# Resource partitioning among small pelagic fish remains stable regardless of food availability in a seasonally pulsed upwelling system

Fany Sardenne, Carl D van der Lingen, Marie Vagner, Yonela Geja, Eleonora Puccinelli, Margaux Mathieu-Resuge, Fabienne Le Grand, Jean-Marie Munaron, Manon Buscaglia, Sandrine Serre, et al.

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## To cite this version:

Fany Sardenne, Carl D van der Lingen, Marie Vagner, Yonela Geja, Eleonora Puccinelli, et al. Resource partitioning among small pelagic fish remains stable regardless of food availability in a seasonally pulsed upwelling system. Progress in Oceanography, 2026, 242, pp.103678. 10.1016/j-pocean.2026.103678. hal-05476218

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Submitted on 26 Jan 2026

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# Resource partitioning among small pelagic fish remains stable regardless of food availability in a seasonally pulsed upwelling system

Fany Sardenne<sup>a,* ID</sup>, Carl D. van der Lingen<sup>b</sup>, Marie Vagner<sup>a</sup>, Yonela Geja<sup>b</sup>,
Eleonora Puccinelli<sup>a,c,d</sup>, Margaux Mathieu-Resuge<sup>a,e ID</sup>, Fabienne Le Grand<sup>a ID</sup>,
Jean-Marie Munaron<sup>a</sup>, Manon Buscaglia<sup>a</sup>, Sandrine Serre<sup>a ID</sup>, Philippe Soudant<sup>a</sup>,
Laure Pecquerie<sup>a</sup>

## ARTICLE INFO

### Keywords:

- Benguela

- Competition

- Fatty acids

- Stable isotopes

- Trophic niche

- Upwelling

## ABSTRACT

Small pelagic fishes (SPF) in eastern boundary upwelling systems share space and must adapt to strong seasonal and inter-annual variations in their environment. This raises questions about how these species share food resources in a changing environment and whether they compete with each other when their food resources are reduced. We approach these questions on three SPF species (sardine *Sardinops sagax*, anchovy *Engraulis encrasicolus* and round herring *Etrumeus whiteheadi*) living on the west and south coasts of South Africa. We combined fish muscle fatty acid profiles as a proxy for prey taxa diversity, stable isotope compositions as a proxy for habitat and trophic position, and satellite-derived chlorophyll-a and zooplankton concentrations as proxies for food availability. We found that the total fatty acid content (energy reserve) of the three species increased with chlorophyll-a concentration, suggesting that chlorophyll-a is a good proxy for food quantity. For the three SPF species, the niche width increased with the amount of chlorophyll-a. However, the niche overlap (i.e. the potential for competition) remained relatively unchanged as chlorophyll-a concentrations increased, though it varied considerably depending on local conditions (from 0 to 100 %), higher on the west coast (influenced by the Benguela upwelling) than in the south coast. The relative trophic level of the species (round herring > anchovy > sardine) was confirmed in both regions and the round herring remained on average 25 % fattier than the other two species. These results suggest that on a relatively short time scale (month) SPF species are more selective when food levels decrease but that competition for resources may or may not operate depending on local conditions. However, integrated over a larger time scale, the three species do feed on different resources (resulting in different trophic levels) and cannot be considered an ecological substitute. The recent changes in their respective biomasses could impact the amount of fat (energy) available to higher trophic levels.

## 1. Introduction

Several theories have been developed to explain the sharing of food resources between co-occurring species, the most common of which are as follows: (i) the ‘optimal foraging’ theory suggests that foragers specialise in times of food abundance, but diversify their diets in times of scarcity. These shifts in foraging behaviour can intensify competition with other consumer species, especially when resource demand exceeds

supply, leading to increased competition and reduced tolerance for niche overlap (Perry and Pianka, 1997; Pianka, 1974). (ii) The ‘niche partitioning’ theory predicts that competing species can coexist through niche differentiation. During periods of food scarcity, animals narrowed their dietary niche width (or breadth) to reduce niche overlap with competitors (Pianka, 1974). These considerations are particularly important for exploited species such as fish, as taking into account the constraints of trophic interactions leads to better predictions of regional

<footnote><sup>a</sup> IRD, Univ Brest, CNRS, Ifremer, LEMAR, IUEM, F-29280 Plouzané, France</footnote>

<footnote><sup>b</sup> Branch: Fisheries Management, Department of Forestry, Fisheries and the Environment, Cape Town, South Africa</footnote>

<footnote><sup>c</sup> Department of Coastal Systems, Royal Netherlands Institute for Sea Research (NIOZ), Texel, the Netherlands</footnote>

<footnote><sup>d</sup> South African Institute for Aquatic Biodiversity, Makanda, South Africa</footnote>

<footnote><sup>e</sup> Univ Brest, Ifremer, BEEP, F-29280 Plouzané, France</footnote>

<footnote>* Corresponding author.
E-mail address: fany.sardenne@ird.fr (F. Sardenne).</footnote>
variations in fish catches (Stock et al., 2017; Szuwalski et al., 2017). However, empirical studies on fish have observed a variety of niche width and overlap responses to increases or decreases in resource availability (e.g. Correa and Winemiller, 2014; Lesser et al., 2020; Pelage et al., 2022).

