02 is a software commonly used for detecting genetic characteristics of past bottlenecks. Here we test the efficiency with which this software detects bottlenecks in two koala populations of known history. The sign test performed well for both populations, particularly under the infinite alleles model for mutation. This suggests this model could be the more realistic for marsupial microsatellites than other mutation models. Under the allele frequency distribution test, the two populations falsely appeared to be at mutation/drift equilibrium. However, this test could detect the bottleneck when only imperfect repeat microsatellites were included in the analysis. We thus recommend further investigation of imperfect repeat microsatellites, which could be more powerful for bottleneck detection. These results underline the cautious approach researchers and conservationists should take when studying the past of unknown populations. Keywords Bottleneck Infinite alleles model Marsupial Perfect and imperfect repeat microsatellites R. Cristescu (&) W. B. Sherwin D. W. Cooper Evolution and Ecology Research Centre, School of Biological, Earth and Environmental Sciences, University of New South Wales, Kensington, NSW 2052, Australia e-mail:
[email protected];
[email protected] K. Handasyde Department of Zoology, University of Melbourne, Melbourne, VIC 3010, Australia V. Cahill Dubbo College, Delroy Campus, East Str, Dubbo, NSW 2830, Australia
Abbreviation IAM Infinite alleles model SMM Stepwise mutation model TPM Two phase model
Introduction It is now known that genetic changes can cause conservation problems (Amos and Balmford 2001; Frankham 2005; Madsen et al. 1996; Newman and Pilson 1997; Sherwin and Murray 1990). Populations that have been severely reduced, or bottlenecked populations, can suffer from loss of genetic variability, increase of inbreeding and fixation of deleterious alleles (Hedrick and Miller 1992; Jimenez et al. 1994; Lande 1994; Lynch et al. 1995; Mills and Smouse 1994; Nei 1987; Nei and Li 1976; Nei et al. 1975). Even after recovering to their previous size, populations that have been through a bottleneck in their history are more prone to extinction (Bijlsma et al. 2000). Aware of this fragility, many researchers and wildlife managers are studying the genetic status of animal populations in an effort to detect bottlenecks (Bouzat et al. 1998; Hoelzel et al. 1993; Madsen et al. 1996; O’Brien et al. 1985, 1987; Packer et al. 1991; Taylor et al. 1994; Whitehouse and Harley 2001). Here we studied two island populations of koalas (Phascolarctos cinereus) which are known to have been through bottlenecks. After European settlement in Australia and the intense fur trade of koalas, the koala species was threatened by extinction. At the beginning of the twentieth century, wildlife managers commenced what is now considered to have been the first marsupial conservation program. Animals were captured on the mainland and translocated offshore to safer habitats. At the founding of the French Island population, around 1890, it is thought
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that only three koalas were introduced. The population expanded rapidly, triggering a vast translocation program. In fact, French Island koalas are the direct or indirect source of almost all populations created or reinforced in Victoria and South Australia (Martin and Handasyde 1999). Thus although the current population size in French Island is thought to be about eight hundred (Gibson and Irvin 2003), this does not reflect the population growth rate. In 1923–1925, 18 animals were taken from the source population of French Island to Kangaroo Island, and founded a new population, which