microsatellite dna analysis and hydrodynamic modelling reveal the extent of larval transport and...

Microsatellite DNA analysis and hydrodynamic modellingreveal the extent of larval transport and gene flow betweenmanagement zones in an exploited marine fish (Glaucosomahebraicum)

OLIVER BERRY,1* PHILLIP ENGLAND,2

DAVID FAIRCLOUGH,3 GARY JACKSON3

AND JIM GREENWOOD1

1CSIRO Wealth from Oceans National Research Flagship andDivision of Marine and Atmospheric Research, PMB 5,Wembley, WA 6913, Australia2CSIRO Marine and Atmospheric Research, G.P.O. Box 1538,Hobart, TAS 7004, Australia3Western Australian Fisheries and Marine Research Laboratories,

Department of Fisheries, Government of Western Australia,P.O. Box 20, North Beach, WA 6920, Australia

ABSTRACT

Determining the extent of dispersal in exploitedmarine fishes is essential for understanding their pop-ulation dynamics and optimising management. TheWest Australian dhufish, Glaucosoma hebraicum, is ahighly sought-after, large and long-lived reef-dwellingspecies, endemic to south-western Australia. Stockassessments indicate that this indicator species isoverexploited. The fishery is managed using a zone-based system, which implicitly assumes a high degreeof demographic independence among zones. Whiletagging studies indicate limited movement of adultG. hebraicum, there is no understanding of the spatialscale of dispersal of its larvae and thus the true extentof interdependence of management zones. We analy-sed 13 microsatellite DNA loci to characterise theextent of gene flow, and conducted particle trackingsimulations to model larval transport in this species.Genetic data demonstrated that some local recruit-ment was likely, but that on a broad scale gene flowbetween the management zones was extensive, and theentire fishery represents a single genetic stock.Hydrodynamic modelling predicted that the majorityof dhufish larvae recruit from within the management

zone where they are spawned, and that inter-annualvariation in current velocities has limited effect on theextent of larval transport. Because management zonesare likely to be largely independent in terms of bothlarval and adult recruitment, heavy localised fishingpressure has the potential to reduce the abundanceand reproductive capacity of this species in highlyfished areas, but it should have limited impact onneutral genetic diversity.

Key words: fish, fisheries, West Australian dhufish,larval dispersal, microsatellite, particle tracking

INTRODUCTION

Spatial management is often used as part of a suite ofmethods for managing fisheries. Usually zoning offisheries implicitly or explicitly attempts to capturedemographically or genetically cohesive ‘stocks’,because this permits relationships between productiv-ity and rates of extraction to be clearly understood(Hilborn and Walters, 1992), and ⁄ or, in the case ofgenetic stocks, retains genetic resources that willensure resilience to environmental heterogeneity andchange (Allendorf et al., 2008). Yet, the inherentdifficulty of characterising patterns of connectivity inmarine environments means that delineating biologi-cally meaningful stocks remains a long-standingchallenge for fisheries managers (Pineda et al., 2007).

A range of techniques have been applied to thisproblem, including analysis of otolith microchemistry,tagging studies, hydrodynamic modelling of egg andlarval dispersal, and microsatellite DNA analysis (e.g.,Hutchinson et al., 2001; Nahas et al., 2003; Newmanet al., 2009). Each method has its limitations in whatit may reveal. For example, identification of stockrelationships using otolith microchemistry relies ondetecting existing geographic variation in chemicalsignatures at the scale of interest, which may not exist(Campana, 1999). Hydrodynamic modelling is a rap-idly developing and increasingly sophisticated appli-cation, but without empirical validation it is difficult

*Correspondence. e-mail: [email protected].

Received 23 August 2010

Revised version accepted 7 December 2011

FISHERIES OCEANOGRAPHY Fish. Oceanogr. 21:4, 243–254, 2012

� 2012 Blackwell Publishing Ltd. doi:10.1111/j.1365-2419.2012.00623.x 243

to determine how well particle modelling is able torealistically represent biological processes in the mar-ine environment (Leis, 2003; Selkoe et al., 2008).Finally, neutral genetic markers have low power toquantify dispersal when populations are large anddispersal is extensive (Whitlock and McCauley,1999), and these are common conditions in the mar-ine environment (Palsbøll et al., 2007). Recentexamples have demonstrated that multidisciplinaryapproaches can largely overcome these individuallimitations and reveal aspects of the temporal andspatial extent of dispersal not evident from individualdatasets (Baums et al., 2006; Selkoe et al., 2006;Botsford et al., 2009; Lowe and Allendorf, 2010).

