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Investigation of tropical eel spawning area in the South-Western Indian Ocean: Influence of the oceanic circulation S. Pous a, * , E. Feunteun b , C. Ellien c a Muséum national d’Histoire naturelle, UMR 7159 (CNRS-IRD-UPMC-MNHN), Laboratoire d’Océanographie et de Climatologie: Expérimentation et Approches Numérique, (LOCEAN), CC100, 4 Place Jussieu, 75252 Paris cedex 05, France b Muséum national d’Histoire naturelle, UMR 7208 (CNRS-UPMC-MNHN-IRD), Département Milieux et Peuplements Aquatiques, Centre de Recherches et d’Enseignement sur les Systèmes Côtiers, 38 rue du Port Blanc, 35800 Dinard cedex, France c Université Pierre et Marie Curie-Paris 06, UMR 7208 (CNRS-UPMC-MNHN-IRD), Département Milieux et Peuplements Aquatiques, Muséum national d’Histoire naturelle, 43 rue Cuvier, CP26, 75231 Paris cedex 05, France article info Article history: Received 6 November 2009 Received in revised form 4 June 2010 Accepted 9 June 2010 Available online 6 July 2010 abstract In the South-Western Indian Ocean (SWIO), four eel species of the genus Anguilla (i.e. Anguilla bicolor bicolor, Anguilla nebulosa labiata, Anguilla marmorata and Anguilla mossambica) were identified, while their respective oceanic spawning area remained unknown. Based on collected larvae, glass eel captures and hydrodynamical conditions, previous studies raised the hypothesis that the eel spawning area might be common to all of those freshwater eel species, and located East of Madagascar. An original modeling approach, based on backward simulations, is developed to assess how the ocean circulation in the SWIO determines the location of the spawning areas and whether a common spawning area for each recruit- ment site where glass eels were found is possible. We use a hydrodynamical model, which reproduces realistically the 3D open ocean circulation in the region, associated with a Lagrangian model that calcu- lates the possible migration pathways of larvae, represented by passive particles. Some biological param- eters, provided by previous otolith microstructures analysis, are taken into account to constrain our simulations. Results suggest the existence of a common spawning area located between 13°S and 19°S and westwards of 60.5°E, although these boundaries vary on the interannual timescale. Salinity fronts were reported beside the boundaries, reinforcing this assumption. We explore the impact of hydrody- namic conditions on recruitment and migration durations from three specific regions within the common spawning area. They all allow migration to each recruitment sites consistent with duration estimated from otolith microstructure analyses. Nevertheless, there is substantial variability on intra-seasonal to interannual timescale in simulated migration durations and arrival success, with specific amplitude to each recruitment site and spawning location. Ó 2010 Elsevier Ltd. All rights reserved. 1. Introduction Eels (Anguilla genus) are highly migratory diadromous species, i.e. a part of their life cycle takes place in fresh water and the other part in sea water. Spawning takes place at depths over 400 m in warm oceans (Tsukamoto, 1992). After hatching, leptocephali are driven by the oceanic currents towards the continental (or islands) coasts, undergoing during this phase a severe mortality. Lepto- cephali are thought to metamorphose into glass eels at their arrival on the continental shelf. Later they colonize coastal and inland waters. After the settlement they become sedentary yellow eel. Their subsequent growth period lasts 3–8 years in males and 8– 15 years in females of the temperate European eel (Feunteun, 2002), but this kind of information is not known for tropical eel. Moreover, duration of the growth period varies strongly, among species and within species, according to environmental parameters such as food availability, water quality and temperature (Jellyman, 1997; Tesch, 2003). After this growth period, a second metamor- phosis occurs: yellow eels change into silver eels and migrate thousands of kilometres to go back to their oceanic spawning areas. Their sexual maturation occurs during this migration. Little is known about this second oceanic migration except that it occurs in the first 1000 m in open ocean, which makes surveys difficult and expensive (Tesch, 1979). However, new tools as pop-up are developed to follow individually eel adults, and this range of migration depths seems to be confirmed (Jellyman and Tsukamoto, 2005; Aarestrup et al., 2009). Nevertheless, most of the keys to understand eel life history are related to reproduction and recruit- ment. Consequently, a particular attention has to be given to the marine phase. Indeed, larval dispersal by oceanic currents 0079-6611/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.pocean.2010.06.002 * Corresponding author. E-mail addresses: [email protected] (S. Pous), [email protected] (E. Feunteun), [email protected] (C. Ellien). Progress in Oceanography 86 (2010) 396–413 Contents lists available at ScienceDirect Progress in Oceanography journal homepage: www.elsevier.com/locate/pocean

Investigation of tropical eel spawning area in the South-Western Indian Ocean: Influence of the oceanic circulation

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Page 1: Investigation of tropical eel spawning area in the South-Western Indian Ocean: Influence of the oceanic circulation

Progress in Oceanography 86 (2010) 396–413

Contents lists available at ScienceDirect

Progress in Oceanography

journal homepage: www.elsevier .com/locate /pocean

Investigation of tropical eel spawning area in the South-Western Indian Ocean:Influence of the oceanic circulation

S. Pous a,*, E. Feunteun b, C. Ellien c

a Muséum national d’Histoire naturelle, UMR 7159 (CNRS-IRD-UPMC-MNHN), Laboratoire d’Océanographie et de Climatologie:Expérimentation et Approches Numérique, (LOCEAN), CC100, 4 Place Jussieu, 75252 Paris cedex 05, Franceb Muséum national d’Histoire naturelle, UMR 7208 (CNRS-UPMC-MNHN-IRD), Département Milieux et Peuplements Aquatiques,Centre de Recherches et d’Enseignement sur les Systèmes Côtiers, 38 rue du Port Blanc, 35800 Dinard cedex, Francec Université Pierre et Marie Curie-Paris 06, UMR 7208 (CNRS-UPMC-MNHN-IRD), Département Milieux et Peuplements Aquatiques, Muséum national d’Histoire naturelle,43 rue Cuvier, CP26, 75231 Paris cedex 05, France

a r t i c l e i n f o

Article history:Received 6 November 2009Received in revised form 4 June 2010Accepted 9 June 2010Available online 6 July 2010

0079-6611/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.pocean.2010.06.002

* Corresponding author.E-mail addresses: [email protected] (S. Pous), feunt

[email protected] (C. Ellien).

a b s t r a c t

In the South-Western Indian Ocean (SWIO), four eel species of the genus Anguilla (i.e. Anguilla bicolorbicolor, Anguilla nebulosa labiata, Anguilla marmorata and Anguilla mossambica) were identified, while theirrespective oceanic spawning area remained unknown. Based on collected larvae, glass eel captures andhydrodynamical conditions, previous studies raised the hypothesis that the eel spawning area mightbe common to all of those freshwater eel species, and located East of Madagascar. An original modelingapproach, based on backward simulations, is developed to assess how the ocean circulation in the SWIOdetermines the location of the spawning areas and whether a common spawning area for each recruit-ment site where glass eels were found is possible. We use a hydrodynamical model, which reproducesrealistically the 3D open ocean circulation in the region, associated with a Lagrangian model that calcu-lates the possible migration pathways of larvae, represented by passive particles. Some biological param-eters, provided by previous otolith microstructures analysis, are taken into account to constrain oursimulations. Results suggest the existence of a common spawning area located between 13�S and 19�Sand westwards of 60.5�E, although these boundaries vary on the interannual timescale. Salinity frontswere reported beside the boundaries, reinforcing this assumption. We explore the impact of hydrody-namic conditions on recruitment and migration durations from three specific regions within the commonspawning area. They all allow migration to each recruitment sites consistent with duration estimatedfrom otolith microstructure analyses. Nevertheless, there is substantial variability on intra-seasonal tointerannual timescale in simulated migration durations and arrival success, with specific amplitude toeach recruitment site and spawning location.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

Eels (Anguilla genus) are highly migratory diadromous species,i.e. a part of their life cycle takes place in fresh water and the otherpart in sea water. Spawning takes place at depths over 400 m inwarm oceans (Tsukamoto, 1992). After hatching, leptocephali aredriven by the oceanic currents towards the continental (or islands)coasts, undergoing during this phase a severe mortality. Lepto-cephali are thought to metamorphose into glass eels at their arrivalon the continental shelf. Later they colonize coastal and inlandwaters. After the settlement they become sedentary yellow eel.Their subsequent growth period lasts 3–8 years in males and 8–15 years in females of the temperate European eel (Feunteun,

ll rights reserved.

[email protected] (E. Feunteun),

2002), but this kind of information is not known for tropical eel.Moreover, duration of the growth period varies strongly, amongspecies and within species, according to environmental parameterssuch as food availability, water quality and temperature (Jellyman,1997; Tesch, 2003). After this growth period, a second metamor-phosis occurs: yellow eels change into silver eels and migratethousands of kilometres to go back to their oceanic spawningareas. Their sexual maturation occurs during this migration. Littleis known about this second oceanic migration except that it occursin the first 1000 m in open ocean, which makes surveys difficultand expensive (Tesch, 1979). However, new tools as pop-up aredeveloped to follow individually eel adults, and this range ofmigration depths seems to be confirmed (Jellyman and Tsukamoto,2005; Aarestrup et al., 2009). Nevertheless, most of the keys tounderstand eel life history are related to reproduction and recruit-ment. Consequently, a particular attention has to be given to themarine phase. Indeed, larval dispersal by oceanic currents

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S. Pous et al. / Progress in Oceanography 86 (2010) 396–413 397

determines at least partly, the location of recruitment and theabundance of recruits. The long larval duration of leptocephalimakes the precise location of their spawning areas important sincespecific current or temperature conditions could affect their sur-vival and then, their recruitment success (Tsukamoto, 1992; Cas-tonguay et al., 1994; Désaunay and Guérault, 1997). Then theleptocephalus stage needs to be better understood. Indeed, theduration of the oceanic larval migration is subject of controversy,particularly for Atlantic eels (Bonhommeau et al., 2008; McCleave,2008).