Small pelagic fish (SPF) that live in mixed schools in eastern boundary upwelling systems (EBUS) are good examples of exploited species that experience fairly drastic seasonal environmental changes while sharing their food resources. Among the four EBUS, the Benguela upwelling system develops along the coast of southwest Africa, between 34°S and 15°S. The northern part of the Benguela EBUS experiences almost continuous upwelling of cold, nutrient-rich waters from a depth of around 200 m, while the southern part (below 27°S) experiences moderate and pulsating upwelling: upwelling occurs during the austral summer (from late September to March), followed by a period of stratification during the austral winter (from March to July), resulting in high plankton growth rates that support the plankton-feeding SPF (Flynn et al., 2020; Huggett et al., 2009). On the Agulhas Bank (beyond 20°E, along the southern coast of South Africa) the upwelling is wind-induced and comparatively weak (Flynn et al., 2020; Koné et al., 2005). Both ecosystems – thereafter referred as the west and south coast of South Africa – support high populations of SPF and the development of intensive fishing in South Africa since the 1950s. Historically, sardine *Sardinops sagax* and anchovy *Engraulis encrasicolus* were the highest catches together accounting for 400 000 tonnes landed each year, however with variability in the relative contribution of the two species to the catches (Coetzee et al., 2008). Since 2007, sardine biomass has been low compared to anchovy (Parker et al., 2020). Alteration of the main biomass between anchovy and sardine is observed worldwide and is generally related to trophic interactions with their preys (Ayón et al., 2011; van der Lingen et al., 2006). Size-based partitioning of food sources may reduce trophic competition between species: anchovy ingests larger prey (>1.5 mm, mainly calanoid copepods and euphausiids) than sardine (>1.2 mm, mainly calanoids, cyclopoids, fish and crustacean eggs) (Ayón et al., 2011; van der Lingen et al., 2006). Both species also consume small amounts of phytoplankton, with sardine preferentially feeding on dinoflagellates, and anchovy on diatoms (Espinoza et al., 2009; van der Lingen, 2002). In addition to their size, the concentration of food particles also affects the feeding behaviour of sardine and anchovy. Fish usually filter food particles non-selectively when they are present in high concentrations, but selectively bite large food particles present in low concentrations (van der Lingen, 1994). At the same time, the redeye round herring *Etrumeus whiteheadi* population has been growing for the last few decade and is now more abundant than sardine and anchovy (de Moor, 2024). Round herring feeds exclusively on zooplankton of larger size classes than the two other species, suggesting a higher trophic level and a reduced trophic competition between this species and the two others (Vorsatz et al., 2015). However, the diet of SPF follows the availability of prey and can therefore be altered by local environmental changes, particularly in terms of food quality (e.g. consumption of gelatinous or toxic microalgae during blooms) (Brodeur et al., 2019; van der Lingen et al., 2016). Understanding the mechanisms of competition or resource sharing between SPF is particularly important, as this could explain some of their biomasses fluctuations (Nakayama et al., 2018), especially in the context of climate change, where biomass, quality and phenology of zooplankton is expected to change with potential cascading effects on higher trophic levels and fisheries (Heneghan et al., 2023; Ratnarajah et al., 2023; van der Lingen, 2021).

Given the modest intraspecific differences in prey type previously observed between Clupoids (Egan et al., 2018), the integrative use of biomarkers reflecting food assimilated in recent times are useful tools to describe the realised trophic niche. Fatty acids and stable isotopes of carbon and nitrogen complement each other to describe trophic relationships among species. Fatty acids are highly diversified lipids (energy), with more than 40 fatty acids commonly observed in the marine

environment, the biosynthesis of which varies among taxa (Budge et al., 2006; Kelly and Scheibling, 2012). Certain groups of plankton can be easily distinguished on the basis of their fatty acid profiles. Consumers, and in particular SPF, are virtually unable to biosynthesise certain fatty acids and therefore obtain them from their diet, e.g. the essential eicosapentaenoic acid 20:5n-3 (Machado et al., 2018) or the 22:1n-11 (Ratnayake and Ackman, 1979). Therefore, the diversity of a consumer's fatty acid profile reflects, to some extent, the diversity of its preys in terms of large taxonomic groups. Stable carbon isotopes (δ¹³C values) reflect the pathways by which carbon enters food webs, in particular whether the carbon originated offshore (very negative δ¹³C values, around -21 ‰) or inshore (less negative δ¹³C values, around -12 ‰). δ¹³C values therefore provide information mainly on the habitat (along a gradient from terrestrial to marine environments) of organisms and possibly their migration between habitats (MacKenzie et al., 2011). Stable nitrogen isotopes (δ¹⁵N values) reflect the trophic position of organisms: the higher the δ¹⁵N value of an organism in a given food web, the higher its trophic level (Minagawa and Wada, 1984; Vander Zanden et al., 2001). Both fatty acids and stable isotopes reflect the food ingested by organisms: their turnover rate ranges from a few weeks to a few months, depending on the mass of the organisms and tissues (Table S1). Although this means that these biomarkers are partly regulated by the metabolism of the organisms, their combined use in trophic ecology often provides a more integrated view of the diet of species than other traditional method used for trophic studies such as stomach content analysis. The turnover of these biomarkers allows for the detection of potential spatial or seasonal changes in the diet of SPF (e.g. Litz et al., 2010; Neven et al., 2025; Pethybridge et al., 2014; Tanaka et al., 2010) as the biomarkers of their zooplankton prey follow those of phytoplankton within a few days (zooplankton turnover rate ~ 9 days; Graeve et al., 2020).

The aim of this study was to better understand the trophic mechanisms that facilitate the coexistence of sardine, anchovy and round herring populations along the South African coast. To do so, we investigated trophic niche variations in relation to fluctuations in resource availability using fatty acids and stable isotopes analysis, as well as food proxies derived from satellite data and numerical models. We specifically examined how the trophic niche width of each species and inter-specific trophic niche overlaps change in response to phyto- and zooplankton availability by testing the following hypotheses: (1) the 'null hypothesis': seasonal resource availability is not variable enough for fishes to adjust their diet, so niche width and overlap with co-occurring species remains relatively constant with plankton availability; (2) the 'optimal foraging' theory: during periods of low resource availability, fishes are expected to reduce their selectivity for preferred resources (i.e. they become more generalists), thereby increasing their trophic niche width and potential for competition; (3) the 'niche partitioning' theory: during periods of low resource availability, fishes are expected to increase their selectivity for preferred resources in order to limit interspecific competition, thereby decreasing both their trophic niche width and overlap with co-occurring species.