In this study we employ both hydrodynamic mod-elling and genetic analysis to characterise the stockstructure of an important species in a south-westernAustralian fishery situated in the South-eastern IndianOcean. This is a region of low productivity and itexperiences unusual oceanographic conditions, nota-bly, a pole-wards flowing boundary current (theLeeuwin Current; Smith et al., 1991). We focus on theWest Australian dhufish, Glaucosoma hebraicum, alarge (maximum length ca. 120 cm), long-lived(maximum age ca. 40 yr) reef-dwelling speciesrestricted to the continental shelf waters of WesternAustralia between Esperance (34�S 122�E) and SharkBay (26�S 113�E), which represents a range ofca. 1800 km (Hutchins and Swainston, 1996; Hespet al., 2002). This species is among the most importantexploited fish in the region, being much sought afterby both recreational and commercial fishers and is akey indicator species for the status of demersal fishresources along the lower west coast of Australia(Fairclough et al., 2010). As part of the multi-speciesWest Coast Demersal Scalefish Fishery (WCDSF),West Australian dhufish are managed towards catchlimits via latitudinal management zones and otherinput and output controls (Fairclough et al., 2010;Fig. 1). Age structure data and assessments of fishingmortality demonstrate that overfishing of dhufish hasoccurred along the lower west coast (Wise et al., 2007;Fairclough et al., 2010).

The present zoning of the WCDSF does not takeaccount of specific biological information regardingthe dynamics of recruitment in dhufish or otherexploited species (Lenanton et al., 2009a). Yet, thecapacity of management zones to sustain abundancesof exploited species depends upon the dynamics ofrecruitment, and in particular the extent of dispersalbetween zones and the age classes contributing todispersal (Kenchington, 1990; Hill et al., 2002; Has-tings and Botsford, 2006). Tagging of adult dhufish

suggests that their movement is limited (StJohn et al.,2009b) and this view is supported by results from apilot study of stable isotopes in crushed whole otoliths,which showed evidence of differentiation amongsamples from each of the WCDSF zones (StJohn et al.,2009a). However, the larval phase is considered to bethe primary period of dispersal in many marine fishes(Ward, 2006), but nothing is known about the extentof such dispersal for dhufish (StJohn et al., 2009a).

Like other marine fishes with pelagic larval stages(ca. the first 20–30 days), the dispersal of larval dhu-fish is likely to be profoundly influenced by oceaniccurrents. There is extensive evidence that the pole-ward flowing Leeuwin Current is responsible for thelong-distance transport of marine fauna in the south-western Australian region (Hutchins and Pearce,1994; Caputi et al., 1996; Beckley et al., 2009; Pearceand Hutchins, 2009). Furthermore, genetic studieshave supported the view that this current promoteslarval transport and genetic homogeneity in marinefishes throughout the region (Ayvazian et al., 2004;Watts and Johnson, 2004).

Figure 1. Map of the study region indicating the samplelocations (circles), and zones of the West Coast DemersalScalefish Fishery (demarcated by dashed lines). Circles mayrepresent more than one sample. Light grey arrow indicatethe approximate trajectory of the Leeuwin current withexamples of its associated eddies, and dark grey arrow indi-cates the approximate trajectory of the Capes Current (seeFeng et al., 2009). Triangles indicate origins of transectsextending perpendicular to the coast, from which particleswere released for tracking simulations. Transect locations arenumbered 1–24 from north to south, and these numberscorrespond to those shown on Fig. 3.

244 O. Berry et al.

� 2012 Blackwell Publishing Ltd, Fish. Oceanogr., 21:4, 243–254.

However, in addition to the strong polewardstransport, strong eddies spin off from the main Leeu-win Current, creating cross-shelf flows at characteristiclocations that may advect larvae offshore and disruptalongshore connectivity (Caputi et al., 1996; Gau-ghan, 2007; Feng et al., 2010, 2011). In addition,during the austral summer (December-February),when most spawning occurs in dhufish, wind-drivencurrents drive flows along the west coast northwardsand inshore of the Leeuwin current (the Capes Cur-rent; Pearce and Pattiaratchi, 1999; Feng et al., 2010),and these are believed to transport the larvae ofmarine fishes (Lenanton et al., 1996, 2009b).Although there is an expectation that hydrodynamicsshould largely dictate the extent of larval dispersal andgene flow in the dhufish, empirical data on how this isrealised is lacking, and there is potential for complexpatterns to be generated. There is also increasingevidence that some larval fish are capable of strongactive swimming, homing and other behaviours thatmay limit the influence of ocean currents (Leis, 2003;Christie et al., 2010). In light of the current status ofdhufish stocks, there is a need to better understand theextent of larval dispersal and gene flow in this speciesamong the management zones of the WCDSF, and toreveal the hydrodynamic processes that underpinthose patterns.

