Molecular Ecology (2009) 18, 4526–4532
NEWS AND VIEWS
COMMENT
An AFLP clock for absolute dating of
shallow-time evolutionary history – too
good to be true?
D. EHRICH,* P. B. EIDESEN,† I. G. ALSOS†
and C . B R O C H M A N N ‡
*Institute of Biology, University of Tromsø, 9037 Tromsø,
Norway, †The University Centre in Svalbard, PO Box 156,
NO-9171, Longyearbyen, Norway, ‡National Centre for
Biosystematics, Natural History Museum, University of Oslo,
PO Box 1172 Blindern, NO-0318 Oslo, Norway
A major drawback of Amplified Fragment Length Polymorphisms (AFLP) as genetic makers for phylogeographic
studies is their lack of a temporal dimension. In a recent
publication in Molecular Ecology, Kropf et al. (2009) proposed a molecular clock for AFLP. In this comment we
evaluate the proposed approach both theoretically and
empirically. A linear increase with time is a prerequisite
to use a genetic distance as molecular clock. Testing the
relationship between genetic distance and time in the
data of Kropf et al. (2009) for linearity revealed that
the relationship was in fact not linear for their pooled
data, as well as for one of the three species analyzed.
Also, the relationship was not linear in two new species,
where divergence times could be inferred from macrofossils. When applying the proposed molecular clock to data
from eight species, dates obtained were plausible in some
cases, but very improbable in others. The suggested
genetic distance was also influenced by intrapopulation
genetic diversity, leading to a potential bias. In the future,
investigations of AFLP mutation rates combined with
phylogeographic modelling may contribute to adding a
time scale to the understanding of AFLP data.
Keywords: amplified fragment length polymorphism, dating, linearity, molecular clock, Nei’s genetic distance
Received 5 June 2009; revision received 28 August 2009;
accepted 15 September 2009
Amplified fragment length polymorphism (AFLP) is a
well-established molecular method in phylogeography,
shallow phylogenetics and population genetics. AFLPs are
DNA-fingerprint markers that are widely distributed
throughout the genome and consist largely of non-coding
Correspondence: Dorothee Ehrich, Fax: +47 77646333;
E-mail: [email protected]
DNA (Meudt & Clarke 2007). Data analysis methods are
continuously being developed to refine possible inferences
from the dominant data produced by this method (Bonin
et al. 2007; Foll et al. 2008). However, compared with DNA
sequences, the classical markers of phylogeography (Avise
2000), a major drawback of AFLP is the lack of understanding of mutation rates and processes. In a recent study in
Molecular Ecology, Kropf et al. (2009) proposed an ‘AFLP
clock for the absolute dating of shallow-time evolutionary
history’. A molecular clock based on AFLPs, which would
allow placing in time recent subdivisions among phylogeographical groups, would be a big step forward and would
allow answering many open questions about the recent history of species. However, reading the work of Kropf et al.
(2009) in detail, it seems that a full solution to this challenge is still to come.
Kropf et al. (2009) used three data sets of alpine plants,
Gentiana alpina, Kernera saxatilis and Atocion rupestris (synonym Silene rupestris), to demonstrate and calibrate the
proposed clock. In each of these three species, distinct
mountain phylogroups were described from each of three
mountain regions (Alps and Massif Central, Pyrenees and
Sierra Nevada). Assuming that the phylogroups arose
postglacially, Kropf et al. (2009) used paleoecological evidence to estimate times since divergence among these
groups and then relate divergence times to genetic distances. They used the slope of a linear regression to infer
a rate of AFLP divergence, and proposed maximum and
minimum rates to estimate a time interval for separation
events. Finally, they applied the proposed rate to additional data sets from Minuartia biflora and Nigella degenii.
In this comment, we evaluate the approach proposed by
Kropf et al. (2009), both theoretically and empirically.
