# Influence of divided ponds on fish diets in polyculture: insights from carbon and nitrogen stable isotopes in trophic webs

Purco Ralairison Ralien, Michael S. Corson, Marc Roucaute, Christophe

Menniti, Kazi Kabir, Joël Aubin, Sarah Nahon

► To cite this version:

Purco Ralairison Ralien, Michael S. Corson, Marc Roucaute, Christophe Menniti, Kazi Kabir, et al. Influence of divided ponds on fish diets in polyculture: insights from carbon and nitrogen stable isotopes in trophic webs. Aquaculture, Fish and Fisheries, 2025, 6 (1), pp.e70161. 10.1002/aff2.70161 hal-05455391

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Submitted on 13 Jan 2026

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ORIGINAL ARTICLE

OPEN ACCESS

# Influence of Divided Ponds on Fish Diets in Polyculture: Insights From Carbon and Nitrogen Stable Isotopes in Trophic Webs

Purco Ralaiarison Ralien<fnref n="1" />,³ | Michael S. Corson<fnref n="1" /> | Marc Roucaute<fnref n="1" /> | Christophe Menniti² | Kazi Ahmed Kabir³ | Joël Aubin<fnref n="1" /> | Sarah Nahon⁴

**Correspondence:** Purco Ralaiarison Ralien (purco-ralaiarison.ralien@inrae.fr; ralienpurco@gmail.com)

**Received:** 7 August 2025 | **Revised:** 2 December 2025 | **Accepted:** 8 December 2025

**Keywords:** diet composition | divided pond | fish polyculture | niche overlap | management

## ABSTRACT

This study analysed the effects of pond-management treatments on the diet composition and niche overlap of fish species by period of the production cycle in a polyculture system of pikeperch, common carp, roach and tench. Between stocking (February) and harvest (October), the treatments consisted of (i) dividing a pond in half with a net fence, stocking adult fish in one half and removing the fence (July) ('divided' treatment) or (ii) keeping a pond open ('open' treatment). Each treatment was replicated in three ponds. Fish and food resources were sampled each period, and their stable isotopes were analysed using mixing models to estimate fish diet compositions. The biomass of food resources did not differ between the two treatments, nor between the halves of the divided ponds with or without adult fish. The specific growth rate and net fish yield of each fish species also did not differ between the two treatments. Between the two treatments, the diet compositions of fish species did not differ, but they did differ between July and October, likely due to the presence of tadpoles in July. The niche of pikeperch did not overlap with those of the omnivorous fish species, as pikeperch are carnivorous. Niches of the omnivorous fish species did overlap somewhat, but less so in the divided pond, likely due to resource specialisation, but this overlap was not reflected in the diet compositions, possibly due to the low fish stocking biomass. Thus, using a divided pond design helps decrease niche competition, which may increase fish productivity.

## 1 | Introduction

Current and future development of fish farming must consider the latter's ability to reconcile productivity with the preservation of natural resources (Boyd et al. 2020). New aquaculture practices are required to increase resource-use efficiency, which is one of the main criteria for minimising environmental impacts of fish farming systems (Engle et al. 2021). In extensive earthen-pond aquaculture, fish are raised at low densities and their growth depends greatly on the natural productivity of the environment,

while in semi-intensive systems, additional feed and/or fertiliser is provided to supplement natural food resources (Edwards 2015). The use of both natural and supplemented resources, which increases fish productivity, depends on the fish species stocked (Mao et al. 2016). To increase the efficiency with which these resources are used, it is necessary to understand how fish species interact with them. A better understanding of the trophic roles of fish in ponds, especially how fish compete for and use food resources in their environment, is essential to manage fish stocking and farming.

This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

© 2025 The Author(s). Aquaculture, Fish and Fisheries published by John Wiley & Sons Ltd.