had consequently been through at least two bottlenecks. However since its foundation, the Kangaroo Island population has grown, possibly exponentially, to a census size of 20,000–30,000 individuals in 2001 (Masters et al. 2004). In fact the population expansion has forced the government to take fertility control measures in order to decrease the growth rate. We tested the ability of the genetic software BOTTLENECK 1.2.02 to detect these bottleneck events under different hypotheses. The program BOTTLENECK 1.2.02 measures the temporary excess of heterozygosity that results from a reduced population size (Cornuet and Luikart 1996; Luikart et al. 1998). For a population at mutation-drift equilibrium, the number of alleles at selectively neutral loci and their heterozygosities fluctuate around an equilibrium between the creation of alleles by mutation and their loss by genetic drift. After a reduction of effective population size, both the number of alleles and their heterozygosities drop, but allele diversity drops more rapidly because the rare alleles are rapidly lost, but their loss has only a weak influence on heterozygosity (Nei 1987). The heterozygosity of the population is thus larger than expected considering the number of alleles found. BOTTLENECK 1.2.02 proposes two different tests to detect this anomaly, the sign test and the allele frequency distribution test. BOTTLENECK 1.2.02 is probably the most frequent method used by researchers to detect bottlenecks (He et al. 2008; Hull et al. 2008; Thomas and White 2007). It is designed to identify recently bottlenecked populations, ‘‘recently’’ being defined by the authors of the program as a few dozen generations (Luikart et al. 1998; Piry et al. 1999). There are reports of much more ancient bottlenecks being detected by the use of conservative markers such as allozymes (Luikart et al. 1998; Ovenden and White 1990). However, power analysis and theoretical models showed that most of the time a heterozygosity excess can only be detected 0.5–5 Ne generations after the initiation of a population reduction (Ne is the effective bottlenecked population size), whereas a distortion of allele frequency distributions is likely to be detectable between 2 and 4 Ne generations (Cornuet and Luikart 1996; Maruyama and Fuerst 1985; Nei and Li 1976). Thus BOTTLENECK
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1.2.02 can only search for recent historical population declines. The use of M, the mean ratio of the number of alleles to the range in allele size, could retain bottleneck signatures for longer (Garza and Williamson 2001). This is less true for populations that subsequently recovered, such as the two in this study, compare to populations that remained at a reduced size (Garza and Williamson 2001). In this study we used microsatellites as neutral loci to run BOTTLENECK 1.2.02. As well as combined analysis of all loci, we partitioned our microsatellites into two sets, one with microsatellites composed of perfect dinucleotide repeats only and one with the imperfect repeats. We did this because it is known that the allele frequency distribution is temporary, and that eventually the bottlenecked population will obtain new rare alleles through mutation (Piry et al. 1999). Perfect repeat microsatellites, with simple dinucleotide repeats, are more prone to mutation via slippage than imperfect microsatellites with interrupted repeats (Goldstein and Clark 1995; Weber 1990). The latter, which mutates more rarely, could thus retain the signatures of bottlenecks for a longer time.