First, we address the issue of linearity, an important prerequisite for the use of a genetic distance as molecular
clock, and re-analyse some of the data presented by Kropf
et al. (2009). We also comment on the estimation of confidence intervals. Second, we investigate the generality of
the approach proposed by Kropf et al. (2009) by applying
it to two additional data sets where information about
divergence dates is available from macrofossils. The two
data sets are from recent phylogeographical studies of
Salix herbacea (Alsos et al. 2009) and Cassiope tetragona
(Eidesen et al. 2007). Third, we apply the proposed rate
to date the colonization of the arctic archipelago Svalbard
after the last ice age for eight species (Alsos et al. 2007),
as Kropf et al. (2009) did for M. biflora. In this case, a
maximum divergence time is given by glacial history.
These data are further used to address the AFLP clock’s
sensitivity to different levels of genetic diversity in the
phylogeographical groups compared. Genetic diversity
within the populations in Svalbard differs indeed consid-
2009 Blackwell Publishing Ltd
NEWS AND VIEWS: COMMENT 4527
erably among these eight species. We also date the separation of the Scandinavian populations from those of the
Alps, and the separation between the Alps and the Pyrenees for the species that occur in these regions.
Linearity and confidence intervals
A first step in determining whether genetic distances measured from AFLP data can be used as a molecular clock is
to test whether AFLP divergence is linearly correlated with
time. Kropf et al. (2009) used Nei’s standard genetic distance (DN72; Nei 1972) to quantify AFLP divergence. DN72
estimates between all pairs of populations belonging to different mountain phylogroups were plotted against estimates of the assumed minimum time since divergence
between mountain phylogroups. A linear regression of
genetic divergence against time was estimated for each
species and for all species together. As values resulting
from pairwise comparisons are not independent, the significance of the regression slopes was tested using a Mantel
test. A Mantel test does not, however, test for the linearity
of an association; it only tests for a positive correlation
between two matrices and ‘can give valid probability levels
for any observed association’ (Mantel 1967, p. 209). It is
important to distinguish between testing the significance of
a regression slope, i.e. the slope is positive and significantly different from zero, and testing for linearity, i.e. the
relation is best described as linear and not for example as
exponential or following any other shape. From Figs 1 and
2 of Kropf et al. (2009), it can be observed that the relation
between divergence and time appears in fact to be not linear in G. alpina and A. rupestris, as well as in the pooled
data set. In all the three cases, the majority of DN72 estimates between the Alps ⁄ Massif Central and the Pyrenees
lay somewhat below the linear regression line, and most
comparisons involving the Sierra Nevada are located above
the regression line, indicating that a nonlinear relation may
provide a better fit to the data.
To test for linearity, we used an ANOVA to compare a linear model with time as a continuous variable (identical to
the regression used by Kropf et al. 2009) to a model with
time as a factor with three levels (t0, t1 and t2). As pairwise comparisons are not independently sampled items,
the significance levels obtained in an ANOVA are inflated.
Therefore, we assessed the significance of the F-values with
a permutation test: populations were randomly assigned to
regions 10 000 times; the two linear models and the ANOVA
were calculated for each of the randomized datasets and
the proportion of F-values equal to or larger than the
observed F was taken as the significance level (pperm). The
same permutation approach was used to assess whether
the parameters of the linear models were different from 0.
All analyses were carried out in R 2.9.0 (R Development
Core Team 2009), if not mentioned otherwise. The model
with time as factor was significantly better than that with
time as a linear variable for A. rupestris and for the pooled
data set (Table 1), showing that the relation between divergence and time was not linear. The ANOVA was not signifi-
2009 Blackwell Publishing Ltd
cant for the two other data sets, where the number of
comparisons was considerably lower. The data of Kropf
et al. (2009) provide only three time points, one of them
being 0, to assess linearity, and thus limit the generality of
possible inference. Nevertheless, our analysis revealed that
the results of Kropf et al. (2009), all together, do not support the linearity of the relationship between the degree of
AFLP divergence and time of isolation.
Looking at the literature, it seems that the linearity of
relations of genetic divergence to time is rarely tested for
directly (see Kumar 2005 for a review; Espinasa & Borowsky 1998 for RAPDs). However, Beerli et al. (1996) tested
parametrically for deviations from linearity when discussing a molecular clock for allozyme frequencies. For
sequence data, the applicability of a molecular clock is usually determined by model choice procedures within the
context of a phylogenetic analysis (e.g. Drummond et al.
