Biological Conservation 109 (2003) 249–258
www.elsevier.com/locate/biocon

Genetic diversity and local population structure of fragmented
populations of Trillium camschatcense (Trilliaceae)
Hiroshi Tomimatsu, Masashi Ohara*
Laboratory of Ecology and Genetics, Graduate School of Environmental Earth Science, Hokkaido University, Sapporo 060-0810, Japan
Received 8 March 2002; received in revised form 15 May 2002; accepted 16 May 2002

Abstract
Trillium camschatcense, a long-lived common woodland herb, has been experiencing intensive habitat fragmentation over the lastcentury in eastern Hokkaido, Japan. We examined the genetic diversity and population genetic structure of 12 fragmented populations with diﬀerent population sizes using allozyme electrophoresis. The percentage of polymorphic loci and mean number ofalleles per locus were positively related to population size, probably due to the stochastic loss of rare alleles (frequency of q <0.1) insmall populations. Populations with 350 ﬂowering plants or fewer had lost almost all of their rare alleles. While the heterozygosity
and inbreeding coeﬃcient were not related to population size, some small populations showed relatively high inbreeding coeﬃcients. In spite of the low genetic diﬀerentiation among overall populations (FST=0.130), local population structuring was recognized between the two geographically discontinuous population groups. Within groups, suﬃcient historical gene ﬂow was inferred,
whereas a low dispersal ability of this species and geographical separation could produce apparent diﬀerentiation between groups.
# 2002 Elsevier Science Ltd. All rights reserved.
Keywords: Allozymes; Forest fragmentation; Genetic diversity; Population genetic structure; Population size; Trillium camschatcense

1. Introduction
The consequences of habitat fragmentation on plant
populations have two aspects, a demographic aspect
and a genetic aspect (Lande, 1988; Schemske et al.,1994). Lande (1988) emphasized demography as beingof more immediate importance for long-term species
viability, and many studies have concentrated on identifying ecological factors which aﬀect population
dynamics, including the disruptions of plant–pollinatorinteractions (e.g. Jennersten, 1988; Aizen and Feinsinger, 1994) and the changes of microclimatic conditions (e.g. Lovejoy et al., 1986; Laurance et al., 1998).
However, recently, attention has been increasingly
focused on the assessment of genetic attributes of remnant populations (e.g. review by Young et al., 1996) andits practical usefulness is now being discussed (e.g.
Crandall et al., 2000; Sherwin and Moritz, 2000).
Theoretically, fragmentation is predicted to cause
genetic erosion owing to founder eﬀects, and subsequently to genetic drift and inbreeding (reviews by Barrett* Corresponding author. Fax: +81-11-706-4525.
E-mail address: ohara@ees.hokudai.ac.jp (M. Ohara).

and Kohn, 1991; Ellstrand and Elam, 1993; Young etal., 1996). The genetic erosion induced by fragmentation
may be harmful to population persistence, because
reduced heterozygosity may lead to decreased individualﬁtness (Rajimann et al., 1994; Oostermeijer et al., 1995)
and the loss of allelic richness may eliminate the opportunities for future adaptation (Frankel et al., 1995). The
theory predicts positive relations of genetic diversity andheterozygosity to population size, and such relationships have been practically identiﬁed for some species
which have recently experienced habitat loss (e.g. van
Treuren et al., 1991; Prober and Brown, 1994). However, because the responses to fragmentation may varydepending on habitat or life history characteristics(Young et al., 1996), more studies on genetic consequences of fragmentation need to be conducted.
We are conducting a series of investigations exploring
the demographic and genetic eﬀects of forest fragmentation on a spring woodland herb, Trillium camschatcense Ker Gawler (Trilliaceae), in the Tokachi region ofeastern Hokkaido, Japan. In this region, anthropogenic
land conversion (e.g. road construction and agriculturaldevelopment) since the 1880s has resulted in a highly
fragmented landscape with a large number of forest