The analysis of geographical trends of leptocephalus size distri-bution have been almost the only way to discover the eel spawn-ing locations (Schmidt, 1923; Jespersen, 1942; Tsukamoto, 1992;Kimura et al., 2006). In the Atlantic and Pacific oceans, the distri-bution of the youngest eel larvae suggests that spawning takesplace in specific areas that may facilitate mating and place larvaein the appropriate landwards-flowing currents (Kimura et al.,1999, 2001; Tsukamoto et al., 2002; Tsukamoto, 2006; Friedlandet al., 2007). It has been suggested that maturing European (Angu-illa anguilla), American (Anguilla rostrata) and Japanese (Anguillajaponica) silver eels make long migrations to spawn in thesubtropical gyres of the North Atlantic or North Pacific oceans(McCleave et al., 1987; Tsukamoto, 1992). In the case of the twoAtlantic eel species, the surface expression of the 22.5 �C isotherm,located near the frontal zone in the Sargasso Sea, forms the north-ern boundary of the spawning area (McCleave, 1993). Tempera-ture fronts in the Sargasso Sea may act as cues that help adulteels to locate the spawning area (Friedland et al., 2007). Jetsassociated with these fronts appear to transport a variety of lepto-cephali species eastwards (Miller and McCleave, 1994). Therefore,changes in the latitude or intensity of these fronts (and the asso-ciated jets) may affect both the spawning location and the subse-quent transport of the leptocephali to continental habitats, andmay partly explain the decline in eel recruitment recorded sincethe early 1980s (Dekker, 2003; Friedland et al., 2007). In the Paci-fic Ocean, the young Japanese eel leptocephali are mostly foundwithin the margins of the North Equatorial Current, flowing wes-terly towards the Japanese coasts, and at a depth ranging between50 and 150 m. Their spawning location corresponds to a salinityfront (Tsukamoto, 1992; Kimura et al., 2001). The reduced salinityin the frontal zone, or some associated features such as odour,may provide migrating eel adults with a cue which triggers cessa-tion of migration and initiation of spawning. As previously statedfor the Atlantic eels and the thermal front, changes in the locationof the salinity front may contribute to the decline in Japanese eelrecruitment observed in the recent decades (Kimura et al., 2001;Kimura and Tsukamoto, 2006; Friedland et al., 2007). The key sim-ilarity in Atlantic and Pacific oceans is the position of the spawn-ing areas connected to major currents, the Gulf Stream for theAtlantic Ocean and the NEC with its northern bifurcation (i.e.Kuroshio Current) for the Pacific Ocean, which can drive the off-springs to their growth habitats and thus complete the recruit-ment of eels (Tsukamoto, 1992).

In the South-Western Indian Ocean (SWIO), four species of An-guilla have been identified: Anguilla bicolor bicolor, Anguilla nebulosalabiata, Anguilla marmorata and Anguilla mossambica (Ege, 1939;Robinet et al., 2007). The anthropogenic impact on those tropicaleel stocks is less documented than for the Atlantic and Pacific eelspecies (Moriarty and Dekker, 1997; Dekker, 2003). Nevertheless,recent observations explain the lack of large eels in most islandsof the Indian Ocean as a consequence of traditional fisheries(Robinet et al., 2007). Indeed, those eel stocks are facing a growinginterest from the international markets in Madagascar and SouthAfrica (Robinet et al., 2008). Of all fishes that breed in the westernIndian Ocean, the genus Anguilla has probably the highest eco-

nomic value per unit weight of fish, and for decades the world-wide demand exceeds the supply (Jackson, 1976).

The eel spawning area in the SWIO has not been discovered yet,despite oceanographic cruises aiming at sampling eel larvae. How-ever, Jespersen (1942), following his observations on small lepto-cephali distribution and hydrographic conditions in this region,suggested that spawning area should be shared by all those fresh-water eel species and located East of Madagascar. Since then, noleptocephalus has been caught. Glass eels or riverine yellow eelsof those four species were sampled in estuaries and rivers of LaRéunion Island, Mauritius Island, Mayotte Island, and on the east-ern coast of Madagascar between 2000 and 2006, highlighting thatthe composition of eel communities seems contrasted between thedifferent islands of this region (Robinet et al., 2008), as Ege (1939)previously suggested. This contrasted distribution may be due: (1)to different migratory routes followed by the eel larvae before theirestuarine recruitment (Robinet et al., 2003, 2008; Réveillac et al.,2008); (2) to different spawning areas or periods among species:A. marmorata and A. mossambica would share the same spawningarea located between Madagascar and the Mascarene ridge, withdifferent spawning periods, while A. bicolor bicolor might spawnfrom an area located westwards and closer to La Réunion Island(Robinet et al., 2003); (3) to different behavioral traits, or intrinsicmetabolism (Robinet et al., 2003, 2008; Réveillac et al., 2008).These hypotheses are mainly based on otolith microstructure anal-yses, which allow to assess the leptocephalus stage duration, theperiod of recruitment as well as the spawning period (Robinet etal., 2003; Réveillac et al., 2008, 2009). However, field data beingsparse, these hypotheses need to be confronted to modelingapproach.

Hydrodynamic modeling provides quantitative methods ofapplying physical oceanographic information to the question oflarval dispersal (Tremblay et al., 1994; Young et al., 1998; Ellienet al., 2004). These models can provide a synoptic view of larvaldistribution for a large range of geographic, hydrodynamic and cli-matic conditions at different spatial and temporal scales. They canalso be used as a tool to determine the relative influence of variouscomponents of the water circulation and biological factors on lar-val dispersal (Barnay et al., 2003; Ellien et al., 2004). Lagrangianmodels have already been used to explain the distribution of theeels and their larvae and to discuss the influence of oceanographicpatterns on larval dispersal and recruitment in the Pacific (Kimuraet al., 1999; Kim et al., 2007) and Atlantic Ocean (Kettle and Haines,2006; Bonhommeau et al., 2009a,b).

In this study, we use a state-of-the-art hydrodynamical model,which realistically reproduces the 3D circulation in the region,associated with a Lagrangian model that reproduces the possiblemigration pathways of larvae. The biological inputs, such as migra-tion durations, together with spawning and recruitment periods,have been deduced from the published results on otolith micro-structure analyses from all eel species. Hence simulations repro-duce migration of eel larvae of the SWIO with no distinction ofthe species.

The aim of this study is to understand how the ocean circulationin the SWIO constraints the location of spawning areas for tropicaleels: (1) can there be a common spawning area to all freshwatereel species sampled in their different recruitment sites? (2) Whatare the leptocephali possible migration routes to the recruitmentsites? (3) What is the variability in the recruitment and migrationduration induced by the environmental conditions? As a first stepto answer these questions, we consider a very simple parameteri-sation of the larvae behavior in order to focus on the impact ofenvironmental conditions alone. We finally discuss the effect ofmore complex behavior (vertical diurnal migration and mortalityscenarios).

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398 S. Pous et al. / Progress in Oceanography 86 (2010) 396–413

2. Data, models and methods

Our results are based on simulations of the oceanic circulationin the SWIO, from a state-of-the-art hydrodynamical model, usedto investigate migration pathways of eel larvae, taking into accountthe few observed biological parameters. In this section, we de-scribe the general features of the ocean circulation in the SWIO, re-view the available observations referring to eel early life traits,present the hydrodynamical and Lagrangian models and finallyintroduce the suite of experiments used in the present study.

2.1. Data

2.1.1. Circulation and hydrology in the SWIOThis study is located in the SWIO, between 5�N and 27�S, 34�E

and 75�E (Fig. 1). We focus on the eastern coast of Madagascar,the island of Mayotte and the Mascarene Plateau includingMauritius and La Réunion islands. The Mascarene Plateau is char-acterized by a series of shallow banks and shoals separated by dee-per ridges, encompassing over 2000 km from The Seychelles at thenorthern end, to Mauritius at the southern end.

Circulation in the SWIO mostly consists of the South EquatorialCurrent (hereafter SEC), which is the northern boundary of the ba-sin-wide anticyclonic subtropical gyre (Stramma and Lutjeharms,1997). The SEC is a broad flow, heading westward, located between10�S and 20�S. New et al. (2007) showed that when the SEC passesacross the Mascarene Plateau near 60�E, it splits into two cores form-ing a northern core between 10�S and 14�S (passing north of Saya deMalha Bank and between Saya de Malha and Nazareth Banks) and asouthern core between 17�S and 20�S (passing between CargadosCarajos Bank and Mauritius). When reaching Madagascar, thosecores form the North-East and South-East Madagascar Currents(hereafter NEMC and SEMC, Schott and McCreary, 2001). The NEMCflows northwards around Madagascar and reaches the ComorosArchipelagos (including Mayotte) whereas the SEMC flows south-wards around Madagascar. Near 25�S, a part of SEMC seems to retro-flect and feed a shallow eastward jet (Palastanga et al., 2007), theSouth Indian Ocean Countercurrent (hereafter SICC).

During the North-East (hereafter NE) monsoon, trade winds areweaker and located further south, which is expected to induce sea-sonal changes of the SEC. Nevertheless, the latter remains unclear(Schott and McCreary, 2001), as illustrated by the climatologicalsurface currents in February (NE monsoon, Fig. 2a and c) and Au-gust (Southwest (SW) monsoon, Fig. 2b and d). Indeed, year-longin situ observations of the NEMC and SEMC suggest that their

Fig. 1. Chart of the southwestern part of the Indian Ocean. Gray lines indicateisobaths 200 m (shaded), 1000 m and 2000 m from the bathymetry of DRAKKARsimulations. Black arrows locate the main currents, namely the South EquatorialCurrent (SEC), the Northern East Madagascar Current (NEMC) and the Southern EastMadagascar Current (SEMC).

seasonal cycles have small amplitude and cannot be significantlydistinguished from the strong intra-seasonal variability (Schottet al., 1988).

