Conservation Genetics 5: 425–437, 2004.
2004 Kluwer Academic Publishers. Printed in the Netherlands.
425
Genetic effects of a persistent bottleneck on a natural population
of ornate box turtles (Terrapene ornata)
Chih-Horng Kuo* & Fredric J. Janzen
Department of Ecology, Evolution, and Organismal Biology, Iowa State University, Ames, IA 50011, USA
(*Author for correspondence: Address: Department of Genetics, The University of Georgia, Athens, GA
30602, USA; fax: +1-706-542-3910; e-mail: chkuo@uga.edu)
Received 25 September 2003; accepted 28 November 2003
Key words: conservation genetics, genetic diversity, microsatellite, population bottleneck, Terrapene ornata
Abstract
Human activities in the past few hundred years have caused enormous impacts on many ecosystems, greatly
accelerating the rate of population decline and extinction. In addition to habitat alteration and destruction,
the loss of genetic diversity due to reduced population size has become a major conservation issue for many
imperiled species. However, the genetic effects of persistent population bottlenecks can be very different for
long-lived and short-lived species when considering the time scale of centuries. To investigate the genetic
effects of persistent population bottlenecks on long-lived species, we use microsatellite markers to assess the
level of genetic diversity of a small ornate box turtle population that has experienced a persistent bottleneck
in the past century, and compare it to a large relatively undisturbed population. The genetic signature of a
recent bottleneck is detected by examining the deviation from mutation-drift equilibrium in the small
population, but the bottleneck had little effect on its level of genetic diversity. Computer simulations
combined with information on population structure suggest that an effective population size of 300, which
results in a census population size of 700, would be required for the small population to maintain 90% of
the average number of alleles per locus in the next 200 years. The life history of long-lived species could
mask the accelerated rate of genetic drift, making population recovery a relatively slow process. Statistical
analysis of genetic data and empirical-based computer simulations can be important tools to facilitate
conservation planning.
Introduction
Human populations have experienced rapid
growth in the past few hundred years and greatly
increased demand for natural resources. Many
species have thus faced rapid extinction, while
others have suffered from the loss of genetic
diversity due to habitat fragmentation and overexploitation (Hoelzel et al. 1993; Halley and
Hoelzel 1996; Hoelzel 1999). Since genetic diversity
is a primary component of adaptive evolution, the
loss of genetic diversity seriously decreases the
long-term survival probability of a population
(Avise 1995; Newman and Pilson 1997; Bouzat
et al. 1998a; Coltman et al. 1998; Saccheri et al.
1998; Reed and Frankham 2003; but also see Reed
and Frankham 2001). For many imperiled species,
active management plans are often required to
prevent extinction. In order to make biologically
sound conservation plans, an understanding of the
genetic diversity remaining in natural populations
is essential (Quattro and Vrijenhoek 1989; Taylor
and Dizon 1996; Friar et al. 2000; Tallmon et al.
2002). Molecular markers are important modern
tools for estimating the level of genetic diversity in
imperiled populations (O’Brian 1994; Haig 1998).
Common measurements of genetic diversity include (i) observed number of alleles, (ii) effective
426
number of alleles (corrected for allelic distribution skew), (iii) observed heterozygosity, (iv)
expected heterozygosity (calculated from the
Hardy–Weinberg expectation), and (v) the proportion of polymorphic loci (Frankel and Soule
1981). To estimate the loss of genetic diversity in
imperiled populations, measurements are often
compared to empirical values obtained from
undisturbed populations of the same or closely
related species (Akst et al. 2002; Gautschi et al.
2002).
The ornate box turtle (Terrapene ornata) is a
small terrestrial turtle that resides in sand prairies
across the central and southern parts of the United
States and northern Mexico (Ernst et al. 1994;
Dodd 2001). The arrival of European settlers and
the accompanying agricultural and industrial
activities in the Midwest US destroyed a large
fraction of the species’ natural habitat, and restricted gene flow among remnant populations. In
addition to habitat loss, the species is further
threatened by collection due to its popularity on
the pet market (Ernst et al. 1994). Habitat fragmentation, restricted gene flow, and reduced population size all raise the concern that populations
will become genetically impoverished, and thus
unable to adapt to future environmental changes
and to develop resistance to new diseases. The
species is listed as endangered (IN, WI), threatened
(IA), or other status of conservation concern (AK,
CO, IL, KS, LA, MO, NE, OK), and is becoming
rare even within its major distribution range
(Levell 1997; Dodd 2001).