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remnants, many of which are smaller than 1 ha (Tan,
1994). Trillium camschatcense commonly occurs in thisregion and sometimes forms large ( > 5 ha), continuous
populations. This species is now often found in theunderstory of small remnant forests surrounded by
pastureland or agricultural ﬁelds, and we can assume
that forest fragmentation divided formerly huge T.
camschatcense populations into small and isolated pieces. Accordingly, T. camschatcense in this region musthave experienced dramatic changes in population sizeand spatial distribution, which may have signiﬁcantly
reduced genetic diversity and altered population genetic
structure. Populations in the region are self-incompatible,
and seed production results from outbreeding by insect
pollination (Ohara et al., 1996). Self-incompatible
plants are expected to be particularly vulnerable togenetic deterioration such as the loss of compatible
mates (Byers, 1995) and increased inbreeding (Ellstrand
and Elam, 1993).
Our previous study (Tomimatsu and Ohara, in press)
showed that seed production and seedling recruitment
was reduced in small, fragmented T. camschatcense
populations. This study explores a genetic aspect offragmentation. We assess the genetic diversity, levels
of inbreeding, and population genetic structure basedon allozyme variation, and discuss the genetic eﬀects offragmentation on T. camschatcense populations.

2. Methods
2.1. Species and study sites
Trillium camschatcense, a corrected name for T.
kamtschaticum Pall. (Fukuda et al., 1996), is a diploid(2n=10), long-lived herbaceous perennial that mainly
occurs in the understory of the broad-leaved deciduous
forests of Hokkaido and northern Honshu, Japan. It
takes more than 10 years for seedlings to become ﬂowering plants, and after reaching ﬂowering, the plants canlive for at least 20 years with continuous ﬂowering every
year (Ohara and Kawano, 1986b). Reproductive plantsof T. camschatcense produce single or sometimes a few
ﬂowers that arise from separate stems originated from therhizome. Each ﬂower has three large white petals and thisspecies reproduces exclusively by seeds (Ohara and
Kawano, 1986a). The ﬂowers are generally visited byvarious insects mostly belonging to Diptera and Coleoptera (Ohara et al., 1991). In study populations, membersof Nitidulidae, Melandryindae, Oedemeridae (Coleoptera), Scatophagidae, and Bibionidae (Diptera) were
often observed (H. Tomimatsu et al., unpublished data).
Regarding population genetics of T. camschatcense,
Kurabayashi (1957) divided populations of Hokkaido
into three geographical regions, North, East, and South,
based on chromosomal variations, and Ohara et al.

(1996) re-examined the patterns of genetic diﬀerentiation with isozymes. Both studies revealed that the
populations of eastern Hokkaido where the present
study was conducted show higher intra and interpopulation genetic diversity than the other geographicalregions (Kurabayashi, 1957; Ohara et al., 1996). Ohara
et al. (1996) also showed that the plants in eastern
Hokkaido are self-incompatible, whereas plants in theother regions are self-compatible.
This study examines 12 populations with diﬀerent
population sizes in the Tokachi region of eastern Hokkaido (Fig. 1; Table 1). Population size, the estimatednumber of ﬂowering plants, was determined by multiplying the population area by the ﬂowering plant density (Table 1).
2.2. Leaf sampling and electrophoresis
At ﬂowering in May 1998, leaf materials were sampled in each population except for the OH population
where the samples were collected in May 2000. Over 40
ﬂowering individuals were randomly sampled per
population, except for two small populations TB and
MB (Table 2). Leaves collected were placed on ice,transported to the laboratory and stored at 80  Cprior to analysis by electrophoresis. Approximately 90
mg of leaf tissue from each individual plant was homogenized in 0.9 ml extraction buﬀer made up of 0.1 M
Tris–HCl (pH 8.0), 0.2 g/ml glycerol, 63 mg/ml Tween
80, 8 mM dithiothreitol (DTT), 0.5% (v/v) b-mercaptoethanol, 0.40% (w/v) b-nicotinamide adenine dinucleotide (b-NAD), 0.45% (w/v) b-nicotinamide adenine
dinucleotide phosphate (b-NADP), 0.3% (w/v) bovine
serum albumin, and 7% (w/v) polyvinylpolypyrrolidone(PVPP; modiﬁed form of Shiraishi, 1988). After the
homogenates were centrifuged (15 000 rpm for 10 min at
4  C), 15 ml of the resulting supernatant was used forpolyacrylamide vertical slab gel electrophoresis. We
preliminarily tested 14 enzymes, and then a total of six
enzyme systems that consistently showed clear andinterpretable banding patterns were examined: aspartate
aminotransferase (AAT; E.C. 2.6.1.1), malate dehydrogenase (MDH; E.C. 1.1.1.37), glutamate dehydrogenase (GDH; E.C. 1.4.1.2), sorbitol dehydrogenase(SODH; E.C. 1.1.1.14), alcohol dehydrogenase (ADH;
E.C. 1.1.1.1), and 6-phosphogluconate dehydrogenase(6PGDH; E.C. 1.1.1.44). The staining procedures were
modiﬁed from Shiraishi (1988). We resolved 11 putativeloci from the six enzyme systems and assumed mendelian inheritance of all loci based on banding patterns
given the expected enzyme unit substructures.
2.3. Data analyses
Standard measures of genetic diversity including thepercentage of polymorphic loci (P), the mean number of