<footnote n="1">¹INRAE, Institut Agro, SAS, Rennes, France | ²UMR 5110 CEFREM, CNRS, Université de Perpignan Via Domitia, Perpignan, France | ³CIRAD, UMR ISEM, Montpellier, France | ⁴MARBEC, Univ Montpellier, CNRS, Ifremer, IRD, INRAE, Sète, France</footnote>
In pond fish polycultures, fish species are combined based on their ability to be raised in the same ecosystem and to use available food resources, as well as on their trophic complementarity (Milstein 1992; Thomas et al. 2021) and potential growth rates. In Europe, pond fish polycultures often combine omnivorous fish species (e.g., common carp [Cyprinus carpio], roach [Rutilus rutilus], tench [Tinca tinca]) and one or more carnivorous fish species (e.g., pikeperch [Sander lucioperca], black bass [Micropterus salmoides], perch [Perca fulviatilis], pike [Esox lucius]) (Wezel et al. 2013). In their natural environments, omnivorous fish often have flexible foraging strategies that vary seasonally depending on the availability of food resources (González-Bergonzoni et al. 2016). Common carp, roach and tench consume a wide variety of food resources, such as benthic macroinvertebrates (BMI), macrophytes and detritus, as well as some zooplankton and phytoplankton (Alaş et al. 2010; Avlijaš et al. 2018; Dadebo et al. 2015; Nahon et al. 2024). Roach have a diet composition similar to that of common carp (Nahon et al. 2020; Zapletal et al. 2014), although they prefer zooplankton (Zapletal et al. 2014). Omnivorous fish are able to consume a wide range of food resources, which decreases interspecific competition and increases the use of natural food in ponds (Yao et al. 2016). Carnivorous species consume BMI and regulate populations of juvenile fish (Aubin et al. 2021).

Understanding trophic interactions in polyculture systems is complex due to the variety of food resources and their spatio-temporal availability, as well as trophic interactions between species (i.e., predation and competition) (Jan et al. 2014). Preferences for food resources also change as individuals develop from fry to adults (Hjelm et al. 2003). Fish food preferences and interspecific competition provide a basis for assessing the ability of species to be raised together in a polyculture system. As diet compositions determine how fish species use food resources, identifying them can thus decrease interspecific competition among assemblages of fish species in polyculture and optimise the use of food resources (Thomas et al. 2021). Knowledge of fish diet compositions in complex systems remains incomplete, however, notably due to a lack of information about feeding behaviours. One method for estimating diet compositions is to analyse stable carbon (δ¹³C) and nitrogen (δ¹⁵N) isotopes of the different compartments of the ecosystem, in order to identify the food resources that fish use and estimate their relative contributions to diets (Fry and Sherr 1989). This method provides an overview of diet composition, as it integrates the isotopic signatures of food resources consumed over a longer period rather than representing consumption at a specific moment. Moreover, digestion influences isotopic signatures little, unlike stomach contents, which helps decrease biases in estimating diet composition (Petta et al. 2020; Tripp-Valdez and Arreguín-Sánchez 2009). Stable isotope values can also be used to represent the isotopic niche of each species. It is defined as a multidimensional space of 'bionomic' and 'scenopoetic' ecological information (i.e., what a species eats and its habitat, respectively), and is thus used to characterise interspecific competition between species (Newsome et al. 2007).

The biomass of natural food resources in earthen ponds changes during a production cycle, driven by seasonal changes and fish foraging (Dong et al. 2022; Thomas and Nielsen 1994), which can influence diet composition and thus fish productivity. In ponds

in Europe, the growth of primary producers (i.e., phytoplankton and macrophytes) begins in early spring and peaks by late spring (Cristofor et al. 2003; Sayer et al. 2010). This increase in food-resource biomass increases consumption by secondary producers and fish, which ultimately causes this biomass to decrease by the end of summer. However, if food-resource biomass were higher in spring, fish might consume even more during a production cycle. One way to increase food-resource biomass could be to exclude stocked fish (and thus their predation) from part of a pond until late spring. The main objective of this study was thus to assess how this pond-management strategy influenced fish feeding behaviour and the pond's overall productivity. In particular, this study aimed to (i) identify the food resources in fish diets in two polyculture systems using δ¹³C and δ¹⁵N isotopes, (ii) determine whether the fish diets varied seasonally, (iii) assess whether protecting food resources from adult fish predation can increase their abundance at the end of the summer, (iv) assess whether and how this strategy of protecting food resources can decrease interspecific competition among fish species and (v) assess whether these changes in the food web are sufficient to influence fish productivity.