As found in the Atlantic and Pacific Oceans, the eel spawningarea is likely to be marked by thermal and/or salinity fronts. Inthe SWIO, there is no clear thermal front in the upper ocean asillustrated along WOCE section IR3 in April 1995 (Fig. 3a). Never-theless the SEC acts as a barrier, separating water masses of south-ern and northern origin with contrasted salinities (Wyrtki, 1971;Schott and McCreary, 2001; New et al., 2007). Tropical SurfaceWater (TSW), carried westwards by the SEC (between 5�S and20�S in the upper 50–100 m) from the Indonesian Seas to the Afri-can coast, has relatively low salinity. It is flanked on its northernside by Arabian Sea High Salinity Water (ASHSW), identified by asalinity maximum under the mixed layer (at 100 m deep approxi-mately) and on its southern side by Subtropical Surface Water(STSW), identified by a salinity maximum at 200–250 m. The sub-sequent surface salinity fronts, located around 5�S and 18�S respec-tively (Fig. 3c) are also observed east of section IR3, on theMascarene ridge around 12�S and 19�S respectively in July 2002(New et al., 2007).

2.1.2. Biological dataIn the SWIO, glass eels or riverine yellow eels of three species

(i.e. Anguilla bicolor bicolor, A. marmorata and A. mossambica) havebeen captured (mostly by electrofishing, but also by traditional netfishing in Madagascar) in estuaries of four islands: La Réunion is-land, Mauritius island, Mayotte and Madagascar (see Table 1 thatsummarizes published information regarding 237 glass eels or el-vers captured in nine recruitment sites of the SWIO from 2000 to2006). We review otolith microstructure analyses that give infor-mation on the recruitment date, the age at recruitment and theduration of marine larval life, based on the counting of dailyincrement rings, from which the spawning period can be deduced(Lecomte-Finiger, 1992; Arai et al., 2001; Kuroki et al., 2007; Réve-illac et al., 2008). In the SWIO, the maximum estimated migrationduration is 180 days, and recruitment mostly occurred from Janu-ary to April, corresponding to a hatching period from August toDecember.

These biological data are based on the assumption that incre-ment rings are deposited daily in otoliths. This assumption is sub-ject of controversy, in particular for European eel (A. anguilla), asthe leptocephali migration duration assessed from modeling study(Kettle and Haines, 2006; Bonhommeau et al., 2009a), or field stud-ies and cohort analyses (McCleave, 2008) is twice as long as thatrevealed by otolith microstructure analyses. This difference couldbe explained by the leptocephalus metabolic rate that may beslower in cold temperature, resulting in a deposit increment everyother day instead of daily as it is currently assumed (Bonhommeauet al., 2009a). However, in warmer waters such as the PacificOcean, the increment rings seem to be deposited on a daily basis.Indeed, Tsukamoto (2006) collected, on their spawning area, newlyhatched A. japonica pre-leptocephali, and the counting of theirgrowth rings showed that they had hatched between 2 and 5 daysbefore their catch. This direct observation validates the pertinenceof considering that increment rings are deposited daily, at least foreel leptocephali in warm waters. In the Indian Ocean, the watertemperature allows us to consider that the published data on oto-liths microstructure analyses constitute a pertinent basis for oursimulation biological inputs.

2.2. Models

2.2.1. The hydrodynamic modelOceanic data used in this study (ocean currents, water temper-

ature and salinity from surface to bottom) is provided by two mod-

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50 cm.s−1

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Fig. 2. Climatological near surface circulation (at 15 m deep) in February (i.e. NE monsoon, left panels) and August (i.e. SW monsoon, right panels) derived from a 1998–2003drifter-based climatology (a and b, Lumpkin and Garraffo, 2005), a 1993–2007 climatology of ocean currents computed from various satellites and in situ measurements (cand d, OSCAR data obtained from JPL Physical Oceanography DAAC and developed by ESR) and from DRAK01 simulation averaged from 1993 to 2002 (e and f, Penduff et al.,2007).

S. Pous et al. / Progress in Oceanography 86 (2010) 396–413 399

eling experiments of the DRAKKAR group (www.ifremer.fr/lpo/drakkar). The first simulation, noted DRAK01 (the ORCA025-G70simulation referred by the DRAKKAR group, 2007) is used for years1988–2002. In order to have a longer dataset of hydrodynamicalconditions, we also use simulation DRAK02 that reproduces years2000–2007. The global ORCA025 coupled ocean/sea-ice model con-figuration developed by the European DRAKKAR collaboration(DRAKKAR group, 2007) is used to perform DRAK01 and DRAK02simulations. An overall description of the model and its numericaldetails are given in Barnier et al. (2006). This model configurationuses the ORCA global tri-polar grid (Madec and Imbard, 1996) at 1/4� resolution (1442 � 1021 grid points and 46 vertical levels). Ver-tical grid spacing is finer near the surface (6 m) and increases withdepth to 250 m at the bottom. Horizontal resolution is 27.75 km atthe equator, 13.8 km at 60�N, and gets to 10 km in the ArcticOcean. The ocean/sea-ice code is based on the NEMO frameworkversion 1.9. (Madec, 2008). It uses a partial step representation ofthe bottom topography and a momentum advection scheme whichboth yield significant improvements (Penduff et al., 2007; Le Som-

mer et al., 2009). The bathymetry of the model is adapted to betterrepresent the different pathways between the banks of the Masca-rene Ridge (New et al., 2007). These global simulations are drivenwithout data assimilation by the hybrid interannual forcing DFS3described in detail in Brodeau et al. (2010) which blends dailyand monthly satellite-derived radiative fluxes and precipitationswith 6-hourly 10-m atmospheric surface variables for turbulentfluxes from the ERA40 reanalysis (DRAK01) or ECMWF analysis(DRAK02). Hence these two simulations only differ in the turbulentfluxes (heat and momentum) at the surface. Circulation andhydrography in DRAK01 and DRAK02 are very similar, except thatthe circulation is slightly more intense in DRAK02 compared toDRAK01. Considering the three years in common of DRAK01 andDRAK02 (2000–2002), we are able to assess the influence of thehydrodynamic forcings on the biological results (i.e. migrationduration).

Barnier et al. (2006) highlighted the performance of the simula-tions in reproducing the strong ocean currents and the variabilitydue to eddies, even in comparison with higher resolution models.

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30˚E 40˚E 50˚E 60˚E 70˚E 80˚E30˚S

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Fig. 3. Hydrography along section WOCE IR3 (location indicated in top panel) in April, 2–18, 1995: temperature and salinity from, respectively, WOCE data (a and c, http://www.ewoce.org/) and DRAKKAR-G70 simulation (b and d).

400 S. Pous et al. / Progress in Oceanography 86 (2010) 396–413

The main features of the global circulation are well represented aswell as the interannual variability (Drakkar group, 2007; Penduffet al., 2007). In the SWIO, the mean simulated circulation comparesreasonably well with that observed, considering that the two cli-matologies derived from observations are not fully consistent witheach other (Fig. 2). The regions of maximum current are very sim-ilar to those found in observations, although the southern core of

the SEC flowing westward south of 17�S is weaker than observed.This southern core feeds the southward current along Madagascar(SEMC), which is also underestimated in the model. Note that thehorizontal resolution of the model remains too coarse to reproducethe circulation in the vicinity of small islands (�20 km offshore)such as La Réunion, Mauritius and Mayotte, but this is likely toaffect only the very last days of migration. Consequently, for the

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Table 1Oceanic larval migration duration and hatching periods, reported in literature, for the 3 Anguilla species in the SWIO. These data are used as biological inputs in the presentmodeling study. References: (1) Robinet et al. (2003), (2) Robinet et al. (2008), (3) Réveillac et al. (2008), (4) Réveillac et al. (2009).

Species Sampling locations Months of capture(N individuals)

Stage atsampling

Oceanic larvalmigration durations(mean ± SD, min–max)(in days)

Hatching periods Ref.

A. bicolor bicolor La Réunion From November 2000 toApril 2001 (11)

Glass eels 46.2 ± 5.8 (39–57) September 2000–January 2001 1

April 2005 (1) Glass eels 151 September 2004 2Mauritius April 2005 (9) Glass eels 110.6 ± 16.7 (87–139) October–December 2004 2Mayotte April 2005 (12) Glass eels 101.8 ± 9.2 (87–117) October–December 2004 2

April 2005 (3) Elvers 97.0 ± 4.4 (92–100) March–August 2004 2

A. marmorata La Réunion From November 2000 toApril 2001 (9)

Glass eels 96.9 ± 26.4 (60–135) September–December 2000 1

April 2005 (15) Glass eels 111.1 ± 15.9 (94–142) August–December 2004 3Mauritius April 2005 (30) Glass eels 139.2 ± 24.0 (91–180) August–December 2004 3Mayotte April 2005 (29) Glass eels 120 ± 13.1 (104–151) September–December 2004 3Madagascar (from northto south)(a) Mananjary From November 2005 to

February 2006 (15)Glass eels 122.5 ± 15.2 (92–145) September–November 2005 2

(b) Farafangana January 2006 (15) Glass eels 108.8 ± 8.4 (94–120) August–September 2005 2

A. mossambica La Réunion From November 2000 toMarch 2001 (12)

Glass eels 102.1 ± 17.2 (72–130) July–October 2000 1

April 2005 (1) Glass eels 113 October 2004 2Madagascar (from northto south)(a) Andevoranto December 2005 (15) Glass eels 82.5 ± 8.2 (70–96) August 2005 4(b) Mahanoro December 2005 (15) Glass eels 84.8 ± 10.8 (64–102) August–September 2005 4(c) Mananjary December 2005 (15) Glass eels 92.5 ± 18.8 (73–131) July–August 2005 4(d) Manakara December 2005 (15) Glass eels 103.0 ± 20.3 (79–152) July–August 2005 4(e) Vangaindrano December 2005 (15) Glass eels 96.6 ± 14.5 (78–127) July–August 2005 4

S. Pous et al. / Progress in Oceanography 86 (2010) 396–413 401

purpose of this study that is to understand better the impact of theopen ocean circulation on the migration of eel larvae in the SWIO,we are confident that the model is reliable. Simulated temperatureand salinity of the upper ocean also compares well with observa-tions (Fig. 3).