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H. Tomimatsu, M. Ohara / Biological Conservation 109 (2003) 249–258

Fig. 1. Locations of the 12 Trillium camschatcense populations examined in the present study. Population numbers correspond to those in Table 1.
Population groups inferred by a neighbor-joining (NJ) tree (Fig. 2) which corresponds to geographical pattern are also indicated by dashed circles.

Table 1
Trillium camschatcense populations examined in this study
Population no.

Population (Abbreviation)

Population area (m2)Forest area (ha)aFlowering plant densityb

Population size

1
2
3
4
5
6
7
8
9
10
11
12

Shibusan (SS)Obihiro (OH)
Motoimatsu (MM)Toyonishi (TN)Kiyokawa (KK)
Hirono-kita (HK)Hirono-minami (HM)
Pepekiki (PK)Toberi (TB)
Rekifune (RF)Monbetsu (MB)Hiroo (HI)

550
1050
18 700
3400
7700
1400
5500
1450
190
60 000480
50 000

0.19
0.39
1.9
0.34
1.0
1.7
1.2
0.23
0.019
31
0.048
30

10
13
24
16
33
15
25
20
6
64
18
63

220
550
18 0002200
10 200
900
5500
1200
46
15 3600350
12 6000ab

Forest area was determined with aerial photographs.
Average numbers of ﬂowering plants in 25 m2 were calculated by randomly placing a 55 m quadrat ﬁve times within each population.

alleles per polymorphic locus (Ap), the mean numberof alleles per locus (A), observed heterozygosity (Ho),and expected heterozygosity (He), were calculated foreach population and for overall populations. Wright’s(1951) F statistics (FIT, FST, and FIS) were calculated foreach polymorphic locus and for overall loci as Weir andCockerham’s (1984) estimates using the computer program FSTAT version 2.9.1 (Goudet, 2000), and the levelsof signiﬁcance were obtained by randomization based
procedures [see Goudet (2000) for details]. Gene ﬂow(Nm) among populations was estimated indirectly fromthe population genetic structure using Wright’s (1951)
equation as modiﬁed by Crow and Aoki (1984): FST ¼
1=ð4Nm þ 1Þ where a=(n/(n1))2 and n is the numberof populations. Slatkin’s (1985) private allele method
also provides equally eﬀective estimates of Nm (Barton

and Slatkin, 1986; Slatkin and Barton, 1989), though, weused only Wright’s method because sampled populations
showed only one private allele.
Genetic divergence among populations was also estimated by calculating Nei’s (1972) standard genetic distance for all possible pairs of populations. The resulting
distance matrix was used to build a neighbor-joining(NJ) dendrogram (Saitou and Nei, 1987) of which thereliability was tested by bootstrapping over loci (Felsenstein, 1985) and given as percentage values over 1000
replications. The DISPAN program (Ota, 1993) wasused to carry out this analysis.
To test the eﬀects of fragmentation, the relationships
between population genetics parameters and the log of
population size were explored with least-square linear
regressions using the REG procedure of SAS version

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H. Tomimatsu, M. Ohara / Biological Conservation 109 (2003) 249–258

