Mar Biol DOI 10.1007/s00227-011-1710-y
ORIGINAL PAPER
Mitochondrial DNA sequence variation in spiny lobsters: population expansion, panmixia, and divergence Eugenia Naro-Maciel • Brendan Reid • Katherine E. Holmes • Daniel R. Brumbaugh Meredith Martin • Rob DeSalle
•
Received: 31 December 2010 / Accepted: 22 April 2011 ! Springer-Verlag 2011
Abstract To investigate population differentiation in a comparative and historical context, segments of the mitochondrial cytochrome c oxidase subunit I gene and the control region were sequenced in Panulirus argus from nine sites along approximately 1,500 km of the Northern Caribbean Sea (n = 326) and analyzed with respect to available panulirid data. A mismatch analysis and Fu’s FS test uncovered a signature of historical population expansion around the time of the Last Glacial Maximum. Significant population structure was not detected in the area. The data supported a hypothesis of panmixia resulting from ongoing larval transport by ocean currents and historical population expansion. Despite high intraspecific divergence levels at COI within Panulirus argus and several other Panulirus species, genetic species identification Communicated by S. Uthicke. E. Naro-Maciel Biology Department, College of Staten Island, City University of New York, Staten Island, NY 10314, USA E. Naro-Maciel (&) ! M. Martin ! R. DeSalle Sackler Institute for Comparative Genomics, American Museum of Natural History, New York, NY 10024, USA e-mail:
[email protected] B. Reid Department of Forest and Wildlife Ecology, University of Wisconsin, Madison, WI 53706, USA K. E. Holmes ! D. R. Brumbaugh Center for Biodiversity and Conservation, American Museum of Natural History, New York, NY 10024, USA K. E. Holmes Wildlife Conservation Society, PNG Marine Program, Kavieng, New Ireland Province, Papua New Guinea
through DNA barcoding was feasible using either a modified distance threshold or a character-based approach.
Introduction Investigating the impacts of past and current biological and physical processes on population connectivity over time is an active area of marine research, with implications for management and our understanding of biological evolution (Palumbi 2003; Palumbi 2004; Cowen and Sponaugle 2009; Weersing and Toonen 2009). Effects of historical changes in environmental or biological conditions on population structure can be multifaceted (Marko 2004; Palero et al. 2008). In certain warm water species including spiny lobsters for example, temperature variation corresponding to climatic cycles can lead to population isolation in glacial refugia, with mixing occurring during warmer periods (Tolley et al. 2005). In other spiny lobsters, however, analysis has revealed historical isolation of groups significant enough to warrant taxonomic revision and elevation to separate species (Gopal et al. 2006; Groeneveld et al. 2006). Such intricate population history can impact current genetic structure in ways that have not been fully explored. The role of planktonic larval duration (PLD) in structuring marine populations, for example, is recognized as complex (Waples 1987; Doherty et al. 1995; Bohonak 1999; Weersing and Toonen 2009), with no clear correlation between PLD and connectivity (Bradbury and Snelgrove 2001; Warner and Cowen 2002; Taylor and Hellberg 2003; Naylor 2006; Leis 2006; Woodson and McManus 2007; Weersing and Toonen 2009), but impacts of population history on these relationships have not always been explicitly considered. Many key findings in the field derive from genetic studies and the analysis of neutral DNA
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markers is a powerful tool for revealing population structure and history (Grant and Waples 2000; Hellberg et al. 2002). The Caribbean spiny lobster (Panulirus argus) is an ecologically important organism in marine communities, and its fishery is among the most valuable in the Caribbean and even worldwide, accounting for an estimated half of the global spiny lobster catch (Lipcius and Eggleston 2000; Cochrane and Chakalall 2001; FAO 2006). P. argus is classified within the family Palinuridae, which includes nineteen species of the widely distributed and intensively fished genus Panulirus. Currently, national P. argus fisheries are considered either stable, fully, or overexploited throughout the species’ range (Cochrane and Chakalall 2001; FAO 2006), which spans tropical and subtropical waters of the Western Atlantic Ocean from North Carolina to Brazil, as well as Bermuda, The Bahamas, and the West Indies (Lipcius and Eggleston 2000). After hatching from egg masses, Caribbean spiny lobsters drift with ocean currents as phyllosome larvae, then metamorphose into a puerulus post-larval stage that recruits to tropical shallow benthic substrates to settle and become juveniles (Lipcius and Eggleston 2000; Butler and Herrnkind 2000). Late juveniles typically move out from shallow areas to the deeper reefs, where reproduction occurs, and this species is known for its queue-style migrations that can involve journeys of 2–3 days in the Caribbean (Lipcius and Eggleston 2000). Laboratory rearing studies support larval durations on the range of 4–6 months in P. argus (Goldstein et al. 2008) with estimates from earlier studies (Sims and Ingle 1967; Farmer et al. 1989) or puerulus research (Ehrhardt and Fitchett 2010) being on the longer end at least for certain populations. Dispersal during this prolonged larval stage has widely been thought to result in panmixia within Panulirus argus subspecies (Ogawa et al. 1991; Glaholt and Seeb 1992; Hately and Sleeter 1993; Silberman et al. 1994a; b); however, the possibility of regional structuring has recently been raised (Diniz et al. 2005). Panulirus argus in Brazil is known to be genetically divergent from and of a different color than Caribbean spiny lobsters and is therefore provisionally considered a separate subspecies (P. argus westonii, Sarver et al. 1998; Sarver et al. 2000; Diniz et al. 2005). However, there had been no reports of structuring within these subspecies despite extensive studies throughout the range until Diniz et al. (2005) carried out an analysis using high-resolution mitochondrial DNA control region sequences. This study detected two divergent lineages within the Caribbean that had also been reported previously (Silberman et al. 1994a), suggested that northern Caribbean groups such as Florida might be distinct from southern areas including Puerto Rico, and called for additional mitochondrial DNA control region research to test these hypotheses (Diniz et al. 2005). The historical demography of Panulirus argus and its effects on population structure remains unknown. However,
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historical analysis of North Atlantic and Mediterranean spiny lobsters provided key insights into patterns of genetic diversity, which were hypothesized to be linked to population reduction and expansion during glacial cycles in Palinurus elephas but not in its congener resident of colder waters, P. mauritanicus (Palero et al. 2008). In other spiny lobsters of the Indian Ocean, genetic analysis detected signatures of population expansion potentially related to the Last Glacial Maximum (LGM) that could be attributed to colonizing of newly available habitat as glaciers melted and flooded previously terrestrial areas (Palinurus delagoae; Gopal et al. 2006, later split into P. delagoae and P. barbarae, Groeneveld et al. 2006). Analysis of P. gilchristi in South Africa suggested that current panmixia and levels of genetic diversity might be linked not only to ongoing larval dispersal, but also to recent expansion related to the LGM (Tolley et al. 2005), a hypothesis whose relevance to other species including P. argus remains untested. In this study, the unknown population history and questioned population structure of P. argus were investigated. The study focused on the Northern Caribbean Sea, an area of unknown population history that includes some of the most abundant populations (Bahamas and Florida; Ehrhardt and Fitchett 2010) and encompasses lobsters from two potentially different stocks and lineages (Diniz et al. 2005). The historical analysis was poised to add a layer of complexity to the hypothesis of panmixia centered simply on current larval dispersal. The possibility of regional structure within a species with such a prolonged larval phase was intriguing in light of the complex relationship between PLD and population structure, with applications for management with respect to loss of genetic diversity, identification of distinct populations, defining structure, marine protected area planning, and regional or international coordination (Lipcius et al. 1997, 2001; Stockhausen et al. 2000; Stockhausen and Lipcius 2001; Cochrane and Chakalall 2001; Diniz et al. 2005). To achieve resolution beyond that available in previous studies, sequencing of two mitochondrial DNA segments, the COI gene and the control region, was carried out. To analyze the divergent lineages observed within Caribbean P. argus in the context of other Panulirus groups, and to contribute to the DNA barcoding initiative (http://www. barcoding.si.edu/whatis.html), distance-based (Hebert et al. 2003) and character-based (DeSalle et al. 2005) analyses were conducted using available COI sequences.
