POPULATION GENETICS, LARVAL DISPERSAL, AND DEMOGRAPIDC CONNECTIVITY IN MARINE SYSTEMS
A THESIS SUBMII lED TO THE GRADUATE DIVISION OF THE UNIVERSITY OF HAWAI'I IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF
MASTER OF SCIENCE
IN
OCEANOGRAPHY
DECEMBER 2007
By Kimberley A. Weersing
Thesis Committee:
Robert Toonen, Chairperson Craig Smith Margaret MeManus We certify that we have read this thesis and that, in our opinion, it is satisfactory in scope and quality as a thesis for the degree of Master of Science in Oceanography.
THESIS COMMITtEE
ii Acknowledgements
My process of attending graduate school, from submitting my application to completing my thesis, was the product of many people's time, energy, and support. It is to these many friends, teachers, and colleagues that I wish to express my deep gratitude.
In particular, I thank Rob Toonen for his mentorship and for providing me with the opportunity to study and conduct research in such a remarkable scientific community. I also thank Craig Smith and Margaret McManus, not only for their critical insights into the development of my project, but also for sharing hands-on research opportunities with me in New Zealand and California. I would like to extend my appreciation to Ben
Bergen, Brian Bowen, Steve Karl, and Andy Taylor for their valuable statistics and experimental design advice. I give special thanks to my funding agencies, HIMB-NWlll
Coral Reef Research Partnership and NSF, and to Eric DeCarlo and Chris Measures for awarding me with a teaching assistantship earlier this year. Finally, I am grateful to
Shimi Rii for her incredible support and friendship during the long grind of data collection, and to Chris Neufeld for helping to make this entire journey possible.
iii Abstract
Population connectivity plays significant roles on both evolutionary and ecological time-scales, however efforts at constraining the magnitude and pattern of demographic exchange between populations of marine organisms has been encumbered by the difficulty of tracking the trajectory and fate of propaguIes. I survey 300 published studies to synthesize life-history and population genetic structure data from a broad array of marine taxa to determine how well pelagic larval duration (PLO) correlates with population genetic estimates of dispersal for benthic organisms. Expanding on earlier studies, I further explore other potential biophysical correlates of population substructure
(genetic marker class, habitat type, and larval swimming ability) that have not been considered previously. In contrast to previous studies concluding that longer planktonic periods confer greater dispersal ability, average PLO was poorly correlated with population connectivity (FSf) except among species in intertidal ecosystems. For species in which minimum, maximum and mean PLO were available, both minimum and maximum PLO are better correlated with Fsr than is the mean estimate. Furthermore, even this weak correlation appears to be anchored by non-pelagic dispersal, because removal of species that lack a pelagic phase entirely (the zero PLO class) from the analysis resulted in a non-significant relationship between Fsr and mean estimated PLO.
A 3-way ANCOVA instead reveals that differences among genetic marker classes
(mtDNA, allozymes, and microsatellites) are responsible for most of the variation in FSf
(F =7.113, df =2, P =0.001). while neither habitat nor swimming ability were significant factors. In contrast to the general expectation that microsatellite-based studies should provide the finest resolution of population structure, this survey finds that significantly
iv higher values of FST are obtained with mtDNA than with either microsatellites or aIlozymes (which were not significantly different). Useful predictors of the pattern and scale of dispersal playa central role in both ecological and evolutionary studies, but as yet remain elusive; this study suggests that mean PLO is at best a weak predictor of population genetic structure and that estimates of dispersal in the sea will need to encompass both behavioral and physical transport processes.
v TABLE OF CONTENTS
Acknowledgements .....•...... •...... ••.•.•.••...... •...... iii
Abstract ....•.•.•...... •...... •...•.....•...... •...... •...•.•.•••...... iv
~stof1Lables ..••.•...•...... •••••••.•••••...... viii
List of Figures ...... ••.•.•.•...... •...... ix
Introduction ...•.•.•.•...... •...... 1
Methods ...... •...... •.•.••.•...... •...... 4
Literature Survey ...... •...... •.••.•.•.•.•...... 4
Definitions and data categorization ...... 5
Effects of study scale and PLD on Fsr .•..•••••.••••.•••.••••••..••••••.•••••••••.•.• .•.•. 7
Effects of additional factors on Fsr ••.•.•.•.••••••.•.••••...••••...••.•.••••••.•.•.•.•.•.•.7
Results ...... ••...... •.•...... •...... 8
Effects of study scale and PLD on Fsr ...... •...•.••.•.••••.•.••••.••••••.•.•.•.•.•..••• 8
Effects of additional factors on FST •••••••••••••••••••••••••••••.•••..•.••.•...... •.••••• 11
Discussion ...... •.•...... 15
Effect ofPLD on Fsr ••••••.••••••••.••••••••••.••.••••.••••••.•.••••.•.••.•.•.•.•.•.....•.• 15
Effects of genetic marker class on Fsr ..••••••.••..••••.•.••••.••••.•.••••.•.•.•.•.•.••• 19
PLD and larval development ...... •.•...... 20
Coupling larval behavior and physical processes •...... 22
Conclusion ...•..•.•.•.•...... 24
Literature Cited ...... ••.•.••••.••••••••...... 26
Appendix A: Raw data ...... •..••.•.•. 0 •••••••••••••••••••••••••••••••••••••••••••••••••••••••••••• 34
Appendix B: Frequency statistics ...... •.•..•.•.•.••.•...... •...... 43
vi Appendix C: Full ANCOV A output ...... •...... 44
Appendix D: Database sources (column 2 of Appendix A) ...... 45
Appendix E: Pelagic larval duration sources ••••••••.....•...... •..52
vii LIST OF TABLES
1. Swimming velocities for various taxa ...... 7
2. Various predictors of FST••••••••••••••••••••••••••••••••••••••••••••• 9
3. Summary of analysis of covariance results ...... 11
4. Bonferroni pairwise comparisons of mean FST values by genetic marker class ...... •.•..•.•...... 12 s. Bonferroni pairwise comparisons of mean FST values by habitat type •••••••••••••••••••••••••••••••••••••••••••••••.• 13
viii LIST OF FIGURES
Figure
1. Global FST versus average, minimum, and maximum pelagic larval duration •.•...... " ...... 10
2. Global FST versus mean pelagic larval duration plotted by habitat type ••••••••••••••••.••••••••••••••••••••••••••••••••••••••• 14
ix Introduction
Dispersal plays a fundamental role in structuring populations and the wide range of developmental modes among marine species are presumed to have broad reaching micro-and macro-evolutionary ramifications (reviewed by Strathman 1985). Patterns and consequences of larval dispersal remain poorly understood, yet their importance in shaping ecological processes and conservation and management policies is unquestioned
(Cowen et al. 2006). For example, a common thread woven throughout the rapidly expanding literature on marine reserves is that reserve configuration needs to reflect the dispersal of individuals between populations, however there is less agreement about how dispersal distance and the magnitude of exchange actually should be estimated (Halpern and Warner 2003, Palumbi 2003, Lockwood et al. 2002).
The relative homogeneity of the marine environment, coupled with a perceived lack of conspicuous geographic barriers, has led to the expectation that most species should be well-mixed, with comparatively little structure among populations (Gaines and
Lafferty 1995, Caley et al. 1996). A recent survey of dispersal in marine and terrestrial environments concludes that this conventional wisdom is supported by available data
(Killian and Gaines 2003). However, the wealth of marine biodiversity in such regions as the Indo-West Pacific speaks of speciation that is driven by forces other than geographic isolation alone (e.g., Carlon and Budd 2002; reviewed by Briggs 2007). Indeed, a growing body of evidence reveals that non-random spatial distributions of dispersers can limit the potential for demographic exchange among marine organisms and that populations rarely constitute fully open or closed systems (Sponaugle et al. 2002).
1 Variation among species in patterns of connectivity complicates our ability to define discrete management units. This complexity is due in part to the challenge of studying dispersal of individuals directly, as most macroscopic marine species have a bipartite life cycle in which sessile or sedentary adults produce tiny planktonic propagules that are difficult or impossible to track (Bradbury and Snelgrove 2001). The small size and weak swimming ability of most invertebrate and early-stage fish larvae has been used to argue that they are passively advected from their natal populations by ambient currents and that their dispersal potential is determined primarily by the length of their pelagic stage (e.g., Grantham et 01.2003, Roberts 1997). For example, Jablonski
(1986) inferred from fossil evidence that greater larval exchange occurs among planktotrophic molluscan larvae than those that exhibit direct development or lecithotrophy. Because non-feeding larvae are more limited in their energy supply, they likely spend less time in the plankton and are thus thought to be less subject to advective dispersal than planktotrophic larvae (Jablonski 1986, Palumbi 1994).
Similar arguments have led to the practice of using pelagic larval duration (PLD) as a metric for estimating dispersal potential and population connectivity (Bohonak 1999,
Lester et al. 2007). In support of this practice, recent reviews of 25 and 32 species
(Shanks et al.2oo3, Seigel et al. 2003, respectively) showed a strong positive correlation between mean PLD and inferred dispersal distance (r =0.61 - 0.90). These studies corroborate the long-standing presumption that longer planktonic larval durations confer greater dispersal ability and that knowledge of PLD can be used as a simple proxy for realized dispersal distance in guiding applications such as marine reserve design.