The large-scale climate variability in the Indian Ocean is wellrepresented in the model, according to the time fluctuations ofthe commonly used climatic indices. The tropical Indian Ocean Di-pole index (hereafter IOD), defined as the difference in sea surfacetemperature (hereafter SST) anomaly in June-November betweenthe western (50–70E, 10N–10S) and the eastern (90–110E, 0–10S) tropical Indian Ocean (Webster et al., 1999; Saji et al., 1999)is in the model positive in 1991, 1994, 1997, 2006 and 2007, andnegative in 1992, 1996 and 1998, which corresponds to observa-tions (http://www.jamstec.go.jp/frcgc/research/d1/iod/). The sec-ond major climatic index for the Indian Ocean, which is thesouthern Indian Ocean Dipole index (hereafter SIOD), defined asthe difference in SST anomaly in December–March between thewestern (55–65E, 27–37S) and the eastern (90–100E, 0–10S) sub-tropical Indian Ocean (Behera and Yamagata, 2001), is in the modelpositive in 1993, 1997, 1999, 2001, 2006 and 2007, and negative in1995, 1998, 2000, 2002 and 2003, which corresponds to observa-tions (http://www.jamstec.go.jp/res/ress/behera/siod.html). Thesetwo indices relate to independent phenomenon, hence are not cor-related to each other (Behera and Yamagata, 2001). They bothinfluence the circulation in the SWIO but with different time lags:the intensity of SEC, SEMC and NEMC tends to increase (respec-tively decrease) in phase (i.e. the same year) with a positive (neg-ative) SIOD event (Hermes and Reason, 2005) and 1–2 years after anegative (positive) IOD event (Palastanga et al., 2006).

2.2.2. The Lagrangian modelWe used the Lagrangian tool ARIANE to simulate the trajectory

of passive particles based on 3D streamlines, in a given velocityfield and subsequent water mass characteristics (http://www.u-niv-brest.fr/lpo/ariane, Blanke and Raynaud, 1997). In our study,

the passive particles are assumed to represent eel larvae. Trajecto-ries are calculated off-line from the 3D 5-days-averaged oceanvariables of simulations DRAK01 and DRAK02. This method allowsto perform a large number of experiments, which would not bepossible using on-line calculation. Backward trajectories are easilycomputed by simply changing the sign of the velocity field. Basedon the location of the recruitment site and the larval life duration,the backward strategy allows us to simulate the migration path-ways of larvae back in time, from each recruitment site to the posi-tion of possible spawning areas.

ARIANE tool offers two types of statistical analysis. The first oneis a qualitative approach (hereafter Qualitative), where one particlestands for one larvae. The initial position of each particle is set (lat-itude, longitude, depth and time), then ARIANE calculates their tra-jectory. As the position in space and time of each particle isrecorded, only a few particles (of the order of 10,000 for our choiceof release) can be followed in a single experiment. Hence they maynot illustrate all the possible pathways of larvae migration.

The Lagrangian tool is also used in a quantitative mode (hereaf-ter Quantitative). In that case, particles are associated with smallwater parcels (500m3) instead of representing larvae directly.The whole water mass of a given geographical section is discretizedin those water parcels, one parcel being released for each elemen-tary transport across the section (Döös, 1995; Blanke and Raynaud,1997). Trajectories of the water parcels are calculated until the par-cel reaches a given geographical section. Hence quantitative exper-iments give access to fluxes between different sections rather thanto individual trajectories. The amount of particles released dependson the intensity of the water flow near each initial geographicalsection and on the duration of the experiment. On average,35,000–600,000 particles are released in the Quantitative experi-ments, which allow to assume that all the possible pathways areinvestigated.

There are two limitations to the Lagrangian model. First, it uses5-day averaged velocity fields from the hydrodynamical model,hence current anomalies at higher frequencies are not taken into

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402 S. Pous et al. / Progress in Oceanography 86 (2010) 396–413

account when calculating off-line the Lagrangian trajectories. Thismay introduce biases in the displacement of particles. However,such high frequency processes imply oceanic adjustment at spatialscales of the order of 10 km (sub-mesoscale processes), which can-not be resolved presently in multidecadal simulations. Besides,Lagrangian calculations are done with a purely advective schemewhich implies that horizontal and vertical diffusion of the particlesare not represented. This may affect the displacement of larvae.Nevertheless, using a purely advective scheme is the only way todo backward calculation.

2.2.3. Biological parameterisationsThere are indications of vertical migration on a diurnal time

scale during the ontogenetic development of the Atlantic and Jap-anese eel larvae (Castonguay and McCleave, 1987; Otake et al.,1998; Tsukamoto, 2009). Nevertheless there is no observation ofthe behavior of eel larvae in the Indian Ocean. As a result, we firstassume that eel larvae may be represented by strictly passive par-ticles and focus on the influence of environmental conditionsalone. Considering the larvae as passive particles implies that theyeventually migrate on the vertical following the ocean currents. Inmost experiments, we release the larvae in the upper layer of theocean (0–100 m depth). However, in Section 4, we discuss theeffect of implementing a particle behavior that mimics the verticaldiurnal migration of larvae, making particles flip every 12 h from50 m to 200 m. This range of depths was chosen based on lepto-cephalus migration in the Atlantic and Pacific Oceans as there isa lack of knowledge about the leptocephalus migration for the dif-ferent eel species in the SWIO. In the Atlantic and Pacific Oceans,leptocephali migrate up to 50 m, which value we choose as upperlimit for vertical migration of leptocephali in the SWIO. The lowerlimit of vertical migration differs from the Atlantic Ocean,where leptocephali reach 300 m deep (Castonguay and McCleave,1987), to the Pacific Ocean where particles only reach 150 mdeep (Kimura et al., 2001). We choose an arbitrary intermediatevalue of 200 m as lower limit of larvae vertical migration in theSWIO.

No realistic mortality is taken into account for the simulationsof larval dispersal, as no data is available on this demographicparameter. However, when the migration duration of a particle ex-ceeds 180 days, the particle vanishes, which can be viewed as acoarse parameterisation of mortality (0% mortality before 180 daysand 100% onwards). In Section 4, we discuss the effect of imple-menting a more realistic parameterisation of mortality: the cumu-lative mortality from 0 to time t, being 1 � e�Mt, where M is theannual mortality rate (Peterson and Wroblewski, 1984).

Otolith analyses of newly hatched Japanese eel leptocephali sug-gests that the spawning is discrete and occurs around 4 days beforenew moon, every month within the spawning period (Tsukamotoet al., 2003; Tsukamoto, 2006). In the forward experiments, spawn-ing is represented as a continuous phenomenon occurring from Au-gust to December in order to investigate all the possiblehydrodynamical conditions and larvae migration pathways,

Lagrangian simulations reproduce a number of particles thatlargely exceeds the number of glass eels captured in the SWIO.The histogram of simulated migration durations exhibits one orseveral modes, suggesting that the mode(s) may be statisticallymore significant than the mean to characterize migration duration.Nevertheless, as observations are insufficient to calculate the modeof observed migration durations, we choose to compare the meansimulated migration duration (and eventually the minimum) tothe observed migration durations. Besides, we are aware that lim-iting migration durations to 180 days may introduce a bias in themean migration duration. Nevertheless, this coarse parameterisa-tion of mortality is unavoidable to ensure that the water particlessimulated by the Lagrangian model mimic eel larvae.

2.3. Simulation design

We first test whether a common spawning area for each recruit-ment site (i.e. Mayotte, Mauritius, La Réunion and the southeastcoast of Madagascar (Mananjary and Vangaindrano), Fig. 1) canbe identified, considering the ocean currents in the SWIO. For thatpurpose, we run qualitative backward experiments BQL1 (coveringyears 1989–2002 using DRAK01, see Table 2 for a description of allexperiments) and BQL2 (covering years 2001–2007 using DRAK02).The initial positions of the particles are in the vicinity of therecruitment sites. Particles are released once a month between Jan-uary and April, at 4 depth levels from the surface to 100 m deep. Atotal of 2000 particles are released at each recruitment site everyyear, which corresponds to about 10 particles per grid cell. Thelocation of each particle is registered every day and for 180 days.The trajectories of the particles back in time allow us to describethe possible migration routes of eel larvae to the different recruit-ment sites. We illustrate these migration routes in 2006 (date atrecruitment) because it reproduces all larvae trajectories observedover the 19 simulated years, and then we evaluate the possibilityof a common spawning area.

The eastward extension of the possible common spawning areais investigated by running backward quantitative experiments(Experiments BQN1-8, Table 2). The initial geographical sectionsconsist in four recruitment areas along the Madagascar coast(North-West, North-East, East and South-East) and the three is-lands previously identified (Mayotte, Mauritius and La Réunion).Particles are released at recruitment sites continuously from thesurface to 100 m deep and from January to April, correspondingto a continuous spawning period from August to December. We de-fine four final sections at fixed longitude ranging westward from70�E to 60.5�E (the location of the central Mascarene ridge). Exper-iments are run for years 1989–2002 using DRAK01 (BQN1-4) andfor years 2001–2007 using DRAK02 (BQN5-8).

