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Biological Invasions ISSN 1387-3547 Biol InvasionsDOI 10.1007/s10530-014-0753-7
An evaluation of the genetic structure andpost-introduction dispersal of a non-nativeinvasive fish to the North Island of NewZealand
Kevin M. Purcell & Craig A. Stockwell
1 23
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ORIGINAL PAPER
An evaluation of the genetic structure and post-introductiondispersal of a non-native invasive fish to the North Islandof New Zealand
Kevin M. Purcell • Craig A. Stockwell
Received: 3 December 2013 / Accepted: 1 July 2014
� Springer International Publishing Switzerland 2014
Abstract The efficacy of invasive species manage-
ment is dependent on a thorough understanding of the
size, origin, and genetic structure of invasive popula-
tions. We evaluated the genetic diversity and structure
of the western mosquitofish, Gambusia affinis, across
the North Island of New Zealand in an effort to better
understand the genetic structure and post-introduction
dispersal mechanisms of this highly invasive estuarine
species. We found clear evidence of founder effects
and significant genetic structure for populations
derived from populations initially established in New
Zealand in the 1930s. Our findings indicate that G.
affinis populations have succeeded through a combi-
nation of localized dispersal and human-assisted
colonization. Additionally, we identify a series of
populations in one region that are apparently genet-
ically isolated from the other regions. This area could
thus represent a ‘‘significant eradication unit’’ where
re-colonization is unlikely. Our results highlight the
utility and value of molecular tools as an efficient
method to facilitate a richer understanding of the
nature and condition of invasive species while iden-
tifying definitive management objectives.
Keywords Invasive fish �Gambusia affinis �Genetic
structure � Post-introduction dispersal � Human-
assisted colonization
Introduction
Understanding how invasive species are genetically
structured is beneficial both for future management
decisions (Ruiz-Navarro et al. 2013) and for the
evolutionary study of invasive biology (Blanchet
2012). Recently, several reviews have expounded on
the importance of understanding not only routes of
invasions but the mechanisms of post-introduction
dispersal (Blanchet 2012; Dlugosch and Parker 2008;
Estoup and Guillemaud 2010; Sax et al. 2007). In
many cases, dispersal occurs both naturally and via
human assistance (Everman and Klawinski 2013;
LaRue et al. 2011). For instance, Everman and
Klawinski (2013) coined the term ‘‘jump dispersal’’
to reflect cases where invasive species are relocated to
a new site by humans and then move naturally until
they encounter a barrier, at which time an assisted
jump is necessary for range expansion. Understanding
how invasive species spread may allow managers to
more effectively target their efforts. For instance,
genetic structure reflects limited gene flow which can
K. M. Purcell (&) � C. A. Stockwell
Environmental and Conservation Sciences Program,
Department of Biological Sciences, North Dakota State
University, Fargo, ND 58108-6050, USA
e-mail: [email protected]
Present Address:
K. M. Purcell
National Marine Fisheries Service, Beaufort Laboratory,
101 Pivers Island Road, Beaufort, NC 28516-9722, USA
123
Biol Invasions
DOI 10.1007/s10530-014-0753-7
Author's personal copy
be used to infer limited immigration, and therefore
could be used to identify areas suitable for eradication.
Such ‘‘significant eradication units’’ have been used to
identify areas that are less likely to be re-colonized
following eradication of invasive species (Ayres et al.
2010; Fraser et al. 2013; Rollins et al. 2009; Sanz et al.
2013).
Evaluating genetic structure is particularly useful
for species that have been widely dispersed. Such is
the case for the western mosquitofish (Gambusia
affinis), and its congener Gambusia holbrooki, both
species were widely introduced in the early twentieth
century largely due to their putative role as a vector
control agent (Pyke 2005). Further, upon establish-
ment, G. affinis and G. holbrooki have strong dispersal
tendencies (Alemadi and Jenkins 2008; Rehage and
Sih 2004). While Gambusia spp. are larvivorous
(Krumholz 1948; Pyke 2008), their role as mosquito
predators has been argued to be questionable (Reddy
and Pandian 1974) and may be outweighed by the
negative impacts of their introductions on native fauna
(Pyke 2008; Vitule et al. 2009; Stockwell and
Henkanaththegedara 2011).