Materials and methods Ear biopsy was performed on 49 koalas on French Island (sampled in 2002–2004), and 46 on Kangaroo Island (2005–2006). The genotyping of those animals was performed as described elsewhere (Cristescu et al. 2009). Using primers shown in Table 1, genetic variation of the two island populations was quantified at fifteen loci (four primers are from Houlden et al. 1996a). Among the loci, 11 were perfect dinucleotide repeats and 4 were imperfect repeats (K 10.1, Pcv 30, Pcv 6.3, Pcv 26). We ran BOTTLENECK 1.2.02 for all microsatellite loci, then only for perfect and imperfect microsatellites. Four microsatellite loci is the minimum number of microsatellites to run the BOTTLENECK 1.2.02 program (Piry et al. 1999). We performed the two tests available in BOTTLENECK 1.2.02 to detect a genetic bottleneck signature. The sign test compares the number of loci that present a heterozygosity excess, to the number of such loci expected by chance only. This test is provided for three mutational models: the infinite alleles model (IAM); the stepwise mutation model (SMM); and a combination of those two extreme hypotheses, the two phase model (TPM, Di Rienzo et al. 1994). In the TPM, the choice of proportion of IAM and SMM can be set by the user. In this study the microsatellites are either of dinucleotide perfect repeats or of imperfect repeats, both of which may tend toward the IAM (Cornuet and Luikart 1996), hence we chose the proportions in favor of IAM (5% of SMM and 95% of IAM). The second test available in BOTTLENECK 1.2.02 is the allele frequency
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Table 1 Perfect and imperfect repeat microsatellites used in the study Locus
Repeat
He
Length in nucleotides
Size range
Nb of alleles
Ho FI
KI
FI
KI
New mutations
Perfect repeat sequences Pcv 6.1
(GT)19
38
225–245
6
0.62
0.43
0.67
0.65
K 2.1
(CA)22
44
178–194
6
0.63
0.52
0.61
0.58
Pcv 2
(CA)22
44
148–156
6
0.71
0.52
0.64
0.45
Pcv 31
(AC)20
40
236–242
3
0.46
0.45
0.42
0.49
Pcv 25.2
(AC)15
30
179–189
3
0.57
0.43
0.58
0.38
Phc 13* Phc 2*
(GT)29 (CA)34
58 68
126–144 196–210
5 2
0.46 0.53
0.43 0.58
0.55 0.38
0.45 0.49
Phc 25*
(GT)49
98
130–178
9
0.44
0.43
0.42
0.40
Pcv 24.2
(CA)29
58
222–227
3
0.26
0.13
0.25
0.15
Phc 4*
(CA)31
62
122–124
2
0.00
0.02
0.00
0.02
Pcv 25.1
(CA)22
44
104–108
3
0.62
0.58
0.66
0.49
3 1
Imperfect repeat sequences K 10.1
(CA)10CG(CA)2
26
143–155
4
0.75
0.56
0.72
0.53
Pcv 30
(TA)3(TC)2TATC(TA)3 TG(TA)2CC(TA)2TC(TA)2 TC(CA)2(TA)3CTA(CA)3 TAAA(CA)4T(CA)13
98
209–215
2
0.36
0.26
0.45
0.45
Pcv 26
(AC)18(AT)2(CA)2
44
204–206
2
0.04
0.00
0.04
0.00
Pcv 6.3
(TC)26GTA(AC)16
87
283–317
5
0.69
0.43
0.67
0.47
He expected heterozygosity, FI French Island, KI Kangaroo Island, * from Houlden et al. (1996a), the remainder from Cristescu et al. 2009
distribution test. This graphical method examines the frequencies of all alleles in a population and compares this to the distribution expected at mutation-drift equilibrium, when rare alleles (i.e. \0.1%) are numerous. More precisely, at equilibrium, the rarest allele class is expected to be much more frequent than the second rarest class. But since the rarest alleles are rapidly lost after a bottleneck, this category of allele proportions drops and the characteristic L-shaped distribution of allele proportions no longer exists. This state is referred to as a mode-shift distortion (Luikart et al. 1998).
Results Fourteen microsatellites were polymorphic in each population (monomorphic loci: Phc 4 in French Island and Pcv 26 in Kangaroo Island) and used for the BOTTLENECK 1.2.02 tests (Table 1). The mean number of alleles was 3.8 in French Island and 2.8 in Kangaroo Island. In the Sign test conducted on all loci (Table 2), the populations were not found to be at mutation-drift equilibrium under IAM (French Island P = 0.039; Kangaroo Island P = 0.011) or TPM (with SMM = 5%, French Island P = 0.047; Kangaroo Island P = 0.015). When all loci were included in