2 In contrast, a number of studies indicate that factors such as larval behavior
(Gerlach et al. 2007, Kingsford et al. 2002) and mesoscale oceanography (reviewed by
Bradbury and Snelgrove 2001; Diehl et ai. in press) are responsible for a significant amount of larval retention and/or self-recruitment. Likewise, many studies continue to emerge with exceptions to the rule of decreased population subdivision in species with long PLD (e.g., Nishikawa and Sakai 2005, Rocha et ai. 2005, Taylor and Hellberg 2003,
Bowen et ai. 2006). The abundance of such counter-examples does not fit well with the strong correlations between PLD and dispersal found in previous analyses (Shanks et al.
2003, Seigel et al. 2003, Grantham et al. 2003) and indicates that for at least some species, population connectivity does not scale linearly with PLD.
These counter examples, coupled with an abundant availability of studies for meta-analysis, argue in favor of revisiting this issue. Here I survey 300 pUblications from ten electronic search queries to expand on these previous studies. After applying selection criteria (detailed below), my analysis ultimately encompasses data on 130 species from 87 of those studies, which I use to assess whether pelagic larval duration and the degree of genetic popUlation differentiation (FST, ¢sr) are significantly correlated for marine taxa with pelagic larvae.
In addition to evaluating PLD, I also explore other potential determinants of gene flow that have not previously been considered elsewhere. The impacts of various biophysical mechanisms on larval transport and settlement are well documented for some species, but these factors are largely absent from models used for designing marine reserves (e.g., Walters 2000, but also see Mace and Morgan 2006). Additionally, different classes of genetic markers (e.g., allozymes, microsateIlites, mitochondrial DNA
3 sequence) are often used interchangeably to estimate population differentiation, but there are many reasons to question quantitative estimates of dispersal based on genetic differentiation (e.g., Whitlock and McCauley 1999, Hutchison and Templeton 1999) and it is unlikely that quantitative results from different classes of genetic markers are directly comparable (e.g., Balloux et al. 2000, Bowen et al. 2005, Bazin et al. 2006). Therefore, I include these factors in my analysis and ask whether estimates of gene flow within species vary by length of planktonic stage, habitat, genetic marker class, or larval horizontal swimming ability. Specifically, I address the questions: 1) Is population differentiation measured with genetic markers highly correlated with PLO?; 2) Are there consistent scales of population structure among species occupying a shared habitat/physical oceanographic region?; 3) 00 different classes of genetic markers produce equivalent scales of population differentiation?; and 4) Is the degree of gene flow within species affected by differences in larval mobility?
Methods
Literature survey
I sampled peer-reviewed literature from various queries on the lSI Web of
Science search engine, spanning from January 1980 to June 2007. Ten different searches resulted in over 1600 hits, from which 300 papers were selected (this was the maximum number of papers that I could process within the timeframe of a masters thesis). These papers were then filtered according to the following criteria (modified from the selection criteria of Kinlan and Gaines 2003). The study must have: 1) surveyed a minimum 3 subpopulations; 2) reported a global or overall F ST or $ST value for the study; 3) examined an organism for which an estimate of PLO is available; 4) included species with sessile or
4 sedentary adults; 5) surveyed multiple loci, if using nuclear data (allozymes or microsatellites); and 6) included species for which sexual reproduction was the primary mode of propagation. From these papers, I created a database that included overall Fsr. pelagic larval duration (minimum, maximum, and mean), genetic marker, habitat, and a number of other factors (Appendix A; the full database will be available as supplemental material on the Toonen-Bowen lab server at the Hawaii Institute for Marine Biology).
Definitions and data categorization
Wright's (1951) hierarchical F-statistics partition population-wide genetic variance (FIT) into population substructure (FST) and nonrandom mating (FIS) components. FST ranges from 0 to I, where 0 represents uniform allele frequencies between subpopulations and 1 indicates that each of the subpopulations is fixed for a different allele (Hartl and Clark 1997). Under any of several specified models, the magnitude of population substructure can then be used to infer the amount and direction of gene flow among populations (e.g., Wright 1931, Beerli and Felsenstein 2001, Hey and Nielsen 2007, Palumbi 2003). Although this framework has some practical limitations (Weir and Cockerham 1984, Neigel2002, Hedrick 2005, Pearse and Crandall
2004), its ubiquity in the literature makes FST a valuable tool for exploring the magnitude of gene flow among populations. Throughout this paper, F ST refers to the global or overall F ST of all sampled populations in the included studies.
I first establish a few definitions used in assigning organisms to a particular habitat or category of swimming ability. Habitats were defined according to their biological andlor geophysical properties. "Reefs" refer to any organo-sedimentary subsurface feature that forms a relief from the surrounding seafloor, that lies in close
5 proximity to islands or continents (in contrast to seamounts), and which hosts biological communities that are unique in comparison to nearby assemblages. Intertidal organisms occupy any substrate that occurs within the zone demarcated by the highest and lowest tide lines. The subtidal habitat is here defined as the region extending from the intertidal out to 100 kIn from shore or from the surface to 200 m in depth (whichever occurs first), exclusive of reefs. Estuaries were also considered initially, but the small sample size ultimately excluded this habitat from the analysis.
Vertical positioning oflarvae in the water column and orientation to environmental cues are likely to be important contributors to patterns of larval dispersal
(Sponaugle et aZ. 2002, Woodson and McManus in press), but I was either unable to find or simplify these data for meta-analysis. Instead, I used a number of studies on the horizontal swimming ability of larval fishes and invertebrates to define levels for larval mobility (Table 1). I tested whether larval swimming ability affects population connectivity by dividing taxonomic groups into two broad categories (weak and strong swimmers) based on the range of reported horizontal swimming velocities within them
(Table 1). I categorized larvae that were capable of maximum horizontal swimming velocities of:S 6 cm/s as relatively weak swimmers, while strong swimmers are those capable of maximum speeds greater than 20 cm/s. There are no taxa with maximum swimming speeds between six and twenty cm/s (Table I), therefore these two groupings reflect a natural break in the data, which creates convenient non-overlapping categories of larval mobility. Organisms with direct development or crawl-away larvae that do not exhibit any swimming behavior were always classified as weak swimmers.
6 Table 1. R anges of reported sWllnmm ve I OClties.. ~or larvae of various taxa. Phylum(N) Swimming References Category of velocities (cm/s) swimming ability Porifera (I) Maldonado and Young 1996, Weak 0.05-0.33 Chia et al. 1984 Cnidaria (19) 0.04-3.0 Chia et aI. 1984 Weak Echinodermata Chia et aI. 1984 Weak 0.01-0.03 (13) Arthropoda (9) Chia et al. 1984, Valero et Strong 0.6-33 al. 1999, Phillips and Olsen 1975 Mollusca (24) Chia et al. 1984, Hidu and Weak 0.02-0.08 Haskin 1978 Chordata (SP McHenry (2005) Weak 0.3-6.0 Urochordata) (4) Chordata (SP Leis and Fisher 2006, Strong Vertebrata) (77) 1.8-65.5 Bellwood and Fisher 2001, Fisher et al. 2000
Effects ofstudy scale and PW on PST
I first explored whether FST values among different species are sensitive to the maximum geographic scales of the individual studies. For example, two studies on the same species might result in different conclusions being drawn about the dispersal ability of that species if one study calculated FSf among several proximate locations while another based the estimate on only very distant populations. To address this potential bias, I log-transformed both F ST and the geographic scale of the study and regressed the former onto the latter. I similarly evaluated various measures of PLD (average, minimum, and maximum) as predictors of FST in order to identify potential differences in their estimates of gene flow.
Effects of additional factors on FST
I use average PLD as a covariate in an analysis of covariance (ANCOV A) to determine if FST differed among genetic markers, habitats, or larval swimming abilities
7 (Table 2). For this study, little to no data were available for some combinations of terms: specifically, weakly swimming reef organisms studied via mtDNA were absent from the surveyed literature. I dealt with the issue of this missing cell by generating Type IV sums of squares to compare treatments for which data were available (Shaw and Mitchell-Olds
1993). The results of the ANCOVA were thus valid for 17 of the 18 total possible combinations between the different markers, habitat types, and swimming abilities.
Sequential Bonferroni tests for multiple comparisons of means were performed to clarify the effects of significant treatment terms. All statistictical analyses were conducted using
SPSS version 15.0 (SPSS Inc. 2006).
Results
Effects ofstudy scale and PW on FST
Eighty-seven of the 300 studies surveyed met all six selection criteria. Global Fsr was not found to be correlated with geographic study scale (r = 0.02, P = 0.07; Table 2).
Consistent with previous reviews that found increased pelagic larval duration coincides with reduced popUlation genetic structure (Bohonak 1999, Siegel et al. 2003), pelagic larval duration was negatively correlated with Fsr, regardless of whether the average, minimum, or maximum PLO was used (Fig. I, Table 2). It is noteworthy that the predictive power ofPLO on Fsris low ((I ranged from 0.097-0.175), and there is considerable scatter in the data across the entire range of pelagic larval durations (Fig. 1).