Materials and methods Sample collection Tissue samples were collected from P. argus at nine locations in Florida, The Bahamas, the Turks and Caicos,
Mar Biol 80°W
76°W
72°W
68°W
AB FL BI
26°N
NA
SS EP LS
22°N
SC
PR 0
18°N 125
250
Kilometers
Fig. 1 Sampling locations with frequency pie charts for each of the two COI haplotype lineages. Abbreviations are as follows: AB Abaco, BI Bimini, EP Exuma Cays Land and Sea Park, FL Florida, LS Lee Stocking Island, NA North Andros, PR Puerto Rico, SC South Caicos, SS San Salvador
and Puerto Rico (Fig. 1). Florida and The Bahamas were selected because they contain areas of high abundance and are among the most important P. argus fisheries in the Western Central Atlantic (FAO 2006; Ehrhardt and Fitchett 2010), while Puerto Rico and the Turks and Caicos were included to expand the study’s range. Spiny lobster samples were obtained mainly by breaking off the tip of a lobster’s antennae when encountered on the reef, a sampling methodology that did not allow for taking measurements or assigning gender. In Florida (n = 29), the Turks and Caicos (n = 24), and Puerto Rico (n = 30), samples were obtained from landings captured at nearby local reefs. Each site was visited only once (Bahamas and Turks and Caicos: 2002–2004, Florida: 2006, and Puerto Rico: 2007), and lobsters missing an antenna were not included to avoid resampling of the same individual. Samples were stored in a sterile lysis buffer or 95% ethanol. Laboratory procedures DNA extractions were performed using a DNeasy Tissue Kit following manufacturer’s instructions (QIAGEN Inc.). Mitochondrial COI was selected for analysis because it has been useful in historical, comparative, or phylogeographic studies (Chow et al. 2006; Inoue et al. 2007; Palero et al. 2008) of other spiny lobsters. Primers PACOI-K5 (CCCT AAGACTTATTATTCGAGC) and PACOI-K6 (AATAAA TGTTGRTANAGRATNGG) were designed and used to amplify and sequence a 564 bp COI segment in P. argus (n = 326), P. argus westonii (n = 1), P. guttatus (n = 4), and P. laevicauda (n = 2) (Table 1). The mitochondrial control region was also selected for analysis because of its
utility in phylogeographic studies of other spiny lobsters (Diniz et al. 2005; Tolley et al. 2005; Gopal et al. 2006) and its potential for increasing resolution and revealing fine-scale population structure in Panulirus argus (Diniz et al. 2005). Primers CRL-F (GCAAAGAATATAGCAAGAATCAA) and CRL-R (GCAAACCTTTTTATCAGGCATC) designed by Diniz et al. (2005) were used to amplify a *700-bp fragment of the mtDNA control region that was truncated to 388 bp for comparison to published sequences. Standard conditions and negative controls were employed for polymerase chain reactions (PCR), using an annealing temperature of 508 for COI and 518 for the control region, and sequencing was carried out in both directions following previously described standard protocols (Naro-Maciel et al. 2007). Sequences were aligned using SEQUENCHER v4.6 (Gene Codes Corporation) and submitted to GenBank (COI: Table 1; control region: JF921208–JF921533). Genetic diversity and differentiation The Arlequin program (v3.11; Excoffier et al. 2005) was employed to calculate haplotype (h) and nucleotide (p) diversities (Nei 1987), as well as number of haplotypes (a) (Table 2). Arlequin was also used to carry out exact tests of population differentiation (Raymond and Rousset 1995), as well as pairwise tests (Table 3) and analysis of molecular variance (AMOVA; Excoffier et al. 1992) using the Tamura–Nei plus Gamma model (Tamura and Nei 1993). This model was selected by the program FindModel. (FindModel 2010), a web implementation of the program Modeltest (Posada and Crandall 1998) used to determine the most appropriate nucleotide substitution model. Significance values were obtained from at least 10,000 permutations. For COI, UST was calculated using the entire sequence and also more conservatively considering first codon positions only. In the AMOVA, genetic structure was tested for at two hierarchical levels: (1) all sites considered as one group and (2) with Puerto Rico belonging to a separate group to test the hypothesis that Florida and Puerto Rico could belong to separate stocks (Diniz et al. 2005). Parsimony median-joining networks of the haplotypes were constructed using Network v4.1 (Bandelt et al. 1999; Fig. 2), and MEGA v4.0 was used to create a neighbor-joining tree for all available control region sequences (Fig. 3). As haplotypes were distributed among two lineages, pairwise tests and AMOVA were also run for each clade separately to check for lineage effects. To investigate population structure further within a geographic context, spatial AMOVAs (using SAMOVA v1.0, Dupanloup et al. 2002) were carried out based on pairwise differences with significance tested over 1,023 permutations. The Isolation by Distance (IBD) Web Service (Jensen et al. 2005) was used to test for IBD using
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Table 1 Species, sampling location, sample size (in parenthesis), number of COI haplotypes (a), GenBank accession numbers, reference or source, percent K2P intraspecific divergence (%ID; mean with range in parentheses), number of pure diagnostic characters (Pu), and number of private diagnostic characters shared by at least 80% of species members (Pr) used in character-based DNA barcoding analysis Taxon
Atlantic Ocean location (n)
a
GenBank accession numbers
Reference/Source
%ID
Pu
Pr
P. argus
Northern Caribbean Sea (Fig. 1); (326)
122
JF921534–JF921859
This study (n = 326)
P. argus: 1.94 (0.00–12.40)
3
1
P. argus westonii
Brazil (1)
1
JF921860
This study, sample from J. Silberman (n = 1)
3
1
P. guttatus
The Bahamas (2)a; Florida USA (3)b, c
5
a
a
This study (n = 2); bThis study, sample from J. Silberman (n = 2); cPtacek et al. (2001) (n = 1)
1.31 (0.61–1.85)
4
0
P. laevicauda
Florida USA (2)a; Brazil (1)b
3
a
a
This study, sample from J. Silberman (n = 2); bPtacek et al. (2001) (n = 1)
0.41 (0.20–0.61)
2
0
P. regius
Congo (1)a; Eastern Atlantic Ocean (1)b
2
a
a
Ptacek et al. (2001) (n = 1); b Palero et al. (2009) (n = 1)
0.61 (0.61–0.61)
1
0
P. versicolor
Palau (1)a; Ryukyu Archipelago Japan (1)b
2
a
a
Ptacek et al. (2001) (n = 1); b Chow et al. unpublished (n = 1)
0.61 (0.61–0.61)
3
0
P. japonicus
Japan (73)
52
a
a
Inoue et al. (2007) (n = 73)
0.98 (0.00–3.32)
2
1
4
a
a
Ptacek et al. (2001), Palero et al. (2009) (n = 1); bChow et al. unpublished (n = 2); cChow et al. (2006) (n = 1)
0.71 (0.41-1.23)
2
0
a
Ptacek et al. (2001) (n = 1); b Chow et al. unpublished (n = 1); cChow et al. (2006) (n = 2)
P. longipes: 3.75 (0.41–5.46)
1
0
a
Ptacek et al. (2001) (n = 1); b Chow et al. (2006) (n = 1)
1
0
JF921861–JF921862; b JF921863–JF921864; c AF339456 JF921865– JF921866; b AF339462 AF339470; bFJ174962
Pacific Ocean Location (n)
P. penicillatus
a
Palau (1) ; Ryukyu Archipelago Japan (4)b
AF339472; bAB244283 N/A AF339468/FJ174970;
b
AB237601, AB237607; c AB193072
Japan (1)c P. longipes bispinosus
Australia (1)a; Ryukyu Archipelago Japan (2)b
4
AF339463;
b
AB237598;
Japan (2)c P. longipes longipes
a
c
AB193071, AB193075
a
b
Philippines (1) ; Japan (1)
2
a
AF339464;
b
AB193074
P. homarus homarus P. homarus megasculpta
Marquesas Islands (1)a a
Oman (1)
1
a
a
Ptacek et al. (2001) (n = 1)
1
a
a
Ptacek et al. (2001) (n = 1)
AF339457 AF339458
P. homarus: 11.96 (11.96–11.96)
1
0
1
0
The superscript letters in the ‘‘GENBANK accession numbers’’ column correspond to the references given in the ‘‘Reference/Source’’ column Mar Biol
Mar Biol Table 2 Sampling location, sample size (n), number of haplotypes (a), haplotype diversity with standard deviation (h ±SD), and nucleotide diversity with standard deviation (p ±SD) of Panulirus argus mitochondrial COI/control region Location
n
a
h (±SD)
p (±SD)
Abaco
20
17/19
0.979 ± 0.025/0.995 ± 0.018
0.017 ± 0.009/0.054 ± 0.028
Bimini
63
33/59
0.968 ± 0.009/0.998 ± 0.003
0.016 ± 0.008/0.050 ± 0.025
Exuma park
23
19/20
0.976 ± 0.022/0.984 ± 0.019
0.023 ± 0.012/0.062 ± 0.032
Florida
29
23/29
0.970 ± 0.022/1.000 ± 0.009
0.016 ± 0.008/0.048 ± 0.024
Lee Stocking Island
64
28/58
0.942 ± 0.015/0.997 ± 0.004
0.015 ± 0.008/0.048 ± 0.024
North Andros
47
28/45
0.959 ± 0.014/0.998 ± 0.005
0.014 ± 0.008/0.054 ± 0.027
Puerto Rico San Salvador
30 26
23/30 21/26
0.982 ± 0.013/1.000 ± 0.009 0.975 ± 0.021/1.000 ± 0.011
0.018 ± 0.009/0.055 ± 0.028 0.021 ± 0.011/0.064 ± 0.032
South Caicos
24
21/24
0.986 ± 0.018/1.000 ± 0.012
0.021 ± 0.011/0.060 ± 0.030
Table 3 Pairwise UST values for the control region (above diagonal) and COI (below diagonal) in P. argus. No values were significant at or below the P = 0.05 level after sequential Bonferroni corrections except for EP vs. LS (exact P \ 0.001), indicated by the symbol ^. Location
AB
Abaco Bimini Exuma Park
BI
EP
-0.021 -0.025
Pairwise comparisons that appeared significant prior to sequential Bonferroni corrections are indicated with the following symbols: * (UST) or + (exact P). Abbreviations as in Fig. 1
FL
LS
NA
PR
?