## 2 | Materials and Methods

### 2.1 | Experimental Design and Management

The experiment was performed in 2023 at the facilities of the Aquatic Ecology and Ecotoxicology unit of INRAE (U3E, Rennes, France, 48°07'13" N, 1°47'33" W), in six earthen ponds, each 0.1 ha (50 m × 20 m) in area and 1 m deep. The rearing conditions were extensive, with no input of additional feed. The four fish species stocked (on 28 February) — pikeperch, common carp, roach and tench — were chosen based on their trophic complementarity (Table 1). Adults of roach and tench, known for their high reproduction rate, were stocked to produce fry as food resources for pikeperch. The stocking biomass of fish was 86.2 ± 0.9 kg ha⁻¹, which fell within the range of those used for extensive fish ponds in Europe (40-100 kg ha⁻¹) (Aubin et al. 2021; Lemmens et al. 2013).

Two treatments were applied in triplicate: 'open' and 'divided', the latter in which a net fence initially separated each pond into two halves (Figure 1). The polyethene fence had a mesh size of 1 cm and was placed through the middle of the pond (water inflow on one side, water outflow on the other), buried ca. 20 cm into the soil, with 50 cm extending above the water surface. The fence was designed to prevent stocked fish from passing through or around it, although fry born to them in early summer could pass through. Technical staff of the U3E experimental site monitored the fish every 2 weeks, carefully observing their behaviour to ensure their well-being and good health. The fish initially had access only to half of the pond into which they had been stocked. As mentioned, this treatment was designed to increase the productivity of BMI, zooplankton and macrophytes by providing them with a temporary refuge from predation by adult fish. All ponds in both treatments had the same stocking biomass to prevent any differences observed between treatments at harvest from being due to differences in the stocking biomass. The fence was removed 132 days after stocking (on 10 July), when we considered that the food resources in only half of the pond
<table><caption>**TABLE 1** | Individual weight (±1 standard deviation), total length, number of fish per pond, biomass (kg ha⁻¹), percentage of total biomass (%) and trophic category in both treatments in February (fish stocking).</caption><thead><tr><th>Characteristic</th><th>Pikeperch</th><th>Common carp</th><th>Roach</th><th>Tench</th><th>Total</th></tr></thead><tbody><tr><td>Individual weight (g)</td><td>46 ± 8</td><td>11 ± 2</td><td>172 ± 30</td><td>471 ± 98</td><td>—</td></tr><tr><td>Total length (cm)</td><td>19.0 ± 0.9</td><td>8.7 ± 0.6</td><td>24.0 ± 1.3</td><td>32.5 ± 2.0</td><td>—</td></tr><tr><td>Number of fish per pond</td><td>9</td><td>88</td><td>21</td><td>8</td><td>126</td></tr><tr><td>Biomass (kg ha⁻¹)</td><td>4.1 ± 0.2</td><td>9.1 ± 0.3</td><td>35.3 ± 0.2</td><td>37.7 ± 1.1</td><td>86.2 ± 0.9</td></tr><tr><td>% of total biomass</td><td>4.8 ± 0.4%</td><td>10.6 ± 0.4%</td><td>41.0 ± 0.5%</td><td>43.7 ± 0.8%</td><td>100%</td></tr><tr><td>Trophic category</td><td>Carnivore</td><td>Omnivore</td><td>Omnivore</td><td>Omnivore</td><td>—</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**FIGURE 1** | Diagram of pond treatments at the sampling periods of February (fish stocking), July (fence removed in divided ponds) and October (fish harvest).</figcaption></figure>

were no longer sufficient to support the fish, thus giving them access to the entire pond. The fish were harvested on 11 October (225 day production cycle).

## 2.2 | Biomasses of BMI, Zooplankton and Phytoplankton

In both treatments, BMI and zooplankton were monitored at stocking (February), before opening the fence in the divided ponds (July) and at harvest (October), while phytoplankton (measured as chlorophyll *a* concentration) were monitored every 2 weeks. BMI were sampled using nets (10 × 10 cm wide, 250 µm mesh size) by scraping the pond bottom along eight 1 m transects per pond, four on each side of the fence in divided ponds and at the same locations in open ponds, with two along the shore and two in the middle. Zooplankton were collected by filtering 10 L of water from the water column through a 250 µm mesh sieve, at two points on each side of the fence in divided ponds and the same locations in open ponds. Biomasses of BMI and zooplankton were estimated as the mean biomass of the samples collected in each pond, following the protocol described by Roucaute et al. (2024). Chlorophyll *a* concentrations were measured using an Algae-Torch fluorometer (bbe Moldaenke, Schwentinental, Germany). Phytoplankton biomass was estimated by multiplying the mean chlorophyll *a* concentration by 100, based on the assumption that chlorophyll *a* represents ca. 1% of total phytoplankton biomass (Reynolds 2006).