## 2. Material & methods

### 2.1. Fish sample collection

To cover a wide range of environmental variation, we sampled the three SPF species on both the west and south coasts of South Africa in two seasons during the biomass and recruits' surveys conducted by the Department of Forestry, Fisheries, and the Environment (DFFE, Cape Towna, South Africa) which is authorised to sample fish, aboard the Africana RV. The first sampling occurred between November 21th and December 14th 2020 (thereafter November) during the upwelling season, mainly on the continental shelf along the South African coast; and the second took place between June 14th and July 20th 2021 (thereafter June), during the non-upwelling season, along both the south and west
coasts of South Africa (Fig. 1). SPF were caught from 14 stations with a midwater trawl within 70 km from the coast. Fish were dead by the time of sampling. They were measured to standard length (SL in cm, to the nearest mm) and weighed (to the nearest 0.05 g). After dissection on board, the gonads were removed and weighted (to the nearest 0.05 g) to calculate the gonadosomatic index (GSI, i.e. weight of gonads divided by total weight of fish excluding gonads). Muscle sample from the white dorsal muscle was dissected and stored at -80 °C in aluminium foil onboard. Ashore, muscle samples were stored at -80 °C in DFFE laboratories, before they were sent in dry ice to the Laboratoire des Sciences de l'Environnement Marin (LEMAR, France) for fatty acid and isotopic analyses.

To properly quantify trophic niche overlap, we selected fish based on the hypothesis of a shared food environment and thus only individuals from multi-species trawls were selected for the analysis. This choice resulted in the section of 254 individuals between 5.0 and 16.0 cm SL (75 anchovy, 85 sardine and 94 herring) (Fig. 1) from 14 trawls (of which 9 trawls contained all 3 species simultaneously). For these selected individuals, muscle samples were freeze-dried for 72 h and ground to a homogeneous powder, then stored again at -80 °C prior further analysis.

## 2.2. Trophic tracers' analysis on SPF

### 2.2.1. Fatty acids analysis

We extracted lipids from ca. 15 mg of dry powders with 6 mL of a

solvent mixture (CHCl₃:MeOH, 2:1, v:v). Extracts were flushed with nitrogen gas, sonicated, and vortexed for 15 min. An internal standard (23:0) was added to a 1 mL aliquot of lipid extract before hydrolysis in 1 ml of KOH-MeOH (0.5 M) for 30 min at 80 °C and trans esterification with 1.6 mL of MeOH:H₂SO₄ (3.4%; v/v) for 10 min at 100 °C. Hexane was added to the formed fatty acid methyl esters (FAME). FAME were analysed on a TRACE 1300 gas chromatograph equipped with a splitless injector and a flame-ionization detector equipped with a DBWAX column (30 m × 0.25 mm ID x 0.2 µm) and with hydrogen as vector gas, at the Lipidocean plateform, LEMAR, France. We processed chromatograms with Chromeleon 7.2 and identified 55 FAME by comparing their retention time with references from commercial (37 component FAME, PUFA1 and PUFA3, Sigma) and in-house standard mixtures from marine fish whose compounds have been certified by mass spectrometry. Quantification of FAME was based on the internal standard recovery. The average analytical variability was 11.9% from lipid extraction to GC analysis. We expressed fatty acids in percentage of total fatty acids (% of TFA) and total fatty acid content in mg g⁻¹ dry weight (sum of all fatty acids).

### 2.2.2. Carbon and nitrogen stable isotope analysis

We weighted an aliquot of bulk powders into tin capsules and analysed by continuous flow on a Flash EA2000 elemental analyser coupled to a Delta V Plus isotope ratio mass spectrometer at the Pôle Spectrométrie Océan, University of Brest, France. Calibration and isotopic corrections were based on reference materials (USGS-61, USGS-62 and

<figure><img src="image_4.png" /><figcaption>**Fig. 1.** Locations of fish sampling (n = 254) from 14 midwater trawls containing mixed schools of SPF, collected during November/December 2020 (austral spring) and June/July 2021 (austral winter) along the coast of South Africa. The dotted vertical line indicates the separation between west and south coast of South Africa (< and >20°E, respectively). The size of each pie chart is proportional to the total number of individuals collected. The number of fishes analysed per region and sampling time is presented in the table at the top of the map.</figcaption></figure>
USGS-63) and on an in house standard Thermo-Acetanilide. Results were reported in the δ unit notation and expressed as parts per thousand (‰) relative to the international standards (atmospheric N₂ for nitrogen and Vienna- Pee Dee Belemnite for carbon). Analytical precision based on replicate measurements (after every 6 samples) of Thermo-Acetanilide standard was < 0.15 ‰ for both δ¹³C and δ¹⁵N values. C: N ratios were determined from % element weight and some were > 3.5. Therefore, bulk δ¹³C values were arithmetically corrected for lipid-content with the equation of Sardenne et al. (2023) developed for these species. Hereafter, δ¹³C refers to δ¹³C corrected for lipid content.