0.019
-0.027
-0.022
-0.017
-0.029
0.007
0.002
0.045?
-0.014
-0.007
-0.009
-0.014
0.026
0.020
?
?
0.026
0.055
Florida
-0.027
-0.018
0.057
Lee Stocking Island
-0.018
-0.010
0.077?*^
–0.019?
North Andros
-0.017
-0.011
0.079?*
-0.023
-0.013
Puerto Rico
-0.030
-0.015
0.018
-0.019
-0.009?
San Salvador
\0.001
0.025
-0.029
0.033
0.046
0.049
-0.004
South Caicos
-0.003
0.020
-0.026
0.025
0.040
0.044
-0.006
0.057
0.065* -0.016?
great circle geographic distances in a Mantel test with significance assessed following at least 10,000 permutations. Furthermore, control region sequences generated in this study were combined with published sequences (Diniz et al. 2005), aligned in MEGA v4.0, and visualized using a neighbor-joining wheel. Demographic history Demographic history was investigated considering the complete P. argus COI data set and also analyzing the two lineages separately. Fu’s FS test (Fu 1997), one of the most powerful for detecting population growth in large samples (Ramos-Onsins and Rozas 2002), was employed to test for equilibrium between genetic drift and mutation. Because a significant FS value can result from either population expansion or selection, the McDonald–Kreitman test (McDonald and Kreitman 1991) was used to test for effects of selection employing P. japonicus as an outgroup in DNAsp v5 (Rozas 2009). The COI gene segment was
0.058*
SS
SC
0.013
-0.018
-0.027
-0.019
-0.016
0.038
0.029
-0.005
-0.006
0.043*
0.039
-0.013
0.045*
0.026
-0.011
0.009
-0.007 -0.010
-0.032
analyzed rather than the noncoding control region to enable testing of selection versus population expansion as alternate hypotheses. In addition, mismatch distributions (Harpending 1994; Schneider and Excoffier 1999) were calculated using Arlequin and DNAsp (Fig. 4). The fit to the population expansion model was tested using the Raggedness statistic (r, Harpending et al. 1993) with 10,000 replicates and alpha = 0.05. Time since population expansion was calculated using the formula: T = s/2u (Harpending 1994), where T is time in years since expansion (Harpending et al. 1993) and u = 2lk, where k = the number of nucleotide sites being analyzed (n = 564), and l is the mutation rate per nucleotide site. The mutation rate was calculated as 3–10 times the interspecific substitution rate (ls = 0.9–1.1% divergence/MY estimated for decapods; reviewed by Ketmaier et al. 2003; see also Palero et al. 2008). As noted by Palero et al. (2008), the intraspecific mutation rate is conservatively expected to be 3–10 times faster than the interspecific substitution rate (Emerson 2007).
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Mar Biol Fig. 2 Minimum spanning network for COI haplotypes. Circles/ovals are proportional to haplotype frequencies (n = 326)
Fig. 3 Neighbor-joining tree of control region sequences (branches have been collapsed to simplify the tree where possible; n = 350 with 326 samples from this study and 24 from Diniz et al. 2005). Region of origin is indicated by symbols at each branch terminus, and the tree is divided by lineage after Diniz et al. (2005). White circles Northern (Florida), gray triangles North/ South Central (Belize and Nicaragua), black squares Southern (Puerto Rico and St. Eustatius), gray diamonds Brazil
To test whether it was appropriate to use a constant mutation rate, two approaches were taken. First, the program MEGA v4.0 (Tamura et al. 2007) was used to
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implement Tajima’s relative rate test for three sequences (Tajima 1993) using P. japonicus as an out-group. In addition, Panulirus COI sequences from Ptacek et al.
Mar Biol
DNA barcoding
Fig. 4 Mismatch distributions for P. argus COI sequences: a all of the samples (n = 326); b Lineage 1 (n = 253); and c Lineage 2 (n = 73). X and Y axes are necessarily at different scales. The bars represent the observed pairwise distances, and the lines represent the distribution expected under a model of recent demographic change (center line) with upper and lower bounds
(2001) were input into PAUP v4.0b10 (Swofford 2003), which was then used to generate a consensus of 100 maximum likelihood (ML) trees in which (1) a molecular clock was assumed and (2) a molecular clock was not enforced. A likelihood ratio test (LRT) was then used to compare the likelihood scores (-lnL) of each consensus tree using the formula LR = 2(lnL1-lnL0), where lnL1 was the score not enforcing a clock and lnL0 was the score assuming a clock. The degrees of freedom (df) were calculated as s–2, where s was the number of taxa (s = 20).