## 2.3 | Mortality and Growth Performance of Fish Stocked

At stocking (February) and harvest (October), for each pond, all individuals of each fish species were counted, measured (total length) and weighed individually, except for the fry. For the fry, only 100 individuals were weighed individually, while the rest were weighed and counted in groups. Fish were also caught, counted and weighed before removing the fence in July, but as not all individuals could be caught, the mortality rate and net yield were not calculated. For adult fish in July and October, the specific growth rate (SGR) and mortality rate of each species were calculated as follows:

- SGR (% day⁻¹) = [ln (final individual weight) – ln (initial individual weight)]/no. of days × 100

- Mortality rate (%) = (no. of fish stocked – no. of fish harvested)/no. of fish stocked × 100

For all fish in October, the net yield of each species was calculated as follows:

- Net yield (kg ha⁻¹) = (final biomass – initial biomass)/pond surface area
## 2.4 | Sampling of Fish and Their Potential Food Resources for Carbon and Nitrogen Stable Isotope Analysis

To identify and quantify the potential food resources used by fish, all fish species stocked and fry from natural reproduction of roach and tench were sampled at two points in both treatments: before opening the fence in the divided ponds (July) and at harvest (October). To facilitate handling, fish were sedated by immersing them in a benzocaine solution (0.4 mL L⁻¹ of water), in accordance with guidelines set by European Union (EU) regulations on the protection of animals used in scientific research (Directive 2010/63/EU).

For adult fish, five individuals of each species were sampled by cutting off a small part of the upper end of the caudal fin (ca. 0.25 cm²). After sampling, the wound was soaked in a 10% povidone-iodine-based antiseptic solution. For the fish fry, the entire body of five individuals was sampled after euthanising them in the same benzocaine solution. Roach and tench fry were grouped into a single fry group, as identifying fry to species was unreliable.

All potential food resources in ponds were also sampled at both points. To sample BMI, a 250 µm net was scraped along the bottom of each pond for ca. 1 m. The BMIs sampled were then washed, filtered through a 500 µm mesh sieve and identified to genus (Table S1). To sample plankton, a 5 L Van Dorn column was dipped into the water column at three points in each pond. The water collected was then filtered successively through sieves of increasingly smaller mesh size. As separating phytoplankton from zooplankton is difficult, plankton samples were divided into two categories depending on the mesh size of the sieves used: ≥ 250 µm (large zooplankton, hereafter 'zooplankton'), and then filtered through pre-combusted (5 h at 450°C) Whatman GF/F filters (considered to be phytoplankton).

To sample macrophytes, leaves (i.e., the part accessible to fish) of each plant taxon were collected in each pond. Three categories of macrophytes were identified: submerged plants, floating plants and filamentous algae (Table S1). To sample sediment organic matter (SOM), sediment cores were taken at three locations in each pond: one near the bank at the water inlet, another near the bank at the outlet and one in the middle. The top 2 cm of each core was sampled.

## 2.5 | Carbon and Nitrogen Stable Isotope Analysis

After being collected, fish, BMI and macrophytes were rinsed with ultrapure water (Milli-Q). All samples were then oven-dried at 60°C until completely dry and then ground using a mill (FastPrep-96) to obtain a homogeneous powder. Each sample was weighed in a tin capsule for simultaneous analysis of δ¹³C and δ¹⁵N.

The δ¹³C and δ¹⁵N values of samples were determined via elemental analyser/isotope-ratio mass spectrometry in continuous-flow mode using an isotope-ratio mass spectrometer (Isoprime GVI, Elementar, Langenselbold, Germany) interfaced with an elemental analyser (EuroEA 3000, Eurovector, Pavia, Italy). The ¹³C:¹²C

or ¹⁵N:¹⁴N ratios are expressed in conventional delta notation in per mil (‰) relative to the levels of ¹³C in Vienna Pee Dee Belemnite and ¹⁵N in the atmosphere, respectively. Repeated measurements of alanine yielded a precision of ± 0.11‰ and ± 0.12‰ for δ¹³C and δ¹⁵N values, respectively. Working standards (alanine, fish gelatine and bovine gelatine) and reference standards (IAEA-N-1, IAEA-N-2, IAEA-CH3 and USGS24 from the National Institute of Standards and Technology, Gaithersburg, Maryland, USA) were used for multipoint calibration.