In this study, we model the transport of dhufish eggsand larvae with hydrodynamic simulations thatencompasses the majority of the range of the dhufish,including the southern coastline of Western Australia.We also employ fine-scale sampling and analysis ofmicrosatellite DNA variation to characterise theextent of gene flow throughout the range of this spe-cies, and to determine whether the hydrodynamicmodel could predict patterns of gene flow. Ourobjective was to determine how well the observedpatterns of gene flow and larval transport are accom-modated by the existing management zones.

METHODS

Sampling

Samples were obtained from recreational and ⁄ orcommercial fishers between Kalbarri (21.51�S,114.00�E) and Esperance (34.07�S, 121.99�E), inWestern Australia between November 2007 andAugust 2008 (Fig. 1). Adipose fin clip samples (ca.5 · 25 mm) were taken and stored in NaCl saturatedDMSO. A total of 466 fish were sampled, and of these,ages of 392 fish (84.4%) were determined followingvalidated methods using otolith growth zones (Hesp

et al., 2002). The majority of sampling locations wereobtained from fishers to the nearest five nautical miles(9.3 km), but some samples had more precise GPScoordinates recorded.

Laboratory and statistical analysis

DNA was extracted with a 96-well silica ⁄ guanidinethiocyanate method as described by Ivanova et al.(2006). Eluted DNA was diluted 1 ⁄ 3 for PCR analysis.Thirteen microsatellite DNA loci were amplified withPCR according to methods described in Burridge andEngland (2009). Products were analysed on an ABI3700 fragment analyser, and alleles were allocated tobins by eye with Genemarker software (Softgenetics,State College, PA, U.S.A.).

The total dataset was checked for variability anddepartures from Hardy–Weinberg and gametic-phaseequilibrium with the exact test based on the methodsof Guo and Thompson (1992) and implemented inthe program Genepop’007 (Rousset, 2008). Signifi-cant departures from Hardy–Weinberg equilibriumwere detected in some loci ⁄ sample region combina-tions and all further analysis was based on either 13loci where analysis did not assume Hardy–Weinbergequilibrium or ten loci when analysis did make thatassumption. An Fst outlier analysis (Beaumont andNichols, 1996) was implemented to test whether lociwere under balancing or positive selection. Thisanalysis was implemented with the program Lositan(Antao et al., 2008). In this analysis we ran 20 000simulations to establish the null distribution of therelationship between genetic subdivision (Fst) andlevel of heterozygosity expected under Hardy–Wein-berg conditions (Hexp) assuming an infinite allelemodel of mutation.

We used Mantel’s permutation approach (Mantel,1967) to test for a significant correlation betweengenetic subdivision (mean Fst) and geographic dis-tance when samples were grouped into ten half-degreelatitude blocks along the west coast (permitting meansample size = 42.4, range 15–79). We also employedMantel tests of correlations between the probability ofparticle transport between blocks and Fst. Thesecalculations were made with the program ZT (Bonnetand Van de Peer, 2002), and employed 3 628 800permutations of the distance matrices. The probabilityof transport between blocks was estimated from par-ticle tracking simulations (see details below). Forblock-wise comparisons we seeded particles withineach block in a region of three 10 · 10 km cellsrunning in a transect starting immediately adjacent tothe coastline (i.e., within 10 km) and running per-pendicular to it. We recorded particle settlement

Multidisciplinary assessment of marine fish dispersal 245


within these same cells. Mean Fst was calculated withGenepop’007 (Rousset, 2008).

We tested for genetic spatial autocorrelation amongsamples with the macro available in GENALEX 6(Peakall and Smouse, 2006). We used a minimumdistance class of 10 km (ca. 6 nautical miles), and10 km intervals up to 150 and 50 km intervalsthereafter. The analysis was run with 999 permutationsof the data to obtain null distributions of the auto-correlation statistic r. Analysis was conducted with the‘multiple populations’ option in GENALEX wherepopulations represented fish of each age class between6 and 14 (samples sizes 18–74, mean 34.2 ± 4.7 SE).This analysis was conducted so that inter-year settle-ment effects would not obscure cohort-specific spatialprocesses. Multiple population analysis is appropriatewhere a common process dictates the pattern of spatialgenetic structure in more than one population(Peakall and Smouse, 2006).

Samples were obtained from four broad samplingregions, three of which represented the three southernmanagement zones defined by the WCDSF (Fairc-lough et al., 2010), and the fourth representing sam-ples collected outside this fishery to the south and eastof the South-west WCDSF fishery zone (Fig. 1). Fif-teen samples were collected north of the Mid-westzone and these were included with Mid-west zonesamples. The sample sizes for each of the four regionswere as follows: Mid-west 179, Metro 139, South-west126, South-east 22. The degree of genetic subdivisionamongst sampling regions overall and between pair-wise combinations as estimated by Fst (h; Weir andCockerham, 1984) was calculated with Genepop’007(Rousset, 2008). Exact tests based on genic andgenotypic data were employed to test for significantdifferentiation under Hardy–Weinberg and nonHardy–Weinberg equilibrium conditions respectively.Exact tests were also employed to test for significantgenic differentiation between dhufish cohorts thatsettled between 2002 (age 6 at capture) and 1994(aged 14 at capture).