## 2.3. Chlorophyll-a and zooplankton concentrations

Chlorophyll-a concentrations derived from satellite data can be proxy for SPF feeding intensity (e.g. Garrido et al., 2008; Quiatuhanga et al., 2025), and zooplankton concentrations derived from numerical models are commonly used as proxy of food availability for SPF (e.g. Brochier et al., 2018; Menu et al., 2023; Raab et al., 2013). Therefore, we used Copernicus marine service to obtain chlorophyll-a concentration (in mg m⁻³) and zooplankton concentration (in g m⁻²) from numerical models in order to cover the geographical and temporal scales relevant for our study. Daily chlorophyll-a estimates were obtained with a 0.25×0.25° cell resolution from the Global Ocean Biogeochemistry Hind-cast dataset (DOI: 10.48670/moi-00019), which provides 3D biogeochemical fields on 75 vertical levels based on the NEMO-PISCES biogeochemical model. Daily zooplankton estimates were obtained with a 0.083×0.083° cell resolution from the Global ocean low and mid trophic levels biomass content hindcast dataset (DOI: 10.48670/moi-00020), which provides 2D biomass fields of zooplankton based on the SEAPODYM-LMTL dynamical population model. For each of the 14 studied trawls, we aggregated chlorophyll-a concentrations by median value simultaneously on three dimensions: (i) in depth, from 5 to 40 m depth to consider that the majority of SPF schools are located between 13 and 39 m in the water column (Lawson et al., 2001), (ii) in space, on a 15 km radius (i.e. 707 km²) around the geographical position of the trawl, to consider the horizontal movement of SPF and the patchy nature of plankton blooms (Moloto et al., 2023; Gibbons and Hutchings, 1996). As the size of the patches and the movement of SPF are unpredictable, the value of 15 km remains arbitrary, and (iii) in time, from the sampling date minus 3 weeks, accounting for the turnover of trophic tracers into SPF muscle, which is several weeks to months (Table S1). As zooplankton concentration data are only available at the surface, they were aggregated only in space and time, using the same criteria as for chlorophyll-a.

## 2.4. Statistical analysis

### 2.4.1. Trophic markers and niche metrics

We calculated trophic markers and niche metrics based on fatty acid and stable isotope compositions to characterise the trophic relationships of the three SPF at three levels of integration: individuals, populations and communities.

**Individual.** We considered four trophic markers at the individual level: δ¹³C and δ¹⁵N values and two trophic markers based on fatty acids, i.e., a diatom trophic marker ('Diatom TM') computed as the sum of 16:1n-7, 16:2n-4, 16:3n-4 and 16:4n-1 (Cañavate, 2019; Puccinelli et al., 2023), and a zooplankton trophic marker ('Zooplankton TM') computed as the sum of 20:1n-9, 20:1n-11, 22:1n-9 and 22:1n-11 (Schukat et al., 2014; Teuber et al., 2014). Dinophyte or haptophyte biomarkers such as C18 PUFAs were not considered as they were too weak to be quantified in most samples. Total fatty acid content was also considered as a variable to consider fish health status.

**Population.** We estimated the species-level response using the trophic niche widths for each species in each of the 14 trawls (thereafter referred as a population). Species population with less than four individuals were excluded, leading to a total of n = 37 trophic niches width. For each

species in a trawl, i.e. each population, we used Bayesian standard ellipse areas (SEAB) obtained from individual coordinates from dimensions 1 and 2 of the Principal Component Analysis (PCA) for scaled fatty acid data and from bi-plots of stable isotope values. The SEAB is the distribution of 4,000 iterations of posterior estimates for the ellipse area of each group and is unbiased with respect to sample size (Jackson et al., 2011). We considered the median of the posterior density for SEAB as the niche width. Two types of niches width were calculated for each population: one based on fatty acid profiles (PCA coordinates) and the other based on isotopic values.

**Community.** We estimated community-level responses using the trophic niche overlap between all species pairs (i.e., two-by-two) within each of the 14 trawls (e.g. for a trawl containing the three species, six niche overlaps were calculated), resulting in a total of n = 64 trophic niche overlaps. Niche overlap for species A was expressed as the proportion (%) of SEAB shared (overlapped) with the SEAB of species B, relative to the SEAB of species A. Two types of niche overlaps were calculated for each trawl: one based on fatty acid profiles (PCA coordinates) and one based on isotopic values.

### 2.4.2. Effect of environmental and biological drivers on trophic tracers

We used linear mixed effects models to analyse the relative importance of chlorophyll-a and zooplankton concentrations (log transformed), sampling region (west or south), time of sampling (November or June), species (anchovy, sardine or round herring), GSI and fish length on the variation of individual, population and community trophic markers and niche metrics (see section 2.4.1) and total fatty acid content (see the equation below). Trawl identity was considered as a random effect. For population and community metrics, we replaced individual size with the median size of the fish species in each trawl, and included sample size as fixed effect to account for potential differences due to unequal sample sizes. For percentage data, i.e. individual fatty acids, diatom and zooplankton TM, all expressed as % of total fatty acids, and overlap proportion, we applied the model to logit transformed data. The model considered was as follows:

$$Y_i = \beta_0 + \beta_1 \log10(\text{chlorophyll-a concentration})_i + \beta_2 \log10(\text{zooplankton concentration})_i + (\beta_3 \cdot \text{Sampling region})_i + (\beta_4 \cdot \text{Time of sampling})_i + (\beta_5 \cdot \text{Sampling region} \cdot \text{Time of sampling})_i + (\beta_6 \cdot \text{Species})_i + (\beta_7 \cdot \text{Fish length})_i + (\beta_8 \cdot \text{Species} \cdot \text{Fish length})_i + (\beta_9 \cdot \text{Fish GSI})_i + u_{\text{Trawl}[i]} + \varepsilon_i$$

With Yᵢ the dependent variable, i.e. trophic markers and niche metrics presented above in section 2.4.1, for the observation i; β₀ the intercept, representing the baseline value of Y when all other predictor variables are at their reference levels or zero; β₁ to β₉ the fixed effects coefficients. These represent the influence of each fixed effect variable (e.g. chlorophyll-a and zooplankton concentrations, sampling region) on the dependent variable; uₜrwl[i] the random effect associated with the Trawl variable. It accounts for the non-independence of observations collected within the same trawl; εᵢ the residual error for the observation i, i.e. part of the variance that is not explained by the fixed or random effects.

