Conserv Genet (2011) 12:517–526
DOI 10.1007/s10592-010-0158-9
RESEARCH ARTICLE
Historic DNA reveals contemporary population structure results
from anthropogenic effects, not pre-fragmentation patterns
Lisa N. Tracy • Ian G. Jamieson
Received: 25 May 2010 / Accepted: 19 October 2010 / Published online: 5 November 2010
Ó Springer Science+Business Media B.V. 2010
Abstract Contemporary patterns of genetic structure
among fragmented populations can either result from historic patterns or arise from human-induced fragmentation.
Use of historic samples collected prior to fragmentation
allows for the origin of genetic structure to be established
and appropriate management steps to be determined. In this
study, we compare historic and contemporary levels of
genetic diversity and structure of an endangered passerine,
the New Zealand mohua or yellowhead (Mohoua ochrocephala), using nuclear microsatellites. We found that a
significant amount of allelic richness has been lost over the
last 100 years. Close to half of this was due to extinction of
birds from entire regions, but almost as much was due to
loss of genetic diversity within extant populations. We
found a pattern of isolation by distance among contemporary populations, which could have resulted from historic
structure due to limited gene flow along a latitudinal cline.
However, we found that minimal genetic structure existed
historically. The pattern of increased structure over time
was confirmed by factorial correspondence analysis. We
conclude that the genetic structure apparent today resulted
from anthropogenic effects of recent fragmentation and
isolation. We emphasize the importance of assessing
genetic structure of populations prior to their fragmentation, when determining the significance of contemporary
patterns. This study highlights the growing importance of
Electronic supplementary material The online version of this
article (doi:10.1007/s10592-010-0158-9) contains supplementary
material, which is available to authorized users.
L. N. Tracy I. G. Jamieson (&)
Department of Zoology, University of Otago, 340 Great King
Street, Dunedin 9054, New Zealand
e-mail: [email protected]
museum specimens as a tool in the conservation of threatened and endangered species.
Keywords Genetic structure Historic DNA Mohoua
ochrocephala Microsatellites Genetic diversity Wildlife conservation
Introduction
Spatial genetic patterns are increasingly studied and applied
to management decisions for threatened species, including
identification of conservation units and estimation of dispersal and migration (Schwartz et al. 2007; Elderkin et al.
2008; Koumoundouros et al. 2009). Understanding the origins of contemporary genetic population structure can also
contribute valuable information in the guidance of management programs (Leonard 2008; Shepherd and Lambert
2008; Boessenkool et al. 2009). However, spatial genetic
patterns across a landscape can arise from entirely different
processes and on different time scales (Reding et al. 2010).
Structure could be the result of historically reduced gene
flow due to natural barriers (such as mountain ranges or
natural habitat gaps) or latitudinal clines (Manel et al. 2003).
Importantly, genetic structure with an extensive history
increases the potential for adaptations to local environments
(Slatkin 1987). On the other hand, genetic structure might
result from more recent processes related to human-induced
decline and fragmentation. Therefore, when contemporary
genetic structure is evident in a threatened species, it is
important to determine whether the observed structure
results from historic processes, and is therefore more likely
to carry evolutionary significance (e.g. Lara-Ruiz et al.
2008), or is merely a consequence of the recent decline,
isolation and fragmentation (e.g. Martinez-Cruz et al. 2007).
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Furthermore, studies using contemporary DNA only will
often overlook this important distinction. Therefore, comparisons of both historic and contemporary patterns of
genetic structure are essential in defining management units
and guiding decisions as to whether augmentation of gene
flow is appropriate or necessary (Leonard 2008).
We illustrate the points made above by using historic
museum specimens and contemporary samples to assess
whether current population structure in a highly fragmented species is a result of recent isolation due to human
induced factors or if it reflects historic population structure
due to natural barriers to gene flow or latitudinal clines.