## 2.6 | Mixing Models, Standard Ellipse Area and Trophic Level

To estimate the contribution of food resources to fish diets, Bayesian stable-isotope mixing models using the R package *simmr* (Parnell et al. 2013) were used. Trophic discrimination factors, which equal the difference between the isotope values of a consumer and its food resources (Minagawa and Wada 1984), were set at 1.0‰ and 3.5‰ for δ¹³C and δ¹⁵N, respectively (Britton and Busst 2018). The potential food resources considered for each fish species were selected based on knowledge of its trophic category (e.g., carnivore, omnivore). For pikeperch, the potential food resources included fry, tadpoles, BMI and zooplankton, while for common carp, roach and tench, they also included phytoplankton, zooplankton and macrophytes. The mixing models were run without using C or N concentrations of the food resources, as the concentrations had not been measured for phytoplankton.

A Bayesian approach was used to estimate the isotopic niche occupied by each fish species during each period using the R package *SIBER* (Jackson et al. 2011). To compare isotopic niche widths statistically among species, between treatments and between periods (July and October), the Bayesian standard ellipse area (SEA<sub>b</sub>), which incorporates uncertainty into ellipse estimates through Markov chain Monte Carlo simulations, was used. The analysis was based on 100,000 posterior draws, which provided robust estimates of niche overlaps and their uncertainties. Differences in SEA<sub>b</sub> were calculated using the probability that the SEA<sub>b</sub> of one species, treatment or period (A) was smaller than that of another (B). Probabilities near 1.0 indicate that A is smaller than B, while probabilities near 0.0 indicate that B is smaller than A. To ease interpretation, we considered that a high (≥ 0.70) or low (< 0.30) probability indicated a significant difference in niche size. Trophic niche overlap among species, between treatments and between periods was estimated using the *maxLikOverlap* function of the *SIBER* package.

The trophic level (TL) of each species by treatment and period was estimated using δ¹⁵N, a reliable indicator of trophic enrichment (Post 2002), as TL<sub>consumer</sub> = λ + (δ¹⁵N<sub>consumer</sub> - δ¹⁵N<sub>phytoplankton</sub>)/3.5; where λ is the TL of the baseline organism (i.e., phytoplankton; λ = 1), and 3.5 is the trophic discrimination factor for ¹⁵N.

## 2.7 | Statistical Analysis

Significant differences (i) between treatments, (ii) between/among periods within a treatment or (iii) between halves of divided ponds with or without adult fish within a
treatment and period in the biomasses of BMI, zooplankton and phytoplankton; the C and N concentrations and C:N ratio of SOM; and the individual weight, mortality rate and growth performance of stocked fish were assessed using Student's t-tests when the data met the assumptions of normality and homoscedasticity (tested by the Shapiro-Wilk and Bartlett's test, respectively). When one of these assumptions was not met (for the biomass of phytoplankton in February, zooplankton in February and July; the C:N ratio of SOM in October, the individual weight of fry in July and the mortality rate of pikeperch, roach and tench), the Kruskal-Wallis test was applied.

For each potential food resource and fish species, differences in δ¹³C and δ¹⁵N values of food resources and fish between treatments, between periods and among treatment × period interactions were assessed using non-linear mixed-effect models developed with the *lme* function of the R package *nlme* (Pinheiro et al. 2007). In the model, the pond nested within the treatment was included as a random effect to consider variable effects of the treatment on each pond. This variance structure made it possible to consider intra-pond correlations and estimate fixed effects more accurately. The contributions of fixed effects were assessed using marginal R² (R²*m*), while the total variance explained by both fixed and random effects was assessed using conditional R² (R²*c*), as implemented in the R package *MuMln* (Barton 2025). Differences were considered significant at *p* < 0.05. Differences in δ¹³C and δ¹⁵N values between fish stocked in each treatment and period were tested using analysis of variance (ANOVA), after verifying normality and homogeneity of variances. Tukey's post hoc test was then applied to identify significant differences between species. All statistical analyses were performed using R software (v.4.2.1) (R Core Team 2024).