We founded no collinearity between the considered variables (vari- ance inflation factors < 3). Their inclusion in the models (including the random term) was based on Akaike's information criterion. Homogeneity of residuals was assessed by plotting residuals against fitted values. For total fatty acid content, an outlier (round herring with a value of 33.7 mg g⁻¹) was removed from the dataset before statistics. For linear models, proportions of variability explained by each variable were reported as ω² but note the sum of the partial ω² values may be greater than 1 due to overlapping variance. For mixed linear models, these proportions were reported as marginal semi-partial r² (sr²).

Results of statistical models were presented in the form of plot of effect sizes, which read as follows: (a) each line corresponds to an explanatory variable (i.e., driver) or, in the case of categorial variable, to
one of its modalities (in this case, a modality is arbitrarily used as a reference, set at 0 and represented by an open square); (b) the vertical dotted line at 0 represents the no-effect line; (c) the horizontal bars correspond to the standardised coefficient (or partial slope) of the reported variable. If the dot (mean) is to the right of the vertical line, the association is positive; if it is to the left, the association is negative. If the horizontal bar (95 % confidence interval) crosses the vertical bar, then the relationship is not significant.

Data analyses and graphics were performed in R (R Core Team, 2020), with packages SIBER (Jackson et al., 2011), effectsize (Ben-Shachar et al., 2020), r2glmm (Jaeger, 2016), and lmerTest (Kuznetsova et al., 2020), and the JAGS program.

# 3. Results

## 3.1. Variability in chlorophyll-a and zooplankton concentrations

Chlorophyll-a concentrations at the 14 trawl positions ranged from 0.39 to 3.42 mg m⁻³. The range of variation was more restricted in the south (0.39–0.99 mg m⁻³, n = 6) than in the west (0.47–3.42 mg m⁻³, n = 8), where the full range of variation is observed in June (Table 1). At the same time, zooplankton concentrations ranged from 2.98 to 8.44 g m⁻² and were 1.5 times higher in the west than in the south, whatever the time of sampling (Table 1). Chlorophyll-a and zooplankton concentrations were weakly correlated overall (Spearman ρ = 0.31; Fig. S1 and S2).

## 3.2. Variability in fatty acids, stable isotope values and derived metrics in small pelagic fishes

Over the whole dataset, the main fatty acids of the three SPF species were 22:6n-3 (min–max = 18.5–50.4 %), 16:0 (20.0–30.7 %) and 20:5n-3 (4.7–16.2 %). The proportions of some fatty acids were particularly variable among individuals: e.g. 16:4n-1 (0.0–3.0 %) and 22:1n-11 (0.0–9.0 %), which were therefore discriminated on axes 1 and 2 of the PCA (both axes accounting for 45.6 % of the total PCA variability; Fig. 2A). On the PCA, 22:6n-3 was the most negatively discriminated fatty acid on PC1, while diatom TM pool (16:1n-7, 16:2n-4, 16:3n-4, 16:4n-1 and 20:5n-3) and zooplankton TM pool (in particular 22:1n-11 and 20:1n-9) were positively discriminated. PC2 discriminated the pool of diatom TM in the lower right corner from the one of zooplankton TM in the upper right corner (Fig. 2A). PC3 accounted for 11 % of the variability and discriminated between the two sampling seasons, with diatom and zooplankton TM both relatively more important in November and 18:1n-7 and 18:3n-3 more important in June (Fig. 2B).

δ¹³C and δ¹⁵N values ranged from –18.9 to –14.5 ‰ and from 9.2 to 14.7 ‰, respectively. The variability is twice higher for δ¹⁵N values (coefficient of variation CV of 7.9 %, mainly due to the high variability of round herring values) than for δ¹³C values (4.4 %, also mainly due to round herring values) (Fig. 2C).

Niche widths calculated from fatty acid profiles were 1.4 times more variable than those derived from stable isotopes (CV = 132 % and 94 %,

<caption>**Table 1**</caption>

Chlorophyll-a and zooplankton concentration (in mg. m⁻³ and g. m⁻², respectively) on the continental shelf of the west and south coast of South Africa (< and > 20°E, respectively) during November/December 2020 (austral spring) and June/July 2021 (austral winter) around the 14 studied trawls. Data obtained from Copernicus (https://data.marine.copernicus.eu/products).

<table><thead><tr><th></th><th>n trawl</th><th>Chlorophyll-a</th><th>Zooplankton</th></tr></thead><tbody><tr><td><strong>June (winter)</strong></td><td>7</td><td><strong>1.56 ± 1.08</strong></td><td><strong>5.67 ± 2.00</strong></td></tr><tr><td>south</td><td>2</td><td>0.52 ± 0.09</td><td>4.73 ± 2.48</td></tr><tr><td>west</td><td>5</td><td>1.98 ± 1.00</td><td>6.04 ± 1.96</td></tr><tr><td><strong>November (spring)</strong></td><td>7</td><td><strong>0.64 ± 0.25</strong></td><td><strong>4.94 ± 1.28</strong></td></tr><tr><td>south</td><td>4</td><td>0.71 ± 0.32</td><td>4.06 ± 0.88</td></tr><tr><td>west</td><td>3</td><td>0.54 ± 0.09</td><td>6.11 ± 0.39</td></tr></tbody></table>

respectively); the opposite trend was observed for proportions of two-by-two overlap (CV = 96 % and 118 %), whose mean values were 28 ± 27 % and 18 ± 21 %, respectively.

## 3.3. Drivers of variability at individual, population and community levels

Regardless of the level of integration, the drivers for fatty acid composition were different from those for isotopic values. No interaction between species and fish length was found for the selected mixed models. Zooplankton concentration and GSI were finally not considered as drivers, as their integration as factors in the mixed models never pass the AIC selection criteria.