However, species with direct development or crawl-away larvae actually lack a pelagic larval stage entirely. Therefore, I also regressed F sr against non-zero PLO data and found that the significant negative relationship between these factors diminished in comparison to when the zero PLO data were also included in the analyses (Fig. I, Table
8 2). Although the removal of species that lack a pelagic stage does not change the significance of the correlation between minimum or maximum PLD and F ST. the relationship is weaker in species with larvae that spend some time in the plankton because the proportion of variation in FI[[' explained by PLD is roughly half that of the complete data set (Fig. 1, Table 2).
T abl e 2 . V anous. pred' IctOrs 0 fFST· Effect N r" p Equation Geographic study scale (km) 149 0.023 0.067 y =0.019x - 0.011 All PLD datal!< (days): AveragePLD 149 0.097 0.000 y =-0.037x + 0.094 MinimumPLD 95 0.164 0.000 y =-0.047x + 0.103 MaximumPLD 95 0.175 0.000 y =-0.046x + 0.113 Non-zero PLD data onlyl!< (days): AveragePLD 135 0.028 0.053 v =-0.023x + 0.073 MinimumPLD 80 0.095 0.006 v =-O.04lx + 0.095 MaximumPLD.. 81 0.090 0.007 v =-0.043x + 0.109 I!< Mimmum and 11llIXlmum PLD data were only available for a subset of the orgamsms surveyed.
9 2 0.30 .----__.I.!!=.!:!2:..L:"""!Z.!.>~"""!Y.L n=149 l =0.097 ____, n= 135 r =0.028 • • • • • • • • 0.25 '. • "• 0.20 • •
0.15 • • • • • • • • • • 0.10 • • •, • • • • •• . • • • • ' ... • ·'. .' • • •• ... ·'. .' • 0.05 . -: . !:. . .: . !: . ..: .' - ...... ,. • 0.OOot..o.!....o...... ----,01:.5-~~1.L;0 ...... ,.;:...-1.115..1.1~ ... 2,l.0=--'0.LO~-LL----.J0.L.:s-~ ...... 1.~0.:.r.~oPIl.511..I1A- 2.L.O----l Log avemge PLD (days) Log avemge non-zero PLD (days)
2 0.30 .----__-'n!!:=9~5L.!r'""'=O""'_'.1"'64::!LJ<"""""""______, n=80 r2 =0.095 • • • 0.25 • • • • • ~ 0.20 •
.s 0.15 • • • • • I I • 0.10 • • • •
0.05
0.OOO'-.0-...... ---'0.5-..!.L.--1-IJ..0:...... >-'-'"-'I.. .5""-'-n.----.J2.LO---'OL.0--'-"-----.J0.5L- ....·..::.....a-JI.O.!....o;.:..C-J .. 1.5w..:!:U-.:2~.0----l Log minimum PLD (days) Log minimum non-zero PLD (days)
___ __--. r-__ 0.30. ~n~=9~5~r~2=O~.1~7~5~~~ ~n~=~8~I~r~2=O~.09~0~~~~_--. • • • • • • 0.25 • • • 0.20 •
0.15 • • • • • • • • 0.10 • • • • • 0.05 • • • 0.5 1.0 2.0 0.0 O.S Log maximum PLD (days) Log maximum non-zero PLD (days)
Figure 1. Overall population genetic structure (Fsr) versus three measures (average, minimum, and maximum) of pelagic larval duration (PLD) for a range of marine vertebrate and invertebrate fauna. The plots on the right share the same y-axes as those on the left but exclude species that lack a planktonic larval stage.
10 Effects of additional factors on FST
I tested for significant differences in FST by 1) marker type, 2) habitat, and 3) larval swimming speed while accounting for the effect of average PLD by incorporating
PLO as a covariate in the ANCOV A model (Table 3). The 3-way ANCOVA explained
47.0% of the variability in FST. Larval swimming ability and habitat were not significant terms in the model; however, I was able to reject the null hypothesis of no differences in
FST among the different classes of genetic markers (Table 3). Further investigation via post hoc comparisons indicated that consistently higher values of FST were obtained with mtDNA than either of the nuclear markers (allozymes and microsatellites), which were not significantly different from one another (Table 4).
Table 3. ANCOVA of biophysical factors on reported population genetic structure for marine taxa with a pelagic larval stage (n = 129). Factor Type Levels or transformation F,df TJ Mean non-zero Covariate Log-transformed to PLO (days) linearize 9.464,1 0.003
Marker Independent AlJozyme, microsatellite, 7.113,2 0.001 mtDNA Habitat Independent Reef, subtidal, intertidal 0.996,2 0.372 Larval horizontal Independent Weak (~ 6 cm/s), strong swimming ability (>20 cm/s) 0.001, 1 0.974
11 Table 4. Bonferroni-adiusted pairwise comparisons of mean FSf values bv marker type. 95% Confidence Interval Mean for Difference' Difference Std. Upper Lower (I) Marker (1) Marker (I-I) Error Sig.' Bound Bound allozyme microsatellite 0.003 0.016 1.000 -0.036 0.042 mtDNA -0.062·· b 0.016 .001 -0.102 -0.022 microsatellite allozyme -0.003 0.016 1.000 -0.042 0.036 mtDNA -0.065·· b 0.020 .004 -0.113 -0.017 mtDNA allozyme 0.062 .< 0.016 .001 0.022 0.102 microsatellite 0.065*·< 0.020 .004 0.017 0.113 '" The mean dIfference IS SIgmficant at the .05 level. a Adjustment for multiple comparisons: Bonferroni b An estimate of the modified population marginal mean (1). c An estimate of the modified population marginal mean 0).
Bonferroni post hoc tests also confirmed the non-significant effect of habitat on
FST (Table 5). However, I reasoned that the three habitat types that I considered have characteristic oceanographic features, such as eddies and fronts, that may affect the patterns of larval transport and recruitment within them. Therefore, I compared regressions of population differentiation and PLD for each habitat type (Fig. 2).
Regardless of the inclusion or exclusion of non-zero PLD data, the relationship between FST and PLD was significantly negatively correlated for intertidal organisms
(Fig. 2A), whereas the regression was only significant for subtidal organisms when zero- value PLD data were included (Fig. 2B). The relationship between the length of planktonic stage and population structure was not significant for reef organisms (Fig.
2C). Finally, I explored clusters of high FST data points that were present in several of the plots (Fig. 1 and 2), but I did not find that these points represented any particular study or group of organisms. Furthermore, inclusion or exclusion of the highlighted points across all the plots did not qualitatively change the relationship or alter the conclusions drawn from the analyses (data not shown).
12 Table 5. Bonferroni-adiusted airwise comparisons of mean FSf' values bv habitat tvoe. 95% Confidence Interval Mean for Difference" Difference Std. Upper Lower en Habitat (J) Habitat (1-1) Error Sil!." Bound Bound Reef Subtidal 0.003" 0.012 1.000 -0.027 0.033 Intertidal -0.003 b 0.Ql8 LOOO -0.048 0.041 Subtidal Reef -0.003" 0.012 1.000 -0.033 0.027 Intertidal -0.006 0.019 1.000 -0.052 0.040 Intertidal Reef 0.003" 0.018 1.000 -0.041 0.048 Subtidal 0.006 0.019 1.000 -0.040 0.052 a Adjustment for multiple. compansons: Bonferroru b An estimate of the modified population marginal mean (J). c An estimate of the modified population marginal mean (1).
13 2 =0.292 n=20 r =0.292 0.30 A. Intertidal n=24 l
0 0 0.25
0 0 0.20
0.15
0.10 0 0 0 0.05 0 0 0 0 0 0 0 o • 0 0 0.00 0.0 0.5 1.0 1.5 2.00.0 0.5 1.0 1.5 2.0
B. Subtidal to 200m (n=46. 1"=0.194. p=O.002) (n=41. 1"=0.026. p=O.312) 0.30 o o 0.25
t; 0.20 ~ ,30.15
0.10 0 0 0 0 0 0 0 0 0 0 0 0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0
2 2 C. Reef(n=72 i=O.Oll. 0=0.383) n=68 r=O.OOI
0 0 0 0 0.25 f- 00 ..0 0
0.20 r
0.15 r- o o o 0 0 0 o 0 o o 0 0 0.10 I- 00 .. o 00 0 o 0 o .. 0 0 00 0.05 ~r:---.r--·o-----.--; ____ 0 0 • • I • • 0 0 0 o 0 I. 0 0 0 0 0.00 ,-,-: ~Llo~--,---o __--,o,-.,-_o~o-'-"I'.~J.L'~...I.~ ':'0_--, o 0 • - 0.0 0.5 1.0 1.5 2.0 0.0 0.5 1.0 1.5 2.0 Log average PLD (days) Log average non-zero PLD (days)
Figure 2. Overall population genetic structure (global Fsr) versus average pelagic larval duration (PLD) plotted separately for intertidal, subtidal, and reef organisms. The plots on the right share the same y-axes as those on the left but exclude species that lack a planktonic larval stage.
14 Discussion
The ability to resolve the pattern and magnitude of demographic exchange in marine organisms would contribute significantly to our understanding of the evolutionary and ecological processes of speciation, adaptation, persistence, and population dynamics.