# 3 | Results

## 3.1 | Effects of Treatment and Period on BMI, Zooplankton and Phytoplankton

The mean biomass of BMI, zooplankton and phytoplankton ranged from 0.602 to 2.646, 0.003 to 1.050 and 1.860 to 4.700 g m⁻², respectively, depending on the treatment and period (Table 2). During each period, the mean biomass of each did not differ significantly between the treatments, except in February, when that of BMI was lower in open ponds. Additionally, within each treatment, the mean biomass of these food resources did not differ significantly among periods, except in February, when that of BMI was significantly lowest and that of zooplankton was significantly highest. Likewise, the C and N concentrations and C:N ratio of SOM did not differ significantly between treatments or among periods.

## 3.2 | Effect of Treatment and Period on Fish Productivity

The mean net yield did not differ significantly between the treatments for pikeperch, common carp, roach, tench or fry between divided ponds (22, 337, 5, 12 and 89 kg ha⁻¹, respectively; total: 463 ± 65 kg ha⁻¹) and open ponds (14, 375, 8, 5 and 54 kg

ha⁻¹, respectively; total: 455 ± 150 kg ha⁻¹) (Table 3). Likewise, SGR did not differ between treatments (Table 3). The mean fish mortality rate varied by species in open and divided ponds (10%–48% and 0%–58%, respectively) (Table 3). The mean mortality rates of pikeperch, common carp, roach and all fish combined did not differ significantly between the two treatments, but for tench, it was higher in open ponds (17%) than in divided ponds (0%).

## 3.3 | Effects of the Treatment and Period on δ¹³C and δ¹⁵N Values of Fish and Their Potential Food Resources

### 3.3.1 | Potential Food Resources of Fish

During the two sampling periods (July and October), seven groups of species in the pond were identified as potential food resources for fish: phytoplankton, zooplankton, macrophytes, SOM, BMI, fry and tadpoles. Tadpoles were observed only in July. Analysis of the non-linear mixed-effect models (Table S4) indicated that during each period, mean δ¹³C and δ¹⁵N values did not differ significantly between treatments for any of the food resources. Within each treatment, mean δ¹³C values did not differ significantly between periods for phytoplankton, zooplankton, SOM or fry, while BMI and macrophytes were ¹³C-depleted by a mean of 1.82‰ (*p* < 0.001) and 2.90‰ (*p* = 0.022), respectively, in October compared to July. Mean δ¹⁵N values did not differ significantly between periods for phytoplankton, zooplankton, SOM, BMI or macrophytes, while fry were ¹⁵N-depleted by 1.12‰ in October compared to July (*p* < 0.001). The treatment × period interaction was significant only for the δ¹³C value of phytoplankton (*p* = 0.003) and the δ¹⁵N value of zooplankton (*p* = 0.038).

### 3.3.2 | Stocked Fish

#### 3.3.2.1 | Effects of the Pond, Treatment and Period for each Fish Species.
The δ¹³C and δ¹⁵N values of fish varied among ponds, even within a given treatment. The variance of this random effect was 26%–58% for δ¹³C and 36%–73% for δ¹⁵N, depending on the species (Table 4). Mean δ¹³C and δ¹⁵N values of each fish species did not differ significantly between the treatments (Table 4). Mean δ¹³C values differed significantly between periods for pikeperch, roach and tench (each *p* < 0.001), but not for common carp (Table 4). In October compared to July, pikeperch, roach and tench were ¹³C-depleted by 0.47‰, 1.30‰ and 1.29‰ in open ponds, respectively, and by 1.25‰, 1.15% and 1.17‰ in divided ponds, respectively. Mean δ¹⁵N values of pikeperch, roach and tench did not differ significantly between periods or treatments, nor did mean δ¹³C values of common carp. Mean δ¹⁵N values of common carp were lower in October than in July, with significant ¹⁵N depletion of 0.64‰ in open ponds and 0.52‰ in divided ponds (*p* < 0.001). The only significant period × treatment interaction was for the δ¹³C value of common carp (*p* = 0.032).