We used microsatellite markers to compare the
genetic diversity of a small isolated population of
T. ornata from fragmented habitat (IL) to a large
relatively undisturbed population from a major
habitat (NE). The goal is to have a better understanding of human impacts on genetic diversity in
remnant populations of this and similarly distributed species. In addition, we developed a computer
simulation algorithm as a tool to forecast future
genetic diversity under different scenarios. The
simulation algorithm employs an overlappinggeneration model that fits the study organism
better than the traditional discrete-generation
model. Ultimately, we want to present fundamental information for the conservation of the
species, and also provide a useful computational
tool for conservation studies involving long-lived
species with overlapping generations.
Materials and methods
Study populations and sample collection
Two ornate box turtle populations were sampled
for the study. The small, isolated IL population has
experienced a relatively recent population bottleneck due to habitat loss and human activities.
Mark-recapture data indicate that the annual adult
survival rate is lower than other populations, and
recruitment is almost nonexistent in this population (F. Janzen unpublished data). Its current
habitat is a sand prairie remnant of approximately
150 ha, surrounded by the Mississippi River and a
heavily developed area (41 550 N, 90 60 W, Carroll
and Whiteside Counties, Illinois, USA). The
development history of the surrounding area traces
back to at least 160 years (Davis 1993; Kett 1993), a
time period equivalent to about six generations for
the species. Our hypothesis is that the population
size experienced a significant decline since the
beginning of this development due to habitat loss.
In addition, the population is further impacted by
increased automobile traffic and human collection
in the past few decades (F. Janzen pers. observ.).
Blood samples were collected from 74 individuals
(41 female, 27 male, 6 juvenile). We believe these
samples include nearly all individuals in the current
population based on our field observation and
mark-recapture work since 1990.
The NE population is a large and relatively
undisturbed population from major habitats
(Valentine National Wildlife Refuge, Cherry
County, Nebraska, USA) and is included in this
study as the reference population. Blood or tail
clip samples were collected by J. Kolbe from 73
individuals (the sex was not identified for all
individuals) in the summer of 1998. Tissue samples
were preserved in Queen’s lysis buffer (Seutin et al.
1991) and stored at 80 C in the laboratory until
DNA extraction.
Microsatellite genotyping procedure
Genomic DNA was extracted by using the High
Pure PCR Template Preparation Kit (Roche),
following the protocol provided by the manufacturer. We tested 16 microsatellite loci (Table 1)
developed for the bog turtle (Clemmys muhlenbergii) by T.L. King et al. (in preparation).
427
Table 1. GenBank accession number, repeat motif, and allele range (bp)
Locus
GenBank accession
number
Repeat motif
Allele range (bp)
CmuA19
CmuB08
CmuB12
CmuB21
CmuB67
CmuB91
CmuD21
CmuD55
CmuD62
CmuD79
CmuD87
CmuD88
CmuD90
CmuD93
CmuD95
CmuD121
AF517227
AF517229
AF517230
AF517231
AF517232
AF517234
AF517236
AF517240
AF517241
AF517243
AF517244
AF517245
AF517247
AF517248
AF517249
AF517252
(CT)7(CA)14
(CAT)10
(CAT)7
(CAT)10
(TGA)13
(ATG)6
(TAGA)15
(GAGA)(CA)(GAGA)3
(GATA)11
(GATA)10(AAT)2(CTGG)(AAT)(AT)5
(GATA)3interrupt(GATA)22
(CTAT)18
(GATA)9
(CTAT)2interrupt(CTAT)18
(GATA)17
(GATA)8
121–156
194–212
184–190
N/A
148
143
149–173
170–227
N/A
159–163
192–250
102–163
113–149
347
137–177
129–166
The reaction volume for the polymerase chain
reaction (PCR) was 12.5 ll. The PCR mixture consisted of 2 ll template DNA solution (with a concentration of approximately 100 ng/ll), 1.25 ll Taq
DNA polymerase10· reaction buffer without MgCl2
(Promega), 1.25 units of Taq DNA Polymerase
(Promega), 4 mM MgCl2, 100 nM of each dNTP
(Takara), and 400 nM of each primer. Forward
primers were labeled with a fluorescent dye (6-FAM
or TET) at the 50 -end. The PCR conditions were as
follows: one denaturing step at 94 C for 2 min, 34
cycles consisting of 94 C for 30 s, 56 C for 30 s, and
72 C for 30 s, and a final elongation step of 72 C for
7 min. Samples were maintained at 4 C after the
PCR amplification. Because the primers were not
developed for the study species, a range of PCR
conditions was tested before genotyping all individuals. The tested annealing temperature ranged from
54 to 60 C, the annealing and extension time ranged
from 20 to 60 s, and the number of amplification
cycles ranged from 32 to 36.