A number of advancements have been made in developing tools to quantify population connectivity in marine environments, including natural and artificial tags (reviewed by
Thorrold et aI.2002) and spatia1ly-explicit physical models of larval dispersal (e.g., Black et at. 1991, Cowen et at. 2000, Baurns et al. 2006). However, the complexity of marine hydrodynamics and variation in life-history strategies within and among species continues to make accurate estimates of connectivity notoriously difficult to obtain
(Sponaugle et al. 2002). In the absence of quantitative estimates of larval exchange among locations (e.g., Cowen et al. 2006), it is appealing to use pelagic larval duration
(PLO) as a proxy for the dispersal potential of benthic organisms (e.g., Bradbury and
Snelgrove 2001). Although PLO is attractive as a simple proxy for estimating dispersal, I found only weak correlations between PLO and FST, (~ranged from 0.028-0.175) and conclude that general acceptance of PLO as a reliable predictor of connectivity is groundless and warrants no further investigation.
Effects ofPW on FST
A relationship between pelagic duration and the potential for larval dispersal is intuitive and several reviews provide empirical support for a strong correlation between
PLO and mean dispersal (e.g., Bohonak 1999, Kinlan and Gaines 2003, Siegel et al.
2003, Shanks et al. 2003, Kinlan et at. 2005, Lester and Ruttenberg 2005). Additiona1ly, it is far cheaper and easier to estimate dispersal potential from mean PLO values and
15 these studies suggest that such inferences are likely to be reasonable approximations of dispersal potential in the field (e.g., Lester et al. 2007, Kinlan and Gaines 2(05).
However, the evidence supporting PLO as a surrogate for dispersal is contrasted by a large number of studies that report population subdivision in species with prolonged planktonic larval stages as well as panmixia in species with brief PLDs (e.g., Nishikawa and Sakai 2005, Taylor and Hellberg 2003, Ayre et al. 1997, Bowen et al. 2(06).
Furthermore, a variety of larval behaviors and regional hydrodynamic processes can contribute substantially to larval retention and self-recrnitment (Sponaugle et al. 2002,
Kingsford et al. 2002, Baums et al. 2006). Numerous exceptions to the rule of proportional decreases in genetic structure with PLO, biophysical mechanisms for self recruitment, and a wealth of available studies reviewed here, all refute a predictive link between pelagic duration and population connectivity.
I considered the effect of various estimates of PLD on realized gene flow between populations (as measured by global FST) with a much larger sample size than has previously been attempted, and with several additional factors included in the analysis.
Consistent with previous studies, I found a statistically significant negative correlation between PLD and FST. This negative correlation is significant regardless of whether the maximum, minimum, or average estimate of PLD was employed, but the variation explained by this relationship varies dramatically (~ranges from 0.10 to 0.18, p < 0.01) depending on which estimate of PLD is used (Fig. 1). Previous studies use mean PLD in their analyses, which I find to be a worse correlate of population genetic structure than either the minimum or maximum PLD. Notably, the significance of this relationship is driven largely by the presence or absence of a pelagic stage rather than the length of
16 pelagic duration per se (Teske et al. 2007) because removal of the zero PLO class from the analysis results in a non-significant relationship between mean PLO and population genetic structure (Fig. 1). The lack of a correlation between non-zero mean PLO and F sr reveals that even the presumably short dispersal potential of several hours to a few days substantially reduces population structure, which supports the argument that the planktonic larval phase may be selected for purposes other than dispersal (Strathmann
1985, Strathmann 1993, Todd et al. 1998). This evidence is not conclusive, as the correlations using minimum and maximum PLO remain significant; however, r drops to roughly half for pelagic larvae compared to the case when non-pelagic (zero PLO) larvae are included (Fig. 1). Overall, this result suggests that it is the presence or absence of a pelagic phase of dispersal which makes the greatest difference to population structure rather than the length of that pelagic phase.
I find it noteworthy that when organisms with direct development or crawl-away larvae (PLO =0) were removed from the analysis, PLO uniformly accounted for less than
10% of the variation in Fsr, which is at odds with previous analyses outlined above (r ranged from 0.61-0.90 for Shanks et al. 2003 and r =0.80 for Siegel et al. 2003).
Additionally, each the minimum and maximum estimates of PLO were stronger predictors of Fsr than the average PLO values typically reported in the literature and used in previous analyses (Table 2). While the full range of larval durations is not always known for an organism (these data were available for only -50% of species), my results indicate that it may be more appropriate to utilize the tails of the PLO distribution rather than average PLO in models estimating dispersal and gene flow. This finding is not wholly unexpected given that the mean PLO is usually the average value of the range and
17 is not likely the highest frequency PLD. Overall, larval dispersal distributions are poorly characterized (Lockwood et al. 2002) and regardless of the measure of PLD employed, the correlation between PLD and FST is weak (r < 0.10).
The disparity between my results and those of recent meta-analyses (Shanks et al.
2003 and Siegel et al. 2003) may be due to a number of methodological differences or statistical biases. First, Shanks et al. (2003) estimated dispersal based on four types of biological data from organisms that may not exhibit representative patterns of dispersal and population connectivity. For instance, direct observations of dispersal were almost exclusively of tunicate larvae, and one-third of the dispersal estimates were calculated from the rate of spread of invasive species. Second, the model-derived estimates of dispersal obtained by Siegel et al. (2003) were based on assumptions that may over simplify the system. For example, the model assumes that each species is comprised of
1000 equally spaced demes each of 1000 individuals organized in a one-dimensional circular array. It is unknown to what extent the simplifying assumptions of this model affect the results, but my examination of the species used by Siegel et al. (2003) revealed no correlation (r = 0.006, p = 0.670) between global FSF and PLD when dispersal estimates are not generated from the use of genetic structure data fed into the model.
Overall, the differences between previous studies and my findings reported here suggest that certain taxonomic or functional groups (such as invasive species) may show a significant correlation between PLD and popUlation genetic structure, but that pattern does not hold when examined across broad taxonomic lines with a large sample size.
With 90% of the variation in population genetic structure left unexplained by PLD, I considered three additional factors that are often mentioned in the literature as likely to
18 impact larval dispersal and estimates of gene flow: I) genetic marker class, 2) mesoscale oceanography, and 3) larval swimming ability.
Effects ofgenetic marker class on FST
Of the three biophysical factors that were considered in my analysis, only genetic marker class was significantly correlated with popUlation structure. Allozymes, mitochondrial DNA, and microsatellites are the most commonly used markers in marine popUlation genetics and are frequently used arbitrarily when estimating population connectivity. As previously mentioned, I found that mtDNA produced significantly higher estimates of FST than either of the nuclear markers considered (allozymes and microsatellites), which were not significantly different from one another.
A number of mechanisms may be responsible for the increased resolution of mtDNA studies. First, the uniparental inheritance of mtDNA reduces effective population size (No) to one-fourth that of nuclear markers (Avise 1994). Consequently, mtDNA experiences increased rates of genetic drift and thus approaches fixation more quickly (and exhibits higher FST values) than nuclear loci (Ballard and Whitlock 2004).
Alternatively, the disparity in the results of mtDNA and the two nuclear markers may be attributed to differences in other inherent characters of the markers, such as mutation rate, degree of polymorphism, or selective pressure (reviewed by Ballard and Whitlock 2004), although there is considerable debate on this topic (e.g., Bazin et al. 2006, Wares et al.
2006).
The standard expectation in the literature is that microsatellites have the greatest resolving power (e.g., Zhang and Hewitt 2003, Weetman et al. 2006, Estoup et al. 2002), yet this survey reveals that mtDNA produces significantly higher values of FST on
19 average, while hypervariable microsatellites show no significant difference from allozymes. The similarity in magnitude of FST calculated from microsatellites and allozymes compared to that detected with mtDNA may be an issue of differences in the mode of inheritance, or a statistical consequence of standardization, as outlined below.
Although there are many caveats to any quantitative estimate of connectivity based on F ST (reviewed by Whitlock and McCauley 1999), overall F ST appears to be a robust indicator of the relative influence of genetic drift and migration in structuring popUlations (reviewed by Hutchison and Templeton 1999). Several recent publications have pointed out that the maximum calculable FST is inversely proportional to the within population heterozygosity (Charlesworth 1998, Hedrick 2005, Meirmans 2006). Thus, for highly polymorphic genetic markers, such as microsatellite loci, the effect is that the maximum possible FST is greatly reduced below one (Hedrick 2005), which may explain why microsatellites routinely showed lower FSTValUes than mtDNA in this survey.
While it is now possible to standardize FST values to account for marker heterozygosity
(Hedrick 2005, Meirmans 2006), I was not able to apply these adjustments to my dataset because the standardizations require data that were invariably unreported in published studies. However, if standardization were the only issue to consider, one might expect a correlation between the relative ranks of marker polymorphism (microsatellites > mtDNA
> allozymes) that does not match the pattern from this survey (mtDNA > microsatellites
= allozymes).