We also used the model-based clustering approachesimplemented in the program STRUCTURE 2.2(Pritchard et al., 2000) to investigate the presence ofgenetic structure without assuming a priori geneticstructure defined by WCDSF management zones. Theprogram STRUCTURE clusters individuals in such away that it minimises departures from Hardy–Wein-berg and gametic-phase equilibrium. We ran ten rep-licate runs at each value of K (number of populations)between 1 and 20 assuming that allele frequencieswere correlated between populations and the popula-tions admixed. Runs consisted of a burn-in phase of

100 000 iterations followed by 150 000 iterationswhere data were collected. Separate runs of STRUC-TURE were conducted where the location of thesample (one of the four sampling regions) was incor-porated as prior information for the model and wherethis information was not incorporated. The extent ofclustering was assessed with the DK method (Evannoet al., 2005).

Hydrodynamic modelling

We modelled the transport of larval fish with aLagrangian particle tracking simulation nested withina global hydrodynamic model generated from theBlueLink Reanalysis (BRAN; Schiller et al., 2008).BRAN is based on the Ocean Forecasting AustraliaModel (OFAM) of ocean circulation, which has aresolution of 1 ⁄ 10� in the Asian-Australian region. Itassimilates observations from satellite SST, altimetricsea-level anomalies, and temperature and salinityprofiles from the Argo float array (Schiller et al.,2008). Comparison of the outputs of BRAN withsurface drifters indicate that it quantitatively repro-duces the shelf circulation in the Australian region,with the observed and reanalysed sea level anomalieshaving a correlation of greater than 0.8 (Schiller et al.,2008). Particle tracking analysis was facilitated by theConnIe2 computer program (http://www.csiro.au/connie2; updated from Condie et al., 2005). ConnIe2enables estimation of the probability that any tworegions are connected by modelled ocean circulationover a specified period of dispersal. It is based onoffline particle tracking within a three-dimensionalhydrodynamic model and incorporates informationfrom wind fields, temperature, salinity, sea level andtides (see Condie et al., 2005; Condie and Andre-wartha, 2008; Schiller et al., 2008). Connie2 alsopermits larvae to have a range of swimming abilitiesand multiple behavioural phases. Particles werereleased at 24 approximately equally-spaced locationsbetween Kalbarri and Walpole and within a transectcomposed of three 10 · 10 km cells running perpen-dicular to the shore (Fig. 1). Although dhufish areknown to spawn throughout their range (Hesp et al.,2002), little is known of the typical depth at whichthey spawn and there is no information on the depthsat which larvae settle. Release transects were used toencompass the likely range of depths and distancesfrom shore where dhufish are likely to spawn (depths10–200 m; Hutchins and Swainston, 1996). Particleswere tracked for a larval duration of 21 days based onthe development of deep-water adapted eyes atapproximately this age (Shand et al., 2001), and theassumption that larvae settle close to the bottom at

246 O. Berry et al.


this time. We initially simulated a generalised larvalfish behaviour that included diurnal vertical migrationof the larvae between the approximate mixed layerdepth during the day (20–30 m; 12 h), and 0–10 mduring the night (Keesing et al., 2006; Leis, 2010).However, considering that ontogenetic changes inlarval behaviour are highly likely (Leis, 2010), we alsomodelled more complex larval behaviour based oninformation available for the dhufish. Dhufish eggs arebuoyant and hatch after ca. 32 h (Pironet and Neira,1998). Eye development of dhufish larvae suggests thatthey are likely to occur in surface waters for the firstweek after hatching (Shand et al., 2001). After thisfirst week the larvae may begin exploiting increasinglydeeper water (Shand et al., 2001), and their early-forming pelvic fins suggests that they develop a rela-tively early specialisation for the pelagic environment,prior to settling in demersal habitats (Pironet andNeira, 1998). We therefore simulated larvae remain-ing within the top 0–10 m for 7 days, followed by7 days of vertical migration between the 0–10 and10–20 m, followed by a final 7 days migrating between10–20 and the 20–30 m mixed zone. This sequenceattempts to represent the increasing exploitation ofdeeper waters in larval dhufish that is indicated byboth fin and retinal development (Pironet and Neira,1998; Shand et al., 2001). Simulated larval fishexhibited random movement at a rate of 2 cm s)1,

based on observations of their relatively sedentarybehaviour in captivity (G. Jenkins, pers. comm.). Ourmodel simulations were replicated on two hydro-dynamic data sets. The first encompassed a represen-tative year of relatively strong northwards CapesCurrent activity (Lenanton et al., 2009a) and wasbased on hydrodynamic observations from 2003. Thesecond encompassed a representative year of strongsouthwards Leeuwin Current activity and was based onobservations from 2000. Simulations were based onthe release of particles into the average currentvelocities experienced between December and March(inclusive), which encompasses the main spawningseason of dhufish (Hesp et al., 2002).