A DNA barcoding approach was used in order to place the divergent lineages observed within Caribbean P. argus in the context of other Panulirus groups, as well as to contribute to the global DNA barcoding initiative. For animal taxa, DNA barcoding involves the use of a standardized segment of COI to provide a cohesive, rapid, and costeffective means of cataloging and inventorying the Earth’s biological diversity (Hebert et al. 2003, 2004; Steinke et al. 2005; http://www.barcoding.si.edu/whatis.html). COI sequences generated in this study were compared to other publicly available Panulirus COI sequences. All sequences were trimmed to 493 bp, and only species with two or more available sequences were included (Table 1). DNA barcoding has traditionally used genetic distancebased procedures, sometimes in combination with tree-building algorithms, as a means of identifying and delineating species (Hebert et al. 2003, 2004; Steinke et al. 2005). However, methodological shortcomings that apply to spiny lobsters, including use of arbitrary thresholds for species delimitation, errors from insufficient sampling or overlap in inter- and intraspecific variation, and incomplete data use have been identified (Sarkar et al. 2002; DeSalle et al. 2005; Rach et al. 2008). A promising alternative is character-based DNA barcoding, in which species identification is accomplished using the presence or absence of diagnostic characters, also known as characteristic attributes (CAs; Sarkar et al. 2002; Rach et al. 2008). As such, both distance-based and character-based analyses were used here. The Characteristic Attribute Organization System (CAOS; Sarkar et al. 2002) was used to identify diagnostic characters for species identification. In a conservative approach, only simple CAs (those occurring at a single nucleotide position; DeSalle et al. 2005) were used. Pure CAs, or those shared among all elements in a clade but absent from members of other clades, and private CAs (which occur in some members of a clade, but are not found in members of other clades) with frequencies above 80% were analyzed following Rach et al. (2008). A guide tree was created using the maximum parsimony module in Phylip (v3.67; Felsenstein 2007) and incorporated into a Nexus file containing COI sequence data in MacClade (v4.06; Maddison and Maddison 2002). The P-Gnome program (Rach et al. 2008) searched each node to identify diagnostic characters using the CAOS algorithm (Table 4). In addition, the program MEGA v4.0 (Tamura et al. 2007) was used to calculate intraspecific as well as mean interspecific pairwise distances using P-distances and the Kimura two-parameter (K2P; Kimura 1980) distance model commonly used in barcoding research, enabling comparisons among studies (Fig. 5; results were similar
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Mar Biol Table 4 Simple diagnostic characters for identifying Panulirus species and subspecies Taxon/bp
5
25 38 44
62
65
P. argus (major clade)
C
A
A
P. argus (minor clade)
C/T A
A
A
T
A/G A
C
A/G G
C
T
C
C
C
A/G C
C
A
A
T
G
A
C
A
G
G
T
C
C
C
A
C
C
A
P. argus westonii
T
A
G
A
T
T
G
A
C
T
T
C
T
C
C
T
C
C
T
A
A
P. guttatus
T
A
A
T
T
A
A
T
A
G
C
T
C
T
C
P. homarus homarus
G
T
A
A
A
A
T
C
C
G
T
T
T
T
C
A
T
G
C
T
T
A
T
A
P. homarus megasculpta A
A
A
A
G
A
A
C
C
G
T
T
C
T
C
T
T
A
T
A
P. japonicus
T
A
C
A/G C/T A
A
T
A
G
T
A/G C
T
C
A/G G
T
P. laevicauda
A
A
A
G
A
A
A
T
T
G
T
T
C
C
C
A
A
T
A
T
P. longipes bispinosus
C/T A
G
G
T
A
A
T
A
G
T
A
C
C/T C
T
A
T
A
T
P. longipes longipes
T
A
G
G
T
A
A
T
A
G
T
A
C
T
C
T
A
T
A
T
P. penicillatus
T
A
A
A
T
A
A
A
A
G
C
A
C
C
C
G
A
C
G
C
P. regius
A
A
A
A
A
A
A
C
T
G
T
A
C
C
C
G
A
C
A
T
P. versicolor
A
A
A
G
A
A
A
T
T
G
C/T A
C
A
C
T
A
T
A
G
Taxon/bp
260
299
P. argus (major clade)
G/T
A/G
P. argus (minor clade)
A/G
A
P. argus westonii
A
A
P. guttatus
T
P. homarus homarus
C
P. homarus megasculpta
338
66 86 116
117 119 122
154 188 199 218
221 242 251
254 T
C/T A/G T
358 368
386
397
404
410
419
447
453
470
476
482
485
488
491
A/G
A
T
A/G
T
A
A/T
C
A
T
A/T
A
A/G
C
T
C/G
A
A
T
A/G
T
A
A
C
A
T
C/T
A
A/G
C
T
G
G
G
T
A
A
A
A
C
A
T
T
A
C
C
T
A
A
A/G
A
T
A
T
G
C
A
A
C
A
A
G
G
T
T
A
A
A
G
A
T
T
A
G
A
C
A
A
G
T
T
T
C
A
A
A
A
A
T
C
A
A
A
C
C
A
A
T
T
T
P. japonicus
C
A
A/C/G
A
T
A
T
T
A/T
A
A
C
C
A
A/G
C
C
C/T
P. laevicauda
C/T
A
G
A
A
A
T
T
A
A
G
C
C
G
A
T
T
T
P. longipes bispinosus P. longipes longipes
C/T C/T
A A
A/G A
A A
C C
T T
T T
T T
A/G G
A/G A
A A
C C
T T
A A
G A
T T
T T
C C
P. penicillatus
T
A
A
A
C
A
T
A
T
A
A
C
T
A
A
T
T
C
P. regius
A
A
T
A
A
A
T
C
G
A
A
C
C
A
A
T
T
T
P. versicolor
A
A
C
A
A
A
T
C
A
A
A
C
A
A
A
T
T
A
Results Genetic diversity and differentiation: control region Analysis of the *388-base pair control region segment revealed 291 haplotypes distributed among two lineages (ntotal = 326; nlineage1 = 253; nlineage2 = 73). There were
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32 30 28 26 24 22 20 18 16 14 12 10 8 6 4 2 0
P. ar gu s P. gu tta P. tu la s ev ic au da P. re gi P. us ve rs ic ol P. or ja po ni P. cu pe s ni ci lla tu s P. lo ng ip es P. ho m ar In us te rs pe ci fic
under the Tamura–Nei model; data not shown). MEGA v4.0 was also used to create a neighbor-joining tree based on pairwise K2P distances for all available COI sequences (Table 1; Fig. 6). Both of these analyses were also performed through the online BOLD interface (Ratnasingham and Hebert 2007), with similar results (data not shown). To test for associations between sample size and intraspecific diversity, Spearman correlation was implemented in R (R Development Core Team 2005).
% K2P
Pure characters for a taxon are in italics, and private characters are in bold
Fig. 5 Inter- and intraspecific divergences in 9 Panulirus species (data references in Table 1). Mean values are indicated with a ‘‘filled circle’’ symbol, and range in values is shown
Mar Biol
P. argus Lineage 1, n = 253
P. argus Lineage 2, n = 73 P. argus westonii P. penicillatus P. penicillatus P. penicillatus P. penicillatus P. guttatus P. guttatus P. guttatus P. guttatus P. guttatus P. longipes bispinosus P. longipes bispinosus P. longipes bispinosus P. longipes bispinosus P. longipes longipes P. longipes longipes P. japonicus, n = 52 P. versicolor P. versicolor P. laevicauda P. laevicauda P. laevicauda P. homarus homarus P. homarus megasculpta P. regius P. regius 0.02 Fig. 6 Neighbor-joining tree for COI haplotypes in 9 Panulirus species (data references in Table 1). Clades with multiple taxa for P. japonicus and P. argus have been collapsed into wedges with height representing number of taxa and width representing maximum distance within clade
34 single nucleotide mutations separating the lineages; however, within each lineage, most alleles differed by one bp (range: 1–11 bp). The G ? C content was 27.60%, the transition-to-transversion ratio was 2.09:1, there were 193 polymorphic loci, and 11 indels were detected, all of which were 1-bp long. Haplotype diversity was high and similar among sites (overall h = 0.999 ± 0.001; range: 0.984–1.000; Table 2), while nucleotide diversity (p) was 0.051 ± 0.025 (range: 0.048–0.064; Table 2). Most of the alleles were unique to one individual (n = 269; 92.4% of total alleles; 82.5% of the sample size). Significant genetic structure was not found. The AMOVAs attributed most of the genetic variation to within-population genetic differences, both when considering all populations as one group (UST = 0.007, P = 0.183, 99.32% variation within populations; exact P = 0.299), and with Puerto Rico in its own group
(UST = -0.008, P = 0.185; 100.79% variation within populations). When the two lineages were analyzed separately, no significant structure was detected (UST \ 0.035, P [ 0.066, [96.53% variation within populations). Statistically significant subdivision was not revealed in any pairwise comparisons following sequential Bonferroni corrections (Table 3). The SAMOVA revealed no significant structure (FST \ 0.033, P [ 0.174; [96.65% variation within populations), and there was no significant relationship between genetic and geographic distance in pairwise comparisons (Z = 64.818, r = -0.188, P = 0.846). Genetic diversity and differentiation: COI The analysis revealed 122 haplotypes distributed among two divergent lineages (n = 326; Fig. 2). There were 16 single nucleotide mutations separating the lineages; however, within each lineage, most alleles differed by one bp (range: 1–4 bp). There were 253 samples in the first lineage and 73 in the second (Fig. 2). The transition-to-transversion ratio was 3.8:1, and there were 101 polymorphic sites. No indels were detected, and the G?C content was 44.32%. The 188 amino acid sequence included a TGA codon at positions 124 and 259; however, following Yamauchi et al. (2002), this was translated as tryptophan rather than as a stop codon. Haplotype diversity was high and similar among sites (overall h = 0.967 ± 0.004; range: 0.942–0.986; Table 2). Nucleotide diversity (p) was 0.017 ± 0.008 (range: 0.014–0.023; Table 2). There were three somewhat frequent haplotypes (range 7–12%), and all others were relatively rare (\5.5%). Most of the alleles were unique to one individual (n = 86; 70% of total alleles; 26% of the sample size). Of the non-singleton alleles, only 4 were private, occurring in two individuals in a single population. The remaining 32 haplotypes were shared among locations. There was no significant genetic structure detected at COI, as expected given similar findings at the more variable control region (in all AMOVAs, UST \ 0.007, P [ 0.205; [99.3% variation within populations; exact P [ 0.083). When the two lineages were analyzed separately, results were also similar (UST \ 0.007, P [ 0.363, [99.30% variation within populations; exact P [ 0.144). Statistically significant subdivision was not revealed in any pairwise UST comparisons following sequential Bonferroni corrections, although one exact P value remained significant (Table 3). Furthermore, the SAMOVA revealed no significant structure (FST \ 0.041, P [ 0.187; [95.86% variation within populations). Neither was there a significant relationship between genetic and geographic distance (Z = 48.293, r = -0.185, P = 0.855).