For each treatment and period, the mean δ¹³C value of pikeperch did not differ significantly from those of the omnivorous fish (Figure 2, Table 5), except for roach being ¹³C-enriched compared
<table><caption>**TABLE 2** | Mean biomass (g m⁻²) of benthic macroinvertebrates (BMI), zooplankton (Zoo.) and phytoplankton (Phyto.), as well as the carbon (C) and nitrogen (N) concentrations (conc.) and C:N ratio of sediment organic matter (SOM) by treatment (Trt.) (open or divided pond) and period (February [fish stocking], July [fence removed in divided ponds] or October [fish harvest]).</caption><thead><tr><th>Item</th><th>Trt.</th><th>Detail</th><th>February</th><th>July</th><th>October</th></tr></thead><tbody><tr><td rowspan="4">BMI (g m<sup>-2</sup>)</td><td>Open</td><td>Entire pond</td><td>1.524 ± 0.231<sup>a A</sup></td><td>0.845 ± 0.268<sup>a B</sup></td><td>0.773 ± 0.094<sup>a B</sup></td></tr><tr><td rowspan="3">Divided</td><td>Entire pond</td><td>2.646 ± 0.532<sup>b A</sup></td><td>1.292 ± 0.228<sup>a B</sup></td><td>0.602 ± 0.090<sup>a B</sup></td></tr><tr><td>Half pond with fish</td><td>2.346 ± 0.093<sup>a A</sup></td><td>1.093 ± 0.454<sup>a B</sup></td><td>—</td></tr><tr><td>Half pond without fish</td><td>2.947 ± 0.930<sup>a A</sup></td><td>1.491 ± 0.664<sup>a A</sup></td><td>—</td></tr><tr><td rowspan="4">Zoo. (g m<sup>-2</sup>)</td><td>Open</td><td>Entire pond</td><td>0.004 ± 0.007<sup>a A</sup></td><td>1.050 ± 0.392<sup>a B</sup></td><td>0.523 ± 0.342<sup>a B</sup></td></tr><tr><td rowspan="3">Divided</td><td>Entire pond</td><td>0.003 ± 0.001<sup>a A</sup></td><td>0.919 ± 1.081<sup>a B</sup></td><td>0.576 ± 0.423<sup>a B</sup></td></tr><tr><td>Half pond with fish</td><td>0.003 ± 0.002<sup>a A</sup></td><td>0.529 ± 0.310<sup>a B</sup></td><td>—</td></tr><tr><td>Half pond without fish</td><td>0.003 ± 0.001<sup>a A</sup></td><td>1.309 ± 1.539<sup>a B</sup></td><td>—</td></tr><tr><td rowspan="4">Phyto. (g m<sup>-2</sup>)</td><td>Open</td><td>Entire pond</td><td>2.390 ± 0.940<sup>a A</sup></td><td>3.730 ± 1.640<sup>a A</sup></td><td>4.670 ± 1.840<sup>a A</sup></td></tr><tr><td rowspan="3">Divided</td><td>Entire pond</td><td>2.410 ± 0.170<sup>a A</sup></td><td>1.860 ± 0.870<sup>a A</sup></td><td>4.700 ± 2.500<sup>a A</sup></td></tr><tr><td>Half pond with fish</td><td>2.343 ± 0.158<sup>a A</sup></td><td>1.687 ± 0.928<sup>a A</sup></td><td>—</td></tr><tr><td>Half pond without fish</td><td>2.481 ± 0.192<sup>a A</sup></td><td>2.029 ± 0.192<sup>a A</sup></td><td>—</td></tr><tr><td rowspan="2">SOM C conc. (%)</td><td>Open</td><td>Entire pond</td><td>—</td><td>1.57 ± 0.12<sup>a A</sup></td><td>1.50 ± 0.21<sup>a A</sup></td></tr><tr><td>Divided</td><td>Entire pond</td><td>—</td><td>1.64 ± 0.22<sup>a A</sup></td><td>1.46 ± 0.19<sup>a A</sup></td></tr><tr><td rowspan="2">SOM N conc. (%)</td><td>Open</td><td>Entire pond</td><td>—</td><td>0.20 ± 0.01<sup>a A</sup></td><td>0.18 ± 0.02<sup>a A</sup></td></tr><tr><td>Divided</td><td>Entire pond</td><td>—</td><td>0.21 ± 0.03<sup>a A</sup></td><td>0.18 ± 0.01<sup>a A</sup></td></tr><tr><td rowspan="2">SOM C:N ratio</td><td>Open</td><td>Entire pond</td><td>—</td><td>7.85 ± 0.72<sup>a A</sup></td><td>8.19 ± 0.50<sup>a A</sup></td></tr><tr><td>Divided</td><td>Entire pond</td><td>—</td><td>7.99 ± 0.17<sup>a A</sup></td><td>8.19 ± 0.58<sup>a A</sup></td></tr></tbody></table>

Note: Different lower-case or upper-case letters indicate significant differences (p < 0.05) between treatments or between/among periods, respectively.