2006). But also for other types of data, such as AFLP, it is
Table 1 Results from linear models estimated for the data
from Kropf et al. (2009). For each species, we estimated first a
model where time is considered a continuous variable (identical to the regressions estimated by Kropf et al. 2009) and then
a model where time is considered a factor with three levels.
The two models were compared with an ANOVA. Significance of
the parameters and of the AMOVA was assessed with a permutation test (see main text). Intercepts for the regression models
are not shown
Coefficients
Gentiana alpina
Time continuous
Time as factor
Intercept
t1
t2
ANOVA:
Kernera saxatilis
Time continuous
Time as factor
Intercept
t1
t2
ANOVA:
Atocion rupestris
Time continuous
Time as factor
Intercept
t1
t2
ANOVA:
Pooled data set
Time continuous
Time as factor
Intercept
t1
t2
ANOVA:
Estimates
2.39 · 10)6
0.060
0.028
0.051
4.08 · 10)6
0.062
0.052
0.081
3.88 · 10)6
0.060
0.036
0.083
3.69 · 10)6
0.060
0.036
0.079
Standard error
6.54 · 10)7
0.009
0.012
0.012
F = 2.419
1.22 · 10)6
0.019
0.023
0.023
F = 1.033
4.69 · 10)7
0.006
0.008
0.008
F = 22.84
3.94 · 10)7
0.005
0.007
0.007
F = 26.15
pperm
0.017
0.049
0.017
>0.1
0.067
>0.1
0.067
>0.1
<0.001
0.014
<0.001
0.004
<0.001
0.004
<0.001
0.002
4528 N E W S A N D V I E W S : C O M M E N T
important to show that a genetic distance has a linear relation to time, at least in a certain time frame, before it can
be calibrated as a molecular clock and recommended to
estimate divergence times.
When estimating a linear regression, the precision of the
regression slope is given as a standard error (SE) calculated
from the sum of squared residuals (Sokal & Rohlf 1995).
Kropf et al. (2009) transformed this SE to a standard deviation (SD) by dividing it by the square root of the sample
size (number of pairwise comparisons). Such a transformation is valid between the SE of sample mean values and
the SD in a sample, but it is neither correct nor meaningful
for the SE of a regression slope (Sokal & Rohlf 1995). Further, the divergence rate estimated from the regression
slope (r = 0.037 DN72 per 10 000 years) was converted to a
time per DN72 unit rate (as 1 ⁄ r = 0.27 million years
Myr ⁄ DN72). To estimate a maximum and minimum for this
rate, the ‘SD’ of the regression slope (inferred as mentioned
above) was inverted in the same way as the rate. This
inverted ‘SD’ was then added to the estimated rate in time
per DN72 unit to provide a maximum: ‘slow rate’ = 1 ⁄ r +
1 ⁄ SD = 0.517 Myr ⁄ DN72, respectively subtracted from it to
provide a minimum: ‘fast rate’ = 1 ⁄ r – 1 ⁄ SD = 0.024
Myr ⁄ DN72. Inverting the SD and the rate separately, before
adding or subtracting them to create a confidence interval
(CI), is not correct, because the resulting CI will be inversely proportional to the magnitude of the original SD. All
together, the CI obtained by Kropf et al. (2009) is, in our
opinion, much too wide. The 95% CI estimated from
the regression slope in a conventional way (±1.96*SE)
represents an underestimation of the real CI, because the
points used to estimate the regression are not independent:
95% CI: 0.0291–0.0447, resulting in 0.22–0.34 Myr ⁄ DN72.
An ad hoc solution may be to consider larger CI, such as
99% CI: 0.0266–0.0473, resulting in 0.21–0.38 Myr ⁄ DN72.
A more correct approach would be to estimate a CI by
bootstrapping either over populations or over AFLP loci.
Note that inverting the CI of a rate always results in an
asymmetric interval around the inverted rate.
Genetic divergence against time in Salix herbacea and
Cassiope tetragona
We further investigated the potential of the approach proposed by Kropf et al. (2009) by applying it to two data sets,
where estimates of divergence times were available from
macrofossil findings. As with Kropf et al. (2009) in the case
of Minuartia biflora, we assumed that populations became
rapidly isolated after colonization of remote areas such as
islands.