PCR products were diluted 1:2 with sterile water, and 1.5 ll of the diluted sample from each
reaction was sent to the DNA Sequencing and
Synthesis Facility at Iowa State University (Ames,
IA, USA). Samples were run on an ABI 377
automated DNA sequencer with a ROX internal
size standard (Applied Biosystem). Data files from
the sequencer were analyzed with GeneScan v.3.0
and Genotyper v.2.0 software (Applied Biosystem). For quality control, three individuals with
blood samples collected from different years were
chosen to perform independent DNA extraction
and genotyping.
Statistical analyses
Genotypic data were analyzed using POPGENE
(Yeh and Boyle 1997) to calculate the observed
number of alleles, effective number of alleles, observed heterozygosity, expected heterozygosity,
and to test for Hardy–Weinberg equilibrium
(HWE) and linkage disequilibrium. Frequencies of
null alleles were estimated following the method
developed by Brookfield (1996). Genetic divergence between populations was measured by Fst
(Weir and Cockerham 1984; Weir 1996) and
Analysis of Molecular Variance (AMOVA, Excoffier et al. 1992; Weir 1996), as implemented in
ARLEQUIN (Schneider et al. 2000).
Heterozygosity excess test and M ratio test
To test whether the bottleneck in the IL population could be inferred from the molecular marker
data, we used the program BOTTLENECK
(Cornuet and Luikart 1996; Piry et al. 1999) to
examine the deviation from mutation-drift
428
equilibrium and the M ratio test (Garza and Williamson 2001) to examine the mean ratio of the
number of alleles to the range in allele size. Separate tests were performed using all loci typed and
only the loci that were in HWE.
When running the BOTTLENECK program,
the two-phase model (TPM; Di Rienzo et al. 1994)
was chosen because it is more appropriate than the
strict one-step stepwise mutation model (SMM;
Ohta and Kimura 1973) for microsatellite data
(Piry et al. 1999). The parameters for TPM were
set to 95% single-step mutations and 5% multiplestep mutations, and the variance among multiple
steps was set to 12 as suggested by Piry et al.
(1999). To assess the robustness of this test, we
performed additional tests with different parameter settings. The single-step mutation percentage in
additional tests ranged from 80 to 99%, and the
variance among multiple steps ranged from 2 to
24. Based on the number of loci in our dataset, the
Wilcoxon test was chosen for the statistical analysis of heterozygote excess.
The parameters for the M ratio test were set as
follows: h = 1, proportion of one-step mutations
(ps) = 0.9, and average size of non-one-step
mutations (Dg) = 3.5 as suggested by the authors
(Garza and Williamson 2001). To assess the effects
of changes in parameter values, additional runs
were performed using h values ranging from 0.1 to
10, ps ranging from 0.8 to 0.99, and Dg ranging
from 2.5 to 6.
(iv) equal allele frequencies using the discretegeneration model. The first simulation scenario
used the actual allele frequency data from the IL
population and the overlapping-generation model
to provide the most realistic projections. The second through fourth simulation scenarios were intended to explore the effects of allelic distribution
skew and the effects of generation models on the
genetic diversity projections. Simulations with
equal allele frequencies were performed by using
an input file that had identical observed numbers
of alleles as the IL population but equal allelic
distribution at each locus (i.e. each allele at the
same locus has an equal frequency).
All simulation parameters except population
size and generation model remained constant and
were set as follows: degree of generation overlap = 100 when using the overlapping-generation
model (i.e. all individuals start with a random age
value that is within the longevity limit), dioecy
with random mating reproductive system, expected longevity = 30 years, age of reproductive
maturation = 10 years (Legler 1960; Blair 1976;
Metcalf and Metcalf 1985; Ernst et al. 1994),
male:female ratio was set to 1:1.5 based on our sex
ratio data from the IL population and previous
studies of other ornate box turtle populations
(Legler 1960; Doroff and Keith 1990), number of
years simulated = 200 years, and number of iterations = 1000.
Computer simulations
Results
To forecast the future genetic diversity level for the
IL population and to make recommendations
regarding sustainable population size, we developed a computer program that simulates the process of random genetic drift (Kuo and Janzen
2003). The simulation algorithm generates hypothetical populations of a fixed size based on the
allele frequencies calculated from input genotypic
data to project genetic diversity decline due to
random genetic drift.