New Zealand’s mohua, or yellowhead (Mohoua ochrocephala), is an endemic forest passerine and is an ideal
species to examine the causes of contemporary spatial
genetic structure for several reasons: (i) mohua had a
continuous historical distribution in a linear landscape
across a latitudinal cline of the South Island of New Zealand (Gaze 1985), which could have given rise to spatial
genetic structure; (ii) human induced decline has resulted
in range reduction and fragmentation into isolated patches
across its historic range (Fig. 1), which could also cause
spatial genetic structure; (iii) museum specimens are
available from before the majority of declines to accurately
assess historic genetic structure. Finally mohua, like many
other threatened New Zealand species, depend on island
reintroductions in order to protect populations from introduced predators (O’Donnell et al. 2002; Jamieson et al.
2008). Understanding changes in genetic diversity among
the remaining but patchily distributed mainland populations allows managers to identify the best sources for
reintroductions to island sanctuaries.
To determine the cause of spatial genetic structure
among contemporary mohua populations, we quantify
nuclear microsatellite DNA variation from contemporary
blood samples and museum skins collected during the early
phase of mohua decline (1880s–1930s). We assess changes
in genetic diversity and genetic structure over time through
a number of methods including isolation by distance on
population and individual levels and factorial correspondence analysis. Our results demonstrate that observed
Fig. 1 Predicted range of
mohua (darkened areas)
1,000 years before present, in
1840 and in 2007 (courtesy of
C. O’Donnell, Dept. of
Conservation). Extant
populations (2007) are circled
for clarity
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Conserv Genet (2011) 12:517–526
contemporary genetic structure is a consequence of
anthropogenic effects and is not indicative of pre-fragmentation patterns.
Materials and methods
Species background
New Zealand biodiversity and landscapes have been drastically affected by the arrival and expansion of Polynesian
and European peoples 750 and 150 years ago, respectively
(Wilmshurst et al. 2008). Humans have significantly
changed the landscape through burning and clear-felling
large tracts of forest and introducing exotic mammalian
predators. Introduced predators, such as stoats (Mustela
erminea), ship rats (Rattus rattus) and brushtail possums
(Trichosurus vulpecula) pose a severe threat to many
native bird species, including mohua (King 1984). Mohua
are currently listed as ‘endangered’ by the IUCN Red List
of Threatened Species (IUCN 2010) and as ‘nationally
endangered’ by the New Zealand Threat Classification
System List (Hitchmough et al. 2007). Mohua were once
widespread across the South Island until the late nineteenth
century, prior to rapid disappearance from some areas
(Fig. 1). In recent years, their patchy distribution has
become further reduced (Gaze 1985; O’Donnell et al.
2002). In their remaining habitat, mohua are particularly
vulnerable to intense periodic predation during stoat
irruptions that follow beech masting events (O’Donnell
1996). Management includes exotic predator trapping and
reintroduction to island sanctuaries and trapped areas on
the mainland (O’Donnell et al. 2002). Mohua have been
translocated to eight offshore island sanctuaries, two
mainland islands and one mainland site between the years
of 1992 and 2008. For a full description of mohua translocation history see Tracy (2008). Removing immediate
threats, such as introduced predators, is clearly the main
concern for conservation of mohua; however, if populations stabilize, whether it is within their remaining range, in
captive-rearing programs, or on island sanctuaries,
Conserv Genet (2011) 12:517–526
ensuring long-term viability by maximizing genetic
diversity becomes an important management strategy (Jamieson et al. 2008; Pujol and Pannell 2008).
Sampling
A total of 60 historic mohua toe pad samples (Mundy et al.
1997) were acquired from 12 museum collections worldwide (Online Resource 1). DNA was successfully extracted
and amplified from 56 of the historic samples. Samples
were obtained only from individuals for which both the
collection date and location were known (Fig. 2a). We
used samples collected between 1872 and 1939 (median
1892), which includes the early phase of major mohua
decline, from across the South Island of New Zealand
(Gaze 1985). Additionally, we used two samples from
Stewart Island collected sometime before their extinction
there in the 1960s. Most samples were categorized into four
regions: Otago, Fiordland, Canterbury and Nelson. Samples outside these regions are included in analyses that use
individual by individual comparisons. Samples were
grouped into regions based on their proximity, historical
habitat gaps and divisions by mountain ranges (i.e.
Southern Alps separate Taramakau from Canterbury)
(Fig. 2a). While errors in museum records are always
possible, we have no reason to doubt the reliability of
historic geographic details given for any of our samples
specifically (Marty 2005).