In the amphi-Atlantic S. herbacea, five distinct main
phylogeographical groups were identified: E Canada ⁄ W
Greenland, the Pyrenees, the Alps ⁄ Carpathians, one E
Atlantic and one W Atlantic group. Four of the main
groups (except the Pyrenees) were further subdivided into
regional subgroups (Alsos et al. 2009). Macrofossil data
were available to date approximately the divergence
between the following groups: The postglacial isolation of
S Scandinavia from the Alps was estimated to be 12 500
calibrated years before present (cal yr), as the most recent
macrofossil records from the area between the current
populations are dated to 12 160–12 795 cal yr and
12 400–12 880 cal yr (Denmark) and 13 400–12 500 cal yr
(Germany ⁄ Baltic Sea) (Alsos et al. 2009 and references
therein). It is likely that both Svalbard and Iceland were
colonized from N Fennoscandia ⁄ Russia. The divergence
between N Fennoscandia ⁄ Russia and Svalbard was estimated to 7900 cal yr, the date of the oldest recorded fossil
from Svalbard (Birks 1991). The oldest fossils found in
Iceland are dated 10 200–9300 cal yr (divergence dated to
9800 cal yr; Alsos et al. 2009 and references therein). The
divergence between W Greenland and NE Canada (the
likely source for colonization of W Greenland) was dated
to 9000 cal yr (Alsos et al. 2009 and references therein). We
also dated the divergence between the Alps and the Pyrenees to 15 800 years, the estimate used by Kropf et al.
(2009), although S. herbacea might have been isolated in the
Pyrenees for a longer time (Alsos et al. 2009).
The genetic structure in the circumpolar C. tetragona ssp.
tetragona showed a strong east–west trend, with a partition
in five main phylogeographical groups (the Siberian group,
the Beringian group, the Canadian group, the E Canadian ⁄ W Greenlandic group and the E Greenlandic ⁄
Scandinavian group; Eidesen et al. 2007). The genetic
pattern, together with glacial history and taxonomy of this
species, supports Beringia as the main source region for
(re-) colonization. However, the strong genetic differentiation between the Siberian group and the remainder suggests that the last westward expansion from Beringia must
pre-date the last glacial maximum. A separate Siberian
refugium, at least during the last glaciation, is supported
by both glacial history and fossil evidence (several fossil
finds of C. tetragona are reported from E Siberia, the oldest
dated to 58 400 14C yr and from Taimyr ca 27 000 14C yr;
Kienast et al. 2001; Eidesen et al. 2007 and references
therein). We therefore assume that the Beringian group
and the Siberian group must have been separated for at
least 60 000 years. The last expansion from Beringia eastwards, into Canada, Greenland and Scandinavia, was probably postglacial. The genetic pattern indicates that
migration occurred through Canada along a northern
route, and then southwards along both coasts of Greenland. The oldest postglacial fossil finds in this area are
from Ellesmere Island (8500 cal yr), Northwest Greenland
(8000 cal yr) and Northeast Greenland (8200 cal yr; Eidesen
et al. 2007 and references therein), suggesting that the split
between the E Canadian ⁄ W Greenlandic group and the
E Greenlandic ⁄ Scandinavian group occurred at least
8000 years ago.
DN72 was calculated between all pairs of populations
belonging to different phylogeographical groups for which
time since divergence could be estimated (see Supporting
Information, Appendix S1 for a list of populations). We
used the software AFLPsurv (Vekemans 2002) instead of
TFPGA version 1.3 (Miller 1997) used by Kropf et al.
(2009), because our data sets were too large for TFPGA.
2009 Blackwell Publishing Ltd
NEWS AND VIEWS: COMMENT 4529
DN72 was estimated from allele frequencies calculated with
the square root method (Lynch & Milligan 1994) assuming
Hardy–Weinberg equilibrium. In addition, as with Kropf
et al. (2009), we calculated DN72 between all population
pairs within phylogeographical groups to estimate DN72 at
time 0. DN72 values were plotted against time and a linear
regression was calculated for each species.