Because, they are one of the most widely dispersed
invasive species, mosquitofish have received recent
attention from molecular ecologists. Notably, the
genetic diversity and structure of European popula-
tions of G. holbrooki has been extensively evaluated,
and more recently populations in Australia have also
been evaluated. Collectively, these studies suggested
that natural and human-assisted dispersal have facil-
itated gene flow among recently established popula-
tions (Ayres et al. 2010, 2012; Dıez-del-Molino et al.
2013; Vidal et al. 2010, 2012). Dıez-del-Molino et al.
(2013) suggested that high gene flow among popula-
tions has limited the erosion of genetic diversity of
mosquitofish populations in Spain, and in turn, they
have argued that high genetic diversity has facilitated
the establishment and spread of invasive mosquitofish
populations.
These previous studies suggest that human-assisted
gene flow plays a critical role in the evolutionary
success of invasive species, but the generality of these
findings should be evaluated, ideally with the same
species, or a species with similar life history attributes.
In fact, a closely related congener, the western
mosquitofish (G. affinis) was also widely introduced
to other regions of the world, most notably to the South
Pacific. G. affinis was initially translocated outside of
its native range in southwestern Texas to the Hawaiian
Islands in the early 1900s (Krumholz 1948; Seale
1917; Van Dine 1907). Once established, the Hawai-
ian Islands served as a ‘‘beachhead’’ with documented
translocations from Hawaii to the Philippines, Guam,
and New Zealand in the early 1930s, with additional
introductions throughout the South Pacific during
World War II (Krumholz 1948; McDowall 1990).
Most of these introductions were conducted as
attempts to biologically control mosquito larvae to
prevent the spread of malaria (Krumholz 1948).
Mosquitofish were originally introduced to New
Zealand from the Hawaiian Islands in 1930, when an
unknown number of fish were established in a pond
located on the Auckland Botanical Gardens (McDo-
wall 1990). In 1933, the first ‘‘wild’’ introduction of G.
affinis individuals in New Zealand was to Lake Ngatu
(McDowall 1990). Following this initial introduction
at Lake Ngatu, numerous un-documented introduc-
tions have occurred throughout the North Island (Ling
2004; McDowall 1990). In a previous study, Purcell
et al. (2012) used molecular markers to verify the
historical origins for the first two populations of G.
affinis established in New Zealand. This study focuses
on describing the genetic structure of G. affinis
populations across the North Island, evaluating the
mechanisms of post-introduction dispersal, and eval-
uating the hypothesis that post-establishment gene
flow has maintained high levels of genetic diversity.
Methods
To examine the genetic structure and diversity of
invasive G. affinis populations on the North Island of
New Zealand we sampled individuals from 15 loca-
tions (Fig. 1) distributed throughout the northern
peninsula and along the eastern coast of the North
Island. Collection sites were chosen from coastal or
estuarine environments ranging from the northern tip
of the northern peninsula, along the eastern coast, to
Hawke’s Bay of the North Island. We sampled at total
of 15 separate geographical locations, many of which
represent independent hydrological units. Locations
are organized into four regions that coincide with four
of the nine regions of local government on the North
Island: Northland Region, Auckland Region, Bay of
Plenty Region, Hawke’s Bay Region (Table 1).