the analysis under SMM, the program also detected a bottleneck signature for Kangaroo Island (P = 0.036), however the program did not find this signature for the French Island population (P = 0.212). With all loci, the allele frequency distribution test showed that no shift in distribution could be detected for either population, which remained in a normal L-shape (Fig. 1a; Table 2). However, the distribution was close to flat for Kangaroo Island. When tests were performed with microsatellites split in two categories, both the French and Kangaroo Island populations showed a mode-shift shape for the imperfect repeat microsatellites only (Fig. 1b; Table 2). The dinucleotide microsatellites remained in the mutation-drift equilibrium L-shape (Fig. 1c; Table 2). The number of loci in each group of microsatellites (imperfect repeat versus dinucleotide) is different and this could influence the result of the allele frequency distribution test. We randomly chose four microsatellites of dinucleotide repeat (K2.1, Pcv24.2, Phc25, Phc2) and performed the test again. The results for both populations were normal L-shape. Also, allele variability can influence the detection of a bottleneck, hence the variability between the perfect and imperfect microsatellites was compared but there was no significant difference (ANOVA P = 0.372). No significance difference was found either for mean heterozygosities (ANOVA
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Table 2 Results from the BOTTLENECK tests for three sets of microsatellites for French and Kangaroo Island: all microsatellite loci, the imperfect and the perfect repeat microsatellite loci Population
Sign test IAM
All loci
Allele frequency distribution SMM
TPM
KI (N = 46)
0.011
0.036
0.015
FI (N = 49)
0.039
0.212
0.047
L-shaped L-shaped
Imperfect repeat microsatellite loci Number of loci = 4
KI (N = 46) FI (N = 49)
0.086 0.281
0.120 0.349
– –
Mode-shift Mode-shift
Perfect repeat microsatellite loci
KI (N = 46)
0.060
0.142
–
L-shaped
Number of loci = 10
FI (N = 49)
0.087
0.065
–
L-shaped
N represents the number of animals genotyped in the study; IAM, SMM, TPM, L-shaped and mode shift are defined in the text, FI French Island, KI Kangaroo Island Sign test values are P values, significant results for sign test are in bold
A 40% 35% 30% 25% 20% 15% 10% 5% 0%
20% 15% 10% 5% 0%
0,0-0,1 0,1-0,2 0,2-0,3 0,3-0,4 0,4-0,5 0,5-0,6 0,6-0,7 0,7-0,8 0,8-0,9 0,9-1,0
0,0-0,1
0,1-0,2 0,2-0,3
0,3-0,4
0,4-0,5
0,5-0,6 0,6-0,7
0,7-0,8
0,8-0,9
0,9-1,0
0,0-0,1
0,1-0,2 0,2-0,3 0,3-0,4
0,4-0,5
0,5-0,6 0,6-0,7
0,7-0,8
0,8-0,9
0,9-1,0
0,0-0,1
0,1-0,2 0,2-0,3
0,4-0,5
0,5-0,6 0,6-0,7
0,7-0,8
0,8-0,9
0,9-1,0
B 35% 30% 35% 25%
30%
20%
25%
15%
20% 15%
10%
10% 5%
5% 0%
0% 0,0-0,1 0,1-0,2 0,2-0,3 0,3-0,4 0,4-0,5 0,5-0,6 0,6-0,7 0,7-0,8 0,8-0,9 0,9-1,0
C
16% 14% 12% 10%
40% 35% 30% 25% 20% 15% 10% 5% 0%
8% 6% 4% 2% 0% 0,0-0,1 0,1-0,2 0,2-0,3 0,3-0,4 0,4-0,5 0,5-0,6 0,6-0,7 0,7-0,8 0,8-0,9 0,9-1,0
0,3-0,4
Fig. 1 Distribution of allele frequencies in French (left column) and Kangaroo (right column) Islands (X axis = frequencies of alleles by category of 0.1, Y axis = percentage of alleles in each frequency
category). a For all microsatellite loci, b for the four imperfect repeat microsatellites, c for the eleven perfect repeat microsatellites
P = 0.768), they were for perfect microsatellites 0.47 for French Island and 0.41 for Kangaroo Island, and in the same order 0.47 and 0.36 for imperfect microsatellites (Table 1). For each of the two sets of microsatellites, probably due to the smaller sample size, the bottleneck signature for the sign test was lost (Table 2).