We pursue our investigations of the influence of environmentalconditions on the larvae migration, by evaluating the success forparticles released in selected regions of the spawning area to reacheach recruitment site. For this purpose, we run forward quantita-tive experiments (Experiments FQN1-5, Table 2) with initial geo-graphic sections located at three chosen spawning sites and finalgeographic sections at the seven recruitment sites. Particles are re-leased continuously from the surface to 100 m deep and from Au-gust to December. Experiments FQN1-3 are run for each year from1988 to 2001 using DRAK01, while Experiments FQN4-5 are run foryears 2000–2006 using DRAK02 to test the sensitivity to differenthydrodynamic forcings. For each year, we examine the proportionsin which the successful particles (i.e. particles that succeed inreaching one of the recruitment sites) share out between therecruitment sites. We also calculate the migration durations fromthe chosen spawning sites to the recruitment sites and comparewith observations for years 2004 and 2005 (Experiments FQN4-5).

Additional sensitivity experiments are discussed in Section 4.We run experiments FQL6-7 to test the effect of vertical diurnalmigration of the particles (particles flip every 12 h between 50and 200 m). In these qualitative experiments, particles are releasedat the same initial location as in reference quantitative experi-ments with no larvae behavior FQN4 and FQN5 (similar to FQN1and FQN3 respectively, but covering years 2000–2006), with thesame frequency of release. Because this implies tracking the posi-tions of 450,000 particles, we only run FQL6-7 for years 2005 and2006. In all experiments above, the mortality does not depend onthe duration of migration as long as they do not exceed 180 days.We also test the influence of a more realistic parameterisation ofmortality by running quantitative experiments FQN8 (whereM = 3.8 yr�1) and FQN9 (where M = 8 yr�1), which are otherwisesimilar to FQN4.

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Table 2List of experiments.

Experiment Type Direction Initial conditions of particle release at Spawning/Recruitment sites Periodsa

Locationb Datec Depthd

Initial Final

BQL1 Qualitative Backward Madagascar (2) MauritiusMayotte Réunion

– January 5, February 5,March 5, April 5

12, 25, 45,100 m

1989–2002

BQL2 Qualitative Backward Madagascar (2) MauritiusMayotte Réunion

– January 5, February 5,March 5, April 5

12, 25, 45,100 m

2001–2007

BQN1-4 Quantitative Backward Madagascar (4) MauritiusMayotte Réunion

E1–E2–E3–E4(respectively)

December–April 0–100 m 1989–2002

BQN5-8 Quantitative Backward Madagascar (4) MauritiusMayotte Réunion

E1–E2–E3–E4(respectively)

December–April 0–100 m 2001–2007

FQN1 Quantitative Forward B1 Madagascar (4) MauritiusMayotte Réunion

August–December 0–100 m 1988–2001

FQN2 Quantitative Forward B2 Madagascar (4) MauritiusMayotte Réunion

August–December 0–100 m 1988–2001

FQN3 Quantitative Forward B3 Madagascar (4) MauritiusMayotte Réunion

August–December 0–100 m 1988–2001

FQN4 Quantitative Forward B1 Madagascar (4) MauritiusMayotte Réunion

August–December 0–100 m 2000–2006

FQN5 Quantitative Forward B3 Madagascar (4) MauritiusMayotte Réunion

August–December 0–100 m 2000–2006

FQL6 Qualitative Forward B1 Madagascar (4) MauritiusMayotte Réunion

August–December 50/200 m 2005–2006

FQL7 Qualitative Forward B3 Madagascar (4) MauritiusMayotte Réunion

August–December 50/200 m 2005–2006

FQN8 Quantitative withmortality M = 3.8 yr�1

Forward B1 Madagascar (4) MauritiusMayotte Réunion

August–December 0–100 m 2000–2006

FQN9 Quantitative withmortality M = 7.6 yr�1

Forward B1 Madagascar (4) MauritiusMayotte Réunion

August–December 0–100 m 2000–2006

a Lagrangian simulations are performed for each year of the period 1988–2002 (resp. 2000–2007) with velocity from the model configuration DRAK01 (resp. DRAK02).Hereinafter, the year of each experiments is the calendar year at the time of spawning for forward experiments and at the time of recruitment for backward experiments.

b Two (resp. four) locations on Madagascar are considered for the qualitative (resp. quantitative) experiments. Location of final (initial) conditions for quantitativebackward (forward) experiments are shown in Fig. 6a (resp. Fig. 7).

c Particles are released at fixed dates for the qualitative experiments and continuously in the given time range (depending on the transport across each initial section) forthe quantitative experiments.

d Particles are released at fixed depth for the qualitative experiments, continuously in the given depth range (depending of the transport across each initial section) for thequantitative experiments BQN1-4, FQN1-5 and FQN8-9, and migrate every 12 h between 50 and 200 m deep in experiments FQL6-7.

S. Pous et al. / Progress in Oceanography 86 (2010) 396–413 403

3. Results

3.1. Backward qualitative migration routes: observation ofconvergence areas

We first illustrate the trajectories that reach the recruitmentsites of Mayotte, Mauritius, La Réunion and the southeast coastof Madagascar (Mananjary and Vangaindrano, Fig. 1) in 2006(Fig. 4). To simplify Fig. 4, we only show trajectories passingthrough the Mascarene Ridge, which constitute a large majorityof the trajectories, the others spreading away in various directions.

Most of the particles reaching Mayotte are transported by thenorthern core of the SEC, between Saya De Malha and NazarethBank (10�S–15�S) directly North of Madagascar, and then by theNEMC to Mayotte (Fig. 5d). Nevertheless, some particles are trans-ported by the southern core of the SEC (17�S–20�S), passing southof Carcados Carajos Shoals, before reaching the northern part ofMadagascar (Fig. 4e and f). As the northern route is shorter andpresumably faster because the currents are more intense, migra-tion duration via the northern route is shorter than via the south-ern route (the minimum migration duration from the MascareneRidge is 60 days via the northern route vs. 110 days via the south-ern route). Besides, the Lagrangian trajectories of particles recruit-ing in Mayotte suggest that the NEMC is partly fed by the southerncore of SEC.

Both recruitment sites in Madagascar are fed by the same twopathways. Particles transported by the northern core reach thecoast of Madagascar near 16�S while other particles transportedby the southern core reach the coast near 17�S (Fig. 4g). Then bothstreams merge and flow southwards banked along the coast. Here

again, the northern pathway is the fastest (the minimum is125 days vs. 160 days for the southern route). Interestingly, thissuggests that the SEMC is partly fed by the northern core of SEC.

Most particles recruiting in Mauritius and La Réunion are alsocoming from the southern core of the SEC (Fig. 4d and f). As Mau-ritius is located close to the Mascarene Ridge, migration durationmay be as short as 50 days for the year considered, which is shorterthan the migration duration estimated from observations (Table 1).The shortest migration duration to La Réunion is slightly longerthan to Mauritius (90 days). Another migration route appears forLa Réunion site, at the beginning similar to the northern one de-scribed for the Madagascar recruitment sites and then, near 21�S,turning east toward La Réunion island (Fig. 4f–h). Migration dura-tion along this second pathway is longer (150 days for the fastestpathway).

If there exists a common spawning area for all recruitmentsites, it has to be located simultaneously within the envelope ofall the possible locations of particles up to 180 days beforerecruitment, relative to each recruitment site (Fig. 5a). As theintersection of all envelopes is not empty in 2006 (shaded areain Fig. 5a), a common spawning area (or several ones) may existin the SWIO. The intersection of all envelopes, referred to as theconvergence zone, is roughly centered near the central Mascareneridge but extends southwestwards to the northern region of Mau-ritius and La Réunion island. Note that the convergence zone is re-stricted to the North-West of La Réunion Island for migrationdurations of 90 days and progressively extends to the North-East,near the Mascarene ridge, for longer migration durations (Fig. 5band c). Considering the 19 years of experiments BQL1 and BQL2individually, there is substantial interannual variability in the

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Fig. 4. Simulated trajectories from the recruitment sites (a) backward in time after (b) 10 days, (c) 20 days, (d) 60 days, (e) 90 days, (f) 120 days, (g) 160 days and (h) 180 daysfor recruitment in 2006.

404 S. Pous et al. / Progress in Oceanography 86 (2010) 396–413

pathways of particles: the shape of trajectories may shift to thenorth or to the south and the eastward extension also exhibitsgreat variability (not shown). These induce substantial changesin the shape of the convergence zone, but the latter always exists,suggesting that there may always be a common spawning area foreel larvae in the SWIO.

3.2. Backward quantitative experiment: extension of the convergencearea east of the Mascarene ridge

East of the Mascarene Ridge, at sections E1, E2 and E3 (Fig. 6a),particles coming from the recruitment sites are confined between9�S and 17�S for recruitment years 2001–2007 (Fig. 6b based on

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Fig. 5. Envelope of the location of particles up to 180 days before recruitment in 2006 for Mauritius, Mayotte, La Réunion and two sites of Madagascar (a). Same for particlesat 90 (b) and 130 (c) days before recruitment. Cross hatched area locates the convergence zone.

S. Pous et al. / Progress in Oceanography 86 (2010) 396–413 405

experiments BQN5-8, Table 2), as well as for recruitment years1989–2002 (not shown), which corresponds to the location ofthe SEC (Fig. 2f). The bulk of particles slightly migrates southwardfrom E1 to E3, due to the southward component of the currentsvelocities. As previously described from the qualitative experi-ments, particles reaching Mauritius are rather located in thesouthern SEC. Closer to the Mascarene ridge, at section E4, the lat-itudinal distribution of the particles is wider, and several peaksappear in the distribution: one at 14�S and several south of16�S. They correspond to the northern and southern routes tothe recruitment sites.

Considering the 19 years of experiments BQN1-8 individually,there are years when particles released from a particular recruit-ment site do not reach (backward in time) the final sections E1,E2 or E3, while particles always reach section E4. If the spawningarea was located at section E3 (respectively E2 and E1), there are2 (respectively 4 and 10) years out of 19 for which no recruitmentwould occur in La Réunion. Similarly, there are few years for whichthere would be no recruitment at Madagascar South-East, Mada-gascar East and Mauritius for a spawning area located at sectionsE2 and E1. Considering these probability results, section E4 is themost favorable eastern boundary for the spawning area in the cen-tral Mascarene ridge.