In S. herbacea, there was no monotonous increase of DN72
with divergence time (Fig. 1). DN72 between the Alps and
S Scandinavia was lower than the estimates for splits
attributed to more recent dates and DN72 between the Alps
and the Pyrenees was much larger than that expected from
a linear relationship. Some of this variation may be a result
of the fact that in this example, minimum divergence times
(Alps-Pyrenees and Alp-S Scandinavia) were mixed with
maximum divergence times (colonization of formerly glaciated areas dated from the oldest fossil appearances). The
estimated regression slope was 0.0243 per 10 000 years
(SE = 0.0026, pperm < 0.001) and 0.0169 per 10 000 years
(SE = 0.0026, pperm < 0.001), when excluding the comparison with the Pyrenees, thus rather close to the rates
estimated by Kropf et al. (2009). For C. tetragona, the
regression slope was 0.0086 per 10 000 years (SE = 0.0004,
pperm < 0.001), which was considerably lower than Kropf
et al. (2009)’s rate. Although for C. tetragona the relation
looked rather linear (Fig. 1), a model with time as a factor
was significantly better (F = 47.54, pperm < 0.001) than a
model with time as a continuous variable, indicating nonlinearity in this case also.
These results suggest that the increase of DN72 with time
is in general not linear. They also show that the proposed
rate, which fitted rather well in the examples used by
Kropf et al. (2009), is far from universal – a possibility
Kropf et al. (2009) were well aware of. In addition to variation because of inexact time estimates and violations of the
assumption of constant population sizes, the lower rates
estimated in this study may be a result of the longer generation times of the dwarf shrubs C. tetragona and S. herbacea
compared with that of the herbaceous plants used by
Kropf et al. (2009), resulting in slower genetic drift and less
mutations per time unit.
Divergence rate and genetic diversity
To further examine the performance of the proposed AFLP
clock and to address its sensitivity to differences in levels
of genetic diversity, we applied it to eight of the nine data
sets analysed in a comparative study addressing immigration to Svalbard (Alsos et al. 2007). Five of these species
are among the most thermophilous species in Svalbard;
thus in situ glacial survival could be excluded. It is likely
that the majority of the thermophilous species colonized
Svalbard during the warm period of the Holocene, which
lasted approximately from 9500 to 4000 years ago (Alsos
et al. 2007 and references therein). In some of the species,
genetic diversity in Svalbard was much lower than that in
other regions, whereas in others the diversity levels were
similar. We did not include the ninth species, Saxifraga rivularis, in this analysis, because it was difficult to delimit
potential source regions for the colonization of Svalbard in
this species, and because we could not exclude glacial survival in Svalbard (KB Westergaard, Tromsø University
Museum, Tromsø, unpublished). Divergence time was estimated between the populations in Svalbard and those in
the region from where postglacial colonization most probably took place. In addition, we estimated divergence time
between the Alps and Scandinavia, and between the Alps
and the Pyrenees for species occurring in these regions. As
with Kropf et al. (2009), we used TFPGA to estimate DN72
between the regions.
DN72 values between 0.0006 and 0.107 were obtained
between Svalbard and the different source regions
Salix herbacea
0.00
0.00
0.05
0.10
0.15
0.20
Nei's standard genetic distance
0.05
0.10
0.15
0.25
Cassiope tetragona
0
5000
10 000
Years before present
15 000
0 10 000
30 000
50 000
Years before present
70 000
Fig. 1 Nei’s standard genetic diversity (DN72) between pairs of populations belonging to different phylogeographical groups plotted
against assumed time since divergence between groups (based on macrofossil findings). Distances between populations within
groups were plotted at time 0 using the method described by Kropf et al. (2009). Confidence and prediction intervals around the
regression lines (95%) are indicated by short and long dashed lines respectively. The thick line shows the divergence rate of 0.037
per 10 000 years proposed by Kropf et al. (2009), plotted with an intercept estimated for each species as the mean DN72 between populations within phylogeographical groups.