A total of 157 contemporary samples were analyzed.
Mohua (145) were sampled in 2006 and 2007 from six key
sites within their remaining range, including the Landsborough Valley, Dart Valley, Murchison Mountains,
Rowallan Forest, Catlins Forest and Blue Mountains
(Fig. 2b). An additional 12 birds were sampled from sites
where only a handful of birds remain (Eglinton, Hurunui,
519
and Caples Valleys). Because these sample sizes were too
small for population comparisons, they were only used in
analyses that incorporate samples on an individual basis.
Mohua were captured in mist-nets and blood (10–50 ll)
was taken by venipuncture of the brachial vein and stored
in lysis buffer (Seutin et al. 1991). For nine samples, DNA
was extracted from other tissue, including: freshly plucked
contour feathers (6), toe pad tissue from recently deceased
birds (2) and tissue from an embryo of a failed nest (1). In
order to ensure that the same bird was not sampled twice,
birds were banded with a unique metal band, except in the
Blue Mountains where retrospective analysis using
microsatellite genotypes confirmed that each sample was
only represented once. Mohua are cooperative breeders
with a primary breeding pair and several ‘helpers’ which
may be related (Elliott 1990). A minimum of seven mistnet sites were sampled within each population to ensure
that multiple family groups were represented.
Unfortunately, mohua no longer occur in many of the
sites where they were sampled (i.e. collected) historically,
but our samples do represent a random distribution of
extant populations within each time period. Incomplete
overlap between historical and contemporary sampling
sites should not affect our general estimates of loss of
genetic diversity, but it could have implications for comparisons of genetic structure. If anything, we would expect
to see greater evidence for isolation by distance among
historic samples because historic samples were collected
over a much greater linear distance (Fig. 2a).
DNA extraction
Historic DNA extractions were performed on one half to
one whole toe pad (Mundy et al. 1997). Samples were first
washed and re-hydrated overnight in 1 ml 10 mM
Fig. 2 Locations and sample
sizes for historic and
contemporary mohua. a Historic
samples are represented with
triangles. Curved lines indicate
historic regions used in
population analyses.
b Contemporary mainland
samples used in population
analyses are represented with
closed circles and marked in
bold. Open circles indicate
contemporary mainland sites
used only in individual-byindividual comparisons
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Tris–HCl pH 8 (Austin and Melville 2006) and were subsequently finely cut into small pieces. DNA was extracted
either with the Invitrogen ChargeSwitch Forensic DNA
Purification Kit or the Qiagen DNEasy Kit following the
animal tissue spin column protocol (all buffers provided,
manufacturer protocols followed). With the Qiagen
DNeasy extractions, the DNA was eluted twice with 200 ll
Buffer AE. All analyses used DNA from the first elution.
Ten percent of Invitrogen extractions were repeated with
the Qiagen DNeasy kit to confirm the reliability of genotypes identified by this method.
Contamination can be a common occurrence when
working with older tissues, such as museum skins (Pääbo
et al. 2004; Wandeler et al. 2007). Extensive measures
were taken to ensure that our results were authentic. All
extractions and amplification set-up took place in a UV
hood in a designated historic DNA laboratory which was
located on a different floor from any laboratory in which
contemporary mohua samples or any avian post-PCR
products had ever been. Further, no materials or clothing
from the PCR lab were ever moved to the historic DNA
lab. One negative control was performed for each extraction and amplification with at least one for every batch of 9
extractions and at least one for every batch of 11 amplifications to monitor for contamination. Replicate extractions
were performed for 10% of samples, chosen randomly.
In addition to contamination, there is also concern that
allele drop-out and artefact alleles (false PCR-generated
bands) may lead to false genotypes when using historic
DNA (Sefc et al. 2003). For historic samples, we calculated
allele drop-out rate and artefact allele rate by replicating
55% of heterozygous genotypes (89 replicated heterozygous genotypes totalling 208 PCR reactions). We found an
allele drop-out rate of less than 1% (2/208). Although the
allele drop-out rate was extremely low compared to other
studies (14% in Miller and Waits 2003 and 12–19% in Sefc
et al. 2003), we minimized any risk of genotyping errors
due to allele drop-out by replicating each homozygous
genotype at least twice. Artefact alleles were not observed
in the subset of replicated heterozygous genotypes.