Discussion
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Sign test All conditions for the efficiency of the sign test are present for both populations in this study: a bottleneck from one to
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several generations previously; a severe reduction of the population size (100-fold); the genotyping of 10–20 loci (but as few as four loci can be used) for at least 30 individuals (Luikart and Cornuet 1998). The time since the bottleneck is also important; the maximum excess of heterozygosity, and thus the maximum power of the sign test, occurs N0 generations after the bottleneck, with N0 equals the number of individuals after the bottleneck (Cornuet and Luikart 1996). Here, N0 = 3 for French Island. With the length of a generation around 5 years the maximum power is expected about 15 years after the introduction, at the beginning of the twentieth century (Martin and Handasyde 1999). Nevertheless, the effect of the bottleneck remained detectable today, except with the SMM hypothesis. For Kangaroo Island, N0 = 18, thus maximal excess should occur 90 years after translocation, around 2013. Accordingly, the sign test detected the bottleneck irrespective of the mutation model chosen. However, the IAM model seemed to be the more sensitive for our data, something that already has been observed in the wallaby Macropus eugenii (Le Page et al. 2000). These results contradict the general mutation theory for microsatellites, which are often described as being closer to the Single Step Mutation model (Piry et al. 1999) or the two phase model (Di Rienzo et al. 1994). It would be worth studying the extent to which this is true among other species. The sign tests did not detect bottlenecks when the number of loci used was small (4 and 11), although the program is meant to work from 4 polymorphic loci (Piry et al. 1999). This suggests that researchers should take negative sign test results obtained from small number of loci cautiously. Allele frequency distribution The lack of alleles at a locus following a bottleneck depends on four factors: the time since the bottleneck, the ratio of effective population size before and after the bottleneck, the locus mutation rate and the size of the sample of loci and individuals. Furthermore, the heterozygosity deficit is time dependent, it reaches a maximum (see above) and then decreases toward a new equilibrium (Cornuet and Luikart 1996). Thus, several reasons could explain the lack of mode-shift distortion after the bottlenecks (except for imperfect repeats). 1.
Power depends on the time elapsed since the bottleneck: The graphic methods are able to detect a bottleneck more efficiently for 2–4 Ne generations, where Ne is the effective size of the population at the bottleneck (Cornuet and Luikart 1996; Nei and Li 1976). In fact, the genetic consequences of a bottleneck may take several generations to be detected, creating a false negative period (Luikart et al. 1998). We cannot
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2.
3.
4.
5.
6.
be sure of how many koalas effectively reproduced and founded the new populations, but roughly we could say that the graphic methods might be efficient during 6–12 generations in French Island (30–60 years) and 36–72 generations in Kangaroo Island (180–360 years or less, considering the 18 founders might not have all reproduced). Our study is thus in the right time frame for those methods to work efficiently. Power is affected by the severity of the bottlenecks (3 and 18 founders), the number of individuals genotyped (46 and 49) and the number of polymorphic neutral loci used (14 when all microsatellites were used). All of these are adequate for a bottleneck to be detected by the allele frequency distribution test of BOTTLENECK 1.2.02. The individuals genotyped should be representative of the population. This is difficult to assess, but the animals were sampled randomly, and we found no genetic structure within the population. Power is low in the presence of a demographic but not a genetic bottleneck (Ne N). Considering the number of animals that founded the population and the breeding system of koalas, where usually not all individuals produce offspring (Martin 1981), it is very unlikely that the census size largely underestimated the effective population size. Immigration can annihilate the bottleneck effects. The two populations are found on islands, thus no immigration is likely to happen. For the graphic methods of detecting the bottleneck, the number of mutations that have arisen since the bottleneck has to be negligible. After a bottleneck, the genetic diversity is expected to shrink, but then with time the number of rare alleles increases more rapidly than the heterozygosity (Chakraborty and Nei 1977; Nei and Li 1976; Nei et al. 1975). The number of new alleles created after a bottleneck depends not only on the mutation rate but also on the growth of the population. For Kangaroo Island for instance, the population exponentially increased from N0 = 18 to Nt = 27,000 (Masters et al. 2004) in 80 years, where Nt is the size of the koala population today. Since Nt = N0ert; the growth rate is very high: r = [ln (Nt/ N0)]/t = 9.2. We also found new alleles in Kangaroo Island, that we did not find in the source population of French Island and that were never described by others there (Houlden et al. 1996b). These are all of perfect repeats. For instance Phc 25 has three new alleles of one-two and three repetitions of the dinucleotide motif. This could explain the incapacity of the BOTTLENECK 1.2.02 graphic test to detect the bottleneck signature. Such a situation has already been described in other species (Le Page et al. 2000).