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Demographic history For the first analysis, all the P. argus COI sequences were combined into one population (n = 326) because no differentiation was revealed among sites. In populations out of equilibrium undergoing population growth, a significant negative Fu’s FS value would be expected and was observed (FS = -24.071, P = 0.002). Although the departure from equilibrium could also be linked to the large number of unique alleles or selection, the McDonald– Kreitman test revealed only nonsignificant differences in proportions of synonymous and non-synonymous substitutions within and among species (G = 3.142, P = 0.076). Despite this evidence of population expansion, the mismatch distribution was bimodal (Fig. 4) and the raggedness index was nonsignificant (r = 0.019, P = 0.999), which are characteristics of a stable population. This apparent contradiction was addressed by analyzing the two lineages separately following Vin˜as et al. (2004), as they could have separate histories. Both FS values revealed departures from mutation-drift equilibrium consistent with population growth (Lineage 1: FS = -26.144, P \ 0.001; Lineage 2: FS = -22.254, P \ 0.001). The mismatch distribution for each lineage was unimodal (Fig. 4; Lineage 1: s = 3.047, CI: 2.660–3.459; Lineage 2: s = 4.4, CI: 1.770–6.611). As expected under a demographic expansion model, the raggedness statistic for Lineage 1 was significant (r = 0.036, P = 0.036) (Fig. 4) and its haplotype network had a star-like shape (Fig. 2). The assumption of a constant mutation rate held up under both tests (RRT : v2 B 3.00; P C 0.083; LRT: v2 = 27.66, P = 0.067), and the time since expansion for Lineage 1 was dated to 12,290–50,072 years ago. However, the raggedness statistic was not significant for the second, smaller lineage (r = 0.023, P = 0.437, n = 73). DNA barcoding Multiple sequences covering the COI fragment amplified in this study were available for eight additional Panulirus species (Table 1). The CAOS program identified simple diagnostic characters capable of distinguishing all taxa at the species level (Tables 1, 4). The two Caribbean P. argus lineages could be differentiated from other taxa and from each other using characteristic attributes (Table 4). Recognized subspecies of P. argus and P. homarus could also be distinguished by identifying characters. However, subspecies of P. longipes could not be distinguished using character-based methods. The average pairwise divergence between P. argus individuals was 1.94% (Table 1), however, individuals belonging to the two different Caribbean lineages were all more than 3% different from one another, and pairwise
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divergences between these individuals and Brazilian lobsters were approximately 12% (Table 1; Figs. 4, 5). Intraspecific divergences (measured as the mean of all pairwise divergences within a species) averaged 2.50% and ranged from 0 to 12.4%. Interspecific divergences averaged 24.82% and ranged from 17.42 to 30.43% (Figs. 5, 6). The highest average intraspecific divergence was in P. homarus, reflecting its two subspecies. The maximum P. argus westonii divergence was also about 12%, although the average is much lower because almost all the comparisons were within P. argus. However, the maximum intraspecific divergence within P. longipes, in which subspecies are also designated, was only *5%. Although intraspecific divergence may be underestimated in some cases due to smaller sample sizes, there was no significant correlation between sample size and mean intraspecific divergence (r = 0.31; P = 0.421), and the clear ‘‘barcode gap’’ between intraand interspecific divergences (Fig. 5) suggested that increasing sample sizes would not significantly alter the effectiveness of species identification through DNA barcoding.
Discussion The Caribbean spiny lobster (Panulirus argus) proved to be a useful organism for investigating effects of historical and contemporary population dynamics on genetic structure in the marine environment. The study revealed previously unknown population history: the occurrence of two distinct lineages with apparently separate evolutionary trajectories followed by mixing and population expansion around the time of the Last Glacial Maximum (LGM). In addition, the comparative analysis of genetic variation observed in Caribbean P. argus revealed similar average intraspecific divergence levels in several Panulirus species, and that the genus lent itself well to species identification through DNA barcoding using either a modified distance threshold or a character-based approach. Furthermore, our study substantiated the hypothesis of panmixia in this region (Ogawa et al. 1991; Glaholt and Seeb 1992; Hately and Sleeter 1993; Silberman et al. 1994a, b). The high genetic diversity, closely related haplotypes, and occurrence of two distinct lineages detected in this analysis were consistent with a history of isolation in two glacial refugia followed by population expansion and mixing around the time of the Last Glacial Maximum. The range in estimated time since expansion was wide, given uncertainty in mutation rates (Tolley et al. 2005; Gopal et al. 2006; Palero et al. 2008). However, assuming that the COI mutation rate for Panulirus argus is on the higher end of the decapod range, which seems reasonable given its genetic diversity, it appears that the population expansion
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revealed in Lineage 1 could be related to increases in suitable warmer, shallow water habitat accompanying dramatic climate shifts and sea level rise around the end of the LGM (Lambeck et al. 2002). Although evidence for expansion was less conclusive in Lineage 2, a targeted study comparing the ability of different methods to detect population growth showed that tests based on the mismatch distribution were conservative, and that Fu’s FS test was the most powerful method for detecting past population growth when sample sizes were large (Ramos-Onsins and Rozas 2002), suggesting expansion occurred in Lineage 2 as well. This scenario matched findings of Holocene population expansion in other spiny lobsters (Pollock 1990; Tolley et al. 2005; Gopal et al. 2006; Palero et al. 2008). We offer the hypothesis that an ancestral population became fragmented in different glacial refugia, leading to the observed lineage divergence. Later, around the time of the LGM, groups may have mixed as populations expanded with warming temperatures and larvae were transported widely during the extended phyllosome stage. This mixing and expansion, in combination with large population size and high mutation rates, could have precluded the loss of genetic diversity due to historical processes observed in other panulirid species (Palero et al. 2008). The lack of significant population structure in the heterogeneous study region revealed by our high-resolution mitochondrial sequence analysis can now be explained with an additional layer of intricacy: recent historical population expansion in combination with ongoing mixing during the extended larval stage (Silberman et al. 1994a; 1994b). Similar findings of genetically unstructured populations with a history of population expansion have been reported in other marine crustaceans (Benzie et al. 2002; McMillen-Jackson and Bert 2004). Lack of population structure was also observed in other spiny lobsters (Ovenden et al. 1992; Tolley et al. 2005; Cannas et al. 2006; Garcı´a-Rodriguez and Perez-Enriquez 2006; Inoue et al. 2007; but see Brasher et al. 1992; Perez-Enriquez et al. 2001; Gopal et al. 2006; Palero et al. 2008). Furthermore, these results were consistent with regional biophysical dispersal models (Roberts 1997; Butler et al. 2008), especially since the exchange of only a few migrants per generation is sufficient to maintain genetic panmixia (Crow and Aoki 1984). The high degree of connectivity revealed in all pairwise comparisons, including between Puerto Rico and Florida located *1,500 km apart, indicated that larval sources may be distant from eventual post-larval recruitment sites, supporting a need for regional management of this shared resource (Stockhausen and Lipcius 2001; Cochrane and Chakalall 2001; FAO 2006). This result complemented similar patterns of connectivity previously detected by