RESULTS

Descriptive statistics

Microsatellite genotypes were obtained from 466individual dhufish. The mean observed heterozygosity(0.48 ± 0.06 SE) and mean number of alleles per locus(5.94 ± 1.38 SE) are consistent with those reported byBurridge and England (2009; Table 1). Exact tests re-vealed that none of the regional samples (aftersequential Bonferroni correction) nor the combineddataset was in Hardy–Weinberg equilibrium(P < 0.050). However, this was driven primarily by

Table 1. Descriptive statistics for thirteen microsatellite DNA markers amplified from 466 samples of the dhufish, Glaucosoma

hebraicum in Western Australia. Values represent mean values across four regional samples.

Locus N Na Hobs Hexp Fis

GheA127 109.5 5.25 0.317 0.469 0.337GheB008 115.5 4.50 0.449 0.450 0.008GheA110 115.2 5.50 0.309 0.322 0.034GheC102 110.7 21.25 0.864 0.887 0.007GheA108 113.7 6.25 0.677 0.722 0.026GheC105 115.2 2.50 0.015 0.015 )0.004GheD102 111.5 4.00 0.632 0.654 0.021GheA129 108.7 4.75 0.612 0.595 0.026GheA010 115.0 3.50 0.510 0.484 )0.035GheD002 113.7 3.50 0.517 0.484 )0.029GheA104 113.7 3.00 0.533 0.539 0.048GheD101 112.5 9.75 0.719 0.704 0.007GheA114 108.7 3.50 0.194 0.221 0.080Ave. Mid-west 172.5 7.00 (1.56) 0.470 (0.067) 0.502 (0.065) 0.072 (0.030)Ave. Metro 136.8 6.31 (1.55) 0.497 (0.066) 0.500 (0.065) 0.008 (0.026)Ave. South-west 119.9 6.23 (1.16) 0.505 (0.067) 0.518 (0.064) 0.024 (0.031)Ave. South-east 21.3 4.23 (0.84) 0.482 (0.062) 0.492 (0.061) )0.002 (0.005)

N, sample size; Na, number of alleles; Hobs, observed proportion of heterozygotes; Hexp, the expected heterozygosity assumingHardy–Weinberg proportions (Nei, 1987); Fis, the extent of departure from Hardy–Weinberg proportions (Weir and Cocker-ham, 1984). Average values are calculated from all thirteen loci. Standard errors in parentheses.



three loci (GheA127, GheA129, GheA114), whichexhibited overall excesses of homozygotes (mean Fis

0.32, 0.04, 0.14 respectively). GheA127 consistentlyexhibited an excess of homozygotes at all allele classesin all four regional samples, strongly suggesting thepresence of null alleles in these samples (vanOosterhout et al., 2006). GheA129 and GheA114exhibited this pattern in 0 and 1 of the four samples.Maximum likelihood estimates of null allele frequen-cies for these loci in regional samples were: GheA127(range 0.09–0.15), GheA129 (range 0–0.09),GheA114 (range 0.07–0.12). When these loci wereremoved from the analysis all regional samples and theoverall dataset was in Hardy–Weinberg equilibrium.No markers were identified as being under positive orbalancing selection with the Fst outlier method.

Genetic subdivision

Estimates of the degree of genetic subdivision (Fst)among management zones was low overall(0.003 ± 0.001 SE) and also low in pair-wise com-parisons (Table 2). An exact test for genic differenti-

ation between the management zones based on tenloci in Hardy–Weinberg equilibrium indicated nooverall significant structure, although the P value wasclose to the a < 0.05 level (v2 = 30.32, d.f. = 20,P = 0.06). Pair-wise tests revealed that the comparisonbetween the northernmost management zone (Mid-west) and the South-east was significant (v2 = 34.31,d.f. = 20, P = 0.02), and the comparison between theMetro zone and South-east was near significant(v2 = 29.65, d.f. = 20, P < 0.08). All other pair-wisecomparisons were non-significant (P > 0.10). Exacttests based on thirteen loci and the distribution ofgenotypes identified significant overall genetic struc-ture (v2 = 53.00, d.f. = 26, P < 0.01). In addition,pair-wise tests showed that comparisons of both theMid-west and Metro with the South-east were signif-icant (both P < 0.04). There was no significant overalldifferentiation between age cohorts 6–14 yr old(v2 = 28.0123, d.f. = 20, P = 0.11). Similarly, afterBonferroni correction for multiple tests, no pairs ofcohorts exhibited significant genic differentiation.