Four sets of simulations were performed to
forecast the decline in microsatellite genetic
diversity under different scenarios: (i) actual allele
frequencies using the overlapping-generation
model, (ii) equal allele frequencies using the overlapping-generation model, (iii) actual allele frequencies using the discrete-generation model, and
Microsatellite genetic diversity and genetic divergence
All 16 microsatellite primer pairs developed for the
bog turtle successfully amplified ornate box turtle
DNA. Changes in annealing temperature and
other PCR conditions had small effects on signal
strength and quality, and did not affect allele
scoring. Two loci were found to be duplicated in
the ornate box turtle genome (CmuB21 and
CmuD62) and three loci were found to be monomorphic in both study populations (CmuB67,
CmuB91, and CmuD93). The five loci above were
excluded from further analyses. The genotyping
results were perfectly repeatable for all loci for the
three individuals with independent blood samples
collected from different years.
429
The IL and NE populations possess similar
values of allele number and heterozygosity (Table
2). The IL population has one more polymorphic
locus than the NE population (CmuD79). The
average observed number of alleles is 7.5 in the IL
population and 8 in the NE population; the average effective number of alleles is 4.7 in the IL
population and 4.3 in the NE population. No
significant difference was found between populations for either allele number measurement
(P > 0.05 for both t-tests). Private alleles accounted for 32% of the total alleles found (33/102,
14 from the IL population and 19 from the NE
population). The mean observed heterozygosity is
0.56 in the IL population and 0.57 in the NE
population; the mean expected heterozygosity is
0.67 in the IL population and 0.69 in the NE
population. Differences between the two populations for both measurements are not significant
(P > 0.05 for both t-tests).
The Hardy–Weinberg tests reveal that each
population has three of the 11 loci typed that
deviate from HWE due to heterozygote deficiency.
CmuD88 and CmuD95 deviate from HWE in both
populations, whereas CmuD55 deviates from
HWE only in the IL population and CmuD90
deviates from HWE only in the NE population.
Using the method developed by Brookfield (1996),
the null allele frequency estimates are 0.21 at
CmuD55, 0.12 at CmuD88, and 0.24 at CmuD95
in the IL population; 0.20 at CmuD88, 0.21 at
CmuD90, and 0.13 at CmuD95 in the NE population. The two populations have completely different sets of 11 allele pairs that exhibit significant
linkage disequilibrium (P < 0.05); this result could
be an artifact caused by the high variability of
microsatellite markers and limited sample sizes.
The Fst value and AMOVA test indicate that
the two populations exhibit a significant genetic
divergence. The Fst value is 0.09891 and is highly
significant (P < 0.001, 1023 permutations). This
Fst value is at the lower end of the range (0.011–
0.631) found in other reptile studies using microsatellite markers (Ciofi and Bruford 1999; Ciofi
et al. 2002; Cunningham et al. 2002; Dever et al.
2002; Beheregaray et al. 2003; Malone et al. 2003).
However, one should note that this study only
includes two populations. AMOVA indicated that
within-population variation in these microsatellite
loci accounts for 90.11% of the total variation
found.
Heterozygosity excess test and M ratio test
Results from the heterozygosity excess test implemented in the BOTTLENECK program are suggestive in the IL population (P = 0.05) and not
significant in the NE population (P = 0.54).
Exclusion of the three loci that deviated from
HWE increases the P value to 0.13 in the IL
population and 0.77 in the NE population. In
additional tests that assess the robustness of this
test, the P values range from 0.03 to 0.10 in the IL
population and from 0.25 to 0.81 in the NE population. In other words, the test of mutation-drift
equilibrium tentatively supports a relatively recent
population bottleneck in the small IL population,
but not in the large NE population.
The M ratio test does not support the existence
of an historical bottleneck in either population.
The P value is 0.27 in the IL population and 0.66
in the NE population. Exclusion of the three loci
that deviated from HWE increases the P value to
0.37 in the IL population and 0.75 in the NE
population. In addition, the test is very sensitive to
changes in parameter settings; in additional tests
using different parameter settings, the P value
ranges from 0.00 to 0.83 in the IL population and
from 0.05 to 0.99 in the NE population.
Simulation projections
Table 3 summarizes simulation projections of the
observed number of alleles and observed heterozygosity retained after a 200-year period compared
to the current level. The observed number of alleles declines at a faster rate than observed heterozygosity, as found in previous theoretical and
empirical studies (Nei et al. 1975; Allendorf 1986;
England and Osler 2001; Cunningham et al. 2002;
England et al. 2003). Based on the actual allele
frequencies, the IL population would retain about
71% of the observed number of alleles and 92% of
the observed heterozygosity if its population size
remained constant at 75 for the next 200 years. In
order to achieve the conservation recommendation of maintaining 90% of genetic diversity for a
200-year period (Soule et al. 1986), the population
size would need to be increased to approximately
300.
