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Modelling the effect of food availability on recruitment success of Cape anchovy ichthyoplankton in the southern Benguela upwelling system a
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V Koné , C Lett & P Fréon
c
a
Département d'Environnement , Centre de Recherches Océanologiques (CRO) , 29 Rue des Pêcheurs, BPV 18 , Abidjan , Côte d'Ivoire b
Institut de Recherche pour le Développement (IRD) – UMI 209 UMMISCO, Centre de Recherche Halieutique Méditerranéenne et Tropicale (CRHMT) , Avenue Jean Monnet, BP 171 – 34203 , Sète , France c
Institut de Recherche pour le Développement (IRD) – UMR 212 EME, Centre de Recherche Halieutique Méditerranéenne et Tropicale (CRHMT) , Avenue Jean Monnet, BP 171 – 34203 , Sète , France Published online: 22 Jul 2013.
To cite this article: African Journal of Marine Science (2013): Modelling the effect of food availability on recruitment success of Cape anchovy ichthyoplankton in the southern Benguela upwelling system, African Journal of Marine Science, DOI: 10.2989/1814232X.2013.796893 To link to this article: http://dx.doi.org/10.2989/1814232X.2013.796893
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African Journal of Marine Science 2013: 1–11 Printed in South Africa — All rights reserved This is the final version of the article that is published ahead of the print and online issue
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AFRICAN JOURNAL OF MARINE SCIENCE ISSN 1814-232X EISSN 1814-2338 http://dx.doi.org/10.2989/1814232X.2013.796893
Modelling the effect of food availability on recruitment success of Cape anchovy ichthyoplankton in the southern Benguela upwelling system V Koné1*, C Lett2 and P Fréon3
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1 Département d’Environnement, Centre de Recherches Océanologiques (CRO), 29 Rue des Pêcheurs, BPV 18, Abidjan, Côte d’Ivoire 2 Institut de Recherche pour le Développement (IRD) – UMI 209 UMMISCO, Centre de Recherche Halieutique Méditerranéenne et Tropicale (CRHMT), Avenue Jean Monnet BP 171 – 34203, Sète, France 3 Institut de Recherche pour le Développement (IRD) – UMR 212 EME, Centre de Recherche Halieutique Méditerranéenne et Tropicale (CRHMT), Avenue Jean Monnet BP 171 – 34203, Sète, France * Corresponding author, e-mail:
[email protected]
Cape anchovy Engraulis encrasicolus adapted its reproductive strategies to the southern Benguela system by spawning over the Agulhas Bank, an area of low productivity that is located upstream of the predominant upwelling system. Frontal jet currents transport eggs and larvae toward the west coast of South Africa, where recruitment takes place. To characterise the recruitment dynamics of Cape anchovy ichthyoplankton, we used an individual-based model forced by a coupled hydrodynamic–biogeochemical model. The results show the importance of food (especially diatoms and copepods) dynamics on the spatial and temporal patterns of recruitment success, and also confirm the importance of the spawning area, timing and water depth on the recruitment success of Cape anchovy larvae. Keywords: 3-D modelling, IBM model, pelagic ecosystem
Introduction The knowledge of small pelagic fish dynamics presents a particular interest both for understanding marine ecosystems functioning and for economic reasons, pelagic fish representing 30% of the world’s fish captures (Tacon and Metian 2009). Small pelagic fish dynamics cannot be fully understood using the different conventional approaches for stock management based on simple hypotheses (spatially homogeneous area, no inter-individual variability, etc.). The development of new approaches is required to take into account the very short life cycle of these fish and the large fluctuations in their abundance due to the environmental (abiotic and biotic) and anthropogenic forcing factors. Using a coupled hydrodynamic–biogeochemical model allows integration of the contrasted physical and biological environment into which the individuals evolve as well as the interindividual variability. However, in a review of such models, Miller (2007) highlighted that few of them (20%) include feeding or growth processes. Yet, food limitation for ichthyoplankton can be a key issue for recruitment success (e.g. Cushing 1990, Heath and Gallego 1997, Miller 2007). Transport from the spawning area of the Cape anchovy Engraulis encrasicolus on the south coast of South Africa (Agulhas Bank) to the recruitment area on the West Coast (St Helena Bay) has been studied by Huggett et al. (2003), Mullon et al. (2003) and Parada et al. (2003, 2008). These authors coupled a three-dimensional hydrodynamic model, based on the Regional Ocean Modelling System (ROMS), with an individual-based model (IBM) in order to study the