Table 2
Sample sizes, genetic diversity estimates, and inbreeding coeﬃcients(FIS) for 12 Trillium camschatcense populationsa
Population

n

P

Ap

A

Ho

He

FIS

SS
OH
MM
TN
KK
HK
HM
PK
TB
RF
MB
HI

42
44
48
48
48
48
48
48
24
48
31
40

18.2
54.5
54.5
54.5
54.5
54.5
54.5
54.5
18.2
54.5
18.2
81.8

2.00
2.33
2.00
2.00
2.83
2.00
2.00
2.67
2.00
2.00
2.50
2.33

1.18
1.73
1.55
1.55
2.00
1.55
1.55
1.91
1.18
1.55
1.27
2.09

0.037
0.105
0.083
0.057
0.068
0.051
0.034
0.131
0.083
0.097
0.067
0.132

0.046
0.100
0.092
0.054
0.078
0.053
0.035
0.125
0.081
0.108
0.080
0.133

0.211
0.0430.107
0.0560.129*
0.057
0.034
0.037
0.0040.166
0.257*
0.072

2.22(0.16)
2.70

1.59(0.09)
2.55

0.079(0.009)
0.076

0.083
(0.009)
0.095

0.074
(0.030)

517

47.7
(5.6)
90.9

Mean
(SE)
Overallan, sample size; P, percent polymorphic loci; Ap, mean number ofalleles per polymorphic locus; A, mean number of alleles per locus; Ho,observed heterozygosity; He, expected heterozygosity (gene diversity);
FIS, Weir and Cockerham’s (1984) estimates of inbreeding coeﬃcient;SE, standard error.
* Signiﬁcant deviation of FIS from Hardy–Weinberg expectations at
P<0.05 level obtained by randomization procedures.

6.12 (SAS Institute, 1997). In this study, the variation ofsample sizes might aﬀect genetic diversity estimates(especially allelic richness, A), because larger sample
sizes could detect more alleles. Then we conducted acomputer simulation to test whether reduced allelicdiversity is due to diﬀerences in sample sizes. Each
population sample was repeatedly reduced at random to
24 individuals, i.e. smallest sample size (Table 2), andwe obtained the distribution of allelic richness among1000 simulations. The simulation was not performed for
the TB population with smallest sample size.

3. Results
3.1. Genetic diversity and levels of inbreeding
Ten of the 11 loci resolved (90.9%; Table 2) werepolymorphic in at least one of the populations examined. In all the populations sampled, 6Pgdh was monomorphic. The percentage of polymorphic loci (P) at the
species level (90.9%) was larger than a value, 65.5% for
outcrossing long-lived perennial species (Hamrick andGodt, 1996). Levels of genetic diversity varied substantially among individual T. camschatcense populations. The percentage of polymorphic loci (P) ranged
from 18.2% (SS, TB, and MB) to 81.8% (HI). The
mean number of alleles per locus (A) was 2.55 at the
species level and 1.59 at the population level. The mean
number of alleles per polymorphic locus (Ap) within