Regional assignments were based on geographical
K. M. Purcell, C. A. Stockwell
123
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Fig. 1 Locations of
Gambusia affinis collection
sites across the North Island,
NZ
Table 1 Summary table of
the genetic characteristics
for all sampled locations,
symbols indicate
(N) number of sampled
individuals, (A) number of
alleles, (AR) effective
number of alleles, (HE) the
expected heterozygosity,
(HO) is the observed
heterozygosity and (PA) is
the number of private
alleles in the sample
Sample ID Location N A AR HE HO FIS PA
Lat Lon
Northland
Lake Waiparera LW -34.95 173.19 30 5.33 4.63 0.64 0.66 0.33
Lake Ngatu LN -35.03 173.20 30 7.55 5.65 0.68 0.67 0.44
Auckland
Stillwater SW -36.38 174.43 30 4.77 3.72 0.55 0.60 0.22
Muriwai Beach MB -36.82 174.42 30 6.11 4.58 0.63 0.64 0.11
Western Springs WS -36.87 173.72 30 4.66 3.48 0.46 0.42 0.33
Auckland Domain AD -36.51 174.77 30 4.44 3.71 0.52 0.53 0.00
Port Waikato PW -37.39 174.73 30 7.44 5.57 0.68 0.73 0.33
Bay of Plenty
Maketu Estuary ME -37.76 176.46 24 5.88 4.82 0.64 0.64 0.44
Matata MT -37.89 176.76 30 6.44 4.90 0.65 0.64 0.33
Whakatane WT -37.91 176.88 30 5.66 4.82 0.68 0.72 0.22
Kukumoa Creek KC -38.00 177.25 30 5.66 4.48 0.59 0.63 0.22
Hawke’s Bay
Watchman Road WR -39.48 176.88 30 4.44 3.58 0.53 0.50 0.00
Napier NA -39.48 176.89 28 4.77 4.05 0.59 0.61 0.11
Pakowahai PS -39.58 176.86 27 4.55 3.76 0.59 0.64 0.00
Hastings HA -39.60 176.87 25 5.11 4.38 0.66 0.65 0.11
An evaluation of the genetic structure and post-introduction dispersal
123
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proximity rather than hydrological association due to
the hypothesized role of human-assisted dispersal in
the invasion of G. affinis to the North Island. For
example, the two Northland Region sampling sites,
Lake Waiperara (LW) and Lake Ngatu (LN), are
proximate to each other (*10 km), however they do
not occupy the same watershed.
The Auckland Region consists of five sampling
locations: Stillwater (SW), Muriwai Beach (MB),
Western Springs (WS), Auckland Domain (AD), and
Port Waikato (PW). Of these five locations two (AD and
WS) are isolated small ponds with no known hydrolog-
ical connections. In fact, the site at AD is a man-made
water body and was included primarily due to its
historical importance as the initial founding location for
the invasion of Gambusia to New Zealand (Purcell et al.
2012). The other three sites are geographically dispersed
with one site, SW, located on the eastern coast of the
northern spit and two sites (MB, PW) located on the
western coast and separated by roughly 67 km of coast
and the broad opening of Manukau Harbor.
The Bay of Plenty Region is located on the
northeastern side of the North Island, and is a hydro-
logically complex region with eight major rivers
feeding into the bay including the Wairoa, Kaituna,
Tarawera, Rangitaiki, Whakatane, Waioeka, Motu
and the Raukokore rivers. We sampled four locations
from around the Bay of Plenty, each separated by
*20 km and each at the terminal end of a separate
river system. The Matata (MT) and Maketu estuary
(ME) sampling locations were both estuarine envi-
ronments adjacent to the Terawera and Kaituna
Rivers. While Kukumoa (KC) and Whakatane (WT)
were sampling locations at the terminal ends of the
Waioeka and Whakatane Rivers, respectively.
The fourth region, Hawke’s Bay, consists of four
locations Watchman Road (WR), Napier (NA), Pak-
owahai (PS), and Hastings (HA) which were more
proximate than samples in other regions and were
uniquely nested within the same river/watershed. The
Western Road and Napier sites were both associated
with the Main outflow channel which feeds directly
into Hawke’s Bay north of the city of Napier, NZ. The
PS and HA locations were both associated with a
different river the Tutaekuri and Ngaruroro Rivers,
respectively, however both rivers share a terminal
estuary which feeds directly into Hawke’s Bay.