PW and larval development
A commensurate increase in gene flow with prolonged PLD is a reasonable expectation that has been supported by empirical evidence in several earlier reviews (e.g.,
20 Bohonak 1999, Shanks et al. 2003, Siegel et al. 2003), yet my results deviate considerably from previous findings. What mechanisms may account for the weak correlation that I found between population connectivity and planktonic duration? First, fish possess otoliths and other calcified tissues that record their growth over time, but the length of the invertebrate larval period is conventionally derived from laboratory observations, and rearing larvae in an artificial environment may not induce typical larval behaviors (Leis 2006, Kingsford et al. 2002, Bradbury and Snelgrove 2001). It is also known that the length of the planktonic larval stage is a plastic life history trait that can vary by an order of magnitude or more for some organisms (e.g., Toonen and Pawlik
200 1, Harii et al. 2002, Addison and Hart 2004). Such variation in PLD can occur due to behaviors such as delayed metamorphosis (reviewed by Pechenik 1990) and larval responses to environmental stochasticity (Pfeiffer-Hoyt and McManns 2005, Bergenius et at. 2005). Additionally, the expectation that dispersal potential is determined by PLD is rooted in the assumption that larvae are largely passive particles (Roberts 1997, Siegel et al. 2003), a conclusion that is now invalidated for many species, including reef fish (Leis and Fisher 2006) and blue crabs (Olmi 1994).
While most of the invertebrate taxa surveyed here are unable to swim effectively against ambient currents as meroplankton (Table 1), the majority of larvae are capable of overcoming vertical current velocities (reviewed by Chia et al. 1984 and Sponaugle et al.
2002). Larvae also possess a variety of sensory structures that enable them to orient to environmental cues (e.g., temperature, light, and salinity), which in combination with vertical swimming behavior allow them to actively position and maintain themselves within a particular water mass (reviewed by Young 1995 and Kingsford et at. 2002).
21 Meroplankters can capitalize on the vertical stratification of the flow field, in which current velocity and direction may vary with depth. Vertical positioning within the water column has been demonstrated by numerous taxa, such as oyster (Dekshenieks et al.1996, Shanks and Brink 2005), crab (Cronin and Forward 1986), and polychaete larvae
(Thiebaut et al.1992), to maximize residence time near food sources, mitigate advection away from and/or aid in transport towards suitable settlement habitat, and for predator avoidance (Young and Chia 1987, Sponaugle et al. 2002, Woodson and McManus in press). The coupling of larval orientation behaviors with mesoscale oceanography provides an elegant example of effective adaptation and habitat utilization; however, such complex biophysical interactions. which are often temporally variable. make a difficult task of discerning useful predictors of population connectivity (e.g., Diehl et al. in press).
Coupling larval behavior and physical processes
Interactions between fluid flow and bottom topography generate oceanographic features such as eddies and fronts that can have important consequences for larval accumulation and transport (Wolanski and Hamner 1988. Pineda 1994. Wolanski and
Spagnol 2000, McCulloch and Shanks 2003, Baums et al. 2006). The three habitats that I surveyed were characterized by their geophysical properties (e.g., depth, relief, tidal range) and as such may also be differentiated by their oceanography. Therefore, I explored the relationship between PLD and population structure for individual habitats
(Fig. 2) with the rationale that if dispersal were a simple product oflarval duration (as opposed to some biological factor or interaction), then species occupying similar habitats would be expected to experience more similar hydrodynamic forces and thus share more similar scales of differentiation. Planktonic larval duration and FST were significantly
22 correlated for intertidal organisms (Fig. 2A• .; =0.29). but not for subtidal or reef species that have a planktonic period. One striking characteristic of the intertidal habitat is that it exists as a narrow band bounded on one side by land. and it is likely that its two dimensionality contributes to circulation patterns that distinguish it from subtidal and reef habitats. While processes allowing cross-shelf transport do exist (see below). the bulk flow is alongshore and one-dimensional. in which case longer planktonic larval periods can confer greater dispersal ability for passive larvae. Alternatively. intertidal organisms may influence their transport and settlement through various behavioral mechanisms.
Interactions between larval behavior and nearshore oceanography have been found to drive the recruitment patterns of many intertidal species. For example. neustonic (surface-dwelling) larvae along the eastern margin of the Pacific Ocean have been documented to be transported offshore during coastal upwelling events and onshore during downwelling events (Farrell et al.1991). However. this pattern was reversed for the larvae of two western Atlantic bivalve species that oriented themselves below the thermocline and were thus carried shoreward in subsurface waters during upwelling but conversely were advected offshore during downwelling (Shanks and Brink 2(05).
Furthermore. two other bivalve meroplankters were retained close to shore by displaying negative geotaxis during downwelling and positive geotaxis during upwelling (Shanks and Brink 2005). Such cross-shelf transport and near-shore retention mechanisms are not unique because a number of other features. such as internal tidal bores and convergent fronts. have also been found to influence the timing and intensity of intertidal larval settlement, as seen in fish and crabs (Pineda 1994). and mussels and barnacles
(McCulloch and Shanks 2003). It seems likely that such species-specific differences in
23 interactions between larval behavior and nearshore oceanography are equally important in determining patterns and magnitudes of larval dispersal. Regardless of the specific mechanism, it appears that PLD has potential use in models of population connectivity for intertidal organisms.
Conclusion
The intuitive connection between PLD and dispersal potential, bolstered by recent studies indicating a strong correlation between the two, has resulted in the length of the planktonic larval stage being used as a convenient measure of dispersal potential for benthic organisms in both ecological (e.g., Lester et al. 2007) and evolutionary (e.g.,
Jablonski 1986) studies. However, there is also a large body of literature documenting high genetic partitioning among organisms with long larval durations or little structure in species with short pelagic periods (reviewed by Swearer et al. 2002, Bowen et al. 2006).
To my eye, the literature seemed to have at least as many exceptions as conforrnants to the rule, and I therefore undertook this study to synthesize a large volume of genetic structure and life-history data across a broad range of marine taxa. My results indicate that PLD is at best a poor predictor of realized population connectivity as estimated by population genetic markers. There are obviously at least two sources of potential error in this relationship, and either or both may be responsible for the poor correlation between