populations was 2.22 with the KK population having
the highest value (2.83).
Expected heterozygosity (He) for the species was 0.095(Table 2). Expected heterozygosity at each population
ranged from 0.035 for HM to 0.133 for HI, with a meanof 0.083. The mean observed heterozygosity at the
population level (Ho=0.079) was slightly lower thanthe expected heterozygosity (He=0.083). Inbreeding
coeﬃcients (FIS) were determined for each population,and two relatively small populations (SS and MB)
showed relatively high FIS values. Randomization tests
detected signiﬁcant deﬁcits of heterozygotes relative toHardy–Weinberg expectations in the KK (FIS=0.129)
and MB (FIS=0.257) populations.
3.2. Fixation indices and genetic diﬀerentiation pattern
Signiﬁcant genetic diﬀerentiations among populations
were found for all polymorphic loci (Table 3). FST
values for individual loci ranged from 0.020 (Sodh-2) to0.203 (Aat-2) with a mean of 0.055, and two loci for
aspartate aminotransferase (Aat-1 and Aat-2) showed 7to 10-fold higher FST values than the other loci, suggesting that the diﬀerentiation among populations ismost evident in these two loci. In fact, the frequencies ofdominant alleles (with highest frequencies in each locus)
were strongly and positively related to the latitude of
populations (linear regressions: R2=0.68, b=0.77,
P=0.001 for Aat-1; R2=0.83, b=1.00, P < 0.001 for
Aat-2), implying the presence of geographical clines.
Signiﬁcant values of FIT were observed in six loci, threeof which (Adh-2, Sodh-2, and Gdh-2), however, were
nearly equal to zero. Because these three loci actually
hold very few polymorphisms, permutations could produce only a small variety of randomized sets of statistics
and then failed to produce useful information (Goudet,
2000). Signiﬁcantly positive FIT was observed when calculated for overall loci, indicating an overall excess ofhomozygotes (Table 4).
The NJ dendrogram of genetic distance among T.
camschatcense populations (Fig. 2) clearly showed thegenetic similarity of geographically adjacent populations. That is, the ﬁrst major branch in the dendrogram
separated the eight northern populations from the four
southern populations, and the pattern obviously corresponds to the geographical pattern (Fig. 1). The grouping of northern and southern populations was
supported by a high (87%) bootstrap value. Consequently, we could classify the study populations geographically into two groups, the North group and theSouth group (Fig. 1; Table 1). Populations of the twogroups are at least 34 km distant from each other, while
the distances between populations within groups areless than 25 km. Associations within each of the tworegional groups exhibited some geographical patterning
as well (Fig. 2). For example, the three populations

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H. Tomimatsu, M. Ohara / Biological Conservation 109 (2003) 249–258
Table 3
Number of alleles and Weir and Cockerham’s (1984) estimates of Wright’s F statistics (FIT, FST, FIS), described for each polymorphic locus
Locus

Aat-1
Aat-2
Adh-1
Adh-2
Sodh-1
Sodh-2
Mdh-1
Mdh-2
Gdh-1
Gdh-2

F statisticsa

Numberof alleles
3
4
3
2
3
2
3
2
3
2

FIT

FST

FIS

0.363 (0.087)***0.185 (0.084)***0.049 (0.057)
0.004 (0.005)*
0.059 (0.060)
0.003 (0.004)*
0.056 (0.059)
0.498 (0.003)***0.059 (0.061)
0.004 (0.005)*

0.151 (0.054)***0.203 (0.042)***0.024 (0.010)***0.028 (0.014)***0.026 (0.009)***0.020 (0.006)***0.023 (0.008)***0.025 (0.008)***0.022 (0.008)**
0.028 (0.014)***0.178 (0.075)***
0.022 (0.074)
0.026 (0.061)
0.032 (0.014)
0.035 (0.063)
0.024 (0.004)
0.033 (0.063)
0.485 (0.003)**
0.038 (0.066)
0.032 (0.014)aStandard errors obtained by jackkniﬁng are given in parentheses. *, **, *** Signiﬁcance for F statistics obtained by randomization procedures(*P<0.05, ** P< 0.01, *** P <0.001).

Table 4
Weir and Cockerham’s (1984) estimates of Wright’s F statistics (FIT, FST, FIS) and estimates of gene ﬂow (Nm) based on FST calculated for overall
populations and for each population groupa

Overall populations
Within groups
North group
South group

FIT

FST

FIS

Nm

0.191 (0.161)***0.130 (0.040)***0.070 (0.068)**

1.41

0.068 (0.022)***0.151 (0.195)***0.031 (0.008)***0.025 (0.030)**

0.038 (0.020)
0.128 (0.173)**

5.98
5.48

a
Standard errors obtained by jackkniﬁng are given in parentheses. **,*** Signiﬁcance for F statistics obtained by randomization procedures(**P<0.01, ***P< 0.001).