2009 Blackwell Publishing Ltd
0.731
0.289
North*
Russia
Greenland
Russia
Greenland
Russia
Scandinavia
Russia ⁄
Greenland†
Arabis alpina
Betula nana
Cassiope tetragona
Dryas octopetala
Empetrum nigrum
Rubus chamaemorus
Salix herbacea
Vaccinium uliginosum
0.0006
160 [130–230]
0.026
7020 [5460–9880]
0.015
4050 [3150–5700]
0.022
5940 [4620–8360]
0.053
14 360 [11 130–20 140]
0.059
15 930 [12 390–22 420]
0.027
7290 [5670–10 260]
0.044 ⁄ 0.107
11 880 [9240–16 720] ⁄
28 890 [22 470–40 660]
Svalbard – source region
0.021
5670 [4410–7980]
0.026
7020 [5460–9880]
0.047
12 690 [9870–17 860]
0.097
26 190 [20 370–36 860]
—
0.218
58 860 [45 780–82 840]
0.013
3510 [2730–4940]
—
Alps–Scandinavia
0.149
40 230 [31 290–56 620]
0.047
12 690 [9870–17 860]
—
0.079
21 330 [16 590–30 020]
—
—
0.208
56 160 [43 680–79 040]
—
Alps–Pyrenees
*In Arabis alpina, all plants growing in formerly glaciated areas of northern Europe, Russia, Greenland and Canada were nearly identical (Ehrich et al. 2007).
†In Vaccinium uliginosum, two populations in Svalbard originated from Russia and one from Greenland (Alsos et al. 2007). The proportion of genetic diversity in Svalbard to
diversity in the source region is given for the populations originating from Russia, as the population originating from Greenland consisted only of four clones.
0.477
0.416
0.837
0.942
0.698
—
Source
region
Species
Div Svalbard ⁄
Div source
Table 2 Nei’s standard genetic distance (1972) between Svalbard and the region from where the species immigrated (source region; according to Alsos et al. 2007), between the
Alps and Scandinavia, and between the Alps and the Pyrenees. Values are missing when a species does not occur in the respective regions. Estimates of the time of divergence
according to the rate proposed by Kropf et al. (2009) are given in the second line, with a confidence interval based on the 99% confidence interval of the regression slope (see
main text) in square brackets. The proportion of average intra-population genetic diversity in Svalbard to intra-population diversity in the source region (Div Svalbard ⁄ Div
source) was calculated from diversities estimated as average number of pairwise differences
4530 N E W S A N D V I E W S : C O M M E N T
2009 Blackwell Publishing Ltd
NEWS AND VIEWS: COMMENT 4531
(Table 2). The very low value for Arabis alpina is explained
by the fact that in this species, all individuals sampled in
formerly glaciated areas were nearly identical and there
was virtually no genetic diversity in the whole region
(Ehrich et al. 2007). In Vaccinium uliginosum, three different
populations in Svalbard are likely to have originated from
two different sources (Alsos et al. 2007). Therefore, two
divergence values were estimated. In the population
originating from Greenland, only four different clones were
sampled. This small sample size may explain the large DN72
estimated. The calculation of DN72 is indeed based on
estimating allele frequencies from the AFLP pattern with
the square root method. To obtain reliable estimates from
this approach, rather large sample sizes are required (Bonin
et al. 2007). Excluding A. alpina and the Svalbard–
Greenland comparison for V. uliginosum, the remaining
values resulted in time estimates between 4050 years in
C. tetragona and 15 930 years in Rubus chamaemorus. Time
estimates varied thus almost by a factor four. For some species, times were close to what is indicated by the fossil
record (S. herbacea and D. octopetala: the oldest fossil
in Svalbard is 7900 cal yr old), or by reconstructions of
climate conditions (sparse arctic vegetation from approximately 9500 cal yr and more thermophilous species from
9000 cal yr). For C. tetragona, the estimated time was
astonishingly short, whereas the large value obtained for
R. chamaemorus was clearly wrong, considering that
Svalbard was still deeply glaciated 15 000 years ago and
that R. chamaemorus is one of the most thermophilous
species growing in the archipelago today.
Divergence times estimated between the other regions
varied even more. The oldest divergence times were
inferred in A. alpina (Table 2), whereas the shortest divergence time was estimated between the Alps and Scandinavia in Betula nana with 3510 years. Assuming that the
alpine populations of B. nana have been isolated from the
populations in Scandinavia since the end of the last glaciation in central Europe (approximately 10 000 years), even
the time estimated with the ‘slow rate’ of Kropf et al.