In comparison, less than 150 individuals are
required to maintain 90% of observed number of
alleles if the population started with an equal
He
7.55
3.75
Mean
St. Dev
4.66
2.66
5.71
2.34
1.27
2.84
6.31
1.46
7.13
7.59
6.93
7.72
1.99
0.56
0.21
0.84
0.58
0.22
0.64
0.47
0.28
0.88
0.66
0.71
0.43
0.47
0.67
0.24
0.83
0.58
0.22
0.65
0.85
0.32
0.87
0.87
0.87
0.88
0.50
8
3.66
9
7
3
6
11
1
14
10
9
8
10
4.27
1.95
2.43
4.28
2.37
3.21
6.74
1
7.06
5.25
5.48
5.76
3.44
0.57
0.24
0.49
0.85
0.56
0.70
0.78
0
0.77
0.46
0.43
0.59
0.68
Ho
0.69
0.25
0.59
0.77
0.58
0.69
0.86
0
0.86
0.82
0.82
0.83
0.71
He
OA – observed number of alleles, EA – effective number of alleles, Ho – observed heterozygosity, and He – expected heterozygosity.
10
4
2
4
10
2
12
10
9
11
9
EA
OA
Ho
OA
EA
NE population
IL population
CmuA19
CmuB08
CmuB12
CmuD21
CmuD55
CmuD79
CmuD87
CmuD88
CmuD90
CmuD95
CmuD121
Locus
Table 2. Measurements of microsatellite genetic diversity
9.27
4.31
13
7
3
6
13
2
15
13
9
11
10
OA
Combined
5.40
3.15
4.61
3.39
1.83
3.37
7.46
1.22
10.41
8.58
7.31
8.52
2.75
EA
0.57
0.18
0.66
0.71
0.39
0.67
0.62
0.14
0.82
0.56
0.58
0.51
0.57
Ho
0.72
0.23
0.79
0.71
0.45
0.71
0.87
0.18
0.91
0.89
0.87
0.89
0.64
He
430
431
Table 3. Percentages of microsatellite genetic diversity retained over a 200-year period based on computer simulations, values greater
than 90% in bold
Population Size
50
75
150
200
300
500
Simulation scenario
Actual allele frequencies,
overlapping-generation
model
Equal allele frequencies,
overlapping-generation
model
Actual allele frequencies, Equal allele frequencies,
discrete-generation model discrete-generation model
OA
Ho
OA
Ho
OA
Ho
OA
Ho
63.55
70.92
82.04
86.00
90.37
94.82
87.81
91.71
95.56
96.65
97.60
98.78
73.34
84.51
96.98
99.06
99.89
100.00
87.54
91.26
95.53
96.63
97.79
98.68
79.60
84.75
91.56
93.75
96.22
98.36
95.10
96.69
98.23
98.69
99.14
99.44
91.98
97.40
99.89
99.99
100.00
100.00
94.85
96.50
98.30
98.76
99.13
99.48
OA – observed number of alleles, Ho – observed heterozygosity.
allelic distribution under the overlapping-generation model. The skewed allelic distribution of the
IL population results in a faster rate of decline in
the observed number of alleles. The probability of
losing a rare allele due to random genetic drift
increases as the frequency of that allele decreases.
Notably, the projections of genetic diversity are
quite different under the overlapping-generation
model and the discrete-generation model. The rate
of decline in genetic diversity is much faster under
the more realistic overlapping-generation model.
To maintain 90% of the observed number of alleles
under the discrete-generation model for 200 years,
a population size of 150 is sufficient when using the
actual allele frequencies. Astonishingly, a population of less than 50 individuals can maintain the
same level of genetic diversity when using an equal
allelic distribution. These numbers are considerably smaller than the 300 individuals suggested by
the most realistic projection using the actual allele
frequencies and an overlapping-generation model,
indicating the importance of selecting appropriate
parameters and simulation model for projecting
genetic diversity.
Discussion
Our study suggests that remnant populations of
long-lived species might appear to be genetically
healthy, even after a persistent population bottleneck that lasts for 100–200 years. This finding
could have complex implications for long-lived
species. On the negative side, the accelerated rate
of genetic drift in such populations could be
masked and thus could be overlooked for necessary conservation efforts. On the positive side, the
genetic diversity still present permits the development of proper conservation plans that could restore populations not only demographically, but
also genetically. This genetic situation is analogous
to the demographic situation facing long-lived
organisms where adults are targeted for removal.