(MM, TN, and KK) with a low geographical separation
clustered together. However, the bootstrap values withineach of the two groups were not so high ( 50%).
Although the level of genetic diﬀerentiation among
overall populations was low (FST=0.130), much lower
FST values for the North and South population groups
were detected (FST=0.031 for the North, FST=0.025 for
the South; Table 4). Estimated gene ﬂow within thesame population groups (Nm=5.98 for the North and
Nm=5.48 for the South) was much higher than thevalue for overall populations (Nm=1.41). Genetic distances among pairs of populations were low within thesame population group, ranging from 0.001 to 0.008 for
the North, and from 0.002 to 0.013 for the South, while
genetic distances among the North and South groups
ranged from 0.008 to 0.060 with a mean of 0.031 (datanot shown).
3.3. Relationships of population size to genetic diversity
estimates
The percentage of polymorphic loci (P) and the mean
number of alleles per locus (A) had signiﬁcant relationships with the log of population size (Fig. 3a and b),
indicating that genetic variation was reduced in small
populations. Three populations (SS, TB, and MB)
with small population sizes (4350 ﬂowering plants)

exhibited low polymorphism (P=18.2%) and low allelicrichness (A=1.18 for SS and TB, 1.27 for MB). Of 28
alleles detected, 13 (46%) were recognized as rare (frequency of q< 0.1). When rare and common (frequencyof q50.1) alleles are analysed separately as conductedin some studies (e.g. Prober and Brown, 1994; Young etal., 1999), all alleles that were not observed in small
populations were rare, and the mean number of common alleles per locus was the same (1.18) for all populations (Fig. 3c). Populations with 350 ﬂowering plants
or fewer, in particular, had lost almost all of their rare
alleles (> 93%). Observed heterozygosity (Ho), expected
heterozygosity (He) and inbreeding coeﬃcients (FIS)
had no relationship with the log of population size
(Fig. 3d–f).
The association between population size and allelicrichness was also corroborated by the simulation
experiment. According to the simulations, larger populations were likely to be less aﬀected by reducing sample
sizes and showed larger allelic richness than those forsmall populations (A=1.18) in most cases. Only two
populations (HK and HM) were severely reduced intheir allelic richness to A=1.18 in approximately 50%
of simulations. Although four populations (OH, KK,
PK, and HI) experienced substantial losses of allelicrichness, more than 90% of simulations were up to 19%
losses of their allelic richness (A51.45 for OH; A51.72

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H. Tomimatsu, M. Ohara / Biological Conservation 109 (2003) 249–258

Fig. 2. Neighbor-joining (NJ) dendrogram (Saitou and Nei, 1987) of
the genetic relationships among 12 Trillium camschatcense populationsin the Tokachi region of Hokkaido, Japan, inferred from a matrix ofstandard genetic distance (Nei, 1972). Numbers in parentheses correspond to those in Table 1. Populations belonging to the South group(Fig. 1) are indicated with asterisks. Bootstrap values have been computed by resampling loci and are given as per cent values over 1000
replications.

for KK; A51.55 for PK; A51.73 for HI; in > 90% of
simulations). The other populations were not reduced inallelic richness in more than 90% of simulations. These
results suggest that the association between population
size and allelic diversity was substantive, not an artifactthat the variation of sample sizes produced.

4. Discussion
4.1. Eﬀects of fragmentation on genetic diversity andlevels of inbreeding
Fragmented populations are predicted to experiencestochastic loss of rare alleles ﬁrst, because only a small
portion of the original gene pool remains after fragmentation (reviews by Barrett and Kohn, 1991; Ellstrand and Elam, 1993; Young et al., 1996).
Subsequently, random genetic drift will reduce allelicrichness and increase interpopulation genetic divergence, however, it takes several generations for drift tohave signiﬁcant impacts. In study populations of T.
camschatcense, at this time, only a few generations(probably one or two) have passed since fragmentation.
On the other hand, elevated inbreeding induced byfragmentation can erode heterozygosity rapidly, leading
to increased inbreeding coeﬃcient.
Small, fragmented T. camschatcense populations havelost rare alleles that were expected to initially be present
at low frequencies (Fig. 3c). Because the eﬀects of drift