Contemporary DNA was extracted from blood samples
using a Chelex resin protocol with 40 lg proteinase K in
5% Chelex (Biorad; Walsh et al. 1991). DNA was
extracted from feather, toe pad, and embryo samples using
the Qiagen DNeasy Kit as outlined above. A negative
control was performed with each extraction to test for
contamination.
Microsatellite marker identification and amplification
We identified 11 polymorphic microsatellite markers
developed for 8 species (Online Resource 2; Primmer et al.
1995, 1996; Crooijmans et al. 1997; Double et al. 1997;
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Stenzler and Fitzpatrick 2002). Polymerase chain reaction
(PCR) was carried out on an Eppendorf Mastercycler ep.
For contemporary samples, PCR was carried out in a total
of 5 ll, containing 0.25 units Bioline MangoTaq DNA
Polymerase, 0.5 lM of each forward and reverse primer,
0.8 mM dNTPs, 19 Bioline MangoTaq Colored Reaction
Buffer, and 1.5 mM MgCl2. Historic samples were treated
similarly as above, except using 10 ll reactions containing
0.5 units Bioline MangoTaq DNA Polymerase. Amplification conditions were optimized for each locus (Online
Resource 2). The PCR profile typically consisted of
2.5 min at 92°C, followed by 30–40 cycles of 30 s at 92°C,
30 s at annealing temperature and 30 s at 72°C followed by
a final extension at 72°C for 4.5 min. PCR products were
resolved on polyacrylamide gels and visualized with SYBR
green I (Invitrogen). Individuals expressing all known
alleles were run, along with a 10 bp ladder on each gel as
size standards.
Scoring, Hardy–Weinberg equilibrium, linkage
disequilibrium
Before statistical analysis was carried out, MicroChecker
(Van Oosterhout et al. 2004) was used to check for scoring
error due to stuttering, large allele drop-out, and null
alleles. Deviations from Hardy–Weinberg proportions and
genotypic linkage disequilibrium (Fisher’s exact tests)
were calculated using GENEPOP v. 4.0 (Raymond and
Rousset 1995). Deviations from Hardy–Weinberg equilibrium were tested using the exact probability test (Guo and
Thompson 1992), with markov chain parameters set at
10,000 dememorizations, 1,000 batches and 10,000 iterations. Sequential Bonferroni corrections for multiple
comparisons were applied where appropriate.
Genetic diversity
Levels of genetic diversity were quantified using both
standardized allelic richness calculated with the rarefaction
method in Fstat version 2.9.3.2 (Goudet 1995) and mean
expected heterozygosity (HE) calculated in GENETIX v.
4.05 (Belkhir et al. 2004). Statistical comparisons between
groups for both allelic richness and heterozygosity were
matched by loci and tested using pairwise Wilcoxon
signed-rank tests in JMP 7.0.2 (SAS Institute Inc. 2007).
Loss of genetic diversity within the extant range was
assessed by comparing contemporary samples with historic
samples that were collected over the same general region
(i.e. all mainland samples south of Canterbury) and
excluded the contemporary Hurunui population due to its
extremely small population size of approximately 10
individuals (G. Elliott pers. comm.). The remaining loss of
allelic richness was attributed to extinction of entire
Conserv Genet (2011) 12:517–526
regions and was calculated from the difference between
overall loss and loss within the extant range.
Genetic structure
Changes in population structure over time were assessed
using two methods, each with its own advantages: isolation
by distance on population and individual levels and factorial correspondence analysis. In a preliminary analysis
we also performed a Bayesian cluster analysis implemented in the program STRUCTURE (Pritchard et al.
2000). This method produced similar results as the other
two analyses. However, because the data did not conform
precisely to an ideal structure model of discrete populations, but rather resembled a classic pattern of isolation by
distance (Pritchard et al. 2007), we do not present the
results from this third method.
First, we assessed patterns of isolation by distance.