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The consequences for the inference of unknown population history are very important. False negatives can occur with the program BOTTLENECK 1.2.02 and populations could be wrongly described as healthy and at mutation-drift equilibrium when in fact they would still be recovering from a past bottleneck. Thus the negative results of graphic tests should be taken cautiously. To increase the sensitivity however, the tests could be repeated with imperfect repeat loci. Their usefulness was already noted for the sign test by Cornuet and Luikart (1996), because imperfect microsatellites are likely to fit the IAM better than perfect ones, and loci evolving under the IAM produce more powerful analysis. In the case of the allele frequency distribution test, a higher mutation rate of perfect repeat loci could influence the bottleneck detection, in comparison to results for imperfect repeats. Microsatellites are known to have high mutation rates principally due to occurrence of slippage (Schlo¨tterer and Tautz 1992). Perfect repeat microsatellites are more prone to slippage (Schlo¨tterer 2000), whereas the interrupted repeat microsatellites are more conservative (Weber 1990). We can note that in our study the two microsatellites presenting new alleles, Phc 25 and Phc 4, have perfect dinucleotide repeats. The length of microsatellites has also been suggested to influence mutation rate (Vazquez et al. 2000; Wierdl et al. 1997), but this has not been obvious in our study (Table 1). However, the length effect does not concern imperfect repeat loci. Even when the size of the perfectly repeated portion is large (as for Pcv 30 and Pcv 6.3 in our study), the microsatellites with repeats which are spilt by an imperfection have unexpectedly low polymorphism (Goldstein and Clark 1995). By dividing our microsatellites in two sets, one of perfect repeats and one of imperfect, we were able to detect the mode-shift distortion in the latter despite the small sample of imperfect repeats. Thus selecting complex microsatellites for the study of past events in wild populations could be more efficient and seems worth further investigations. Although the results are in accord with theoretical expectations for the power of imperfect repeats in bottleneck detection, it has to be noted that using so few microsatellites (four) for the allele frequency distribution test increases the type one error, meaning that a stable population could be classified as having been through a bottleneck. Although we know it is not the case for our study, we cannot exclude the possibility of having a mode-shift result due to a type one error. Another fact of concern is the degree of polymorphism at two loci that lack variation in one of the two populations (Phc 4 in FI, Pcv26 in KI). Not enough polymorphic loci may result in a reduced power to detect a bottleneck (Luikart and Cornuet 1998). However that should have been the most impairing with the imperfect repeat group constituted by only four microsatellites, group
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in which the bottleneck was indeed detected. Finally, since bottlenecks increase linkage disequilibrium, additional complications could arise in such extreme bottlenecks due to selection at linked loci, particularly if the selection type is heterozygote advantage or balancing selection (Luikart et al. 1998). This could possibly counteract the loss of rare alleles. However, this effect should be independent of the type of marker studied, and thus should not contribute to the mode-shift distortion reported here.
Conclusion Genetic software programs are getting more numerous by the minute. Their importance depends upon their power. The results of BOTTLENECK 1.2.02 were accurate for most of the analyses we performed. However, some events in the studied population, such as exponential growth in our case or immigration, as well as some sampling characteristics, can produce false negatives. Thus caution is recommended for studies of populations with totally unknown past, especially if the two tests performed by BOTTLENECK 1.2.02 are inconsistent. Further reflection on the development and use of some more powerful markers would be an important step. For instance imperfect repeat microsatellites sound promising, and further studies could test if our results can be generalized. Imperfect repeat microsatellites have the double advantage of fitting the IAM better and hence being more powerful, as well as having a lower mutation rate that could make them retain the bottleneck signature longer (Cornuet and Luikart 1996). We cannot emphasize enough the importance of further investigating the use of imperfect repeat microsatellites to analyse possible bottlenecks. If the genetic side of species conservation is not evaluated, extinction risks will be underestimated and therefore the conservation programs developed would be more likely to fail (Brook et al. 2002; Vucetich and Waite 1999). Acknowledgments This worked has been funded by ARC linkage grant (LPO560344). We thank K. Carlyon for providing koala samples, B. L. Carlsson and A. Wilton for technical assistance. We also thank the Rangers from French Island National Park (Parks Victoria) and the members of the Koala Management Program (Department for Environment and Heritage) for their support and assistance in the field. We would also like to thank two anonymous reviewers for their most helpful comments.
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