(2009), 6720 years, was considerably lower than that
expected. However, it is possible that there has been some
gene flow among these areas in the early Holocene through
surviving patches of B. nana in Europe, north of the Alps.
From the comparisons between these eight species, it is
evident that the level of intra-population diversity influences the estimates of DN72. Genetic diversity within populations was in general low in A. alpina compared with that
in other species (Ehrich et al. 2007), and in this species, the
longest divergence times overall were estimated. In S. herbacea, the divergence between the Pyrenees and the Alps
was particularly large (Fig. 1), and the population from the
Pyrenees was one of the populations with the lowest diversity in that data set (Alsos et al. 2009). The species with the
highest time estimates between Svalbard and the source
region were those with the most severe reduction in
genetic diversity between the source region and Svalbard
(Table 2). The relation between time and the proportion of
average intra-population genetic diversity in Svalbard to
2009 Blackwell Publishing Ltd
that in the source region was significant (linear model,
parameter
estimate = )16572,
SE = 4127,
t = )4.016,
P = 0.01; excluding A. alpina). Repeated immigration to
Svalbard during the Holocene (Alsos et al. 2007) could
explain the lower time estimates obtained for species with
a small reduction of genetic diversity in Svalbard compared with that in the source region, but cannot explain
the very large time estimates obtained in other species.
Several of the populations in Svalbard, as well as S. herbacea from the Pyrenees, are small and isolated outposts of
the species’ range. In small and isolated populations, as
well as in populations descending from a small number of
founders, genetic drift is enhanced, explaining low genetic
diversity and large estimates of divergence times. In such
cases, a molecular rate based on frequency differences is
unlikely to be applicable.
Conclusions
Altogether our results indicate that the claimed absolute
dating of shallow-time evolutionary history based on AFLP
data is too good to be true. Of course, AFLP band frequencies are subject to genetic drift and, in general, AFLP divergence, quantified with any genetic distance measure,
increases with time. This general relationship seems, however, not sufficient to allow the calibration of an AFLP
clock. We have shown that the relation between AFLP
divergence, as measured by DN72, and time is not linear,
both for some of the data sets presented by Kropf et al.
(2009) and for two additional data sets. Although the large
interval between the ‘fast rate’ and the ‘slow rate’, together
with the uncertainty of phylogeographical histories, makes
it difficult to find examples where estimated dates are not
plausible, we obtained several surprising results when we
applied the proposed rate to eight data sets from a comparative phylogeographical study. Our analyses have also
shown that the suggested genetic measure, DN72, is clearly
influenced by intra-population genetic diversity, biasing
inferred divergence times.
Considering the processes leading to genetic differentiation between two isolated phylogeographical groups,
including both the complexity of real population histories
and the molecular mechanisms underlying the variation
revealed by the AFLP method, the proposed approach converting a summary statistic to a divergence time is clearly
an oversimplification. In the future, it would be interesting
to further investigate AFLP mutation processes and rates,
and to attempt combining such knowledge with phylogeographical modelling.
Acknowledgements
We thank Matthias Kropf for sending us his data and explaining some of the analyses; Nigel G. Yoccoz for valuable discussions and advice about statistics; Andreas Tribsch for
commenting on the manuscript and Inger Skrede and Gro
Hilde Jacobsen for allowing us to use the data sets of Dryas
4532 N E W S A N D V I E W S : C O M M E N T
octopetala and Empetrum nigrum. The three anonymous reviewers contributed to improve the manuscript.
References
Alsos IG, Eidesen PB, Ehrich D et al. (2007) Frequent long-distance
plant colonization in the changing Arctic. Science, 316, 1606–1609.
Alsos IG, Alm T, Normand S, Brochmann C (2009) Past and future
range shifts and loss of diversity in dwarf willow (Salix herbacea
L.) inferred from genetics, fossils and modelling. Global Ecology
and Biogeography, 18, 223–239.
Avise JC (2000) Phylogeography. The History and Formation of Species.
Harvard University Press, Cambridge, Massachusetts.
Beerli P, Hotz H, Uzzell T (1996) Geologically dated sea barriers
calibrate a protein clock for aegean water frogs. Evolution, 50,
1676–1687.