Chlorophyll-a concentration and species explained 13 % and 11 % of the variability of fatty acids at the individual level on PC1, respectively. Chlorophyll-a concentration was positively correlated with PC1 and species ordered on PC1 (round herring > anchovy > sardine) (Fig. 3A). Species and fish length explained 42 % and 21 % of the variability on PC2, respectively, with round herring having higher values on PC2 and length decreasing with PC2 (Fig. 3A). Time of sampling, species and fish length explained 60 %, 28 % and 18 % of the variability on PC3, respectively, with fish caught in November and round herring having lower values on PC3 (df = 247, t = 6.8, p < 0.001 and t = -7.2, p < 0.001, respectively) and fish length decreasing with PC3 (df = 247, t = -5.0, p < 0.001). Species explained < 1 % of the variability in diatom TM. The interaction between time of sampling and region explained 31 % and species < 1 % of the variability in zooplankton TM, despite there was twice as much zooplankton TM in round herring than in the two other species (5.8 ± 4.0 % vs 2.5 ± 2.4 %; df = 246, t = 6.8; p < 0.001). Chlorophyll-a concentration and species explained 22 % and 1 % of the variability in the 22:6n-3 proportion, respectively. The 22:6n-3 proportion decreased with increasing chlorophyll-a (df = 248, t = -3.6, p < 0.001) and was the highest in sardine (df = 248, t = 2.1, p < 0.05) (Fig. 4A). At the population level, chlorophyll-a concentration explained 26 % of the variability in niche width (Fig. 3A & Fig. 4D). At the community level, region and time of sampling explained 23 % and 5 % of the variability in overlap proportion, respectively, with overlap proportion being highest on the west coast (Fig. 3A) and in June (df = 61, t = 2.1, p < 0.05).

Regarding total fatty acid content of SPF (in concentration), chlorophyll-a concentration and species explained 21 % and 12 % of the variability in total fatty acid content, respectively: total fatty acid content increased from 6.5 to 12.1 mg g⁻¹ when chlorophyll-a concentration increased from 0.4 to 3.4 mg m⁻³ (df = 247, t = 3.3, p < 0.01), and is 25 % higher in round herring than in the two other species (9.8 ± 5.3 vs 7.5 ± 3.3 mg g⁻¹; df = 247, t = 4.9, p < 0.001) (Fig. 4B).

For isotopic composition, the drivers were different. At the individual level, fish length and species explained 18 % and only 3 % of the variability in δ¹³C values, respectively (Fig. 3B). Regardless of the species, δ¹³C values increased by 0.12 ‰ for each cm of standard length, from about –17.4 ‰ at 5 cm to –16.0 ‰ at 16 cm (Fig. 4C). On the other hand, species explained 48 % of the variability in δ¹⁵N values (e.g. δ¹⁵N values were 12.0 ± 0.1 ‰ for round herring > 11.5 ± 0.1 ‰ for anchovy > 10.6 ± 0.1 ‰ for sardine from the south) and region 28 %, with δ¹⁵N values systematically higher of 0.7 ± 0.2 ‰ for individuals from the west. At the population level, isotopic niches (derived directly from both δ¹³C and δ¹⁵N values) are influenced by the same drivers as at the individual level. Fish length explained 26 % of the isotopic niche width (isotopic niche width increased with fish length), species explained 16 % (sardine had a smaller isotopic niche width than the two other species) and region explained 6 % (isotopic niche width was greater in the west than in the south) (Fig. 3B). At the community level, both species and fish length explained 1 % of the variability in overlap proportion, with higher overlaps for anchovy than for the two other species and that increase with fish length (Fig. 3B).
<figure><img src="image_5.png" /><figcaption>**Fig. 2.** Individual muscle composition of the three small pelagic fish species from South Africa (n = 254 individuals). Fatty acid profiles are represented by a PCA in A (PC1 and PC2) and B (PC1 and PC3) with only the fatty acids best represented superimposed (correlation > 0.5). Lipid-corrected δ¹³C and bulk δ¹⁵N values are represented in C. Colours indicate species and dot shapes indicate trawls (ZA01-ZA14). Solid dots correspond to trawl hauls from June (austral winter) and open dots to those from November (austral spring), [s] indicate trawl hauls from the south and [w] these from the west coast of South Africa. Ellipses represent 95 % of the data.</figcaption></figure>

## 4. Discussion

We investigated variations in the trophic relationships of three exploited SPF species in relation to fluctuations in food resources availability along the west and south coasts of South Africa. Trophic relationships (niche width and overlap) were estimated using fish fatty acid profiles as biomarkers of prey diversity and stable isotope values of carbon and nitrogen as biomarkers of habitat and trophic level, while food resources availability was estimated using chlorophyll-a and zooplankton concentrations (satellite and model estimates). Trophic niche width increased with chlorophyll-a concentration in the three SPF species (Fig. 5A), while niche overlaps were more dependent on region (Fig. 5B). The use of satellite-derived chlorophyll-a concentration as an indicator of food availability for SPF has been validated, since total fatty acids content (energy reserves) in the three species increase alongside chlorophyll-a concentration (Fig. 5C). Although the nitrogen isotopic values varied by region, interspecific differences in trophic levels were confirmed in both regions (Fig. 5D). It should be noted, however, that the concentration of zooplankton surprisingly had no effect on the

trophic markers and metrics considered.

**A mixed hypothesis of trophic niche partitioning but low competition**

The diversity of prey taxa (based on fatty acid profiles) for the three SPF increased with the amount of chlorophyll-a available, in line with the 'niche partitioning' theory (hypothesis 3: in periods of high resource availability, fish reduce their prey selectivity, increasing their trophic niche width). This seems to be a consequence of the filtration-feeding mode, which is observed when the concentration of small particles is high and which is less selective than the bite-feeding mode (van der Lingen, 1994). However, in contrast to the 'niche partitioning' theory, the proportion of resource overlap between species remained relatively unchanged with resource availability, which is more consistent with the null hypothesis. Although neither of the hypothesis tested is fully supported by our results, this mixed pattern has already been observed in various aquatic ecosystems, such as for frugivorous fishes (Correa and Winemiller, 2014) and pelagic predators (Portner et al., 2025).