PLD and F ST reported here. On the side of pelagic larval duration, factors such as larval behavior and mesoscale oceanography are likely to influence larval dispersal ability. On the side of population genetic structure, issues such as marker type and FST standardization may confound the relationship between PLD and population genetic structure. I recognize that my results are at odds with previous analyses and argue that
24 accurate estimates of dispersal and population connectivity will need to consider the interplay of all these factors in the framework of standardized F ST estimates. Such integrated research is currently being pursued with the development of coupled biological-physical models that are validated with genetic data (e.g. Dawson et al. 2005,
Pfeiffer-Hoyt and McManus 2005; see also Flow, Fish and Fishing biocomplexity project by D. Siegel and colleagues).
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33 Raw data Dfordata
pseudocurala ICu.cunoaria miniata
IAcropcll'8 cuneata paJifera
gibbosa gibbosa pa1mata clarkii
gigas 9 0 1050 7 10 8.5 0.98 maxima 10 0.156 9200 7 10 8.5 0.98 lrimaculatus 11 0.72 17500 22 26 23 1.38 12 0.015 1100 14 1.18
ascensionis 13 0.091 10500 43 56 71 1.86 monodon 14 0.01 SOOO 14 1.18 caurinus IS 0.036 2300 60 90 75 1.88 60 1.79
68 lOS
melaoopus atripectoralis 24 cluysozona 47 24
balaooides Iividus lisbethae
purpuratus 800 28 147 27 670 5 7 aruanus 28 1550 citrinellus 28 250 1550 xanthwa 28 0.0145 1550 25 1.41 glauca 28 0.0178 1450 25 1.41 f1avissimus 28 0.0208 15S0 45 1.66 diekii 28 0 1450 25 1.41 25 1.41
34 Appendix A· Contlnned · :swun Species Marker Phylum Class ability Sb'ODgylocentrotus droebacbiens microsatellite Echinodermata echinoid weak Cucumaria pseudocurnta mtDNA Echinodermata holothuroid weak Cucumaria mioiaIa mtDNA Echinodermata holothuroid weak Corisjulis microsatellite Vertebrata osteichthyes strong Stylophora . . aIlozvme Cnidaria anthozoa weak Pocillopora damicornis a110zyme Cnidaria anthozoa weak Acropora cuneata aIlozyme Cnidaria anthozoa weak Acropora palifera aIlozyme Cnidaria anthozoa weak Pocillopom damicornis a110zyme Cnidaria anthozoa weak Botrylloides magnicoecum aIlozyme Urochordata aacidacean weak Stolonica austraIis a110zyme Urochordata aacidacean weak Pyura gibbosa gibbosa aIlozyme Urochordata aacidacean weak Acropora paImata microsatellite Cnidaria anthozoa weak Ampbiprion clarkli aIlozyme Vertebrata osteichthyes strong Tridacna maxima a110zvme Mollusca bivalve weak Tridacna derasa a110zyme Mollusca bivalve weak Tridacna gigas a110zyme Mollusca bivalve weak Tridacna maxima a110zyme Mollusca bivalve weak Dascyllus trimacu1atus mtDNA Vertebrata osteichthyes strong CaIlIchirus is1agrande microsatellite Anhropoda crus:tm:ean strong Myripristis jacobus mtDNA Vertebrata osteichthyes strong Holocentru.s asc:ensionia mtDNA Vertebrata osteichthyes strong Penaeus monodon microsatellite Anhropoda crustacean strong 80bastes caurinus microsatellite Vertebrata osteichthyes strong Sebastes rastrelliaer microsatellite Vertebrata osteichthYes stron2 Sehastes auriculalus microsatellite Vertebrata osteichthyes strong Pachygmpsus crassipes mtDNA Anhropoda crustacean strong Myripristis bemdti mtDNA Vertebrata osteichthyes strong Eucyclogobius newbenyi mtDNA Vertebrata osteichthyes strong C1evelandiaios mtDNA Vertebrata osteichthyes strong Acanthocbromis pnlya.:anthus aIlozyme Vertebrata osteichthyes strong Ampbiprion melanopus aIlozyme Vertebrata osteichthyes strong Cbromis atripectoralis aIlozyme Vertebrata osteichthyes strong Pterocaesio cluysozona aIlozyme Vertebrata osteichthyes strong S ni2ricans aIlozvme Vertebrata osteichthyes stronR Synalpheus pectlniger aIlozyme Anhropoda crustacean strong 80mihalanus balanoides microsatellite Anhropoda crustacean strong Paracentrotus !ividus mtDNA Echinodermata echinoid weak Epiactis iisbethae a110zyme Cnidaria anthozoa weak Epiactis ritteri a110zyme Cnidaria anthozoa weak Anthopleura elegantissima aIlozyme Cnidaria anthozoa weak Strongylocentrotus pmpumtus aIlozyme Echinodermata echinoid weak Haliotis midae microsatellite Mollusca gastropod weak Dascyllus aruanus a110zyme Vertebrata osteichthyes strong Chaetodon citrinellus aIlozyme Vertebrata osteichthyes strong Chaetodon quadrimacu1atus aIlozyme Vertebrata osteichthyes strong Chromis lIlIDthura a110zyme Vertebrata osteichthyes strong Chrysiptera gIanca aIlozyme Vertebrata osteichthyes strong Forcipiger f1avissimus aIlozyme Vertebrata osteichthyes strong PJectrogIyphydodon dickii a1lozyme Vertebrata osteichthyes strong Pomacenttus pavo aIlozyme Vertebrata osteichthyes strong
35 Appendlx A· Continued · Log Log MIn Log MaX Species Habitat LogFst Distance PLD PLD Strongylocentrotus droebacbiens coastnl 0.00 3.02 1.46 217 Cu=narla pseudocurata intertidal 0.29 3.59 0 0 Cucumaria miniata coastnl O.oI 3.38 1.04 I.l8 Corisjulis coastnl 0.05 3.68 1.46 1.63 Stylophom llistillata reef 0.04 3.26 0 0.7 Pocillopora damlcornis reef 0.00 3.26 0.3 202 Acropora euneata reef 0.02 3.26 Acropom palifera reef O.oI 3.26 Poclllopora damlcornis reef 0.02 3.08 0.3 2.02 Bollylloides magnicoecum coastnl 0.08 2.28 0 0 Stolonica austraIis coastnl 0.08 2.15 0 0 Pyum gibbosa gibbosa coastnl 0.00 2.33 Acropora paImata reef 0.02 3.26 0.78 1.32 Ampbiprion clarkii coastnl 0.00 3.00 Tridacna I1IlIldma reef 0.00 3.02 0.9 1.04 Tridacna derasa reef O.oI 3.02 0.9 1.04 Tridacna gigas reef 0.00 3.02 0.9 1.04 Tridacna I1IlIldma reef 0.06 3.96 0.9 1.04 Dascyllus trimaculatus reef 0.24 4.24 1.36 1.43 Calliehirus islagrande coastnl 0.01 3.04 Myripristis jacobus reef 0.00 3.99 1.61 1.77 Holocentrus ascensionis reef 0.04 4.02 1.64 1.76 Penaeus monodon coastal 0.00 3.70 Sebastes caurinus coastnl 0.02 3.36 1.79 1.96 Sebastes rastreIliger reef 0.00 3.11 Sebnstes auriculntus coastnl 0.02 3.26 Pacbygmpsus cmssipes intertidal 0.00 3.40 1.84 2.04 Myripristis berndti reef 0.08 4.36 Eucyelogobius newbenyi coastal 0.28 2.85 0.6 1.49 CleveIandiaios estuary 0.01 2.85 I.l8 1.46 Acan1hocbromis polyacanthus reef 0.25 3.00 0 0 Amphiprion me1annpus reef 0.05 3.00 1.2 1.36 Chmmis atripectomIis reef 0.02 3.00 1.28 1.4 Pterocaesio ehIysozona reef 0.00 3.00 1.58 1.68 Stegastes nigricans reef 0.02 3.00 1.4 Synalpheus pectiniger reef 0.06 3.24 0.48 SemibaIaous balanoides intertidal 0.01 3.04 1.34 1.56 Paracentrotus lividus coastnl 0.00 3.40 1.32 1.61 Epiactis lisbethae intertidal 0.13 3.11 0 0 Epiactis ritteri intertidal 0.06 3.08 0 0 Anthopleum elegantissima intertidal 0.06 3.23 Strongylocentrotus purpumIUS intertidal 0.01 2.90 1.46 217 HaIiotis midae coastnl O.oI 2.83 0.78 0.9 Dascyllus aruanus reef O.oI 3.19 Chaetodon citrinellus reef 0.00 2.40 Chaetodon quadrimaculatus reef O.oI 3.19 Chmmis xanthum reef O.oI 3.19 Chrysiptera g1auca reef 0.01 3.16 Forcipiger flavissimus reef om 3.19 Pleetroglypbydodoo dield; reef 0.00 3.16 Pomacentrus pavo reef O.oI 3.19