DK was consistently low across all values of Kbetween 2 and 20 indicating that no well-definedclusters were recovered from model-based clusteringimplemented with STRUCTURE. Individuals typi-cally were assigned equally into K partitions insequential runs of K = 1–20 both when prior locationdata was and was not incorporated.

There was a weak, but statistically significant cor-relation between the distance between samplingblocks and mean Fst (r = 0.278, P = 0.037). None ofthe four models of larval transport showed a significantcorrelation between probability of connectivity andmean Fst. Significant, but weak (r < 0.015) spatialautocorrelation was identified at distances £40 km(Fig. 2).

Table 2. Estimates of genetic subdivision (Fst) betweenpairs of regional samples.

Mid-west Metro South-west South-east

Mid-west – )0.0004 )0.0002 0.0091*Metro 0.0001 – )0.0015 0.0048South-west 0.0002 0 – 0.0045South-east 0.0083* 0.0062* 0.0024 –

Values above diagonal are based on 10 loci that were inHardy–Weinberg equilibrium. Values below diagonalinclude three loci that were not in Hardy–Weinberg equi-librium. *Values significantly different from zero in exact tests.

0.025

0.020

0.015

0.010

0.005

0.000

–0.005

–0.0100–20 0–30 0–40 0–50 0–60 0–70 0–80 0–90 0–100 0–110 0–120 0–130 0–140 0–150 0–200 0–2500–10

Distance (km)

r

Figure 2. Correlograms showing change in spatial autocorrelation statistic (r) with increasing distance classes. Dashed lineindicates confidence limits around an autocorrelation of zero derived from permutations. Error bars indicate 95% confidencelimits around data points based on bootstrap resampling.

248 O. Berry et al.


Hydrodynamic modelling

Particle tracking revealed a pattern of moderatelyextensive transport of simulated larvae during a21 day period, with some particles being transportedup to 520 km and across three WCDSF zones (Figs 3and S1). However, invariably the highest percentageof particles re-settled at their initial release site, al-though this percentage varied between 13 and 0.2%for the generalised larval behaviour models, and 10and 0.2% for the gradual descent models. Further-more, most particles settled within the WCDSF zonewhere they were released. Under a strong Capes

Current scenario, the percentage of particles settlingwithin the WCDSF zone from where they were re-leased was 85.5, 69.8, and 88.1% for the Mid-west,Metro and South-west zones respectively, under thegeneralised larval behaviour model, and 92.4, 71.2,and 86.3% under the gradual descent model. Under astrong Leeuwin Current scenario, the percentage ofparticles that recruited within the WCDSF zone fromwhere they were released was 87.8, 79.1, and 79.7%for the Mid-west, Metro, and South-west zones,respectively, under the generalised larval behaviourmodel, and 87.6, 81.8, and 75.0% under the gradualdescent model.

(c)

(a) (b)

(d)

Figure 3. Connectivity matrices derived from particle tracking simulations. Cells are colour coded according to the probabilityof receiving particles after 21 days. Lines indicate the zone boundaries of the West Coast Demersal Scalefish Fishery. (a)Particles modelled under strong Capes Current and simple larval behaviour; (b) strong Capes Current and gradual descent larvalbehaviour; (c) strong Leeuwin Current and simple larval behaviour; (d) strong Leeuwin Current and gradual descent larvalbehaviour.



Broadly, particles released from all points alongthe coast travelled both north and south, and overallthe extent of transport did not differ greatly betweenthe generalised model of larval behaviour and thegradual descent model (Figs 3 and S1). Under a strongCapes Current there was greater transport to thenorth, particularly in the Metro WCDSF zone, andunder a strong Leeuwin current there was greatertransport to the south (Figs 3 and S1). All modelledscenarios indicated that a fraction of particles releasedin the Mid-west zone would be transported south andsettle in the Geographe Bay to Cape Naturaliste re-gion (Fig. 3). The two gradual descent models pre-dicted a greater fraction of particles would settle inthis region, and would originate from both the Metroand Mid-west WCDSF zones (Fig. 3c,d), particularlyunder a strong Leeuwin current conditions. All mod-elled scenarios identified the same regions of relativelyhigh larval retention in each of the WCDSF zones,and in particular the Geographe Bay region in theSouth-west zone (Fig. 3).

DISCUSSION

Gene flow is extensive throughout the range of theWest Australian dhufish G. hebraicum. However, boththe analysis of microsatellite variation and hydrody-namic modelling also provide evidence that larvaldhufish are likely to recruit locally. Together, theseresults establish that the WCDSF management zonescomprise a single genetic stock, but that the zones arelikely to be largely independent in terms of larvalrecruitment.