The three species seemed to benefit equally from the increase in resource availability, as their total fatty acid content (proxy for energy
<figure><img src="image_6.png" /><figcaption>**Fig. 3.** Standardized effect sizes (mean and 95 % confidence interval) for individual, population and community responses of SPF to environmental and biological drivers. (A) Responses according to fatty acid profiles, i.e. taxon origin of carbon, and (B) Responses according to stable isotope values, i.e. feeding habitat and trophic position. Stars indicate significance level of drivers (* for p < 0.05, ** for p < 0.01 and *** for p < 0.001). See section 2.4.2 for instructions on reading this type of graph.</figcaption></figure>

reserves) increased with chlorophyll-a concentration while their niche overlap remain unchanged. It suggests that changes in chlorophyll-a concentration, rather than competition for resources, are more likely to drive fluctuations in SPF populations. However, this hypothesis needs to be confirmed over a longer period of time and over a wider range of chlorophyll-a concentrations, because the range of chlorophyll-a concentration estimated in our study (0.39–3.42 mg m⁻³) is more restricted than the range of variation observed over the last 20 years in the studied area (0.09–5.43 mg m⁻³; satellite-derived data from Copernicus). Nevertheless, this suggests that this SPF community is adapted to seasonal changes in its habitat: thanks to generalist diets, these species have maintained a relatively constant level of dietary overlap (average overlap of 28 %, but ranging from 0 to 100 %). Thus, despite a spatial overlap between SPF species of around 60–80 % along the South African coast (Drapeau et al., 2004), trophic overlapping in terms of prey taxa diversity appeared to be highly variable according to local conditions of the trawl stations. Similarly, in the case of SPF from the Bay of Biscay (European sardine, anchovy and sprat), dietary overlap in terms of prey species was high and depended on local availability, but

this did not seem to generate competition (Bachiller and Irigoien, 2015). Long-term monitoring of the trophic ecology of SPF would be required to verify whether this low trophic overlapping is independent of the biomass of each SPF, or whether it varies with the dominance (in biomass) of a species. As the dietary overlap among large pelagic predators was also found to be stable despite annual variations in diet composition (Portner et al., 2025), this may be a widespread pattern of resilience to environmental variability.

### Regional contrasts in interspecific relationships

Trophic overlap in term of prey taxa diversity is observed to occur at a higher proportion in the western region, where there was a notable variation in chlorophyll-a concentration. This pattern is probably related to the non-selective filtration feeding that occurs when particle concentrations are high (see the previous paragraph). At the same time, the higher overlap in the west, particularly in June (winter), is associated with an average 0.7 ± 0.2 ‰ higher δ¹⁵N values than those from the south (especially for round herring). The following two hypothesis can be put forward to explain regional differences in δ¹⁵N values: (i) baseline differences caused by ¹⁵N-enriched nitrate uptake by
<figure><img src="image_7.png" /><figcaption>**Fig. 4.** Individual values of (A) 22:6n-3 proportion in fish muscle according to chlorophyll-a concentration, (B) total fatty acids content in fish muscle according to chlorophyll-a concentration and (C) lipid-corrected δ¹³C values according to fish length. Population metric with (D) Niche width of each species (median value) obtained from muscle fatty acid profiles as a function of chlorophyll-a concentration. sr² = marginal semi-partial r².</figcaption></figure>

phytoplankton during upwelling events (Puccinelli et al., 2023) that are more frequent and intense on the west (i.e. under the influence of the Benguela system), while they only sporadically occur on the south (Lutjeharms et al., 2000), (ii) differences in the size/life stage of zooplankton consumed, particularly given that interspecific dietary differences based on prey size of the same taxon may limit food competition in Clupoids (Egan et al., 2018). The hypothesis that food chains are longer in the west than in the south seems unlikely, as upwelling regions generally have short trophic chains and a great efficiency of energy transfer between trophic levels (Pauly and Christensen, 1995). In both the western and the southern regions, δ¹⁵N values and zooplankton TM were consistent with the known trophic level of the species present (round herring > anchovy > sardine) (Horton, 2024; Vorsatz et al., 2015). These differences in trophic level, but also in energy content (round herring contains 25 % more fatty acids than the other two species, especially during the austral summer), suggest that these species cannot be considered as ecological substitutes and that changes in their biomass could have an impact on the food web. For example, the energy content of SPF is important for higher trophic levels, as they require high-energy prey, particularly during the breeding season (Grémillet et al., 2008; IJsseldijk et al., 2021).

### Possible metabolic influence on lipid-free δ¹³C values

Regardless of the quantity of food available, we observed an increase in δ¹³C values (though not in δ¹⁵N values) with the size of the individuals. This suggests that an ontogenetic change in feeding habitat or in prey type is occurring. Differences in dietary habits between juveniles and adults have already been observed in the North Atlantic, based on the stomach contents of the sardine *Sardina pilchardus* and anchovy

E. encrasicolus (Garrido et al., 2015). However, the increase in δ¹³C values with size length may also be the consequence of a metabolic process resulting in a reduction of growth rate with size length. Given the differences in sardine growth rate previously observed between the west and south coasts (Sakamoto et al., 2022), we could hypothesise that such a regional effect influences δ¹³C values. However, we did not observe any regional effect on δ¹³C values. The mechanism underlying the observed increase in δ¹³C values with fish size remains unclear, and the available data are insufficient to advance our understanding of this phenomenon. In a previous study conducted 33 years ago along the South African coast, a negative relationship was observed between δ¹³C values and size length for anchovy and round herring of a similar size (n = 19) (Sholto-Douglas, 1992). In contrast, the relationship between δ¹³C values and body weight was found to be non-significant for adult anchovy from Japan (n = 272) (Tanaka et al., 2010; Tanaka et al., 2008). In order to elucidate the influence of the environment and metabolism on the δ¹³C values of SPF, one potential approach could be to apply the Dynamic Energy Budget theory to stable isotopes (Pecquerie et al., 2010).