impact of environmental conditions (except food) on larval recruitment. During their transport, eggs and larvae are subjected to fluctuations in both physical (current, temperature, salinity) and biological (food availability) factors. The aim of the present study is to extend these former modelling approaches by investigating the impact of food availability (especially diatoms and copepods) on the spatial and temporal patterns of Cape anchovy larval recruitment. Juvenile and adult Cape anchovy have been shown to be opportunistic feeders that consume a large variety of prey of different sizes, ranging from diatoms and copepods to larger crustaceans, including euphausiids (James 1987, 1988, James and Findlay 1989, James and Probyn 1989, James et al. 1989a, 1989b). Few studies, however, have investigated in any detail the diet of E. encrasicolus larvae in situ, with the notable exception of studies in the Mediterranean Sea (Tudela and Palomera 1997, Conway et al. 1998, Tudela et al. 2002, Catalan et al. 2010, Morote et al. 2010), which showed the dominance of early stages of copepods (copepodites, nauplii and eggs) in the diet of the larvae. Other studies (reviewed by Conway et al. 1998) concur with these findings, with the exception of Ferreira and Ré (1993) who found >80% tintinnids in anchovy larvae collected in a Portuguese estuary. Phytoplankton do not appear to be important in the diet of first-feeding anchovy larvae, although Regner (1971) recorded green amorphous material in the guts of the earliest stage larvae. Brownell (1983) reared Cape anchovy larvae in the laboratory by feeding them on different cultured organisms of
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phytoplankton and zooplankton, and found the best growth rates using the calanoid copepod Paracartia africana and cyclopoid Oithonina nana as prey. In this study, we focused on diatoms and copepods to examine the impacts of these prey on Cape anchovy early stage spatial and temporal dynamics. To address this objective, we used a coupled physical–biogeochemical model as forcing of an individual-based model. We concentrated our study on the impact of food availability on anchovy survival and transport success from the anchovy spawning area to where they recruit. The results of the simulations are then discussed in relation to survey data collected in the southern Benguela over many decades. Modelling approaches and methods Coupled hydrodynamic–biogeochemical model The hydrodynamic model is based on ROMS, adapted to the southern Benguela upwelling subregion in its PLUME (Primitive equation non-Linear Upwelling Model of the Benguela Ecosystem) configuration by Penven (2000) and Penven et al. (2001). A detailed description of ROMS is provided by Shchepetkin and McWilliams (2003, 2005). The system solves the free-surface, primitive equations in an Earth-centred rotating environment based on the Boussinesq approximation and hydrostatic vertical momentum balance. PLUME is discretised in coastline and terrain-following curvilinear coordinates. Horizontal resolution ranges from 9 km at the coast to 16 km offshore. There are 20 vertical levels on the vertical dimension, mostly near the surface in order to better resolve the upper ocean variability. In order to resolve mesoscale features, the baroclinic time-step was set to 30 min and the barotropic time-step to 38 s. The model was forced using the atmospheric forcing fields based on monthly climatology derived from the Comprehensive Ocean-Atmosphere Data Set (COADS) (Da Silva et al.
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1994). The momentum forcing is given by the longitudinal and latitudinal components of the wind stress. The biogeochemical model used in our study simulates the functioning of the first trophic levels of the Benguela ecosystem. It contains eight state variables: the pool of dissolved inorganic nitrogen is represented by nitrate (NO3) and ammonium (NH4); phytoplankton by small (flagellates) and large (diatoms) organisms; zooplankton by ciliates and copepods; and detritus by slow (small detritus) and fast (large detritus) sinking particles. The differentiations flagellates/diatoms and ciliates/copepods are relevant with regards to the a priori knowledge of the main phytoplankton and zooplankton organisms present in the southern Benguela region (Shannon and O’Toole 1999). The initial and boundary conditions of nitrate distribution are given by an analytical profile derived from Conkright et al. (1994). The main processes represented in the model (plankton growth and mortality, zooplankton excretion, nitrification, remineralisation, and sinking of detritus) and the parameters used in the model are described in details by Koné et al. (2005) and Koné (2006). The solution of the biogeochemical model was analysed by Koné et al. (2005). The main features of diatoms’ horizontal distributions are represented on a seasonal scale in Figure 1, which shows marked difference between the coast and offshore and between summer and winter. The standing stocks of copepods simulated by the biogeochemical model are overestimated in autumn compared to in situ data. In late spring, the simulated spatial distribution and abundance of copepods are similar to those of the in situ observations (Figure 2). The biogeochemical model used in this study performs better over the Agulhas Bank and offshore (oligotrophic areas), where the small organism size classes (nanoplankton) play a key role in the functioning of the ecosystem. Also, the general patterns simulated by this model are more
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temperature, diatoms and copepods fields) were stored every two days (to save memory space) and were used as forcing factors in the IBM. These fields were interpolated in time to feed the IBM in which the time-step was one hour. This time interval is longer than the 30 min coupling time-step between the hydrodynamic and biogeochemical models, because larval processes occur at a lower velocity than hydrodynamic and biogeochemical processes. There is no feedback of this later on the coupled hydrodynamic– biogeochemical model.