Genetic stock structure

A single genetic stock is a recurring pattern observedin several marine fishes in south-western Australiathat have pelagic larvae, including Australian herring(Arripis georgianus), sea mullet (Mugil cephalus), tailor(Pomatomus saltatrix), common blowfish (Torquigenerpleurogramma), and Australian salmon (Arripis trut-taceus) (MacDonald, 1980; Shinzig, 1992; Lenantonet al., 1996; Ayvazian et al., 2004; Watts and John-son, 2004). Although these species share a larval lifehistory that facilitates extensive gene flow, their adultbehaviours differ considerably, with some undertakingextensive migrations to spawn (e.g., A. georgianus,A. trutta, P. saltatrix; Lenanton, 1978; Lenantonet al., 1996; Ayvazian et al., 2004), whereas othersmigrate between estuarine or sheltered embaymentsinto near-shore marine environments to spawn(T. pleurogramma, Mugil cephalus; Chubb et al., 1981;Potter et al., 1988). The West Australian dhufish

differs from all these species in being a demersal reef-dweller and also because both tagging and otolithmicrochemistry indicate that adults are likely to berelatively sedentary (Lenanton et al., 2009a) andtherefore, unlike the other species, dhufish gene flowwould be facilitated primarily by passive and ⁄ oractive larval dispersal.

Limits to gene flow

While gene flow is extensive in dhufish, the consistentgenetic structure detected between the Indian andSouthern oceans, suggests that a partial barrier to geneflow may exist at this location. This observation haslimited significance for the management of dhufishwithin the WCDSF because fish on the south coast areoutside its boundaries and catches there are low.However, it does represent an unusual biogeographicpattern. Cape Leeuwin is a major geographical featurein south-western Australia, but with the exception ofthe highly sedentary estuary catfish Cnidoglanis mac-rocephalus (Ayvazian et al. 1994), it has not beenassociated with genetic subdivision in marine species(e.g., Waters et al., 2004; Peuker et al., 2009). Howthis subdivision is maintained in dhufish is unclear,but Cape Leeuwin coincides with the genesis of thenorth-flowing Capes Current during the period ofdhufish spawning (Pearce and Pattiaratchi, 1999),suggesting that this current and ⁄ or its interaction withthe south-flowing Leeuwin current may be important(Lenanton et al., 2009b). While our particle trackingsimulations do not predict an abrupt change in larvaltransport in this region, our simulations do indicatethat the lower South-west WCDSF zone has morecomplex recruitment dynamics than elsewhere in thefishery because it encompasses both high retention andalso captures long-distance recruits from the north ofthe fishery. While the BRAN hydrodynamic modelused here captures most of the meso-scale watermovements in our study region, it under-estimates theinfluence of wind-driven surface currents especiallythose on the continental shelf (Feng et al., 2011).More detailed hydrodynamic modelling that incorpo-rates higher resolution of the wind-driven flows andshallower environments may provide further expla-nation for the observed genetic subdivision in thisregion.

The scale of gene flow

The weak population structuring detected overallindicates that gene flow among WCDSF zones isextensive in the dhufish, but it does not imply com-plete dispersal throughout the region each generation.Computer simulations demonstrate that even when

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broad scale genetic differentiation is low or absent,isolation by distance effects, such as the one we de-tected, may develop under restricted gene flow (Sokaland Wartenberg, 1983; Epperson, 2005). Therefore,the observation of positive spatial autocorrelation atsmall spatial scales (ca. 40 km), and a significant iso-lation by distance correlation is consistent with geneflow in the dhufish being limited by distance, thusimplying that some self-recruitment occurs at scalessmaller than individual management zones. Thisbroadly agrees with the results of hydrodynamic sim-ulations, where although particles routinely crossedmanagement zone boundaries and were transportedfurther than tagging data suggests that adults move(StJohn et al., 2009b), they generally traversed withina fraction of the range of the dhufish and self-recruitment within WCDSF zones was more commonthan recruitment from adjacent zones.

Recruitment and the role of currents

The dhufish is characterised by inter-annual variationin recruitment strength (Lenanton et al., 2009a), andit has been speculated that this may in part reflectvariation in the strength of the prevailing currents,and that the Capes Current in particular may play arole in distributing larvae from the south of theWCDSF northwards (Wise et al., 2007; Lenantonet al., 2009b). Although our particle tracking simula-tions are likely to under-estimate the influence of theCapes Current (Feng et al., 2011), and can only pro-vide a representative snapshot of actual currentvelocities, they did reveal effects of stronger Capes andLeeuwin currents on the rates of self-recruitment insome regions, in particular the Geographe Bay area,which experienced greater self-recruitment under astrong Capes Current. They also indicated a trendtowards greater northwards transport of larvae under astrong Capes Current scenario, particularly in theMetro WCDSF zone (Figs 3b,d and S1), and con-versely greater southwards transport under a strongerLeeuwin current scenario (Figs 3a,c and S1). Overall,however, the extent of larval transport differed littleunder the two extremes of Cape and Leeuwin currentstrengths, suggesting that inter-annual variation incurrent velocities are unlikely to profoundly impactthe scale of larval transport in this species, althoughother factors associated with the currents could impacton egg ⁄ larval mortality and growth as well as thereproductive behaviour of adults (Paris et al., 2007).