Specimens from each location were collected from
shore, euthanized with a lethal dose of clove oil and
immediately preserved in 75 % ethanol. Genomic
material was extracted from preserved tissue samples
employing the Puregene tissue extraction protocol
(Gentra Systems). All tissue samples were assayed for
nine microsatellite loci following the conditions
detailed in Purcell et al. (2011). Amplicons were
analyzed on a 3,730 DNA Analyzer (Applied Biosys-
tems) and genotypes were scored using GeneMarker
1.85 (SoftGenetics). All genotypes were visually
assessed for accuracy and the entire data set was
examined for the presence of null alleles using
MICROCHECKER 2.2.3 (Oosterhout et al. 2004).
Our data set was examined for deviations in Hardy–
Weinberg Equilibrium (HWE) with 1,000 iterations of
exact probability tests implemented in GENEPOP 4.2
web interface (Raymond and Rousset 1995; Rousset
2008) using adjusted significance thresholds based on
a sequential Bonferroni correction (Rice 1989). The
presence of linkage disequilibrium (LD) was evaluated
using the MCMC methods also implemented in
GENEPOP. To evaluate the genetic diversity within
our sampling locations the observed (HO) and
expected (HE) heterozygosity and the mean number
of private alleles (PA) was calculated using the
GENALEX 6.4 (Peakall and Smouse 2006). We
calculated the allelic richness (AR) within sampling
locations using FSTAT 2.9.3 (Goudet 1995) because
its algorithm has been shown to account for variation
in the number of alleles resulting from variations in
sample size (Leberg 2002).
Patterns of genetic relatedness between sampling
locations were examined using pairwise FST (Weir and
Cockerham 1984) using FSTAT 2.9.3, and due to the
relatively recent introduction (c. 1930s) of G. affinis to
the North Island we also conducted tests of genic and
genotypic divergence using Markov chain methods in
GENEPOP 4.2. We evaluated an isolation by distance
(IBD) hypothesis using a Mantel test of Spearman
rank correlation coefficients which examined the
correlation between genetic and geographic distance
among sampling locations, also implemented in
GENEPOP 4.2. Contemporary migration rates
between sampled populations were calculated using
a Bayesian inference approach implemented in the
program BayesAss 3.0 (Wilson and Rannala 2003).
We conducted 20 replicate MCMC simulations each
with a unique seed value, which consisted of a total of
5 9 106 iterations. The MCMC chains were sampled
every 1 9 103 iterations after an initial burn-in of 106
K. M. Purcell, C. A. Stockwell
123
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iterations. Mixing parameters were adjusted to ensure
parameter acceptance rates within suggested ranges
(DA and DF were both set to 0.30) as suggested in the
program documentation. We calculated a Bayesian
deviance value (Faubet et al. 2007) for each of the 20
replicate MCMC chains, using a previously published
R-script (Meirmans 2014; Supp.) to identify which
analysis chain had the highest likelihood, which was
used for further analysis.
Finally, we used two Bayesian assignment tests to
sort sampled genomes into putative groups. We used
STRUCTURE 2.3.3 (Pritchard et al. 2000) due to its
robust history and its ability to identify mislabeled
individual genomes. Using this approach we ran
simulations for possible clusters (K) ranging from 1
to 15. For each possible K value we ran 10 independent
simulations consisting of 400,000 MCMC replicates
with 100,000 burn-in replicates (Gilbert et al. 2012).
Results of the STRUCTURE analysis were post-
processed using the program STRUCTURE HAR-
VESTER 0.6.93 (Earl and vonHoldt 2012) a web-
based platform for visualization and implementation
of the Evanno method (Evanno et al. 2005) to properly
interpret the number of population clusters. To further
evaluate structure we used BAPS 5.2 (Corander et al.