lower resolution mitochondrial RFLP analysis among sites as distant as Venezuela, Florida, and other Caribbean locations (Silberman et al. 1994a). Mixing within the wider Caribbean region was also reflected in the neighbor-joining tree, where our data from the Northern Caribbean Sea fell within two divergent lineages that also included published control region sequences from throughout the Caribbean and Brazil (Fig. 3). Although a previous study suggested Puerto Rico haplotypes might cluster predominantly separately from the other groups constituting a different genetic stock (Diniz et al. 2005), haplotypes from both of these clades were mixed within the region, with Puerto Rico haplotypes found in both clades (Fig. 3). Silberman et al. (1994a) also noted the mixing of locations among the two lineages detected in similar proportions to those found here using restriction enzyme analysis, highlighting the absence of location-specific clustering. In this research, the divergent lineages within P. argus were investigated using character-based approaches not previously employed for this group, as well as more traditional distance-based analyses. The two lineages within Caribbean P. argus were more than 3% divergent (Table 4; Figs. 5, 6), which is consistent with the 2.9% divergence reported by Silberman et al. (1994a). Furthermore, these two lineages could be distinguished by characteristic attributes (Table 4). CAs were also used to successfully distinguish P. argus of the Caribbean from the Brazilian form, P. argus westonii (Sarver et al. 2000; Diniz et al. 2005, Table 1), and previously reported high levels of divergence among these groups were confirmed. These patterns are consistent with the Amazon River being an effective barrier to larval dispersal. Furthermore, no P. argus westonii haplotypes were found within our study area despite extensive sampling, in contrast to the two individuals that grouped with the Brazilian form but were found in Florida during surveys in the early 1990s (Silberman et al. 1994a; Sarver et al. 2000). This suggests that, of the alternate hypotheses proposed (Silberman et al. 1994a; Sarver et al. 2000), rare incidences of migration between hemispheres mediated by large-scale oceanographic processes such as El Nin˜o events would seem more likely than co-occurrence of both forms in the Caribbean. The comparative analysis of genetic variation in P. argus with respect to other Panulirus taxa revealed similar average intraspecific divergence levels in groups containing no subspecies (\2%), as well as relatively high intraspecific divergence throughout the genus (Fig. 5), and provided insights into the utility of this group for species identification through DNA barcoding. The DNA barcoding community has proposed an empirical 2–3% intraspecific distance threshold (Hebert et al. 2003), however, this threshold was violated in other Panulirus species that included subspecies designations. Pairwise divergences
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between individuals of different subspecies in P. longipes and P. homarus were greater than 3% (Table 1, Fig. 5). While the average pairwise divergence between P. argus individuals was below this threshold, as noted above individuals belonging to the two different Caribbean lineages were all more than 3% different from one another, and pairwise divergences between these individuals and Brazilian lobsters were approximately 12% (Table 1; Figs. 4; 5). However, species in the Panulirus genus also tended to be highly differentiated (17–31%) at mitochondrial COI. While only species for which two or more individuals were available were analyzed here, high interspecific COI divergence was reported for the genus as a whole in a phylogenetic study prior to the onset of DNA barcoding (Ptacek et al. 2001). Due to high interspecific divergence, COI or other mitochondrial markers have successfully been used to establish species identity in several Pacific Panulirus species whose pelagic larvae were otherwise difficult to identify (Chow et al. 2006; Garcı´a-Rodrı´guez et al. 2008). Previous barcoding studies have indicated that interspecific divergences of only 2–3% should be sufficient for distinguishing invertebrate species (Hebert et al. 2003), and the interspecific divergences among Panulirus species reported here were well above this threshold. As interspecific COI divergences were quite high between Panulirus species (Fig. 5; Ptacek et al. 2001; Chow et al. 2006), raising the divergence threshold for species identification would be a reasonable means of accounting for the full range of intraspecific diversity while allowing for accurate identification in this genus. A ‘‘barcode gap’’ was clearly visible between the maximum intraspecific divergence (12%) and the minimum interspecific divergence (17%) observed in this study (Fig. 5), and the modified threshold could be set within this gap. Furthermore, for all of the species examined here, characters existed that could reliably diagnose all individuals from other panulirids and may provide a preferable alternative to somewhat arbitrary distance thresholds for taxonomic identification (DeSalle et al. 2005). In addition, character states could be used to identify subspecies and, for Caribbean P. argus, lineage of origin. The two lineages observed within Caribbean P. argus do not correspond to geographically separate groups, and it is likely that the divergence in these lineages resulted from their particular demographic histories discussed above. Although there are no reports in the literature concerning speciation within Caribbean P. argus, further research would be needed to state with absolute certainty that these lineages are not reproductively isolated and do not represent cryptic species. Indeed, this study did not encompass the full range of Panulirus argus, focusing instead on populations of the Northern Caribbean Sea that were
123
abundant, unsampled, or hypothesized to possibly belong to different stocks (Diniz et al. 2005). To further investigate reproductive isolation, or to test the contrasting and generally accepted Pan-Caribbean theory of panmictic population structure in Panulirus argus (Lyons 1980), additional regional sampling and analysis, as well as inclusion of nuclear genome data (Diniz et al. 2004), would be instrumental. Recruitment studies and biophysical modeling of spiny lobster dispersal are needed to determine whether this species’ genetic distribution is affected by the complex oceanographic structure of the Caribbean, composed of retentive and advective regions (Roberts 1997; Cowen et al. 2006; Butler et al. 2008; Cowen and Sponaugle 2009; Ehrhardt and Fitchett 2010). Furthermore, there may be ecologically relevant differentiation that is poorly resolved by genetic data, requiring an integration of information on ecological and evolutionary timescales (Levin 2006; Ehrhardt and Fitchett 2010). In conclusion, this study pointed to productive avenues of further research, and advanced understanding of this commercially valuable group by testing hypotheses of population structure and providing new insights into species identification and population history of Panulirus argus. Acknowledgments For samples or unpublished data, we thank Kenny Broad, Kevin Buch, Seinen Chow, Alondra Diaz, Nariaki Inoue, Aurea Rodriguez, Shane K. Sarver, Hideo Sekiguchi, Jeffrey Silberman, Patrick J. Walsh, and K and B EZ Dive in Bimini. Craig Dahlgren, Elizabeth Hemond, Carrie Kappel, and other members of the Bahamas Biocomplexity Project provided additional field assistance. We thank the students funded through NSF’s Research Experience for Undergraduates program, Ballington Kinloch, Catherine Munsch, Anthony Petroso, Caroline Storer, and Matthew Winfield, for laboratory assistance, and Steve Palumbi and Tom Oliver for discussions about this research. We are grateful to Jeffrey Silverman for producing Fig. 1. We thank The Bahamas Department of Marine Resources, the Department of Environment and Coastal Resources in the Turks and Caicos Islands, and the Departamento de Recursos Naturales y Ambientales of Puerto Rico for providing research permits for our work, as well as the School for Field Studies in the Turks and Caicos. E.N.M., K.E.H., and D.R.B. were supported in part by an NSF Biocomplexity in the Environment grant (OCE-0119976) to D.R.B. Additional support was provided by NOAA Grant #NA05SEC4691002, as well as George Amato and the AMNH’s Center for Conservation Genetics, and Eleanor Sterling and the Center for Biodiversity and Conservation.