36 Appendix A' Conllnued lJata UJollal· :study Min Max Avg Log Species Source Fst scale PLD PLD PLD PLD Sparisoma viride 29 0.0188 2000 47 80 57 1.76 Acanthochromis polyacantbus 30 0.0603 23 0 0 0 0.00 Ostmbinchus doederleini 30 0.0298 23 16 27 21 1.34 Pomacentrus coelestis 30 0 23 18 20 19 1.30 Sebastes atrovirens 31 0.0014 800 60 90 75 1.88 E' Ius morio 32 0 1000 30 40 40 1.61 Cynoscioo nebuJ08us 32 0.027 2300 20 132 CeUana ornata 33 0.83 1400 3 II 7 0.90 CeUana mdians 33 0.14 1440 3 II 7 0.90 Sebastes cmmeri 34 0.001 1100 90 1.96 Pseudopterogorgia elisabethae 35 0371 444 3 7 5 0.78 Haliotis cracherodii 36 0.039 300 5 IS 7.5 0.93 BalanophyUia elegans 37 0.2832 3000 I 5 3 0.60 BalanophyUia elegans 38 0.2 1100 I 5 3 0.60 Paracyatbus steamsii 38 0.0039 1100 35 1.56 PteraJlOROD kauderni 39 0.18 203 0 0 0 0.00 Bedew hanleyi 40 0.14 180 0 0 0 0.00 Cominella Iineolata 40 0.523 162 0 0 0 0.00 Morula marginalba 40 0.017 162 28 35 31.5 1.51 PatirieUa exigua 41 0.462 230 0 0 0 0.00 PstirieUa calcar 41 0 230 10 1.04 Uttorina saxatilis 42 0.078 288 0 0 0 0.00 Uttorina Iittorea 42 0.021 288 28 42 35 1.56 Pandalus borealis 43 0.0331 11000 90 120 105 2.03 Nasa vlamingii 44 0.065 16300 90 1.96 Chondrilla oucula 45 0.21 3000 I 2 2 0.48 Eupomacentrus partitus 46 0.1 125 28 1.46 Ostrea eduIis 47 0.019 11000 10 1.04 Batbygobius 8Oporator 48 0.18 2220 30 1.49 Seriatopom hystrix 49 0.089 610 I 0.30 Gasterosteus aca1eatas 50 0.024 2230 35 40 35 1.56 Echinometra lucunter 51 0.261 12700 14 1.18 Heliocidaris erytbrngramma 52 0.6218 3400 3 4 3.5 0.65 Heliocidaris tubercuJata 52 0.1171 1000 21 1.34 Aplysia ca1ifomica 53 0.0148 2490 35 1.56 StroollYlocentrotus fmnciscanus 54 0.031 1000 51 152 100 2.00 Acantbaster planci 55 0.07 12500 10 42 21 1.34 Acropora tennis 56 0.015 500 4 9 7 0.90 Stylophom pistillata 56 0.142 500 7 0.90 PhaUusia oigm 57 0.083 8000 0.5 I 0.8 0.24 Strongylocentrotus purpumtus 58 0.176 1500 48 74 60 1.79 Pomatoschiatas mlnutus 59 0.026 ISO 28 42 35 1.56 Chtbamalus mootagui 60 0.116 3500 19.5 131 Cbtbama1us stellatus 60 0.057 5700 23 138 Hemigmpsus oregonensis 61 0.192 1260 21 28 24.5 1.41 Acantburus triostegus 62 0.0886 1900 60 70 65 1.82 DascyUus aruaDUS 63 0.0076 1900 20 26 23 1.38 Acantburus triostegus 64 0.0881 1850 60 1.79 Acantburus triostegus 65 0.038 400 60 1.79 Slegastes olgricans 65 0.049 400 24 1.40 EPinephelus merm 65 0 400 39 1.60
37 Appendix A' Continued · "WlID Species Marker Phylum Class ability Sparisoma viride allozyme Vertebrata osteichthyes strong Acanthocbromis polyacanthus microsateIIite Vertebrata osteichthyes weak Ostorhinchus doederleini microsateIIite Vertebrata osteichthyes strong Pomacentrus coelestis microsateIIite Vertebrata osteichthyes strong Sebastes atrovirens microsateIIite Vertebrata osteichthyes strong Epinephelus morio mtDNA Vertebrata osteichthyes strong Cynoscion nebulOSWI mtDNA Vertebrata osteichthyes strong Cellana ornata mtDNA MoUusca gastropod weak CeUana radians mtDNA MoUusca gastropod weak Sebastes crmneri microsateIIite Vertebrata osteichthyes strong Pseudopterogorgia eIIsabethae microsateIIite Cnidaria anthozoa weak HaIlotis cracherodii ailozyme MoUusca gastropod weak BaianophyIIia elegans allozyme Cnidaria anthozoa weak BaianophyIIia elegans allozyme Cnidaria anthozoa weak Paracyathus steamsii allozyme Cnidaria anthozoa weak kaudemi microsateIIite Vertebrata osteichthyes strong Bedeva hanIeyi allozyme MoUusca gastropod weak Cominella Iineolata allozyme MoUusca gastropod weak Morula marglnaIba ailozyme Mollusca gastropod weak Patirielia exigua allozyme Echinodermata asteroid weak PatirieIIa culcar allozyme Echinodermata asteroid weak Uttorina saxatiIis ailozyme Mollusca gastropod weak Uttorina Httorea allozyme Mollusca gastropod weak PandaIus borealis allozyme Arthropoda crustacean strong Nasa vIamingii mtDNA Vertebrata osteichthyes strong ChondriIIa nucuIa allozyme Porifera demospongiae weak Eupomacentrus partitus allozyme Vertebrata osteichthyes strong Ostrea edulis microsateIIite Mollusca bivaive weak Bathygobius soporator allozyme Vertebrata osteichthyes strong Seriatopora hystrix microsateIIite Cnidaria anthozoa weak Gasterosteus aculeatus microsateIUte Vertebrata osteichthyes strong Echinometra lucunter mtDNA Echinodermata echinoid weak HeIIocidaris erytbrogramma mtDNA Echinodermata echinoid weak Heliocidaris tubercuIata mtDNA Echinodermata echinoid weak Aplysia culifomlca microsateIIite MoUusca gastropod weak Strongylocentrotus fnmciscanus allozyme Echinodermata echinoid weak Acanthaster planci allozyme Echinodermata asteroid weak Acropora tennis ailozyme Cnidaria anthozoa weak Stylophora pistillata allozyrae Cnidaria anthozoa weak Phallusia nigra allozyme Urochordata ascidecean weak Strongylocentrotus purpuratus mtDNA Echinodermata echinoid weak Pomatoschistus minutus microsateUite Vertebrata osteichthyes strong Chthamalus montagui allozyme Arthropoda crustacean strong Chthamalus stellatus allozyme Arthropoda crustacean strong Hemigrapsus megonensis mtDNA Arthropoda cmstacean strong Acanthurus triosteRus alloZYme Vertebrata osteichthyes strong Dascyllus 8IUBDUS ailozyme Vertebrata osteichthyes strong Acanthurus triostegus allozyme Vertebrata osteichthyes strong Acanthurus triostegus allozyme Vertebrata osteichthyes strong Stegastes nigricans allozyme Vertebrata osteichthyes strong Epinephelus merra allozyme Vertebrata osteichthyes strong
38 Appendix A' Continued · Log Min Log Max Species Habitat LogFst o!:ce PLD PLD Sparisoma viride reef 0.01 3.30 1.68 1.91 Acanthochromis polyacanthus reef 0.03 1.36 0 0 Ostorhiru:bus doederleini reef O.oI 1.36 1.23 1.45 Pomacenttus coelestis reef 0.00 1.36 1.28 1.32 Sebastes atrovirens coastal 0.00 2.90 1.79 1.96 Epinephelus morlo reef 0.00 3.00 1.49 1.61 Cynoscion nebulosus estuary O.oI 3.36 Cellana ornata intertidal 0.26 3.15 0.6 1.08 Cellana radians intertidal 0.06 3.16 0.6 1.08 Sebastes cramerl coastaI 0.00 3.04 Pseudopterogorgia e1isahethae reef 0.14 2.65 0.6 0.9 Haliotis CrIIl)herodii intertidal 0.02 2.48 0.78 1.2 Balanopby1lia elegans intertidal 0.11 3.48 0.3 0.78 Balanopbyllia elegans intertidal 0.08 3.04 0.3 0.78 Paracyathus steamsii coastal 0.00 3.04 Pterapogon kaudemi reef 0.07 2.31 0 0 Bedeva hanleyi estuary 0.06 2.26 0 0 Cominella Iineolala coastal 0.18 2.21 0 0 Morula marginalba coastaI 0.01 2.21 1.46 1.56 Patirlella exigua coastaI 0.16 2.36 0 0 Patirlella calcar coastaI 0.00 2.36 Littorlna saxatiJis intertidal 0.03 2.46 0 0 Littorlna Iittorea intertidal 0.0\ 2.46 1.46 1.63 Pandalus borealis coastaI 0.01 4.04 1.96 2.08 Naso vlamingii reef 0.03 4.21 Chondrilla nucuIa coastaI 0.08 3.48 0.3 0.48 Eupomacenttus partitus reef 0.04 2.\0 Ostrea edalis coastaI 0.01 4.04 Bathygobius soporator coastaI 0.07 3.35 Serlatopora bystrix reef 0.04 2.79 Gasterosteus acuIeatus coastal 0.01 3.35 1.56 1.61 Echlnometra lucunter coastal 0.\0 4.\0 Heliocidaris eJYIbrogramma intertidal 0.21 3.53 0.6 0.7 Heliocidaris tubercuJata intertidal 0.05 3.00 Aplysia califomica intertidal 0.01 3.40 Stronavlocentrotus franciscanus coastaI 0.01 3.00 1.72 2.18 Acanthaster planci reef 0.03 4.\0 1.04 1.63 Acropora tenuis reef 0.0\ 2.70 0.7 1 Stylophora pistillata reef 0.06 2.70 PhaIlusia nigra intertidal 0.03 3.90 0.18 0.3 Strongylocentrotus intertidal 0.07 3.18 1.69 1.88 Pontatoschlstus minutus coastal 0.01 2.18 1.46 1.63 Chthamalus montagui intertidal 0.05 3.54 Chthama1us ste1latus intertidal 0.02 3.76 Hemigrapsus oregonensis estuary 0.08 3.10 1.34 1.46 Acanthurus triostegus reef 0.04 3.28 1.79 1.85 Dascyllus aruanus reef 0.00 3.28 1.32 1.43 Acanthurus triostegus reef 0.04 3.27 Acanthurus triostegus reef 0.02 2.60 StegaSIes nigricans reef 0.02 2.60 Epinephelus merra reef 0.00 2.60
39 bifasciatum 66 0.0002 3000 38 78 45 Digricaudus 67 0.224 792 18 hubbsi 67 0.081 365 24 67 0 484 50 68 0.008 2400 21 28 24.5 1.41
chirurgus bivittatus 70 0.77 5100 24.2 1.40 maculipinna 70 0.88 3200 29 1.48 poey 70 0.23 5100 25 1.41 70 0.83 4200 26 1.43
72 0.38 900 1 4 2.5 0.54 72 0.03 900 1 4 2.5 0.54 fasciolatu. 73 0.003 2500 34 35 34.5 1.55
1eucostictus blvittatus atIanticus
tbompsoDi lou!sianensis monodon
nodosa chilensis
82 0.001 1280 21 42 32 1.52 83 0.444 760 0 0 0 0.00 83 0.002 720 21 28 24 1.40 83 60 1.79 83 60 120 70 1.85 83 0.017 60 120 53 1.73 holderi 83 0.028 60 1.79 punctipinDis 83 0.003 35 1.56 anaIi.