Population differentiation among contemporary populations and among historic regions was measured using the h
estimator (Weir and Cockerham 1984) of Wright’s (1969)
FST in GENETIX v. 4.05 (Belkhir et al. 2004). Significance
levels (0.05) were assessed using 1,000 permutations. Only
populations with minimum sample size of seven were
included (Fig. 2). In both time periods, the relationship
between genetic divergence and geographic distance was
explored graphically by plotting both pairwise FST and
FST/(1 - FST) (Rousset 1997) against geographic distance
in kilometres. The relationship was tested using Mantel
tests in GENEPOP v. 4.0 (Raymond and Rousset 1995).
Analysis with distance versus either FST or FST/(1 - FST)
produced similar results, so only distance versus FST is
presented. To exclude the possibility of individuals caught
at the same mist-net being related and hence causing
inflated FST values, the above analyses were repeated using
a minimized contemporary data set, where only one randomly selected individual caught at each mist-net site was
included.
Analysis of isolation by distance was also carried out on
an individual basis which included samples that do not fall
within a thoroughly sampled population or region. This is
especially important for assessing historic patterns, as not
all samples belonged to a particular region. Isolation by
distance was calculated using pairwise individual-by-individual genetic (d2) (Peakall et al. 1995) and geographic
(kilometres) distance matrices and Mantel tests using the
program GenAlEx (Peakall and Smouse 2006). For both
population and individuals analyses, slopes between trendlines were compared using t tests with degrees of freedom
estimated from the matrix size (N) (Manly 2007) and
standard error values approximated from Mantel P values.
ANCOVA tests were also performed to test the difference
of slopes between time periods and showed similar results.
521
However, because the data points are not independent, we
present the more conservative t test with lower degrees of
freedom.
Secondly, population structure in each time period was
visualized using factorial correspondence analysis (FCA)
by population in GENETIX v. 4.05 (Belkhir et al. 2004).
FCA is used to investigate the genetic similarity of individuals based on allele frequencies and genotypes. FCA by
population compares genotypes of individuals within the
dataset and plots individuals on two composite axes that
optimize the differences between the analyzed individuals
using the average inertia of predefined groups.
Results
Amplification, scoring, Hardy–Weinberg, linkage
disequilibrium
All 157 contemporary samples amplified at all loci, except
for feather samples at two loci, corresponding to a 100%
extraction success and [99% amplification success
(range = 0.82–1.0). Of 60 historic samples, 56 were successfully extracted, indicating 93.3% extraction success.
There was 88.4% amplification success for historic samples
from which DNA was extracted (range = 0.36–1.0). For
historic DNA, we encountered less than 1% allele drop-out
rate and no incidents of artefact alleles, which were calculated from 208 replicate PCR reactions of 89 heterozygous genotypes. Replicate extractions and negative
controls detected no contamination during the extraction or
amplification processes. Analysis by MICROCHECKER
(Van Oosterhout et al. 2004) revealed no scoring error due
to stuttering or large allele drop-out. All populations and
loci were in Hardy–Weinberg equilibrium apart from one
population (Landsborough) at one locus (Hru7) which
showed homozygote excess. There was no evidence for
linkage disequilibrium.
Changes in genetic diversity
We estimate that mohua have experienced a significant loss
(22.6%) of allelic richness over the last 100 years
(Table 1). Nearly half of this estimated loss (9.6%) has
occurred within the contemporary range, whereas the
remaining proportion (13.0%) reflects loss of alleles due to
extinction of entire regions (Table 1). Differences in heterozygosity (HE) between historic and contemporary samples were not significant (Table 1). When considering
genetic diversity among contemporary populations, the
Dart Valley and Blue Mountains exhibited highest levels of
allelic richness, both significantly higher than the Murchison Mountains (Fig. 3). The Dart Valley further showed
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Table 1 Comparison of genetic diversity in contemporary populations relative to historic populations in all samples and excluding extinct
regions, in mohua
Populations
compared
All samples
Excluding
extinct regions
Time period
Historic
n
HO
HE
T
P
Alleles per
locus
Allelic richness
T
P
Loss of allelic
richness (%)
-0.407
0.346
3.73
3.64
-2.405
0.019
22.6
3.27
2.82
3.00
3.18
2.88
2.60
-1.554
0.076
9.6
56
0.310
0.365
Contemporary
157
0.324
0.351
Historic
Contemporary
27
155
0.290
0.327
0.349
0.352
0.064
0.527
Fig. 3 Allelic richness of contemporary populations. Horizontal
brackets indicate populations with significant differences in allelic
richness based on Wilcoxon paired sign-rank tests and P \ 0.05.