References Bandelt H, Forster P, Rohl A (1999) Median-joining networks for inferring intraspecific phylogenies. Mol Biol Evol 16:37–48 Benzie JA, Ballment E, Forbes AT, Demetriades NT, Sugama K, Haryanti MoriaS (2002) Mitochondrial DNA variation in IndoPacific populations of the giant tiger prawn, Penaeus monodon. Mol Ecol 11:2553–2569 Bohonak AJ (1999) Dispersal, gene flow and population structure. Quart Rev Bio 74:21–45
Mar Biol Bradbury IR, Snelgrove PVR (2001) Contrasting larval transport in demersal fish and benthic invertebrates : the role of behavior and advective processes in determining spatial pattern. Canad J Fish Aquat Sci 58:811–823 Brasher D, Ovenden JR, White RWG (1992) Mitochondrial DNA variation and phylogenetic relationships of Jasus spp. (Decopoda: Palinuridae). J Zool Lond 227:1–16 Butler MJ, Cowen R, Paris C, Matsuda K, Goldstein J (2008) Long PLDs, Larval Behavior, and Connectivity in Spiny Lobster. 11th International Coral Reef Symposium. Ft. Lauderdale, USA, p 123 Cannas R, Cau A, Deiana AM, Salvadori S, Tagliavini J (2006) Discrimination between the Mediterranean spiny lobsters Palinurus elephas and P. mauritanicus (Crustacea: Decapoda) by mitochondrial sequence analysis. Hydrobiologia 557:1–4 Chow S, Suzuki N, Imai H, Yoshimura T (2006) Molecular species identification of spiny lobster phyllosoma larvae of the genus Panulirus from the northwestern Pacific. Mar Biotechnol 8:260–267 Cochrane KL, Chakalall B (2001) The spiny lobster fishery in the WECAFC region—an approach to responsible fisheries management. Mar Freshwater Res 52:1623–1631 Cowen RK, Sponaugle S (2009) Larval dispersal and marine population connectivity. Annual Review of Marine Science 1:443–466 Cowen RK, Paris CB, Srinivasan A (2006) Scaling of connectivity in marine populations. Science 311:522–527 Crow JF, Aoki K (1984) Group selection for a polygenic behavioral trait: estimating the degree of population subdivision. Proc Natl Acad Sci USA 81:6073–6077 DeSalle R, Egan MG, Siddall M (2005) The unholy trinity: taxonomy, species delimitation and DNA barcoding. Philos Trans R Soc Lond B Biol Sci 360:1905–1916 Diniz FM, Maclean N, Paterson IG, Bentzen P (2004) Polymorphic tetranucleotide microsatellite markers in the Caribbean spiny lobster, Panulirus argus. Mol Ecol Notes 4:327–329 Diniz FM, Maclean N, Ogawa M, Cintra IHA, Bentzen P (2005) The hypervariable domain of the mitochondrial control region in Atlantic spiny lobsters and its potential as a marker for investigating phylogeographic structuring. Mar Biotechnol 7:462–473 Doherty PJ, Planes S, Mather P (1995) Gene flow and larval duration in seven species of fish from the Great Barrier Reef. Ecology 76:2373–2391 Dupanloup I, Schneider S, Excoffier L (2002) A simulated annealing approach to define the genetic structure of populations. Mol Ecol 11:2571–2581 Ehrhardt NE, Fitchett MD (2010) Dependence of recruitment on parent stock of the spiny lobster, Panulirus argus, in Florida. Fish Oceanog 19:434–447 Emerson BC (2007) Alarm bells for the molecular clock? No support for Ho et al’.s model of time-dependent molecular rate estimates. Syst Biol 56:337–345 Excoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491 Excoffier L, Laval G, Schneider S (2005) Arlequin (version 3.0): an integrated software package for population genetics data analysis. Evol Bioinform Online 1:47–50 FAO (2006) National reports presented at the fifth regional workshop on the assessment and management of the Caribbean Spiny Lobster, FAO, Rome Farmer MW, Ward JA, Luckhurst BE (1989) Development of spiny lobster (Panulirus argus) phyllosoma larvae in the plankton near Bermuda. Proceedings of the 39th Gulf and Carib Fish Inst 39:289–301
Felsenstein J (2007) PHYLIP (Phylogeny Inference Package) Department of Genome Sciences. University of Washington, Seattle, Washington FindModel (2010) http://www.hiv.lanl.gov/content/sequence/findmodel/ findmodel.html Fu Y-X (1997) Statistical tests of neutrality against population growth, hitchhiking and background selection. Genetics 147:915–925 Garcı´a-Rodrı´guez FJ, Perez-Enriquez R (2006) Genetic differentiation of the California spiny lobster Panulirus interruptus (Randall, 1840) along the west coast of the Baja California Peninsula, Mexico. Mar Biol 148:621–629 Garcı´a-Rodrı´guez FJ, Ponce-Dı´az G, Mun˜oz-Garcı´a I, Gonza´lezArmas R, Perez-Enriquez R (2008) Mitochondrial DNA markers to identify commercial spiny lobster species (Panulirus spp.) from the Pacific coast of Mexico: an application on phyllosoma larvae. Fish Bull 106:204–212 Glaholt R, Seeb J (1992) Preliminary investigation into the origin of the spiny lobster, Panulirus argus (Latreille, 1804), population of Belize, Central America (Decapoda, Palinuridea). Crustaceana 62:159–165 Goldstein J, Matsuda H, Takenouchi T, Butler IV MJ (2008) The complete development of larval Caribbean spiny lobster Panulirus argus (Latreille, 1804) in culture. J Crustac Biol 28:306–327 Gopal K, Tolley KA, Groeneveld JC, Matthee CA (2006) Mitochondrial DNA variation in spiny lobster Palinurus delagoae suggests genetically structured populations in the south-western Indian Ocean. Mar Ecol Prog Ser 319:191–198 Grant WS, Waples RS (2000) Spatial and temporal scales of genetic variability in marine and anadromous species: implications for fisheries oceanography. In: Harrison PJ, Parsons TR (eds) Fisheries Oceanography: An Integrative Approach to Fisheries Ecology and Management. Blackwell Science, Oxford, pp 63–93 Groeneveld JC, Griffiths CL, Van Dalsen AP (2006) A new species of spiny lobster Palinurus barbarae (Decapoda, Palinuridae) from Walters Shoals on the Madagascar Ridge. Crustaceana 79:821–833 Harpending HC (1994) Signature of ancient population growth in a low-resolution mitochondrial DNA mismatch distribution. Hum Biol 66:591–600 Harpending HC, Sherry ST, Rogers AR, Stoneking M (1993) Genetic structure of ancient human populations. Curr Anthr 34:483–496 Hately J, Sleeter TD (1993) A biochemical genetic investigation of spiny lobster (Panulirus argus) stock replenishment in Bermuda. Bull Mar Sci 53:993–1008 Hebert PDN, Cywinska A, Ball SL, deWaard JR (2003) Barcoding animal life: cytochrome c oxidase subunit 1 divergences among closely related species. Proc Roy Soc Lond B 270:313–321 Hebert PDN, Penton EH, Burns JM, Janzen DH, Hallwachs W (2004) Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proc Nat Acad Sci USA 101:14812–14817 Hellberg ME, Burton RS, Neigel JE, Palumbi SR (2002) Genetic assessment of connectivity among marine populations. Bull Mar Sci 70(Suppl):273–290 Inoue N, Watanabe H, Kojima S, Sekiguchi H (2007) Population structure of Japanese spiny lobster Panulirus japonicus inferred by nucleotide sequence analysis of mitochondrial COI gene. Fish Sci 73:550–556 IV ButlerMJ, Herrnkind WF (2000) Puerulus and juvenile ecology. In: Phillips BF, Kittaka J (eds) Spiny Lobsters: Fisheries and Culture, 2nd edn. Blackwell Scientific, Oxford, pp 276–301 Jensen J, Bohonak AJ, Kelley ST (2005) Isolation by distance, web service. BMC Genetics 6:13 Ketmaier V, Argano R, Caccone A (2003) Phylogeography and molecular rates of subterranean aquatic Stenasellid Isopods with a peri-Tyrrhenian distribution. Mol Ecol 12:547–555
123