Birks HH (1991) Holocene vegetational history and climatic change
on west Spitsbergen – plant macrofossils from Skardtjørna, an
Arctic lake. The Holocene, 1, 209–218.
Bonin A, Ehrich D, Manel S (2007) Statistical analysis of amplified
fragment length polymorphism data: a toolbox for molecular
ecologists and evolutionists. Molecular Ecology, 16, 3737–3758.
Drummond AJ, Ho SYW, Phillips MJ, Rambaut A (2006) Relaxed
phylogenetics and dating with confidence. Plos Biology, 4, 699–710.
Ehrich D, Gaudeul M, Assefa A et al. (2007) Genetic consequences
of Pleistocene range shifts: contrast between the Arctic, the Alps
and the East African mountains. Molecular Ecology, 16, 2542–
2559.
Eidesen PB, Carlsen T, Molau U, Brochmann C (2007) Repeatedly
out of Beringia: Cassiope tetragona embraces the arctic. Journal of
Biogeography, 34, 1559–1574.
Espinasa L, Borowsky R (1998) Evolutionary divergence of AP-PCR
(RAPD) patterns. Molecular Biology and Evolution, 15, 408–414.
Foll M, Beaumont MA, Gaggiotti O (2008) An approximate Bayesian computation approach to overcome biases that arise when
using amplified fragment length polymorphism markers to study
population structure. Genetics, 179, 927–939.
Kienast F, Siegert C, Dereviagin A, Mai DH (2001) Climatic implications of Late Quaternary plant macrofossil assemblages from
the Taymyr Peninsula, Siberia. Global And Planetary Change, 31,
265–281.
Kropf M, Comes HP, Kadereit JW (2009) An AFLP clock for the
absolute dating of shallow-time evolutionary history based on
the intraspecific divergence of southwestern European alpine
plant species. Molecular Ecology, 18, 697–708.
Kumar S (2005) Molecular clocks: four decades of evolution. Nature
Reviews Genetics, 6, 654–662.
Lynch M, Milligan BG (1994) Analysis of population genetic-structure with RAPD markers. Molecular Ecology, 3, 91–99.
Mantel N (1967) Detection of disease clustering and a generalized
regression approach. Cancer Research, 27, 209–220.
Meudt HM, Clarke AC (2007) Almost forgotten or latest practice?
AFLP applications, analyses and advances. Trends in Plant Science, 12, 106–117.
Miller MP (1997) Tools for Population Genetic Analysis (TFPGA): a
Windows Program for the Analysis of Allozyme and Molecular Population Genetics Data, Version 1.3. Northern Arizona University,
Flagstaff.
Nei M (1972) Genetic distance between populations. American Naturalist, 106, 283–292.
R Development Core Team (2009) R: A Language and Environment
for Statistical Computing. R Foundation for Statistical Computing,
Vienna.
Sokal RR, Rohlf FJ (1995) Biometry. W. H. Freeman and Company,
New York.
Vekemans X (2002) AFLP-SURV Version 1.0. Laboratoire de Génétique et Ecologie Végétale, Université Libre de Bruxelles, Bruxelles.
Dorothee Ehrich is working on ecology and phylogeography of
arctic animals and plants, and is interested in methodological
questions relating to the analysis of molecular data. Pernille
Bronken Eidesen and Inger Greve Alsos are associate professors at the University Centre in Svalbard. PBE is working with
phylogeography and molecular ecology of arctic plants and
IGA is interested in phylogeography, ecology and dispersal of
arctic and alpine plants. Christian Brochmann’s main research
interest is the history and evolution of plants in arctic and
alpine regions.
doi: 10.1111/j.1365-294X.2009.04387.x
Supporting information
Additional supporting information may be found in the online
version of this article.
Appendix S1 Number of individuals analysed (n), proportion
of variable markers (var) and genetic diversity estimated as the
average proportion of pairwise differences (div) in the populations used to estimate divergence among regions for Salix herbacea and Cassiope tetragona. Population names are given as in
the original publications (Alsos et al. 2009 for S. herbacea and
Eidesen et al. 2007 for C. tetragona).
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