Vertical bars are standard error. Populations have been reduced to
varying degrees, with population size estimates from Department of
Conservation Surveys indicating Dart/Caples (5000), Catlins (2000),
Blue Mountains (500), Landsborough (300), Rowallan (100), Murchison Mountains (100), Eglinton (20) and Hurunui (10)
significantly higher allelic richness than the Landsborough.
The Rowallan Forest and Catlins Forest populations displayed intermediate levels of allelic richness (Fig. 3).
Expected heterozygosities of contemporary populations
ranged from 0.29 to 0.37, but were not significantly
different.
Changes in population structure
We employed two approaches to determine genetic population structure: isolation by distance analysis by population and by individual, and factorial correspondence
analysis (FCA). Each analysis confirms that there is a
strong pattern of genetic structure among contemporary
samples that did not exist historically.
The correlation between genetic distance (FST) and
geographic distance (kilometres) showed a highly significant relationship among contemporary populations, but no
corresponding relationship historically (Fig. 4; for a table
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Fig. 4 Isolation by distance patterns in contemporary and historic
samples. The difference between contemporary and historic slopes is
significant (P \ 0.01). Contemporary: R2 = 0.530, P = 0.010. Historic: R2 = 0.113, P = 0.125. A table of FST values that generated
this graph are available in Online Resource 3
of values that generated this graph, see Online Resource 3).
Even though the historic populations occurred over a
greater geographic distance (see the x axis of Fig. 4), the
slope of the trend in contemporary samples (0.0009,
SE = 0.00023) was nine times steeper than in the historic
samples (0.0001, SE = 0.00005) (df = 10, t = 3.20,
P = 0.010). When only one randomly selected individual
was selected from each mist-net site, the same pattern of
isolation by distance emerged, with an identical slope
(0.0009), negligible change in intercept (-0.061) and R2
value (0.543), and significant isolation by distance
(P \ 0.001). The slope was still significantly different from
the historic samples (df = 10, t = 11.2, P \ 0.001). This
allowed us to rule out the possibility that the higher FST
values among the contemporary populations were due to
catching related individuals in the same mist-net.
Analysis of isolation by distance on the individual level
confirmed the results of isolation by distance analysis using
FST values between populations. Contemporary samples
showed a significant pattern of isolation by distance
(P \ 0.001), whereas historic samples did not (P = 0.113).
The slope of the trend in contemporary samples (0.00162)
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due to extinction of birds from entire regions, but almost as
much was due to loss within extant populations (see
Table 1). Mohua have been restricted to isolated populations in 3% of their original range (Robertson et al. 2007)
and have become extinct in some regions (i.e. Nelson)
while other populations have remained relatively large
([1,000 individuals). It is likely that loss of genetic
diversity over time is related to both range reduction and
fragmentation and decrease in population size.
Change in genetic structure
Fig. 5 Factorial correspondence analysis by population illustrates
patterns of genetic structure among a historic regions and b contemporary populations
was 6.5 times steeper than in the historic samples (0.00025)
and this difference was significant (df = 213, t = 3.17,
P \ 0.001).
Genetic structure in each time period was visualized
using factorial correspondence analysis (FCA) by population. This illustration confirms the trends observed in isolation by distance analysis. Historically, some structure
between regions is observed (Fig. 5a); however, there is
evidence of greater structure over much shorter distances
among contemporary populations (Fig. 5b). For example,
the population furthest north (Landsborough) is distinct
from the two populations in the southeast (Catlins and Blue
Mountains) in that their distributions across the FCA plot
do not overlap, with samples from populations found in
between these two areas plotted intermediately (Fig. 5b).