As expected from the location of the sections, the mean migra-tion duration to the recruitment sites, averaged over years 2004and 2005 to compare with observed migration durations in Table 1,

increases from section E4 to section E1 (Fig. 6c). At sections E3 andE4, the distribution of migration durations as a function of the lat-itude indicates that migration is the shortest for MadagascarNorth-East, Mayotte and Madagascar North-West, via the directnorthern route, and for Mauritius via the southern route, especiallyfor a spawning area at section E4. The structure is more complexfor the other recruitment sites, and depends on the section thatis considered. Among all particles released in 2005 at La Réunion,none reaches (backward in time) section E1, which is incompatiblewith field observations. Particles released in 2005 at La Réunionthat reach section E2 have an average migration duration of175 days and a minimum migration duration of 171 days (notshown), which largely exceeds observations. These two points givemore credit to a common spawning area located in the vicinity ofsections E3 and E4 (assuming that the common spawning area islocated east of the Mascarene Ridge). Indeed, for a spawning arealocated near section E4 (respectively E3), there are particles thatrecruit in 4 (respectively 3) sites with an average migration dura-tion of 110 days that corresponds to the ensemble average ofobservations over all species and years. Concerning the recruit-ment sites of Madagascar East and South-East, the average migra-tion duration exceeds 140 days even for a spawning area locatednear section E4, but the minimum migration duration for particlesthat recruit in 2005 (respectively 88 and 101 days, not shown) iscompatible with observations. However, the backward simulationto sites located at the west of E4 was technically impossible.

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0 20 40 0 20 40 0 20 40 0 20 40

30S

25S

20S

15S

10S

5S

0

E4 (60.5oE) E3 (63oE) E2 (66oE) E1 (70oE)

Probability density function (no unit)

2000−2006 (b)

Mayotte Mauritius La Réunion Madagascar NWMadagascar NEMadagascar EMadagascar SE

60100140180 60100140180 60100140180 6010014018030S

25S

20S

15S

10S

5S

0

2004−2005 (c)

Migration duration (day)

(a)

E4 E3 E2 E1

35oE 40oE 45oE 50oE 55oE 60oE 65oE 70oE

0o

5oS

10oS

15oS

20oS

25oS

Fig. 6. Location of the recruitment sites (Mayotte in dark blue, Mauritius in light blue, La Réunion in green and Madagascar in red, gray, purple and light pink) and the finalsections (black dashed lines) used for the quantitative backward experiments BQN1-8 (a). Statistical analysis of particles passing through sections E1, E2, E3 and E4 from eachrecruitment site in quantitative backward experiments BQN5-8: probability density function over years 2000–2006 as a function of latitude (b, no unit) and migrationduration (c, days) on average from 2004 to 2005. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

406 S. Pous et al. / Progress in Oceanography 86 (2010) 396–413

3.3. Forward quantitative migration: arrival success for a spawningarea on the Mascarene ridge

From a spawning area B1 that encompasses the bulk of back-ward trajectories through section E4 (Fig. 7), arrival success tothe different recruitment sites exhibits interannual variability(Fig. 8a and b, left). A large majority of particles that recruit alwaysreaches Mayotte or the North-East site of Madagascar (respectively45% and 33% of particles that recruit on average over years 1988–2006). Arrival success to Mauritius (respectively Madagascar East)ranges from 4% to 12% (respectively from 1% to 11%). La Réunion,North-West and South-East sites of Madagascar receive always lessthan 6% of successful particles and there are years when the arrivalsuccess drops to less than 1%. If the southern extension of thespawning area B1 is reduced (16�S instead of 17�S), the arrival suc-cess in Mauritius decreases from 8% to 4% on average over years

1988–2006, and there are years when it drops to less than 1% aswell as for La Réunion (not shown). Hence recruitment in Mauri-tius and La Réunion depends crucially on the southernmost exten-sion of the spawning area. For each site, arrival success alsodepends on the spawning date. However, the best spawning date,based on the arrival success, changes from one year to the other,as illustrated in Fig. 9 (left).

On average over years 2000–2006 and over all particles that re-cruit (experiment FQN4), simulated migration durations to therecruitment sites of Mayotte and Mauritius are of the same orderof magnitude as observed, while those to La Réunion and Madagas-car are overestimated (Fig. 8b, right and Tables 1 and 3). For aspawning event in 2004, simulated migration duration is the short-est for Mayotte and the North-East site of Madagascar (91 and93 days resp.) and the longest for the East and South-East sites ofMadagascar (163 and 165 days), while in 2005, the shortest migra-

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B1B2B3

35oE 40oE 45oE 50oE 55oE 60oE 65oE 70oE

0o

5oS

10oS

15oS

20oS

25oS

Fig. 7. Location of the recruitment sites (Mayotte in dark blue, Mauritius in lightblue, La Réunion in green and Madagascar in red, gray, purple and light pink) andthe spawning areas B1 B2 and B3 (black boxes) used for the quantitative forwardexperiments FQN1-5, FQL6-7 and FQN8-9. (For interpretation of the references tocolour in this figure legend, the reader is referred to the web version of this article.)

S. Pous et al. / Progress in Oceanography 86 (2010) 396–413 407

tion duration is recorded for the recruitment site of Mauritius(97 days in average), the longest being still in Madagascar Eastand South-East with 159 and 166 days respectively. However, inthe observed data set, La Réunion seems to be the recruitment sitewith the shortest migration durations, the longest durations beingobserved either in Madagascar, Mayotte or Mauritius depending onthe years of recruitment and the eel species (Table 1). In experi-ment FQN1, average migration durations are 4–19 days longercompared to FQN4, except for La Réunion (Fig. 8a, right and Table3).

It is important to note that, in spite of the differences betweenthe mean simulated and observed migration durations, for eachrecruitment site, there are always particles that recruit withinthe observed migration duration. Indeed, the simulated minimummigration duration is 47 days for Mayotte, 51 days for Mauritius,

1988 1990 1992 1994 1996 1998 2000

100

80

60

40

20

0

Arriv

al s

ucce

ss (%

)

Mayotte MauritiusLa RéuniMadagasMadagasMadagasMadagas

2000 2002 2004 2006

100

80

60

40

20

0

Arriv

al s

ucce

ss (%

)

Mayotte MauritiusLa RéuniMadagasMadagasMadagasMadagas

Fig. 8. (a) Arrival success for each site in 1988–2001 (left) and migration duration (days)that the spawning area is at B1 (see Fig. 7). (b) same for 2000–2006.

82 for La Réunion for a spawning in 2004, 88 days for MadagascarEast and 102 days for Madagascar South-East for a spawning in2005 (which roughly corresponds to observations on glass eels col-lected in Andevoranto and Mananjary).

Migration duration depends on the spawning date on the intra-seasonal time scale (Fig. 8a and b, right), but the function linkingmigration duration to the spawning date is very noisy and doesnot exhibit a clear intra-seasonal signal. In addition, the migrationduration for a specific spawning date varies from one year to theother (Fig. 9, right), showing important interannual variability.

There is no simple link between interannual fluctuations in themean migration duration to each recruitment site and the large-scale climatic indices for the Indian Ocean IOD and SIOD (Fig. 10,correlations are not significant at 95% level), presumably becausethese two climatic indices are not related to each other and influ-ence the circulation in the SWIO with different time lags. Never-theless in 1997, IOD and SIOD both contribute to a strengtheningof the SEC, NEMC and SEMC (IOD is negative in 1996 while SIODis positive in 1997, Fig. 10a), which may induce the decrease inthe mean simulated migration duration to all recruitment sites re-corded in 1997 (Fig. 10b). This illustrates that the IOD and SIODhave an impact on the eel larvae migration in the SWIO throughthe ocean circulation, although this impact is complex.

3.4. Forward quantitative migration: arrival success for otherspawning areas

We test the arrival success and migration duration from twoother spawning areas within the convergence zone, to the recruit-ment sites: spawning area B3 is located in the westernmost part ofthe convergence zone while spawning area B2 is located in be-tween B1 and B3 (Fig. 7). In both cases (spawning area in B2 orB3), there are 70% of particles released that recruit to all sites onaverage over years 1988–2001, which is higher than for a spawn-ing area in B1 (where only 35% of released particles recruit on aver-age over the same time period).

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Fig. 9. Arrival success for each site as a function of the spawning date (left), and migration duration (days) as a function of the spawning date (right), for two consecutiveyears: 2004 (top) and 2005 (bottom) when observations are available.

Table 3Mean and minimum migration duration at each recruitment site averaged over 1988–2001 for experiments FQN1, FQN2 and FQN3 and over 2000–2006 for experiments FQN4and FQN5. We also indicate the mean and minimum migration durations at each recruitment site for experiments FQN4 for years 2004 and 2005 when observations are available.

Experiment Spawning site Periods Depth Migration duration at recruitment sites (mean/minima)

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408 S. Pous et al. / Progress in Oceanography 86 (2010) 396–413

For a spawning area located in B2, arrival success to the recruit-ment sites of Madagascar and La Réunion is generally higher thanfor a spawning area in B1 (Fig. 11a, left). This is consistent withqualitative backward experiments, as B2 is located closer to LaRéunion and Madagascar. Actually, the North-East site of Madagas-car recruits most of the particles released in B2 (42% of all particlesthat recruit on average over years 1988–2001). Arrival success inMayotte is lower for a spawning area located in B2 than for aspawning area located in B1, which directly feeds the recruitmentsite via the northern route. Arrival success is the poorest at Mauri-tius, where it averages to 3% and is larger than 1% only in 1988,1992 and 1997. This is consistent with the circulation in the regionas currents passing through B2 are mostly westward, hence theydo not directly reach Mauritius (Fig. 2). Migration durations for aspawning area in B2 towards all recruitment sites are, on average,10–30 days shorter than for a spawning area located in B1, exceptfor Mayotte where it is 13 days longer (see Table 3). As for aspawning area in B1, migration durations depend on the spawningdate and do not show any intra-seasonal modulation whatever therecruitment site considered (Fig. 11a, right). Again, there is largeintra-seasonal and interannual variability in both arrival successand migration durations (not shown).