realistic than those simulated by the single compartment model like the Nutrient-Phytoplankton-ZooplanktonDetritus (NPZD) model (Koné et al. 2005). Therefore, we used some of the outputs of this 8-compartment model as forcing factors of the IBM. Even if some variables from the biogeochemical model are not used in the IBM, they still play a key role in the functioning of the modelled ecosystem and therefore on the retained outputs. The hydrodynamic and biogeochemical models are coupled online with a time-step of 30 min, i.e. the same value as the baroclinic hydrodynamic model internal time-steps. The hydrodynamic–biogeochemical model and the IBM model are coupled ‘offline’. The outputs of the coupled hydrodynamic–biogeochemical model (zonal [u], meridional [v] and vertical [w] components of velocity,
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and Parada (2003), who did not find a satisfactory linear growth, we used the following growth model for Cape anchovy eggs:
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Figure 3: Composite distribution map of anchovy eggs using a CalVET net during annual pelagic spawner biomass surveys over the period 1983–2000 (after van der Lingen et al. 2002) with spawning and the nursery grounds overlaid (see text for abbreviations)
environmental factors on the dynamics of small pelagic fish (sardine and anchovy) in the main eastern boundary currents. In the Benguela upwelling system, the model was used by Huggett et al. (2003), Mullon et al. (2003) and Parada et al. (2003, 2008) to study the transport of anchovy eggs and larvae from the spawning area over the Agulhas Bank to the nursery area along the west coast of South Africa (Figure 3). More recently, it was used in the Humboldt (Lett et al. 2007, Brochier et al. 2008a) and in the Canary (Brochier et al. 2008b, 2011) upwelling systems. None of these models considered food availability for the larvae and therefore we developed the model to represent the first three stages of the anchovy life cycle: egg, yolk-sac larvae and feeding larvae (Figure 4), as detailed below. Eggs The IBM model initially contains 5 000 eggs released uniformly within the different spawning areas. Spawning areas are defined according to field observations of anchovy egg distribution over the Agulhas Bank (van der Lingen et al. 2002; Figure 3). Following Huggett et al. (2003), we divided the Agulhas Bank into five spawning areas (Figure 3): western Agulhas Bank (WAB), central Agulhas Bank inshore (CABin), central Agulhas Bank offshore (CABoff), eastern Agulhas Bank inshore (EABin) and eastern Agulhas Bank offshore (EABoff). The spawning duration was set to 30 d, the estimated period that new eggs enter the population each day (Huggett et al. 2003, Mullon et al. 2003), and the choice of this parameter did not change significantly the particle recruitment success. In our study, spawning occurs throughout the year. Three lethal temperature thresholds were considered for egg mortality: 13 °C, 14 °C and 15 °C, centred around the average lethal value of 14 °C reported by King et al. (1978). We assumed that during their transport, eggs die when they reach an ambient temperature equal to the critical lethal temperature threshold. Three spawning depth levels were considered: 0–25 m, 25–50 m and 50–75 m. Following Mullon et al. (2003)
L 0 exp »k (1 e
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where L(t) is egg size (mm) at time t (d) and temperature T (°C). We simplified the original expression of T as follows: (T nT c). We used the parameter values determined by Parada (2003) to fit the experimental data of King et al. (1978): L 0 0.025 mm, k 5.1493 (constant), n 0.2041 (slope of growth rate), and c 2.0833 (constant). The development from the egg stage to yolk-sac larval stage in the model occurred when the length of the individual (egg) was >2.8 mm, which corresponds to the average length of Cape anchovy at hatching, but corresponds to different egg ages (King et al. 1978). Indeed, in laboratory experiments, hatching time varied from 48 min at 23.2 °C to 4.4 h at 11.9 °C (King et al. 1978). Yolk-sac larvae The yolk-sac larval growth is given by the following linear expression: L(t) gt b with g a1 a2T