were expected to be minor, the decrease in allelic richness in small populations was attributable to genetic
bottlenecks at the time of fragmentation. The results
indicate that populations are expected to lose 0.17 allelicrichness for each reduction of their population size by 1/
10 of the original [Fig. 3b; A=1.00+0.17 log (population size)]. Several studies have also shown a loss of rare
alleles when populations were reduced in size for someperennial plant species, Salvia pratensis and Scabiosa
columbaria (van Treuren et al., 1991), Eucalyptus albens(Prober and Brown, 1994), Gentiana pneumonanthe(Rajimann et al., 1994), Rutidosis leptorrhynchoides(Young et al., 1999), and Swainsona recta (Buza et al.,2000). On the other hand, population size should havelittle eﬀect on expected heterozygosity (gene diversity),
because alleles lost are mainly rare. As expected, we
observed no signiﬁcant relationship between expected
heterozygosity and population size (Fig. 3e).
Although no eﬀect of population size on observed
heterozygosity and inbreeding coeﬃcients was observed in
the present study, inbreeding coeﬃcients in some small
populations were relatively high, and two populations(KK and MB) showed signiﬁcant deviations from
Hardy–Weinberg expectations (Table 2). There are two
possible explanations for this result. First, if population
substructuring has aﬀected sampling, inbreeding coeﬃcients would be overestimated. That is, the patchy distribution of related individuals may generate a Wahlundeﬀect. It was unlikely to explain the result, since fertile
individuals seemed to be uniformly distributed within asmall area. Second, signiﬁcant amounts of inbreeding
could occur within these populations. Fragmentation,
coupled with localized pollinator movements and seeddispersal, may have resulted in signiﬁcant biparental
inbreeding, causing heterozygote deﬁciencies. Whereas
the evidence of inbreeding was shown in some small
populations, the FIS value of the smallest population(TB) did not diﬀer from the Hardy–Weinberg expectation (Table 2). In this population, we found no seedlingrecruitment possibly because of low seed productionand environmental deterioration (Tomimatsu andOhara, in press). Some fertile plants may have survived
fragmentation owing to the long life span of this species.
Hence, the inbreeding coeﬃcient of the TB population
would only reﬂect the pre-fragmentation mating system,
and relatively high FIS values in some small populations
could reﬂect fragmentation. Further careful observationis needed to conﬁrm whether pollination systems havechanged after fragmentation by the comparison of pollinator fauna and their behavior to those of large andunfragmented populations.
4.2. Local population structure
The analyses of NJ dendrogram and F statistics
clearly showed the apparent genetic diﬀerentiation

H. Tomimatsu, M. Ohara / Biological Conservation 109 (2003) 249–258

255

Fig. 3. Relationships between the log of population size (estimated number of ﬂowering plants) and population genetics parameters; (a) percent
polymorphic loci, (b) mean number of alleles per locus, (d) observed heterozygosity, (e) expected heterozygosity and (f) inbreeding coeﬃcient. Leastsquare linear regressions are indicated in those cases where the slope of the regression line was signiﬁcantly diﬀerent from zero (P< 0.05). The
relationship between population size and allelic richness for rare (closed circles; frequency of q <0.1) and common (open circles; frequency of q50.1)
alleles are also shown (c).

between the two geographical groups (Fig. 2; Table 4).
The pattern could have resulted from the low dispersal
ability of this species and geographical separation. Pollinators of T. camschatcense are mostly small Diptera andColeoptera species (Ohara et al., 1991; H. Tomimatsu etal., unpublished data) whose ﬂight distances are thought
to be relatively short. The seeds are dispersed by ants,
whose dispersal distance was reported to be up to 3.3 m(Ohara and Higashi, 1987). The two groups are geographically separated by the small hills where this plant

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H. Tomimatsu, M. Ohara / Biological Conservation 109 (2003) 249–258