2008), a Bayesian program that uses a geographic
prior, to investigate the genetic structure of sampled
locations due to its assignment accuracy at low FST
values (Latch et al. 2006), and its ability to conduct
spatially explicit Bayesian analysis. In BAPS, we
examined our dataset for clusters, consisting of [3
individuals (Corander et al. 2003), using the admixture
model with the geographical location of each sampling
point serving as a spatial prior in the model. We ran
simulations for Kmax ranging from 2 to 15, with 20
replications for each possible Kmax value.
Results
Our data set of 434 individuals assayed for 9
microsatellite loci showed no indications of the
presence of null alleles, and no significant patterns
of deviation from HWE and LD. Site specific heter-
ozygosity values were consistent among populations
within three of the regions with the notable exception
of the Auckland Region where heterozygosity varied
widely (Western Springs HE = 0.46; HO = 0.42; Port
Waikato HE = 0.68; HO = 0.73; Table 1).
The overall FST estimate for all sampled locations
was 0.180. We conducted 105 pairwise comparisons
with FST estimates ranging from 0.017 to 0.330
(Table 2); 101 of the 105 comparisons were found to
be significant after adjusting alpha for multiple
comparisons. Of the 4 non-significant comparisons
one (WT 9 ME) was within a region and two
(LN 9 HA; LN 9 WT) included the initial wild
introduction site at Lake Ngatu. We observed pairwise
FST values within Hawke’s Bay which were qualita-
tively lower (FST = 0.017–0.094) than comparisons
among sites within the other three sampling regions.
Aside from this there was no consistent pattern of
differentiation within or among sampling sites or
regional water bodies. A similar pattern of differen-
tiation was found in our examination of genotypic and
genic allele frequency differentiation, with a low
number of significant locus differences among the
Hawke’s Bay sites but a consistently higher number
between and among the sampling locations in the three
other regions (Table 2).
We examined the correlation between genetic
divergence and geographic distance between sampling
sites, while we found no significant correlation
(P = 0.070) to support an IBD hypothesis we did
observe a subtle positive correlation between genetic
and geographic metrics (Fig. 2). Bayesian clustering
analysis using the program STRUCTURE gave the
highest support for the presence of 3 distinct clusters
within our sampling locations (K = 3; Fig. 3 (inset)).
The geographic distribution of cluster assignments by
STRUCTURE further supported the absence of geo-
graphic distance as a driver in the genetic structuring
of sampled locations (Fig. 3). One cluster consisted of
the four populations sampled in the Hawke’s Bay
Region (HA, NA, PS, WR). A second cluster included
populations from three regions including the two
populations from the Northland Region (LW, LN),
two populations from the Auckland Domain Region
(WS, AD) and two populations from the Bay of Plenty
Region (MT, KC). A third cluster included three
populations from Auckland Domain Region (MB,
PW, SW) and two populations from the Bay of Plenty
Region (ME, WT).
Further, clustering analysis using the program BAPS
indicated a significantly higher degree of genetic
structure in comparison to the STRUCTURE results.