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where L(t) is larval length (mm) at time t (d), g is the growth rate (d–1), b is length at hatching (mm) and T is the temperature (°C). We used a linear increase for growth rate g with temperature T, i.e. g 0.02 0.03T, determined as the best compromise between simplicity and accordance between the simulated growth and observed growth by Brownell (1983) and Thomas (1986) at different temperatures (Figure 5). Yolk-sac larvae start feeding at length 4.3 mm, which is equivalent to the average length at which they develop a functional jaw (Parada 2003). Three lethal temperature thresholds were considered for the yolk-sac larval mortality: 11 °C, 12 °C and 13 °C, according to data provided by Parada (2003). We assumed that larvae would die when they reach an ambient temperature below these temperatures. This mortality criterion was also applied to the feeding larval stage. Feeding larvae In the absence of information on the growth of feeding larvae, the same growth formulation as for yolk-sac larvae was used except that a growth limitation by food was included. Every time-step (t), the length of the larvae increased by L gƒt, with g as the temperature-dependent growth rate established for yolk-sac larvae and ƒ a food-dependent limiting factor. This limiting factor followed a MichaelisMenten formulation: ƒ Food (Ks Food), where Food is the prey concentration (mmol N m–3) and Ks the halfsaturation constant (mmol N m–3). This formulation was adapted from Rilling and Houde (1999). For simplicity, we did not incorporate mortality by starvation in the model. Feeding larvae that reached the recruitment area along the West Coast before the end of a 60 d dispersal period, and having attained a length of >14 mm, were considered as recruited larvae. The 14 mm threshold was used as an approximate equivalent of the 14 d threshold used
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Yolk-sac larvae Mortality = if ambient T < 11, 12 and 13 °C Growth = f(temp) Length (2.8–4.3 mm)
Recruitment Position = nursery area Length 14 mm Age max. 60 d
End of the loop over individuals End of the loop over time Figure 4: Flow chart of the individual-based model (IBM) of the early life stages of anchovy. T corresponds to the simulated temperature (°C)
in previous simulations (Mullon et al. 2003, Parada et al. 2003). Feeding affects larval growth (size), and this in turn influences the likelihood that larvae arrive at sufficient size in the recruitment area. It does not, however, affect the likelihood of an individual moving from any point in the model domain to another. Our ‘Standard’ simulation represents an ideal case where food is not limiting and only the temperature-dependent growth model was included. In the ‘Diatoms’ (respectively ‘Copepods’) simulations we added simulated diatoms (respectively copepods) concentration as a food-dependent limiting factor. We tested two values of the Michaelis-Menten half-saturation constant (Ks 0.25 and 0.5), whereas Ks 0 corresponds to the Standard simulation. Results Lethal temperatures influenced egg mortality to varying degrees (from 2% to 16%; data not shown), but the general patterns were similar. Also, lethal temperatures did not result in significant change in larval mortality. The results presented below are based on the averages over the lethal temperature values.
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Figure 5: Cape anchovy larval and post-larval growth curves: data and trend curves at 18.5–19.5 °C and 20–23 °C (after Brownell 1983), and at surface temperature (14–23 °C) at the time of collection (Thomas 1986); simulated growth curves at 18.5 °C, 20 °C and 23 °C
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As expected from the addition of a food-dependent limiting factor, the amplitude of the transport success was lower in the Diatoms and Copepods simulations compared to the Standard, but this decrease was more important in the Diatoms (68%) than in the Copepods (46%) simulation. In all simulations, spawning area was a determining factor in transport success, which showed a very pronounced spatial variability (Figure 6a, d). In the Standard simulation, the maximum transport success was obtained for individuals released in the WAB area with about 20% of them successfully reaching the nursery area along the West Coast. When the dynamics of diatoms were taken into account, the most favourable area remained the WAB when Ks 0.25. In contrast, when Ks 0.5, the most favourable area changed to CABoff. For both Ks values, the spatial pattern obtained in the Copepods simulation was very
similar to the one obtained in the Standard. Moving away from the WAB, the transport success decreased gradually and reached very low values in the EABin and EABoff. The seasonal variability of transport success was significant in all the simulations (Figure 6b, e). In the Standard, the maximum transport success was in summer (January) and thereafter decreased gradually until it reached very low values in autumn (minimum in May), and subsequently increased until it reached appreciable values in spring. The Diatoms simulation (when Ks 0.25) displayed the same patterns. When Ks 0.5, the seasonal variability was less pronounced but there was still a significant minimum in June–July. The maximum was reached in spring (October). In the Copepods simulation, the maximum transport success also occurred in October for both Ks values, but the minimum was reached in March.