### Mismatch between zooplankton and SPF diet

The lack of effect of zooplankton concentration on the trophic metrics studied was surprising, as the diet of SPF is mainly based on zooplankton. However, a previous study in the region has already shown that there was no relationship between the abundance of zooplankton and that sardine and anchovy, based on observations from at-sea surveys (Grémillet et al., 2008). Here too, there appears to be a mismatch between SPF and zooplankton, with implications for trophic metrics. The following hypotheses can be put forward to explain why zooplankton
<figure><img src="image_8.png" /><figcaption>**Fig. 5.** Schematic summary of the main drivers explaining the variability in trophic biomarkers and SPF relationships based on effects size: (A) Niche width increased with chlorophyll-a concentration for all three species; (B) Niche overlaps were the highest to the west; (C) Total fatty acid content in SPF muscle increased with chlorophyll-a concentration, with the highest levels for round herring; and (D) δ¹⁵N values were the highest to the west, with round herring > anchovy > sardine in both regions.</figcaption></figure>

concentration was not a reliable proxy of resource availability for SPF: (i) it is highly likely that the detected regional effects actually mask or include an effect of zooplankton concentrations, since these concentrations were higher in the west, where SPF indeed have higher trophic levels (as indicated by δ¹⁵N values) and higher levels of zooplankton TM (22:1n-11 in particular); (ii) the observed surface concentration of zooplankton (i.e. from sampling to minus three weeks) does not accurately represent the true available biomass for SPF. This could be due to a time lag in detection, caused by recent top-down pressure from SPF (or other competitors), which has already consumed a significant proportion of zooplankton by the time they are observed (Mcginty et al., 2014; Plagányi et al., 2000); (iii) the trophic biomarkers we used do not enable detailed detection of diet diversity over the previous weeks. A better match between zooplankton concentration and SPF diet diversity might have been obtained using less integrative but more species-specific tools, such as eDNA analysis of stomach contents (e.g. Jan et al., 2025).

In our configuration of numerical models and data aggregation, the correlation between chlorophyll-a and total fatty acid content in SPF suggests that satellite-derived chlorophyll-a concentration provides a more accurate representation of the available food supply for SPF than modelled zooplankton concentration.

### Caveats and perspectives

Finally, it is important to consider the limitations of this study. From a methodological perspective, satellite-derived concentrations of chlorophyll-a and zooplankton were estimated and indirect measures whose reliability is difficult to assess. It would be particularly relevant to use long-term series of observational data to quantify and describe the taxonomic diversity of the resources available to SPF. From an ecological perspective, we did not consider the biomass/abundance of the

studied species, the presence of other SPF species that may compete (scad, mackerel, jellyfish...), nor the presence of SPF predators (tuna, seabird...), that all could affect the trophic metrics monitored and should be considered in future studies.

## 5. Conclusion

The diet diversity the three SPF increased with the amount of food resources available (chlorophyll-a concentration), while the overlap proportion between species remained relatively unchanged but highly variable according to local conditions. Changes in chlorophyll-a concentration are therefore more likely to drive fluctuations in SPF populations than competition for resources. Trophic overlap was higher in the west, i.e. under the influence of the Benguela upwelling system, with no particular effect on the fish's energy reserves, which increased with the concentration in chlorophyll-a. Consequently, satellite-derived chlorophyll-a concentration has been posited as an indicator of the available food supply (energy) for SPF. The known trophic level of the species (round herring > anchovy > sardine) was confirmed in both regions, suggesting stability of trophic levels and that these species cannot be considered ecological substitutes.

### CRediT authorship contribution statement

**Fany Sardenne:** Writing – original draft, Methodology, Formal analysis, Data curation, Conceptualization. **Carl D. van der Lingen:** Methodology. **Marie Vagner:** Writing – review & editing, Project administration, Investigation, Funding acquisition. **Yonela Geja:** Resources, Investigation. **Eleonora Puccinelli:** Writing – review & editing,
**Investigation, Formal analysis. Margaux Mathieu-Resuge:** Writing – review & editing, Methodology, Formal analysis, Data curation. **Fabienne Le Grand:** Validation, Resources, Methodology, Data curation. **Jean-Marie Munaron:** Validation, Methodology. **Manon Buscaglia:** Validation, Methodology. **Sandrine Serre:** Investigation, Data curation. **Philippe Soudant:** Writing – review & editing, Supervision, Resources. **Laure Pecquerie:** Writing – review & editing, Supervision, Project administration, Investigation, Funding acquisition.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

We would like to thank participants in FRS *Africana* for collecting fish samples during DFFE surveys and Romain Gonzalvez (LEMAR lab) for help with sample preparation. We also thank two anonymous reviewers for their constructive comments and suggestions, which have improved the quality of this work. This study is a contribution to the OMEGA project, supported by ISblue project, Interdisciplinary graduate school for the blue planet (ANR-17-EURE-0015) and co-funded by a grant from the French government under the program "Investissements d'Avenir" embedded in France 2030. This study has been conducted using E.U. Copernicus Marine Service Information; DOI: 10.48670/moi-00019.

## Appendix A. Supplementary material

Supplementary data to this article can be found online at https://doi.org/10.1016/j.pocean.2026.103678.

## Data availability

The data of the study are openly available in DataSuds repository at https://doi.org/10.23708/OJJNGM. Data reuse is granted under CC-BY license.

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