BAPS provided the highest support for a Kmax = 13,
indicating that 11 sampled sites represented unique
An evaluation of the genetic structure and post-introduction dispersal
123
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Ta
ble
2O
ver
all
FST
=0
.18
0
LN
AD
MT
KC
LW
WS
PW
MB
SW
PS
WR
NA
HA
ME
WT
LN
–0
.08
93
0.0
78
0.0
50
20
.09
17
0.1
79
40
.12
05
0.1
43
90
.24
22
0.1
31
30
.15
01
0.1
04
70
.13
10
.17
49
0.1
40
8
AD
9/8
–0
.19
15
0.1
50
.20
81
0.1
34
10
.23
06
0.2
43
40
.33
01
0.1
96
0.2
39
20
.20
10
.22
89
0.2
82
0.2
47
8
MT
7/7
9/9
–0
.13
97
0.1
49
40
.24
23
0.1
59
20
.18
90
.25
46
0.1
30
80
.14
96
0.1
06
60
.18
02
0.2
04
20
.19
23
KC
8/8
8/8
8/8
–0
.08
53
0.2
30
90
.16
48
0.1
79
50
.24
06
0.1
97
90
.22
34
0.1
69
30
.19
98
0.1
99
60
.19
72
LW
8/8
9/9
9/9
9/9
–0
.24
85
0.1
28
90
.16
91
0.2
44
50
.18
26
0.2
10
20
.16
86
0.1
68
70
.18
92
0.1
55
6
WS
9/9
7/7
9/9
8/8
8/8
–0
.22
44
0.2
37
30
.30
66
0.2
03
80
.24
86
0.2
11
50
.24
18
0.2
99
40
.27
01
PW
7/9
9/9
9/8
8/9
9/9
9/9
–0
.12
07
0.1
48
30
.15
51
0.1
75
60
.12
24
0.0
96
70
.12
46
0.0
79
7
MB
9/8
9/9
9/9
9/9
9/9
9/9
9/9
–0
.12
19
0.1
95
0.2
61
60
.20
05
0.1
74
10
.13
79
0.1
23
SW
9/9
9/9
9/8
9/9
9/9
9/9
8/8
8/8
–0
.26
23
0.3
10
90
.25
89
0.2
10
70
.11
75
0.1
62
4
PS
9/9
7/8
9/9
9/9
9/9
9/9
9/9
9/9
9/9
–0
.08
28
0.0
17
80
.09
48
0.2
14
30
.15
79
WR
9/9
9/9
9/9
9/8
8/9
8/9
8/9
9/9
8/9
6/6
–0
.04
38
0.1
04
30
.24
07
0.2
05
3
NA
9/9
8/8
9/9
8/8
9/9
9/9
9/9
9/9
9/9
3/3
4/4
–0
.07
84
0.2
03
40
.15
72
HA
9/9
8/8
9/9
9/9
8/9
9/9
7/7
9/9
8/8
7/7
8/8
7/7
–0
.12
84
0.0
88
9
ME
8/8
9/9
8/8
8/9
9/9
9/9
7/7
8/8
8/7
9/9
9/9
8/9
9/9
–0
.14
76
WT
9/9
9/9
9/9
9/9
8/8
9/9
7/6
9/9
9/9
9/9
9/9
9/9
6/8
8/8
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K. M. Purcell, C. A. Stockwell
123
Author's personal copy
genetic units. The remaining four sampled locations
clustered into two additional populations (Auckland
Domain, Lake Ngatu) and (Watchman Road, Napier), of
which the former two sites represent the first site of
introduction at Auckland Domain in 1930, followed by
the first release into the ‘‘wild’’ at Lake Ngatu (McDo-
wall 1990; Purcell et al. 2012) and the latter two sites are
both in the Hawke’s Bay Region.
Fig. 2 Relationship
between genetic distance
(F/1 - F) and the natural
logarithm of geographic
distance between all sample
locations
Fig. 3 Spatial orientation of sampled locations indicating a
mixed distribution relative to initial founding locations (AD,
LN). Bayesian clustering assignments for sampled individuals
based on the STRUCTURE algorithm (inset). Colours represent
cluster assignments
An evaluation of the genetic structure and post-introduction dispersal
123
Author's personal copy
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MT
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LW
WS
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MB
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WR
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WT
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97
0.0
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74
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78
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82
AD
0.0
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78
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07
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MT
0.0
08
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.01
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.00
79
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K. M. Purcell, C. A. Stockwell
123
Author's personal copy
The average proportion of immigrants in our 15
sampled populations was 0.146 with a range from
0.106 to 0.325 (Table 3). The average contemporary
pair-wise migration rates between sampled popula-
tions was 0.010 with a ranged from 0.006 to
0.204 (Table 3, diagonal). Of the 210 pairwise
migration comparisons only 10 were[0.01 (Table 3,
emboldened values), with five of the these 10
comparisons coming from populations sampled within
the Hawke’s Bay region, which was identified by
STRUCTURE as a single cluster [see above; Fig. 3
(inset)].