Significance of larval behaviour

Despite the broad agreement between genetic andparticle tracking data that some local recruitment is

likely to occur, on a study-wide scale, hydrodynamicmodelling was a poor predictor of genetic structure.There may be several explanations for this. First, itmay reflect the limitations of our modelling approachin shallow water (Condie et al., 2005). Second, itmay stem from the low levels of genetic subdivisionthat were observed, which provide little power todetect differences between regions (Table 2, and seeLowe and Allendorf, 2010). Finally, the poor corre-lation may partially reflect inaccuracies in the larvalbehaviours we incorporated into our particle trackingsimulations. Although the dhufish has a well-described larval ontogeny because of aquacultureresearch on the species (Pironet and Neira, 1998;Bradbury and Snelgrove, 2001; James et al., 2002),the effects of larval behaviour on particle transport inour models makes this conclusion plausible. Larvalbehaviour was a determinant of the scale of particletransport in our models, and while it did not varyenough to change the general conclusions drawnfrom the simulations, it introduced some significantdifferences in the extent of transport between thenorth and south of the WCDSF (Figs 3 and S1). Thiseffect reflects the different exposures of particles towind-driven currents near the surface under the dif-ferent larval behaviour scenarios, and is significantbecause it is the product of vertical migration alone,illustrating how fish may strongly regulate theirtransport without horizontal swimming (Bradbury andSnelgrove, 2001; Parada et al., 2008). This result,along with the increasing evidence that fish larvaeare capable of a variety of complex behaviours andstrong swimming performance (Leis, 2006, 2010)underscores the importance of including larvalbehaviour into particle tracking simulations. Whileever-more sophisticated particle tracking modelscontinue to be developed (Cowen and Sponaugle,2009), there remains a strong case for coupling thatdevelopment with fundamental research into thebehaviour of pelagic larvae of marine organisms (Leis,2007).

Summary and management implications

Existing otolith microchemistry data suggests thatadult dhufish rarely cross between WCDSF zones(StJohn et al., 2009a). The consequence of this rela-tively sedentary life history is that without sufficientlarval recruitment, intense fishing pressure may causelocalised stock depletions, indicated by high fishingmortality rates and reduced average ages of dhufish,and this has already been observed (Hesp et al., 2002;Lenanton et al., 2009a). Our results show that whilegene flow in dhufish is limited spatially, it is sufficient



to replenish genetic diversity throughout the WCDSF,meaning that localised fishing pressure is unlikely toimpact future adaptive potential in this species. Par-ticle tracking simulations indicate that demographicconnectivity resulting from larval transport is highlyunlikely to be so extensive. Simulations of both asimple and complex larval life-history suggest thatlarvae are likely to recruit from a moderately largearea, but usually within the same WCDSF zone,making fish stocks less able to accommodate intenselocal fishing pressures. This study illustrates thepotential for genetic and demographic stock structuresto be considerably different, and it highlights the valueof considering multiple sources of data for the ‘holistic’identification of stock structure in exploited species(Begg and Waldman, 1999).

ACKNOWLEDGEMENTS

We gratefully acknowledge the recreational anglerswho provided samples for this project, and Ian Keayand Brett Crisafulli for collection and processing ofsamples as well as ageing of fish. We also thank DirkSlawinski, Evan Weller and Ming Feng for advice onparticle tracking and hydrodynamic modelling, andRod Lenanton, Dan Gaughan and anonymousreviewers for valuable comments on an earlier version.We thank Greg Jenkins for his comments on thebehaviour of larval dhufish in an aquaculture envi-ronment. This research was financially supported bythe Western Australian Marine Science Institute(WAMSI) Node 4 (Fisheries Ecosystems, Project 4.4:Captured Species Assessments).

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SUPPORTING INFORMATION

Additional Supporting Information may be found inthe online version of this article:

Figure S1. Examples of connectivity envelopes forlarval dhufish (Glaucosoma hebraicum) predicted withparticle tracking simulations implemented in Connie2.

Please note: Wiley-Blackwell is not responsible forthe content or functionality of any supporting mate-rials supplied by the authors. Any queries (other thanmissing material) should be directed to the corre-sponding author for the article.

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