Discussion
Loss of genetic diversity
Mohua have lost a significant amount of allelic richness
(22.6%) over the last 100 years. Close to half of this was
A clear pattern of genetic structure was detected among
contemporary mohua populations that was not explained
by historic population structure alone. This was supported
by evidence from isolation by distance analysis at the
population and individual levels and factorial correspondence analysis. The structure observed among contemporary populations could be described as a pattern of isolation
by distance, which describes increasing genetic differences
among populations separated by increasing geographical
distance and is usually explained by varying levels of gene
flow across a landscape (Rousset 1997). However, we
suggest a similar pattern may appear in a declining species
in which the populations on the periphery of its range have
experienced the greatest gene flow restriction for the longest period of time. Greater genetic differentiation in
peripheral populations compared with central strongholds
has been documented in many cases (Eckert et al. 2008;
Rogell et al. 2010). Patterns of isolation by distance were
not always observed (Rogell et al. 2010), but a more linear
landscape may result in the appearance of isolation by
distance. If peripheral populations are more vulnerable to
genetic drift, then one may expect little scatter in an isolation by distance analysis, as we see in this study.
Historic samples were collected over a wide time period
(1872–1939) even though the major decline in mohua
range occurred in the late nineteenth century (Gaze 1985).
Although an earlier and shorter time-frame for the historic
samples would have been ideal, this was not possible given
the limited museum collections. Importantly, we do not
expect the time-frame over which the historic samples were
collected to have affected the overall conclusions. If some
genetic diversity had been lost before or during the historic
sampling period, then the reported values represent a
minimum loss of genetic diversity. If changes in genetic
structure had occurred during but not after the historic
sampling period, then we would not have been able to
detect a temporal change in genetic structure, yet clear
differences in structure were apparent between the two
sampling periods.
Although contemporary and historic sample sites do not
cover the same areas in all cases we would have expected
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to see greater differences in Fst values among historic
samples simply because they were collected over a much
greater linear distance (see Fig. 4). In fact, we found less
evidence for isolation by distance in historic samples, and
therefore conclude that the stronger pattern of isolation by
distance in contemporary populations emerged between the
two sampling periods.
When genetic structure is seen among contemporary
populations, it is important for conservation purposes to
determine whether this pattern is a natural remnant of
historic population structure or whether it is the result of
more recent, human-induced changes. In the case of mohua, it is clear that the observed pattern of isolation by
distance is not a remnant of the past; rather, it is induced by
recent anthropogenic events. In support of this conclusion,
it is known that at least one population (Landsborough)
underwent a population bottleneck down to *14 individuals in 1992 (O’Donnell 1996). This has resulted in a shift
of allele frequencies which differentiates it from other
populations (Online Resource 3, 4). Two other populations
in the southeast (Blue Mountains and Catlins) have also
experienced similar shifts in allele frequencies but to a
lesser extent, although neither population has undergone
known bottlenecks as severe as seen in the Landsborough
Valley.
Most of the detected differences over time and between
populations reflect losses of rare alleles and changes in
allele frequencies, rather than losses of common alleles
(Online Resource 4). Most of the historic genetic diversity
of the current range still exists, but allele frequencies are
changing, especially in longer isolated areas. Although
changes in allele frequencies alone would not necessarily
translate into decreased adaptability, they are an early
warning sign of genetic endangerment (Gilpin and Soule
1986). If the populations are declining due to extrinsic
factors, then genetic factors are mostly irrelevant in terms
of short-term extinction risk (Jamieson 2007a, b; Jamieson et al. 2008). However, if management action can
stabilize threatened populations, then ensuring the longterm viability of these managed populations through
maximizing genetic diversity becomes important, otherwise genetic drift could lead to further loss of genetic
variability and consequent increase in extinction risk for
local populations. In this study we used neutral nuclear
microsatellite markers, which presumably indicate variation across the genome (see Li et al. 2002 for discussion
of this issue). In the future, a study of variation in
functional genes, such as in the major histocompatibility
complex (Mhc) which affects immune function, may be
useful to assess loss of genetic diversity at specific
functional genes (Westerdahl et al. 2000; Hansson and
Richardson 2005).