Delayed sexual maturity of these organisms provides a juvenile pool that can buffer the chronic
disturbance, but can also mask the inevitable
population extinction (Congdon et al. 1993;
Congdon et al. 1994; Heppell 1998).
Comparisons between the two study populations
The two study populations of ornate box turtles
exhibited about the same level of genetic diversity
for all four measurements we employed. This result
might indicate that the two populations have
approximately the same effective population size
historically. This recent and ongoing bottleneck
experienced by the IL population apparently did
not affect the level of genetic diversity represented
by the microsatellite loci we analyzed. This surprising result might be explained by the long lifespan of this species and/or the severity of the
bottleneck.
Previous studies indicate that the ornate box
turtle has a relatively long lifespan in the wild and
432
in captivity. Legler (1960) expected the species
could live up to 50 years in the wild, and Blair
(1976) estimated the species has a lifespan of
approximately 30 years based on 23 years of field
studies. Metcalf and Metcalf (1985) reported an
average life span of approximately 22 years, and
that a nearly complete population turnover occurred over the course of 25 years. Ernst et al.,
(1994) reported a record of 42 years for a female
box turtle that lived 22 years in captivity.
The relatively long lifespan of ornate box turtles might also explain the similarity in allelic
composition between the two populations. Although the two populations have probably been
separated for thousands of years, the generation
number since last exchanging migrants could be
comparatively low. Phylogeographic studies of
mtDNA in other turtle species (e.g., Weisrock and
Janzen 2000; Starkey et al. 2003) suggest that
current populations in the Midwest originated
from the southern US and that dispersal occurred
after the last glaciers retreated about 10,000 years
ago. The allele set found in these two populations
might, then, simply be representative of the
ancestral southern US gene pool.
In addition to the relatively long lifespan of the
species, the severity and pattern of the bottleneck
experienced by the IL population might also help
to explain the similar level of genetic diversity
found in the two populations. England et al.
(2003) found that diffuse population bottlenecks
over several generations have a lower impact on
the number of alleles compared to short intense
bottlenecks. The actual population decline might
be a gradual process in the IL population rather
than a sudden one-step decline; thus, the population could still maintain most of its rare alleles
present before the population decline.
Previous studies have pointed out that interpretation of Fst values per se might be of little biological
relevance due to certain complications (Hedrick
1999; Balloux and Lugon-Moulin 2002); thus, we
only assess the statistical significance of the Fst value
by permutation tests and compare it to other microsatellite studies on reptile species. Our hypothesis
to explain the small but significant Fst value and the
among-population variation is that the two populations were not significantly divergent before the
human impacts occurred. In other words, we
hypothesize that the recent population bottleneck in
the IL population accelerated the drift rate in the
past century, and resulted in this significant Fst value
and among-population variation. Because the bottleneck was probably not severe, both the Fst and
among-population variation estimates are small
compared to other studies (e.g., Ciofi and Bruford
1999; Ciofi et al. 2002; Dever et al. 2002; Beheregaray
et al. 2003; Malone et al. 2003). We also emphasize
that the statistical significance of the low Fst value in
our study may not reflect a true biological difference
between the two populations examined.
Detecting the population bottleneck by using
molecular markers
Several methods have been developed to detect historical population bottlenecks from molecular marker data (Cornuet and Luikart 1996; Luikart and
Cornuet 1998; Beaumont 1999; Garza and Williamson 2001). The method developed by Beaumont
(1999) is useful for detecting a long-term gradual
change but is not appropriate for detecting severe
bottlenecks or a recent population decline; thus, we
only examine our dataset using the heterozygosity
excess test and the M ratio test. We found that the
heterozygosity excess test was more sensitive
and robust in detecting the historical bottleneck that
occurred in the IL population than was the M ratio
test.
The heterozygosity excess test developed by
Cornuet and Luikart (1996) examines if the population in question deviates from mutation-drift
equilibrium, and is useful for detecting historical
population bottlenecks (Luikart and Cornuet
1998; Luikart et al. 1998a, b; Spencer et al. 2000).
The test indicates that the NE population did not
experience historical bottleneck; the P value for
the IL population data is suggestive (P = 0.05) of
a bottleneck, however, but is not highly significant.
Additional tests with different parameter settings
verified that this result is relatively insensitive to
the parameter settings. The reason that this heterozygosity test failed to obtain a highly significant
P value for the IL population data could be due to
the long lifespan of the species and the pattern of
decline of the IL population. The heterozygosity
excess test is most sensitive for detecting historical
population bottlenecks when the bottlenecks are
severe (n < 40, Luikart and Cornuet 1998), which
is probably not the case for the IL population.