species is rarely found. The hills may be a barrier togene ﬂow and promote diﬀerentiation between groups.
The earlier studies concerning the population genetics ofT. camschatcense (Fukuda et al., 1960; Kozuka andKurabayashi, 1960; Fukuda, 1990; Ohara et al., 1996)
have mainly focused on wide range genetic diﬀerentiation within Hokkaido. They examined the patterns ofgenetic diﬀerentiations and possibilities of migration
among and within three geographical groups in Hokkaido, ﬁrst described by Kurabayashi (1957), to elucidate the genetic, ecological and paleogeographicalforces producing the observed pattern. The present
study indicated that signiﬁcant interpopulation genetic
diﬀerentiations have also occurred on a small, regional
scale (within a radius of 30 km). Such local population
structure is of conservation concern for the maintenanceof adaptive genetic variation (Crandall et al., 2000).
By contrast, the genetic diﬀerentiation within the two
geographical groups was very low. The high level ofgene ﬂow estimates (Nm) showed the possibility that
there was a suﬃcient gene ﬂow within groups (Table 4).
However, Nm is a historical estimate under theassumption of migration-drift equilibrium (Wright,
1951). These estimates do not infer any pattern ofongoing gene ﬂow and therefore any eﬀects of fragmentation on gene ﬂow. Ecological and genetic theory
expects that gene ﬂow rates, as measured by the fractionof immigrants per generation, increase as the size of the
focal population decreases (Ellstrand and Elam, 1993).
If the amount of pollen introduced from outside thepopulations remains invariant, the relative fraction of
immigrant pollen grains or seeds increases (Handel,
1983). Indeed, recent experimental studies showed someevidence of a high gene ﬂow rate among small and isolated populations (Young et al., 1993; Goodell et al.,1997). However, the expectation will not be applicable if
the assumptions of the theory are not met, namely, if theamount of immigrant pollen varies among populations.
This situation can occur when population isolation isgreater than the foraging range of dispersers or when theavailability of pollinators greatly diﬀers among populations. Small T. camschatcense populations suﬀered low
pollinator availability (Tomimatsu and Ohara, in press).
Thus, the limited dispersal ability of this species and low
pollinator availability in small populations may limit
gene ﬂow among these populations.
4.3. Implications for conservation
The results of this study suggest that reductions in
population size have led to losses of rare alleles in small
T. camschatcense populations. To retain high allelicdiversity including many of rare alleles, populations
with at least 550 ﬂowering plants are required (Fig. 3c).
At sizes below this, some elevation of inbreeding coeﬃcient (Fig. 3f) and reduced seed production (Tomimatsu

and Ohara, in press) also occurred. Because small
populations will suﬀer from gradual eﬀects of genetic
drift, larger population sizes may be needed to avoid
further allelic losses in the long term. In spite of the lowlevel of genetic diﬀerentiation among overall populations, substantial genetic variations are those between
the two geographically discontinuous populationgroups, which should attract management concern. The
results showed the evidence of historical genetic unexchangeability, which may lead to a recommendationthat the two geographical groups would be distinct
evolutionarily signiﬁcant units (ESUs; Moritz, 1994;
Crandall et al., 2000) and be managed separately. Further information on other molecular markers and ecological distinctiveness will clarify appropriate
management actions (Crandall et al., 2000).
The genetic deterioration may be related to remnant
population viability. The loss of allelic variation may
lower individual ﬁtness, because the allelic richness ofneutral molecular markers can be correlated with adaptive variations (Ouborg et al., 1991; Lynch et al., 1999).
Young et al. (2000) showed that population size of
Rutidosis leptorrhynchoides had a positive relationshipwith allelic variation at the self-incompatibility (S)locus, as well as allozyme diversity, indicating that
reduced allelic variation was likely to reduce mateavailability. This could also contribute to reduced seedproduction in small populations of T. camschatcense(Tomimatsu and Ohara, in press). In spite of its
increasing need for species conservation, only a few
studies have attempted to integrate immediate genetic
eﬀects of fragmentation into models of population viability (Burgman and Lamont, 1992; Menges and Dolan,
1998; Oostermeijer, 2000). Further study with a longterm ﬁeld survey of demography will enable us to
examine genetic eﬀects on remnant population viability.

Acknowledgements
The authors are grateful to Shuji Oiki for technicalsupport throughout the allozyme electrophoresis andItsuro Koizumi, Jérôme Goudet, and anonymous
reviewers for valuable comments on data analysis andon the manuscript. This study was supported by the
Sasakawa Scientiﬁc Research Grant from the Japan
Science Society, the Hokscitec Foundation, Grants-inAid from the JSPS for Scientiﬁc Research (10440230,
11691175, 13640637), and Research Fellowships of the
JSPS for Young Scientists.
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