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Implications for conservation
Increases in population structure due to anthropogenic
events have been seen in other species of conservation
concern including the Spanish imperial eagle (Aquila
adalberti), the Canadian peregrine falcon (Falco peregrinus), the Northeastern beach tiger beetle (Cicindela dorsalis dorsalis) of the United States, and the common
hamster (Cricetus cricetus) in the Netherlands (MartinezCruz et al. 2007; Brown et al. 2007; Goldstein and DeSalle
2003; Smulders et al. 2003). In each case, it was concluded
that the current spatial structure has no evolutionary significance and that isolated fragments should be managed as
a whole. Our study of mohua reveals another case where
current population structure of strong isolation by distance
cannot be explained by pre-fragmentation patterns.
Our results also suggest that it would be appropriate to
mix mohua populations in cases where it may be beneficial.
For example, the Hurunui population of mohua in Canterbury, and the Eglinton population in Fiordland (maximum of 10 and 20 remaining individuals each) are
receiving intensive predator control to protect mohua and
other endangered species (G. Elliott, pers. comm.).
Assuming the predator control measures are successful, it
may be beneficial to transfer birds from another area to
augment the small Hurunui populations to prevent
inbreeding. When mixing of populations is critical to the
survival of the species, populations that are most similar
genetically and in terms of local adaptation are preferable
(Edmands 2007). Our results suggest that mixing mohua
populations is suitable, at least from a genetic perspective,
because historically there were minimal genetic
differences.
On the other hand, mixing of mohua populations in
order to restore gene flow may not be immediately necessary for two reasons. First, the differences between populations are mostly due to varying allele frequencies rather
than unique alleles. Second, intentional mixing should only
be used for populations that are severely genetically
depauperate or are clearly suffering from inbreeding
depression (Edmands 2007). Inbreeding depression is difficult to detect in wild populations, but there has been no
indication that even the most diverged populations, such as
the Landsborough and Catlins, are suffering from
inbreeding induced population declines (Department of
Conservation, unpublished surveys). This recommendation
does not preclude the need for mixing populations in the
future, especially if they remain isolated and experience a
prolonged and severe bottleneck.
Additionally, we recommend that when mohua are
transferred to predator-free island sanctuaries, it is preferable to source birds from populations that have maintained
Conserv Genet (2011) 12:517–526
higher levels of genetic diversity, such as the Dart Valley
or the Blue Mountains, although sourcing a large number
of founders should be the first priority. If attaining a sufficient number of founders is difficult due to logistical
constraints, then adding or sourcing birds from a slightly
less genetically diverse source would be suitable.
Overall, we conclude that evidence from genetic analysis of historical and contemporary populations of mohua
does not necessarily justify augmentation to restore gene
flow, but can be useful for deciding whether to mix populations and for identifying suitable source populations for
reintroductions. We give an example where historic patterns of genetic structure give insight into the interpretation
of contemporary genetic structure. Genetic structure among
contemporary populations alone cannot confirm the existence of separate units prior to fragmentation and isolation.
This study highlights the importance of museum specimens
in threatened species conservation (Wandeler et al. 2007;
Leonard 2008).
Acknowledgments We are grateful to the New Zealand Department
of Conservation, including H. Edmonds, R. Cole, C. O’Donnell, G.
Elliott, J. Kemp and especially to G. Loh and B. Lawrence for
logistical support and collection of samples. We are also grateful to
M. Efford, D. Dawson, R. Laws, R. Paterson, N. Babbage, B. Rhodes,
B. Masuda, M. Somerville, G. Pickerell and D. Hegg for assistance in
the field. Laboratory work was greatly facilitated by T. King and B.
Star. We are indebted to S. Boessenkool for her advice and support in
the laboratory, discussion of ideas and comments which greatly
improved the manuscript. We thank all of the museums listed in
Online Resource 1 for their willingness to contribute tissue samples.
Funding for this research was provided by the Department of Conservation and Landcare Research (contract no. C09X0503), the University of Otago, Forest and Bird JS Watson Trust and N. Babbage of
Mohua Inc. LNT was supported by the University of Otago Postgraduate Scholarship and Publishing Bursary. Permits to conduct this
research included Department of Conservation research permits (SO21285-FAU) and a University of Otago Animal Ethics permit (87/05).
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