Mar Biol Kimura M (1980) A simple method for estimating evolutionary rate of base substitutions through comparative studies of nucleotide sequences. J Mol Evol 16:111–120 Lambeck K, Esat TM, Potter EK (2002) Links between climate and sea levels for the past three million years. Nature 419:199–206 Leis JM (2006) Are larvae of demersal fishes plankton or nekton? Adv Mar Biol 51(2006):57–141 Levin LA (2006) Recent progress in understanding larval dispersal: new directions and digressions. Integrative and Comparative Biology 46:282–297 Lipcius RN, Eggleston DB (2000) Ecology and Fishery Biology of Spiny Lobsters. In: Phillips BF, Kittaka J (eds) Spiny Lobsters: Fisheries and Culture, 2nd edn edn. Blackwell Scientific, Oxford, pp 1–41 Lipcius RN, Stockhausen WT, Eggleston DB, Marshall LS Jr, Hickey B (1997) Hydrodynamic decoupling of recruitment, habitat quality and adult abundance in the Caribbean spiny lobster: source–sink dynamics? Mar Freshw Res 48:807–816 Lipcius RN, Stockhausen WT, Eggleston DB (2001) Marine reserves for Caribbean spiny lobster: empirical evaluation and theoretical metapopulation recruitment dynamics. Mar Freshw Res 52:1589–1598 Lyons W (1980) Possible sources of Florida’s spiny lobster population. Proc Gulf Carib Fish Inst 33:253–266 Maddison D, Maddison WP (2002) MacClade: Analysis of phylogeny and character evolution. Sinauer Associates, Sunderland, MA Marko PB (2004) ‘What’s larvae got to do with it?’ Disparate patterns of post-glacial population structure in two benthic marine gastropods with identical dispersal potential. Mol Ecol 13:597–611 McDonald JH, Kreitman M (1991) Adaptive protein evolution at the Adh locus in Drosophila. Nature 351:652–654 McMillen-Jackson A, Bert TM (2004) Genetic diversity in the mtDNA control region and population structure in the pink shrimp Farfantepenaeus duorarum. J Crustac Biol 24:101–109 Naro-Maciel E, Becker JH, Lima EHSM, Marcovaldi MA, DeSalle R (2007) Testing dispersal hypotheses in foraging green sea turtles of Brazil. J Hered 98:29–39 Naylor E (2006) Orientation and navigation in coastal and estuarine zooplankton. Mar Freshw Behav Phys 39:13–24 Nei M (1987) Molecular Evolutionary Genetics, Columbia University Press, New York Ogawa M, Oliveira GM, Sezaki K, Watabe S, Hashimoto K (1991) Genetic variations in three species of spiny lobsters, Panulirus argus, Panulirus laevicauda and Panulirus japonicus. Revta Investnes Mar, Habana 12:39–44 Ovenden J, Brasher DJ, White RWG (1992) Mitochondrial DNA analyses of the red rock lobster Jasus edwardsii supports an apparent absence of population subdivision throughout Australasia. Mar Biol 112:319–326 Palero F, Abello P, Macpherson E, Gristina M, Pascual M (2008) Phylogeography of the European spiny lobster (Palinurus elephas): Influence of current oceanographical features and historical processes. Mol Phylogenet Evol 48:708–717 Palero F, Crandall KA, Abello P, Macpherson E, Pascual M (2009) Phylogenetic relationships between spiny, slipper and coral lobsters (Crustacea, Decapoda, Achelata). Mol Phylogenet Evol 50:152–162 Palumbi SR (2003) Population genetics, demographic connectivity, and the design of marine reserves. Ecol Appl 13:S146–S158 Palumbi S (2004) Marine reserves and ocean neighborhoods: the spatial scale of marine populations and their management. Ann Rev Env Res 29:31–68 Perez-Enriquez R, Vega A, Avila S, Sandoval JL (2001) Population genetics of red spiny lobster (Panulirus interruptus) along the Baja California Peninsula, Mexico. Mar Freshw Res 52:1541–1549
123
Pollock D (1990) Palaeoceanography and speciation in the spiny lobster genus Jasus. Bull Mar Sci 46:387–405 Posada D, Crandall KA (1998) Modeltest: testing the model of DNA substitution. Bioinformatics 14:817–818 Ptacek MB, Sarver SK, Childress MJ, Herrnkind WF (2001) Molecular phylogeny of the spiny lobster genus Panulirus (Decapoda: Palinuridae). Mar Freshw Res 52:1037–1047 Rach J, DeSalle R, Sarkar IN, Schierwater B, Hadrys H (2008) Character-based DNA barcoding allows discrimination of genera, species and populations in Odonata. Proc Roy Soc Lond B 275:237–247 Ramos-Onsins SE, Rozas J (2002) Statistical properties of new neutrality tests against population growth. Mol Biol Evol 19:2092–2100 Ratnasingham S, Hebert PD (2007) Bold: the barcode of life data system (http://www.barcodinglife.org). Mol Ecol Notes 7:355–364 Raymond M, Rousset F (1995) An exact test for population differentiation. Evolution 49:1280–1283 Roberts CM (1997) Connectivity and management of Caribbean coral reefs. Science 278:1454–1457 Rozas J (2009) DNA sequence polymorphism analysis using DnaSP. Methods Mol Biol 537:337–350 Sarkar I, Thornton J, Planet PJ, Schierwater B, DeSalle R (2002) A systematic method for classification of novel homeoboxes. Mol Phyl Evol 24:388–399 Sarver S, Silberman JD, Walsh PJ (1998) Mitochondrial DNA sequence evidence supporting the recognition of two subspecies or species of the Florida spiny lobster Panulirus argus. J Crustac Biol 18:177–186 Sarver S, Wilson D, Walsh PJ (2000) The occurrence of the provisional Brazilian subspecies of spiny lobster—Panulirus argus westonii—in Florida waters. Fish Bull 98:870–873 Schneider S, Excoffier L (1999) Estimation of past demographic parameters from the distribution of pairwise differences when the mutation rates vary among sites: application to human mitochondrial DNA. Genetics 152:1079–1089 Silberman JD, Sarver SK, Walsh PJ (1994a) Mitochondrial DNA variation and population structure in the spiny lobster, Panulirus argus. Mar Biol 120:601–608 Silberman J, Sarver SK, Walsh PJ (1994b) Mitochondrial DNA variation in seasonal cohorts of spiny lobster (Panulirus argus) postlarvae. Mol Mar Biotech 3:165–170 Sims HW, Ingle RM (1967) Caribbean recruitment of Florida’s spiny lobster population. Q J Fla Acad Sci 29:207–242 Steinke D, Vences M, Salzburger W, Meyer A (2005) TaxI—-a software for DNA barcoding using distance methods. Phil Trans Roy Soc Lond B: Biol Sci 360:1975–1980 Stockhausen WT, Lipcius RN (2001) Single large or several small marine reserves for the Caribbean spiny lobster? Mar Freshw Res 52:1605–1614 Stockhausen WT, Lipcius RN, Hickey BH (2000) Joint effects of larval dispersal, population regulation, marine reserve design, and exploitation on production and recruitment in the Caribbean spiny lobster. Bull Mar Sci 66:957–990 Swofford DL (2003) PAUP*. Phylogenetic analysis using parsimony (and Other Methods). Sinauer associates, Sunderland, Massachusetts Tajima F (1993) Simple methods for testing molecular clock hypothesis. Genetics 135:599–607 Tamura K, Nei M (1993) Estimation of the number of nucleotide substitutions in the control region of mitochondrial DNA in humans and chimpanzees. Mol Biol Evol 10:512–526 Tamura K, Dudley J, Nei M, Kumar S (2007) MEGA4: molecular evolutionary genetics analysis (MEGA) software version 4.0. Mol Biol Evol 24:1596–1599
Mar Biol Taylor MS, Hellberg ME (2003) Genetic evidence for local retention of pelagic larvae in a Caribbean reef fish. Science 299:107–109 R Development Core Team (2005) R: a language and environment for statistical computing, reference index version 2.2.1. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-90005107-0, URL http://www.R-project.org Tolley KA, Groeneveld J, Gopal K, Matthee CA (2005) Panmixia in spiny lobster, Palinurus gilchristi, supports a recent population expansion. Mar Ecol Prog Ser 297:225–231 Vin˜as J, Bremer JA, Pla C (2004) Phylogeography of the Atlantic bonito (Sarda sarda) in the northern Mediterranean: the combined effects of historical vicariance, population expansion, secondary invasion, and isolation by distance. Mol Phylog Evol 33:32–42
Waples RS (1987) A multispecies approach to the analysis of gene flow in marine shore fishes. Evolution 41:385–400 Warner RR, Cowen RK (2002) Local retention of production in marine populations: evidence, mechanisms and consequences. Bull Mar Sci 70(suppl): 245–249 Weersing K, Toonen RJ (2009) Population genetics, larval dispersal, and connectivity in marine systems. MEPS 393:1–12 Woodson CB, McManus MA (2007) Foraging behavior can influence dispersal of marine organisms. Limnol Oceanogr 52:2701–2709 Yamauchi M, Miya M, Nishida M (2002) Complete mitochondrial DNA sequence of the Japanese spiny lobster, Panulirus japonicus (Crustacea: Decapoda). Gene 295:89–96
123