Discussion
Diversity within sampled sites on the North Island
showed little qualitative differences or nested patterns
of geographic association. The single consistent
pattern within the sampled variation was that the Lake
Ngatu site seemed to represent a peak of genetic
diversity (HO, AR, PA) within the 15 sampled NZ sites.
This finding supports our hypothesis that most the
sampled North Island populations descended from the
initial ‘‘wild’’ introduction site at Lake Ngatu (Purcell
et al. 2012). The levels of genetic diversity (HO, HE,
AR) within the NZ sites all showed signs of founder
effects when compared to previously published esti-
mates, employing the same genetic markers, for
populations within the native range of G. affinis
(Purcell et al. 2012). For instance, allelic richness and
heterozygosity for the ancestral Texas populations
varied from 11.44 to 12.33 and 0.75 to 0.76, respec-
tively, and were both considerably more diverse than
even the most diverse New Zealand population
(AR = 3.48 - 5.67; HE = 0.46 - 0.68).
These founder effects are not unique, a number of
studies on other aquatic invasive species (Ayres et al.
2012; Grapputo et al. 2006; Peacock et al. 2009;
Stockwell et al. 1996) have reported similar findings.
Despite these founder effects, G. affinis has managed
to spread extensively in the 80 years since its initial
introduction into the wild. These findings further
support the work of Rollins et al. (2013) that indicates
that reduced neutral diversity may not limit the
evolutionary potential and spread of invasive species.
In a number of cases, invasive species with limited
neutral diversity have undergone contemporary evo-
lution (Rollins et al. 2009). For instance, populations
of G. affinis introduced to Nevada had limited neutral
diversity (Stockwell et al. 1996), yet underwent
contemporary evolutionary divergence (Stockwell
and Weeks 1999). Similarly, non-native populations
of guppies (Poecilia reticulate) maintained high levels
of additive genetic variance for a variety of morpho-
logical traits (Brooks and Endler 2001) despite
reduced neutral genetic diversity (Lindholm et al.
2005).
The pairwise FST evaluations showed that many of
the sampled site comparisons where significantly
differentiated suggesting limited dispersal and some
degree of genetic structure. Of the 4 populations that
were not significantly differentiated two comparisons
involved the original founding site at Lake Ngatu.
These findings are particularly significant given recent
findings (Rehage and Sih 2004; Rehage et al. 2005)
indicating that Gambusia, is a genus with a natural
predilection for dispersal (Brown 1985; Congdon
1994; Cote et al. 2010). While significant levels of
genetic structure have been reported for invasive G.
holbrooki (Sanz et al. 2013), their study was con-
ducted at a much larger scale, where inter-site
dispersal would be limited. The sampling scheme
employed in the current study captures sites within
similar geographical regions and in some cases the
same watershed covering distances ranging from 3 to
608 km. In a study of invasive G. holbrooki in
Australia, Ayres et al. (2010) reported similar patterns
of population differentiation which included spatial
scales similar to those in our study; they suggested that
the pattern was a product of human-assisted coloni-
zation. Similar findings were reported in a study of the
round goby, an invasive to Lake Michigan, which
based on pairwise FST values, indicated limited
dispersal and high levels of human assisted transport
(LaRue et al. 2011). Our overall findings from FST
seem indicative of limited natural dispersal, especially
between our geographical regions. These results
concur with contemporary dispersal estimates deter-
mined using Bayesian inference which indicated low
levels of pair-wise migration. These migration rates
were considerably lower than those reported for
introduced G. holbrooki populations examined
recently in northeastern Spain (Dıez-del-Molino
et al. 2013). However, unlike populations examined
in Spain (Dıez-del-Molino et al. 2013), our popula-
tions seem to be undergoing limited dispersal and
show significant indications of regional genetic
An evaluation of the genetic structure and post-introduction dispersal
123
Author's personal copy
structure. Contrary to recent findings that extensive
post-establishment gene flow plays and important role
in maintaining high genetic diversity (Dıez-del-Moli-
no et al. 2013) our analysis suggests that when gene
flow is present, predominantly on a local scale, it does
little to ameliorate the initial founder effects.