For a spawning area located in B3, arrival success to Mayotte islower than for a spawning area located in B2, while the East andSouth-East recruitment sites of Madagascar receive most of the re-leased particles (65% of all particles that recruit on average overyears 1988–2001, Fig. 11b, left). Arrival success at La Réunionand Mauritius are higher for a spawning area in B3 compared toB2. Migration durations from a spawning area located in B3 to-wards all recruitment sites of Madagascar and Mayotte are 10–30 days shorter than for a spawning area located in B2 (Fig. 11b,

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S. Pous et al. / Progress in Oceanography 86 (2010) 396–413 409

right). Migration duration from spawning area in B3 to Mauritius issimilar to that from spawning area in B2, and averages to 100 days.Most interestingly, migration duration from spawning area in B3 toLa Réunion is 84 days on average, hence shorter than migrationduration to Mauritius, consistent with observed data, which isnot the case for spawning areas in B1 nor B2 (see Table 3). Besides,migration durations increase for recruitment sites from the North-East to the South-East Madagascar, as for a spawning area in B1and B2. As for the other spawning areas, there is substantial in-tra-seasonal to interannual variability in both arrival success andmigration durations (not shown).

4. Discussion

4.1. Sensitivity to hydrodynamic model and biologicalparameterisations

Two simulations of the hydrodynamical model (DRAK01 andDRAK02) have been used to reproduce the trajectories of passiveparticles in the SWIO from the possible spawning areas to recruit-ment sites, knowing that these two simulations differ only in theturbulent fluxes at the surface. Forward quantitative simulationFQN4, based on DRAK02, gives migration durations similar to thosein FQN1 (based on DRAK01), the difference being of the order of 0–20 days. This may be interpreted as the standard deviation of themigration durations as a function of the hydrodynamic forcings.As noted above, migration duration from spawning area at B1 torecruitment sites, averaged over years 2000–2006, is 4–19 daysshorter than those averaged over 1988–2001 (excepted for La Réu-nion). This should not be interpreted as decadal variability of thelarval dispersion, as the difference is of the order of the standarddeviation of the migration durations related to the hydrodynamicmodel.

In all experiments discussed above, larvae are considered aspassive particles, transported only by the advective currents onthe horizontal as well as on the vertical, although eel larvae are

known to be capable of vertical migration. Field studies have re-ported evidences of diel vertical migration, for Japanese and Atlan-tic eel leptocephali, from the upper ocean at night to depths from150 to 300 m (respectively for Pacific and Atlantic eel leptocephali)during the day (Castonguay and McCleave, 1987 for Atlantic eel,Otake et al., 1998; Kimura et al., 2001 for Japanese eel). The variousprocesses that contribute to the control of the vertical distributionof eel leptocephali, depend on the larval stage (Castonguay andMcCleave, 1987 for Atlantic eels, Otake et al., 1998; Tsukamoto,2009; Yamada et al., 2009 for Japanese eels); they may facilitatethe larvae survival and growth and finally help to ensure a success-ful migration. We test this hypothesis by running additional exper-iments with a parameterisation of the larvae vertical behavior(FQL6-7 compared to FQN4-5 for years 2005–2006, see Table 2).For a spawning area located in B3, there are indeed more particlesreleased that recruit to all sites. Arrival success to all recruitmentsites is very similar to the reference experiments with no verticalbehavior, except for Mauritius where arrival success decreasesfrom 2% to 0% for the 2 years of the sensitivity experiment (spawn-ing in 2005 and 2006). The difference in migration duration (±0 to20 days) is within the range of variability related to the hydrody-namic conditions. The effect of including the larvae vertical behav-ior is different for a spawning area located in B1: there are lessparticles released that recruit to all sites. The arrival success de-creases (eventually to 0) for all recruitment sites but Mayotte, be-cause particles following the northern route cannot pursue theirmigration southward at depth along the coast of Madagascar, pre-sumably due to a vertical shear in ocean coastal currents. Thisactually questions the realism of imposing vertical migrationsevery 12 h even in the presence of a vertical shear in the ocean cur-rents considering the present resolution of the model. The meanmigration durations increase by 15–45 days, presumably becausemore particles follow the southern route that is longer, exceptfor Mayotte where the mean migration duration remains the same.Finally, it is important to keep in mind that there are no observa-tions of the behavior of eel larvae in the Indian Ocean, hence little

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is known about how this behavior depends on the hydrodynamicconditions, which is required to assert the realism of the parame-terisation that we tested here. Also the absence of continentalslope in the vicinity of volcanic islands in the Indian Ocean mayinfluence the larval behavior at the time of recruitment, but noinformation is available on this phenomenon.

The results that we present in this study do not include a real-istic larval mortality. We ran additional experiment FQN8 that in-clude a more realistic parameterisation of mortality, with constantmortality rate M = 3.8 yr�1, which is the most realistic value for eelleptocephalus in Atlantic Ocean, assessed from a combination ofLagrangian simulations and eel ecology parameters (Bonhommeauet al., 2009b). When compared to the similar experiment with norealistic mortality (FQN4), there are less particles released that re-cruit to all sites (15% in FQN8 and 43% in FQN4), but the propor-tions in which successful particles share out between thedifferent recruitment sites is very similar. The mean migrationduration to all sites over years 2000–2006 in FQN8 is of the orderof 10 days smaller than in FQN4, while the minimum migrationdurations remain the same. This value of 3.8 yr�1 belongs to thevery low mortality rates among fish species but in regards to thevery long leptocephalus migration duration in Atlantic Ocean (i.e.21 months), it is the only way to ensure arrival of enough juvenilesin their recruitment sites (Bonhommeau et al., 2009b). In the In-dian Ocean, eel larval duration is shorter (i.e. maximum of6 months), so that the mortality rate could be stronger, withouthaving such drastic consequences on larvae arrival success. Inaddition, the water temperature in the Indian Ocean is warmerthan in Atlantic Ocean, presumably leading to a faster metabolismfor leptocephali of the different eel species, hence a stronger mor-tality. Indeed, it has been shown that the mortality rate is stronglyrelated to temperature, among other environmental parameters(Pepin, 1991; Wegner et al., 2003), and increases as water temper-ature increases (Houde and Zastrow, 1993). Besides, mortality rateis likely to be different between the different eel species of the In-dian and Atlantic Oceans. For example, among the tuna of thegenus Thunnus, instantaneous larval mortality rate of Atlantic Blue-fin tuna (Thunnus thynnus) has been assessed to 0.2 d�1 (Scott et al.,1993), while it ranges between 0.16 and 0.41 d�1 for Yellowfintuna (Thunnus albacares) (Lang et al., 1994) and between 0.5 and0.66 d�1 for Southern Bluefin tuna (Thunnus maccoyi) (Daviset al., 1991). As a result, we also tested a mortality rates of7.6 yr�1 (experiment FQN9). There are only 6% of the released par-ticles that recruit to all sites. The mean migration durations to allsites decreases further compared to the experiment with no realis-tic mortality rate, but the trends among recruitment sites still re-mains the same, hence our results are still valid. For highermortality rates, there are years when arrival success decreases to0 for some recruitment sites, which affects our results, but maxi-mum migration durations are not consistent with observationsany more (less than 143 days for M P 19 yr�1).

4.2. Interpretation of our results in the context of biological knowledge

Following our simulations results, a common spawning area canbe defined, boundaries of which are variable from one year to an-other, but which remains centered near the central region of theMascarene Ridge (i.e. located westwards of 60.5�E, while northernand southern boundaries oscillated respectively around 13�S and19�S) (Fig. 5). This finding is consistent with Jespersen’s hypothesis(1942). Recent studies have shown that for Atlantic (resp. Pacific)eel species, thermal (resp. salinity) fronts are supposed to be iden-tified as landmarks by the eels to locate the spawning areas (McC-leave, 1993; Friedland et al., 2007 for the Atlantic and Tsukamoto,1992; Kimura et al., 2001 for the Pacific). In the SWIO, there is nothermal front associated to the SEC (Fig. 3a). On the other hand,

and as discussed above, two salinity fronts are associated withTSW that is advected westwards by the SEC, both in observations(Antonov et al., 2006) and in the hydrodynamic model. Duringthe spawning period (August–December), the TSW vein is narrow,and the associated salinity fronts are located near 12�S at 60�E, andnear 19�S at 56�E (Fig. 12). These two fronts are located North andSouth of the common spawning area defined here. It is importantto note, though, that there is substantial interannual variabilityin the location of these salinity fronts. Besides, the presence ofthe Mascarene Ridge may act as a geographic or topographic land-mark for the eels in the SWIO.