We found that the M ratio test was very sensitive
to the changes in parameter settings and generated
433
a wide range of P values for both populations. In
addition, the M ratio test is most sensitive when the
pre-bottleneck population size is large (h = 10).
Using a theta of 10 represents a population with an
effective population size of 5000, which is an unlikely number for ornate box turtle populations.
Although the parameter we chose (h =1, effective
population size = 500) is more realistic for the
species, the sensitivity of the test is low under this
setting (Garza and Williamson 2001).
As discussed in the previous section, the life
history of the species and the pattern of decline in
the IL population could result in a lower probability of losing rare alleles. Thus, the impact of this
bottleneck could be greater on allelic distribution
skew than on the average number of alleles per
locus. This pattern could explain why the heterozygosity excess test (based on allele frequency
data) is more sensitive than the M ratio test (based
on ratio of the number of alleles to the range of
allele size) for our dataset.
Genetic diversity projections for the IL population
Computer simulations indicate that a hypothetical
population that has the same number of alleles at
each locus as the IL population but equal allelic
distribution could maintain 90% of its observed
number of alleles for 153 years and 91% of its
observed heterozygosity for 200 years with a constant population size of 75. This finding might
explain why the small IL population maintains the
same level of genetic diversity as the large NE
population. Considering that the actual population decline for the IL population could be a
gradual process rather than a sudden crash, we
might expect that the IL population has maintained most of its historical genetic variation before the bottleneck.
The rate of random genetic drift in T. ornata is
relatively slow when measured by the temporal
unit of year, but this fact certainly does not make
the species immune to the process when the population size declines. Our simulation results demonstrated that the IL population would lose 30%
of its observed number of alleles in the next
200 years if the population size remained at 75
(Table 3). To maintain 90% of the observed
number of alleles over a 200-year period, as recommended by Soule et al. (1986), a population size
of 300 is required. One cautionary finding of our
simulation study is that only 150 individuals are
required to maintain the same level of genetic
diversity under a traditional discrete-generation
model (Table 3). Considering that the overlappinggeneration model is more realistic for long-lived
organisms such as turtles and large mammals, this
important factor must be taken into account when
making conservation plans for such long-lived
organisms.
Another important consideration is that the
simulation model is based on several ideal population assumptions (complete random mating,
constant population size, no selection, no migration, no mutation), thus the ‘population size’ in
this simulation model should be viewed as effective
population size. The direct use of the population
size guideline obtained from the simulations in
practical conservation planning may not be
appropriate. The effective population size is difficult to estimate in natural populations (Pope
1996), and the ratio of effective population size to
census population size (Ne/N) can vary widely
depending on the life history of the organism in
question (Nunney 1995). Previous studies found
that for long-lived species with overlapping generations, the effective population size is generally
close to half of the breeding adult number (Nunney 1991, 1993; Nunney and Elam 1994). We
found that adults account for approximately 92%
of the IL population, and Legler (1960) reported a
Kansas population consisting of 84% adults.
Based on the simulation results and population
structure information, a census population size of
700 is therefore desirable for the IL population to
maintain 90% of its observed number of alleles in
the next 200 years.
Although migration and mutation are not included in this simulation model, we believe that
these two factors would have little influence on the
projections made for the IL population. Based on
the species’s distribution range (Ernst et al. 1994;
Dodd 2001) and the IL population’s current habitat
condition, it is unlikely that immigration into the IL
population could occur naturally. The rate and
pattern of microsatellite mutations are affected by a
complex set of interacting factors, such as repeat
length, base composition, flanking sequences, and
taxonomic groups (Balloux and Lugon-Moulin
2002). Using a conservative estimate of l =5 · 104
(Garza and Williamson 2001), there will only be one
expected mutation event per locus in 200 years when
434
the population size is 300; thus, exclusion of mutation from our simulation model would have little if
any effect on projected outcomes.
The simulation results agree well with the 50/
500 conservation rules laid out by Franklin (1980).
Using an effective population size of 50, the IL
population can still maintain 90% of the observed
number of alleles for 36 years and 90% of the observed heterozygosity for 167 years. This result
indicates that maintaining an effective population
size of 50 can be a short-term solution to minimize
inbreeding depression for the species. When the
effective population size is increased to 500 in the
simulation, the IL population can maintain 94% of
the observed number of alleles and 99% of the
observed heterozygosity for 200 years. This result
suggests that maintaining an effective population
size of at least 500 can effectively minimize the
depletion of genetic diversity for an ornate box
turtle population.