Our study also suggests multiple introductions of
fish among regions. For example, three populations in
the Auckland Region (Stillwater, Muriwai Beach and
Port Waikato) group with two populations from the
Bay of Plenty Region (Maketu Estuary and Whaka-
tane). Further, another grouping includes 6 popula-
tions from three of the four regions (Fig. 3; Lake
Ngatu, Lake Waiparera, Auckland Domain, Western
Springs, Matata and Kukumoa Creek). This pattern of
association is suggestive of a shared introduction
history and multiple introductions across regions.
The findings from our STRUCTURE analysis
identified three distinct population clusters. Only one
genetic cluster was geographically discrete, Hawke’s
Bay. The other two clusters suggested a mixture of
sampling sites with spatial locations ranging from the
Bay of Plenty to the northern most sample locations on
the North Island. This long-range dispersal most likely
reflects repeated human-assisted colonization of new
sites across the various regions that we sampled. Such
dispersal may be intentional; however, new popula-
tions may have been co-established with the inten-
tional introduction of aquatic plants (Peacock et al.
2009). Again given what we know about the river
basins structure of the North Island, and the putative
importance that was placed on Gambusia as a vector
control agent in the twentieth century, it seems likely
that the spatially mixed distribution of populations is
most parsimoniously explained based on human
assisted post-colonization coupled with limited local-
ized dispersal within a larger watershed hierarchy.
Our findings also suggest that Hawke’s Bay may
represent a ‘‘significant control unit’’, where control
efforts could be targeted. However, it is important to
acknowledge that eradication of mosquitofish is
exceptionally difficult due in many regards to the
same biological and reproductive traits that make this
species such an efficient invasive (Ruiz-Navarro et al.
2013). For instance, one surviving pregnant female
could re-establish a population and thus undermine
expensive eradication efforts. In addition, the ques-
tionable impact of G. affinis within the various
environments of New Zealand (Ling 2004) should be
considered prior to spending valuable resources in
removal attempts.
Finally, the results of this study support the current
recommendations (Palsbøll et al. 2007; Schwartz et al.
2007) for extended use of molecular genetic tools for
the mitigation and management of invasive species.
Molecular tools provide particularly powerful frame-
work for evaluating dispersal of invasive species (Le
Roux and Wieczorek 2009) and our work has shown
that predicting the invasion success and potential for
dispersal of invasive species is a more complicated
that traditional theory would suggest and highly
dependent on the genetic and geographic conditions
of the system. Our findings are an example the
importance of molecular tools in understanding
unique invasion scenarios and their utility for the
cost-effective and fruitful management of invasive
species.
Acknowledgments We thank Nick Ling for introducing us to
this study system and identifying some of the collection sites.
We also thank Brandon Kowalski for providing many of the
samples used in these analyses, and we thank Makenzie
Stockwell, Jan Terfehr and Monica Gruber for assistance in
collecting mosquitofish. We also thank Pete Ritchie for
logistical assistance and two anonymous reviewers for
valuable insights on this manuscript. This work was supported
by funds from the NDSU, Environmental and Conservation
Sciences Graduate Program Postdoctoral Fellowship to KP, a
NDSU Centennial Grant award to CAS and a North Dakota
EPSCoR and National Science Foundation Grant EPS-0814442
to CAS. Additional funds from a NDSU President’s Travel
Grant, and from the NDSU College of Science and Mathematics
supported CAS during field collections.
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