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Figure 6 (c and f) shows the impact of spawning depth on the transport success. In the Standard simulation, transport success increased with depth from 0–25 m to 50–75 m. In contrast, in the Diatoms simulation, the optimal spawning depth for being transported to the West Coast was located in the subsurface layer (25–50 m) for both Ks 0.25 and 0.5. In the Copepods simulation, transport success also increased with depth, but the optimum depth was less marked. In all simulations, the minimum transport success was obtained for the surface layer (0–25 m). The size frequency distributions of the recruited larvae simulated by the Standard simulation showed that, as expected, the further the spawning was from the nursery area the larger the size of recruits (Figure 7a–e). Indeed, in the coastal area (EABin), the size distribution mode was 38 mm, whereas in the WAB, most of the recruiting larvae ranged in size from 14 and 15 mm. In the Diatoms simulation (Figure 7f–o), the size frequency distributions of the recruited larvae were very different, with small-sized individuals dominating in both simulations with Ks 0.25 and 0.5. There were no recruited larvae >21 mm when Ks 0.5, whereas the largest recruited larva was near 28 mm when Ks 0.25. In the Copepods simulation, the size frequency distributions of the recruited larvae were very similar to those exhibited by the Diatoms simulation, with the size of the recruited larvae ranging between 14 mm and 24 mm (Figure 7p–y). Discussion The recruitment of anchovy in the southern Benguela ecosystem is constrained by the transport success of ichthyoplankton from the spawning areas on the South African south coast toward the nursery area along the West Coast. Our simulations show that it depends mainly on the geographical location of spawning, more precisely its proximity to the nursery area. The optimal transport success was found in the Standard simulation for individuals released over the WAB (Figure 6a). The addition of moderate food limitation (Ks 0.25) did not change this pattern. However, the addition of strong food limitation (Ks 0.5) in the model showed that the dynamics of diatoms changed the spatial patterns of transport success, in contrast to the dynamics of copepods. Indeed, the optimal spawning area was in the Diatoms simulation offshore of the Central Agulhas Bank (CABoff) (Figure 6a), whereas it remained the WAB in the Copepods simulation. This change in the optimum spawning area recurred in the Diatoms simulation for stronger food limitation (Ks 1; data not shown). The reason why the results of the Copepods simulation did not mirror those of the Diatoms lies in the difference in their spatial distributions. The relatively high concentration of copepods in the WAB (Figure 2) suggests that copepods exert strong grazing pressure on diatoms, so that there is a relatively weak fraction of diatoms available to the higher levels of the trophic web. Our results concur with those of Huggett et al. (2003) who used in situ collections over the Agulhas Bank to show that maximum egg concentrations occur over the WAB and CABoff areas. In their modelling studies, Huggett et al. (2003), Mullon et al. (2003) and Parada et al. (2003,
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2008) also found that spawning area explained a large part of transport success variability, and that the most favourable spawning area is the WAB. Our model shows the importance of the CABoff as a spawning area, in particular when strong food limitation is taken into account. Chlorophyll concentrations in this area between the 200 and 500 m isobaths are higher than those in the WAB (Koné 2006). This could favour larvae that are not transported quickly to the West Coast and makes CABoff a better compromise than the WAB between transport to the West Coast and subsequent growth. Retrospective data analysis of anchovy egg distributions suggests that, from 1994, CABoff has became the principal spawning area whereas, in the past, the WAB was considered the main spawning area (van der Lingen et al. 2002, Roy et al. 2007). Despite the relatively high biomass of prey in the eastern part of the Agulhas Bank, the recruitment success for the simulated individuals derived from this area (EABin, EABoff) is poor (Figure 6a, d). This situation could be explained by the fact that the released individuals are either retained on the South Coast (Lett et al. 2006), trapped by the Agulhas Current and then by its retroflection and entrained toward the Indian Ocean or to the south, or advected offshore into the south Atlantic Ocean. In all cases, those released are unlikely to reach the West Coast and contribute significantly to recruitment along that coast. It should be noted that the spatial patterns derived from the model results, particularly in the Diatoms simulation, are sensitive to the Ks value (Figure 6). Our simulations indicate marked temporal variability in transport success, with optimal transport success occurring when spawning takes place during summer or spring (Figure 6b, e). These results are in good agreement with the reproductive strategy of anchovy. Barange et al. (1999) reported that the conditions for survival and recruitment are better in spring (October–December) than in summer (January–March). From data collected over the period 1965–1967 between 18° and 21° E, Crawford (1981) showed that spawning of anchovy takes place from September to March, with a peak in December. Similarly historical data (1965–1968) from collections taken in the south-western Cape province show that the spawning season started in August and ended in March, whereas, during the period 1977–1978, spawning occurred between September and May with a peak in October and January (Shannon et al. 1984). Further evidence of a spring and summer spawning for anchovy has been shown by Shelton (1986), who showed that spawning takes place from October to May with two peaks in October and January. Samplings along the Sardine/Anchovy Recruitment Project monitoring line over the period 1995–2002, to quantify egg and larval transport toward the nursery area, showed a peak in abundance in December for eggs and in February for larvae (van der Lingen and Huggett 2003). These spring and summer peaks from the survey data are shown in our simulations. In the Standard simulation, transport success increased with depth, so the depth interval that showed the greatest success was between 50 and 75 m (Figure 6c, f). However, the vertical distributions of anchovy eggs observed by Shelton and Hutchings (1982) along a transect off the Cape Peninsula showed a peak in abundance between 20 and
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15 20 25 30 35 40 15 20 25 30 35 40 RECRUITED LENGTH CLASS (mm)
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CABoff
Figure 7: Size frequency distributions of recruited larvae for the different areas and simulations. Note the different scales on the y-axis
0.002
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DIATOMS SIMULATION Ks = 0.25 Ks = 0.5
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CABin
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EABin STANDARD SIMULATION Ks = 0
TRANSPORT SUCCESS (%)
Downloaded by [IRD Documentation] at 07:50 22 July 2013
8 Koné, Lett and Fréon
COPEPODS SIMULATION Ks = 0.25 Ks = 0.5
9
Downloaded by [IRD Documentation] at 07:50 22 July 2013
African Journal of Marine Science 2013: 1–11
40 m. Dopolo et al. (2005) investigated the vertical distribution of anchovy eggs over the WAB and found a decrease in abundance with depth from 0 to 100 m. Osman et al. (2010) found a similar pattern over the EAB for early-stage eggs with an abundance peak at 30–40 m for middle-stage eggs. When we took into account the influence of trophic conditions on larval growth, the optimal depth for transport success changed from 50–75 m to the subsurface layer (25–50 m) in the Diatoms simulation and to 25–75 m in the Copepods simulation (Figure 6e, f). The former simulation result is thus in better agreement with in situ data. The change from 50–75 m to 25–50 m optimal depth level is due to the fact that the diatoms profile exhibited a subsurface maximum at about the 20–30 m depth range (Koné 2006). Therefore, even if spawning between 50 and 75 m provides better conditions for transport, spawning between 25 and 50 m leads to a better compromise between transport and growth. Zooplankton diel vertical migration is lacking in the biogeochemical model, which could affect the availability at depth of zooplankton. Copepods may be predominantly within a particular depth stratum, but at night they most likely migrate towards the surface. Nonetheless, the absence of diel vertical migration schemes in both the biogeochemical model (for zooplankton) and the IBM model (for anchovy larvae) may compensate for each other. Other sources of vertical distribution of eggs and larvae, such as their buoyancy, as well as turbulence, were not incorporated into our model. Mullon et al. (2003) and Parada et al. (2003) demonstrated by simulation a value of 1.025 g cm–3 as the optimum egg buoyancy for transport, which we used in our model. Larval buoyancy is unknown and is only influential during a few days before active swimming takes place. Turbulence is an important factor, but is poorly represented in hydrodynamic models. Therefore, we chose to use only the vertical velocity to affect the individual positions. Estimates of relative decrease in amplitude of recruitment success due to the incorporation of food-dependent growth are not of crucial interest in our study, because it depends largely on an empirical parameterisation (especially the Ks value). More interesting is the finding that the decrease is larger with a diatom diet than a copepod diet, using the same Ks value in both simulations. Cape anchovy larvae are opportunistic feeders (James 1987, 1988, James and Findlay 1989, James and Probyn 1989, James et al. 1989a, 1989b), which is why we also performed a simulation where the larvae feed on all available prey. The patterns displayed in this simulation did not show major changes when compared to the Copepods simulation (results not shown). The size frequency distributions of the recruited larvae showed a large range of size classes, from 14 to 42 mm total length (TL). This range is consistent with the anchovy larval and post-larval size frequency data collected using a Methot frame trawl during annual surveys conducted over the nursery area in late summer/autumn (van der Lingen and Huggett 2003). These data showed that small larvae of