The female-biased adult sex ratio that we found
in the IL population is consistent with previous
studies of other ornate box turtle populations.
Legler (1960) reported a 1:1.7 male:female ratio
from a population in Kansas, whereas Doroff and
Keith (1990) reported a 1:1.56 male:female ratio
from a population in Wisconsin. These findings
lead us to believe that this biased adult sex ratio
might be the norm in this species, and we used this
number to set the sex ratio parameter in all our
simulations. However, this biased sex ratio only
reduces the effective population size to 96% of the
census size and has little effect on drift rate. The
simulation results under 1:1 and 1:1.5 male:female
ratio were not significantly different (data not
shown).
declines substantially when the animals reach
maturation: survivorship of adults is estimated at
81–96% year1 (Blair 1976; Metcalf and Metcalf
1985; Doroff and Keith 1990). However, automobiles are the major threat to these animals in
developed areas, killing more adults than all
predators combined (Ernst et al. 1994; Gibbs and
Shriver 2002). Consequently, fences may be required as well to keep the turtles from surrounding roads.
Several additional factors make the population
recovery of ornate box turtles difficult without active management, such as slow maturity, high
mortality for juveniles, and the potential threat from
pet trade collectors. The species has a low intrinsic
population growth rate, thus utilizing individuals
from large healthy populations in recovery plans of
imperiled populations might be a reasonable option.
The genetic divergence data indicated that the IL
population and NE population have similar allelic
compositions, and the among-population variation
only accounted for a small portion of the total
variation found. This genetic similarity suggests that
the NE population could be a candidate for helping
the recovery of the IL population, if no other populations that are more appropriate could be found.
On the other hand, introduction of individuals from
other genetically dissimilar populations could be an
option to increase the genetic diversity of the IL
population (Ingvarsson 2002). The population size
of the IL population can be increased either through
the contribution of eggs (preferable) or translocation of adult individuals (less desirable because of
potential disease and homing issues) from other
populations (Dodd and Siegel 1991; Moore 1993).
Conservation guidelines for the IL population
Conclusions
Previous studies indicated that the population
density of ornate box turtle populations is 2.9–
13.1 adults/ha (Legler 1960; Doroff and Keith
1990). This finding suggests that the current 150ha habitat of the IL population might be adequate to support the desirable census population
size of 700. Previous studies identified adult survival as being vital in the management of
declining populations of long-lived organisms
(Lande 1988; Congdon et al. 1993; Congdon
et al. 1994; Heppell 1998; Belzer 2002). Mortality
of the species is high at the juvenile stage, but
In addition to rendering populations more susceptible to environmental stochasticity (Lande
1993; Foose et al. 1995), bottlenecks also reduce
the average number of alleles per locus and heterozygosity (Houlden et al. 1996; Bouzat et al.
1998b; Akst et al. 2002; Cunningham et al. 2002).
Organisms with different life histories and reproductive systems could experience different rates of
decline in genetic diversity. However, this decline
would inevitably lead to a decrease in fitness
(Gautschi et al. 2002; Beheregaray et al. 2003) and
an increase in extinction probability (Newman and
435
Pilson 1997; Frankham 1998; Saccheri et al. 1998).
Our study demonstrates that long-lived organisms
have a relatively slow rate of decline in genetic
diversity, but are not immune to the process. The
long lifespan of these species can be a doubleedged sword. On the one hand, it considerably
slows down the rate of drift; on the other, it also
makes the recovery process relatively slow. Careful
investigations are needed to identify populations
that appear to be genetically healthy but have an
accelerated drift rate that is masked by the long
lifespan. Computer simulations based on empirical
data could be a useful tool to help conservation
planning in this regard. Conservation will be much
more difficult when populations actually become
genetically impoverished, and is much more
effective and easy to implement when the populations are still genetically healthy.
Acknowledgements
We thank Jason Kolbe and all other Janzen lab
members for helping with tissue sample collection.
Tissue samples were collected under permits from
the Illinois DNR, the Nebraska Game and Parks
Commission, and the US Fish and Wildlife Service. The sequences of microsatellite primers were
kindly provided by Tim King (USGS). This work
was supported by grants from the Chelonian Research Foundation Linnaeus Fund and the Society
for Integrative and Comparative Biology Grantsin-Aid of Research program to C.-H. K., and NSF
grants DEB-9629529 and DEB-0089680 to F.J.J.
This manuscript is greatly improved with the
helpful comments from A. Caccone, J. Nason, and
two anonymous reviewers.
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