undatum 84 0.014 5410 0 0 ebanos 85 0.041 10000 14 28 morio 86 0 1870 30 50
40 Appendix A· Continued · "WIDI Species Marker Phylum Class ability Haemulon flavolineatum microsateIlite Vertebrata osteichthyes strong Thalassoma bifasciatum microsateIlite Vertebrata osteichthyes strong Axoclinusnigricautius mtDNA Vertebmta osteichthyes strong Malacoctenus hubbsi mtDNA Vertebrata osteichthyes strong Ophioblennius steindachneri mtDNA Vertebrata osteichthyes strong Pecten jacobaeus allozyme Mollusca bivalve weak Pecten maximns allOZYJDe Mollusca bivalve weak Acanthurus chirurgus mtDNA Vertebrata osteichthyes strong Halichoeres bivittatus mtDNA Vertebrata osteichthyes strong Halichoeres maculipinna mtDNA Vertebrata osteichthyes strong Halichoeres poey mtDNA Vertebrata osteichthyes strong Halichoeres radiatua mtDNA Vertebrata osteichthyes strong Crassostrea virginica microsateIlite Mollusca bivalve weak Mo_ annularis microsateI1ite Cnidaria anthozoa weak Montastraea faveolatu microsateIlite Cnidaria anthozoa weak Stegastes fascio1atus allozyme Vertebrata osteichthyes strong Haemulon flavolineatum mtDNA Vertebrata osteichthyes strong Abudefduf saxatilis mtDNA Vertebrata osteichthyes strong Stegastes leucostictus mtDNA Vertebrata osteichthyes strong Halicboeres bivittatus mtDNA Vertebrata osteichthyes strong Ophioblennius atIanticus mtDNA Vertebrata osteichthyes strong Thalassoma bifasciatum mtDNA Vertebrata osteichthyes strong Holocenlnls ascensionis mtDNA Vertebrata osteichthyes strong Gnatholepis thompsoni mtDNA Vertebrata osteichthyes strong Lepidophthalmus IOllisianensis allozyme Anhropoda CIUStacean strong Penaeus monodon allozyme Anhropoda crustacean strong BIacatinus evelynae mtDNA Vertebrata osteichthyes strong AdaIaria proxima allozyme Mollusca gastropod weak Goniodoris nodosa allozyme Mollusca gastropod weak MytiIus chilensis allozyme Mollusca bivalve weak Seriatopora hystrix microsatellite Cnidaria anthozoa weak Holothurla nobills allozyme Echinodennatu holothuroid weak Panopea abrupta allozyme Mollusca bivalve weak Panopea abrupta microsatellite Mollusca bivalve weak Bmbiotoca jacksoni allozyme Vertebrata osteichthyes weak ParaIabrax clathratus allozyme Vertebrata osteichthyes strong GireIIa nigricans allozyme Vertebrata osteichthyes strong Lyduypnus dalli allozyme Vertebrata osteichthyes strong Semicossyphus pulcher allozyme Vertebrata osteichthyes strong ADoclinus hnlderi allozyme Vertebmta osteichthyes strong Chromis punctipinnis allozyme Vertebrata osteichthyes strong CIinocottus anaIis allozyme Vertebrata osteichthyes strong MediaIuna californiensis allozyme Vertebrata osteichthyes strong Buccinum ImdahlJD microsatellite Mollusca gastropod weak Chanos chanos allozyme Vertebrata osteichthyes strong Epinephelus morio microsatellite Vertebrata osteichthyes strong Lu\janus erythropterus mtDNA Vertebrata osteichthyes strong
41 Appendix A· Continned · LogMln Log Max Species Habitat LogFst D~ce PW PW Haemulon f1avolineatum reef 0.00 3.48 1.15 1.32 Thalassoma bifasciatum reef 0.00 3.48 1.59 1.9 Axoclinus nigricaudus reef 0.09 2.90 MaIacoctenus hubbsi coastal 0.03 2.56 Opbloblennius stelndachneri coastal 0.00 2.68 Pecten ilU:Obaeus coastal 0.00 3.38 1.34 1.46 Pecten maximus coastal 0.00 3.38 1.34 1.46 Acanthurus ehlnugus reef 0.01 3.94 1.75 1.86 HaJieh"""", bivittatus reef 0.25 3.71 HaJieh"""'" maculipinna reef 0.27 3.51 HaJich"""'" poey reef 0.09 3.71 HaJich"""", radiatus reef 0.26 3.62 Cmssostrea virginica estumy 0.00 2.48 1.18 1.34 Montastraea annularis reef 0.14 2.95 0.3 0.7 Montastraea faveo1ata reef 0.01 2.95 0.3 0.7 Stegnstes fasciolntus coastal 0.00 3.40 1.54 1.56 Haemulon f1avolineatum reef 0.09 3.49 1.15 1.32 Abudefduf saxatilis reef 0.09 3.49 1.26 1.48 Stegnstes leucostictus reef 0.13 3.49 1.3 1.49 HaJieh"""'" bivittatus reef 0.11 3.49 1.36 1.43 Opbloblennius atlanticus reef 0.08 3.49 1.46 1.48 ThaIassomn bifasciatum reef 0.14 3.49 1.59 1.9 Holocentrus ascensionis reef 0.11 3.49 1.67 1.71 Gnatholepis thompsoni reef 0.09 3.49 1.78 2.09 LepidophtbolmllS louisianensis estumy 0.09 3.15 Penaeus monodon coastal 0.01 3.45 E1acatinus evelynae reef 0.25 3.30 1.34 1.41 AdaIaria proxima intertidal 0.12 3.20 0.3 0.48 Goniodoris nodosn intertidal 0.00 3.20 1.85 1.96 Mytllus ebllensis intertidal 0.01 3.26 Seriatopora hystrix reef 0.06 1.78 0.08 0.48 Holothuria nobi1is reef 0.00 3.11 Panopea abrupta coastal 0.00 3.11 1.34 1.63 Panopea abrupta coastal 0.00 3.11 1.34 1.63 Bmbiotoca jackson! reef 0.16 2.88 0 0 Paralabrax elathratus coastal 0.00 2.86 1.34 1.46 Girella nigricans coasta1 0.00 2.84 1.79 2.08 Lythrypnus daIJi coasta1 0.00 2.70 1.79 2.08 Semicossyphus pulcher coastal 0.01 2.88 1.79 2.08 Alloclinus holder! coastal 0.01 2.81 Cbromis punctipinnis coasta1 0.00 2.88 CIinocottus anaIis coasta1 0.02 2.84 Medialuna ca1ifomiensis coasta1 0.00 2.84 Bucclnum undatum coasta1 0.01 3.73 0 0 Chanos ebanos estumy 0.02 4.00 1.18 1.46 Epinepbelus morio reef 0.00 3.27 1.49 1.71 Lu~anus erythropterus reef 0.04 3.48
42 Appendix B: Frequency statistics for surveyed species
N Porifera 1 Arthropoda 11 Echinodermata 14 Phylum Cnidaria 19 Mollusca 24 Chordata (Urochordata) 4 Chordata (Vertebrata) 76
Demospongiae 1 Holothuroidea 3 Asteroidea 3 Crustacea 11 Class Echinoidea 8 Bivalvia 11 Gastropoda 13 Anthozoa 19 Ascidacean 4 Osteichthyes 76
Estuary 7 Habitat Intertidal 24 Coastal 46 Reef 72
Microsatellite 31 Marker mtDNA 38 Allozvme 80 Larval swimming ability ~6.0cmls Weak 65 >20cmls Strong 84
43 Appendix C: Full ANCOVA output Full ANCOV A output of biological and physical effects on reported population genetic structure includinl!: sums of s uares and all interaction terms. (n = 129. ? =0.470) Type IV Degrees of Mean Source F Sig SS freedom Square Corrected .254 17 .015 5.781 .000 Model Intercept .060 I .060 23.347 .000 Logl>LD .024 I .024 9.464 .003 Marker .037 2 .018 7.113 .001 Habitat .005 2 .003 .996 .372 Swim 2.77E-006 I 2.77E-006 .001 .974 Marker * .012 4 .003 1.186 .321 Habitat Marker * Swim .000 2 .000 .091 .913 Habitat * Swim .015 2 .007 2.844 .062 Marker * .013 3 .004 1.688 .174 Habitat * Swim Error .286 III .003 Total .778 129 Corrected .540 128 Total
Full ANCOVA output of biological and physical effects on reported population genetic structure including sums of squares and all interaction terms. PLD term refers to all available data (includinl!: zero-value PLD). (n = 142; r = 0.484) Type IV Degrees of Mean Source F Sig SS freedom Square Corrected .340 17 .020 6.847 .000 Model Intercept .180 1 .180 61.529 .000 LogPLD .097 1 .097 33.108 .000 Marker .043 2 .021 7.299 .001 Habitat .005 2 .002 .782 .460 Swim .001 I .001 .330 .567 Marker * .014 4 .003 1.177 .324 Habitat Marker * Swim .001 2 .000 .130 .878 Habitat * Swim .014 2 .007 2.332 .101 Marker '" .017 3 .006 1.932 .128 Habitat'" Swim Error .362 124 .003 Total 1.057 142 Corrected .702 141 Total
44 Appendix D: Database sources (column 2 of Appendix A)
1. Addison JA, Hart MW (2004) Analysis of population genetic structure of the green sea urchin (Strongylocentrotus droebachiensis) using microsatellites. Marine Biology 144:243-251
2. Arndt A, Smith MJ (1998) Genetic diversity and population structure in two species of sea cucumber: differing patterns according to mode of development. Molecular Ecology 7:1053-1064
3. Aurelle 0, Guillemaud T, Afonso P, Morato T, Wirtz P, Santos RS, Cancelo ML (2003) Genetic study of Coris julis (Osteichtyes, Perciformes, Labridae) evolutionary history and dispersal abilities. Comptes Rendus Biologies 326:771- 785
4. Ayre OJ, Davis AR, Billingham M, Llorens T, Styan C (1997) Genetic evidence for contrasting patterns of dispersal in solitary and colonial ascidians. Marine Biology 130:51-61
5. Ayre OJ, Hughes TP (2000) Genotypic diversity and gene flow in brooding and spawning corals along the Great Barrier Reef, AustraIia. Evolution 54:1590-1605
6. Ayre OJ, Hughes TP, Standish RJ (1997) Genetic differentiation, reproductive mode, and gene flow in the brooding coral Pocillopora damicomis along the Great Barrier Reef, Australia. Marine Ecology Progress Series 159:175-187
7. Baums m, Miller MW, Hellberg ME (2005) Regionally isolated popUlations of an imperiled Caribbean coral, Acropora palrrulfa. Molecular Ecology 14:1377-1390
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