The hypothesis of a single spawning area in the SWIO for A.mossambica is highly probable, as this species is endemic of the re-gion (observed only on the eastern coasts of Africa: from Kenya toSouth Africa, in Madagascar, Maurice and La Réunion, Fishbasedata: www.fishbase.org, version (09/2009), Froese and Pauly,2009). The hypothesis must be discussed for A. marmorata as thisspecies is widely distributed through the Indo-Pacific region, fromthe western Indian Ocean to the North Pacific and South Pacificoceans (Ege, 1939). Such a pandemic species is likely to have sep-arate spawning areas in the different regions of the Indian Ocean,since tropical eels are characterized by shorter larval migrationduration than temperate species (Kuroki et al., 2006). For the In-dian Ocean, Jespersen (1942) suggested the existence of at leasttwo spawning areas located respectively Southwest of Sumatrafor the leptocephali captured in the eastern Indian Ocean, and Eastof Madagascar for the one caught in the western Indian Ocean. Re-cent genetic studies on this species (Gagnaire et al., 2009) confirmthe existence of two populations, leading to the hypothesis of twogeographically and/or temporally separated spawning areas be-tween SWIO and Sumatra populations. Hence it is conceivable thatthe common spawning area described in our modeling results in-clude the SWIO spawning area for A. marmorata. Beside, Minegishiet al. (2008) and Gagnaire et al. (2009) observed genetic similari-ties between SWIO and Sumatran populations. In particular,Gagnaire et al. (2009) observed cytonuclear desequilibrium onindividuals from Mauritius and La Réunion suggesting the possibil-ity of larval exchanges from Sumatra to these easternmost islandsof SWIO. Backward simulations from the SWIO have been run totest this hypothesis (not shown). It appears that even with a larvalmigration duration as long as 365 days (which exceeds observa-tions), passive particles do not reach Indonesia. Such transoceanicmigrations, if they exist, are necessarily rare and depend on verystrong meteorological events, which are not taken into accountin our model, due to the 5-days averaged hydrodynamical data.

We explore the impact of hydrodynamic conditions on recruit-ment and migration duration from the common spawning area tothe recruitment sites. Because the size and boundaries of the com-mon spawning area change with time, we test three potentialspawning areas (B1, B2 and B3), fixed in time, located within theaverage common spawning area. There are remarkable differencesin larval supply and migration durations to the different recruit-ment sites depending on the spawning area. Indeed, our simula-tions highlight that some years, the potential spawning areas B1and B2 do not provide larval supply with realistic migration dura-tions to all recruitment sites. Nevertheless, the observed distribu-tion of eel communities in estuaries and rivers of the SWIO iscontrasted between the different recruitment sites, with A. bicolorbicolor and A. nebulosa labiata being dominant grossly North ofMadagascar whereas A. marmorata and A. mossambica dominatein the South (Watanabe, 2003; Robinet et al., 2007 and 2008). Thissuggests that different spawning areas may be linked to differenteel species. For example, as B1 seems to favor larval supply onMayotte and North-East of Madagascar (Fig. 8) and A. bicolor bicoloris dominant in the North of Madagascar (Watanabe, 2003; Robinetet al., 2007, 2008), it is conceivable that its spawning area is

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located near B1. At the opposite, the absence of A. marmorata inSeychelles Islands strongly suggests that the spawning area for thisspecies is not located in the vicinity of B1, and may be in moresouthern areas than A. bicolor bicolor.

On the other hand, A. marmorata and A. mossambica recruit in thesame rivers and estuaries in the southern region of SWIO. Indeed,during the Dana expedition in the SWIO (December 1929–January1930), Jespersen (1942) captured leptocephali of A. marmorata andA. mossambica in the same region (i.e. North and West-Southwestof Madagascar), but with a difference in their development stages:leptocephali of A. mossambica were considerably more advancedthan those of A. marmorata. More recently, in La Réunion Island, Rob-inet et al. (2003) observed an earlier recruitment for A. mossambicathan for A. marmorata, with the same time lag in their hatching per-iod (deduced from otoliths analyses), suggesting a 2 months differ-ence in their respective spawning dates. The possibility of acommon spawning area with a temporal difference in the spawningperiods for A. marmorata and A. mossambica is conceivable, as oursimulations highlight the great variability in larval migration trajec-tories according to the release period. This temporal variability inthe migration routes may indeed explain the observed differencesin the proportion of the two species, in La Réunion Island in 2000–2001, as it was suggested by Robinet et al. (2003). However our re-sults do not exclude the possibility of a separate spawning area inmore southwestern zones. Indeed, this fits with the shorter migra-tion durations observed in this species (Robinet et al., 2008; Réveil-lac et al., 2009) than in A. marmorata.

In Madagascar, Réveillac et al. (2009) observed that larval dis-persal durations for A. mossambica increase from north to south onthe eastern coast. The same gradient in larval dispersal durationsis observed in our simulations covering years 1988–2001, whateverthe spawning area (B1, B2, and B3). Indeed, for a spawning area in B1(resp B2 and B3) simulated migration durations increase on averagefrom 150 to 156 days (resp, from 120 to 137, and from 91 to112 days) between the recruitment sites located East and South-East of the Malagasy eastern coast. This modeling result suggeststhat this gradient in larval dispersal duration is only related to thehydrodynamical conditions. The migration duration is related tothe length of the migratory pathway along the eastern coast of Mad-agascar, as the current flows southwards along this coast.

From ageing studies, it has been observed that larval migrationduration is shorter to reach La Réunion than Mauritius (Table 1).Among the three potential spawning areas tested within this study,only B3 allows such pattern, with a mean migration duration of84 days towards La Réunion, vs. 100 days to reach Mauritius. How-ever, to confirm this observation, more biological data are needed.Indeed, this assumption lays on few individuals observed on differ-ent years c.a. for A. bicolor bicolor, 11 individuals were observed in

2000 in La Réunion, leading to a mean migration duration of 46.2(±5.8) days, and 1 individual in 2005 with a migration duration of151 days. These data were compared to nine individuals capturedin Mauritius in 2005, with a mean migration duration of 110.6(±16.7) days. This assumption is more convincing when comparisonis made on A. marmorata on 2005: on 15 individuals captured in LaRéunion, the mean migration duration reached 111.0 (±15.8) days,vs. 139.0 (±24.0) days for 30 individuals captured in Mauritius.

The present study shows that larval migration duration de-pends on the spawning date and the inherent hydrodynamic con-ditions. Such a larval migration duration plasticity has alreadybeen reported by Réveillac et al. (2008) on A. marmorata in differ-ent recruitment sites of the SWIO, and also between eel species fora same recruitment site in La Réunion (Robinet et al., 2003). Thisplasticity has been explained by Réveillac et al. (2008) by boththe individuals intrinsic metabolism and the environmental condi-tions such as sea temperature and food availability. In our model,as larvae are considered as passive particles, individual variabilityis not taken into account. Still, variability in larval migration dura-tion has been highlighted, which is only linked to the hydrodynam-ical variability and the different migratory pathways followed bylarvae. Besides, our results clearly show that migration durationas a function of the spawning date is very noisy, and does not ex-hibit a clear intra-seasonal signal (Fig. 8a and b, right), suggestingthat there is no best spawning date for each spawning area to eachrecruitment site. This is consistent with the non significant sea-sonal variation of larval duration observed in A. mossambica thatrecruited in Madagascar’s East coast in 2005 (Réveillac et al., 2009).

5. Conclusion

As the eel recruitment in the Atlantic and Pacific has beendeclining in the recent decades, eels stocks in the SWIO are facinga growing interest from international fisheries. Although severalstudies described the eel community distribution, and despiteoceanographic cruises aiming at sampling eel larvae, little isknown about the eel spawning area in the SWIO. The objective ofthis study is to investigate the eel spawning area in the SWIO usinga modeling approach that simulates migration trajectories consis-tent with available observations.

Preliminary experiments, which calculate trajectories from therecruitment sites of the SWIO backward in time, suggest that theremay be a common spawning area for all recruitment sites, roughlybounded between 13�S and 19�S and located westwards of 60.5�E,which yields migration durations to the recruitment sites roughlyconsistent with observations. This area is bounded by salinityfronts that may serve as cue for adult eels to locate the spawning

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area. Interannual simulations show that the common spawningarea has varying size and boundaries, but always exists from1988 to 2006. We describe several migration pathways from thespawning area to each recruitment site of the SWIO. Note that thiscould imply multi-modal distribution of some characteristics (e.g.migration duration, age at recruitment, spawning period) of theglass eels that recruit, but observations are insufficient to test thishypothesis.

We explore the impact of hydrodynamic conditions on recruit-ment and migration duration from three specific regions of thecommon spawning area, located on the Mascarene ridge and Northof Mauritius and La Réunion. The migration duration and arrivalsuccess to each recruitment site depends on the region that weconsider. The spawning area located North of La Réunion (B3)seems to yield migration durations to all recruitment sites thatfit the best with observed migration durations. Nevertheless,observations suggest that there may be different spawning areasdepending on the eel species considered, which is not excludedby the simulations, as long as they are within the large commonspawning area that we define. Besides, arrival success and migra-tion durations are different when taking into account diel verticalmigration of the eel larvae. Still, observations are required to assertthe realism of the vertical behavior that was tested here. Also,implementing a more realistic larvae behavior would require ahydrodynamical model that reproduces more realistically the cir-culation in the coastal regions.

The important highlight of this study is the substantial variabil-ity on intra-seasonal to interannual timescale in simulated migra-tion durations and arrival success, with amplitude specific to eachrecruitment site. There is no best spawning date for each spawningarea to each recruitment site that would be common to all years.Migration pathways to the recruitment sites are influenced by boththe IOD and the SIOD that are climatic phenomena independent ofeach other, which seem to induce a complex interannual variabilityin arrival success and migration durations. Such variability cannotbe validated yet because observations are too sparse. To concludeon the most probable location of the spawning area, more oceano-graphic surveys and estuarine samplings are needed on all therecruitment sites, to assess (1) the spatial distribution of eel larvaein the SWIO and (2) the temporal variability of larval supply to thedifferent recruitment sites. A spatio-temporal monitoring of therecruitment is highly recommended.

Acknowledgments

We thank the Drakkar group for providing the hydrodynamicaldata. The Drakkar model has been run at the IDRIS computing cen-tre of CNRS, Orsay, France. We also thank B. Blanke and N. Grimafor helping us with Lagrangian tools. Fig. 3 was created usingODV software (Schlitzer, R., Ocean Data View, http://odv.awi.de,2010). We thank E. Réveillac and T. Robinet for their work on oto-lith microstructure analyses. We appreciate the detailed commentsof Sylvie Dufour in helping us to improve the manuscript. Wethank the two anonymous reviewers for their very constructivecomments.

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