<table><caption>**TABLE 3** | Individual weight (g), specific growth rate (SGR, % day⁻¹), net yield (kg ha⁻¹) and mortality rate (%) of each fish species and the fry (roach and tench) by treatment (open or divided pond) and period (July, October or overall).</caption><thead><tr><th>Period</th><th>Item</th><th>Treatment</th><th>Pikeperch</th><th>Common carp</th><th>Roach</th><th>Tench</th><th>Fry</th><th>Total</th></tr></thead><tbody><tr><td rowspan="4">July</td><td rowspan="2">Individual weight (g)</td><td>Open</td><td>80 ± 24<sup>a</sup></td><td>387 ± 50<sup>a</sup></td><td>210 ± 18<sup>a</sup></td><td>583 ± 88<sup>a</sup></td><td>1.92 ± 0.08<sup>a</sup></td><td>—</td></tr><tr><td>Divided</td><td>101 ± 37<sup>a</sup></td><td>393 ± 16<sup>a</sup></td><td>201 ± 5<sup>a</sup></td><td>579 ± 64<sup>a</sup></td><td>1.00 ± 1.00<sup>a</sup></td><td>—</td></tr><tr><td rowspan="2">SGR (% day<sup>-1</sup>)</td><td>Open</td><td>0.42 ± 0.17<sup>a</sup></td><td>2.75 ± 0.09<sup>a</sup></td><td>0.14 ± 0.07<sup>a</sup></td><td>0.15 ± 0.14<sup>a</sup></td><td>—</td><td>—</td></tr><tr><td>Divided</td><td>0.60 ± 0.29<sup>a</sup></td><td>2.75 ± 0.05<sup>a</sup></td><td>0.12 ± 0.01<sup>a</sup></td><td>0.17 ± 0.09<sup>a</sup></td><td>—</td><td>—</td></tr><tr><td rowspan="4">October</td><td rowspan="2">Individual weight (g)</td><td>Open</td><td>266 ± 17<sup>a</sup></td><td>886 ± 141<sup>a</sup></td><td>239 ± 17<sup>a</sup></td><td>660 ± 95<sup>a</sup></td><td>7.4 ± 8.00<sup>a</sup></td><td>—</td></tr><tr><td>Divided</td><td>321 ± 66<sup>a</sup></td><td>935 ± 144<sup>a</sup></td><td>207 ± 11<sup>a</sup></td><td>606 ± 45<sup>a</sup></td><td>5.1 ± 3.00<sup>a</sup></td><td>—</td></tr><tr><td rowspan="2">SGR (% day<sup>-1</sup>)</td><td>Open</td><td>0.7 ± 0.00<sup>a</sup></td><td>1.98 ± 0.07<sup>a</sup></td><td>0.14 ± 0.02<sup>a</sup></td><td>0.14 ± 0.0<sup>a</sup></td><td>—</td><td>—</td></tr><tr><td>Divided</td><td>0.96 ± 0.10<sup>a</sup></td><td>2.00 ± 0.60<sup>a</sup></td><td>0.08 ± 0.02<sup>a</sup></td><td>0.12 ± 0.03<sup>a</sup></td><td>—</td><td>—</td></tr><tr><td rowspan="4">Overall</td><td rowspan="2">Net yield (kg ha<sup>-1</sup>)</td><td>Open</td><td>14 ± 5<sup>a</sup></td><td>375 ± 117<sup>a</sup></td><td>8 ± 5<sup>a</sup></td><td>5 ± 6<sup>a</sup></td><td>54 ± 28<sup>a</sup></td><td>455 ± 150<sup>a</sup></td></tr><tr><td>Divided</td><td>22 ± 5<sup>a</sup></td><td>337 ± 64<sup>a</sup></td><td>5 ± 3<sup>a</sup></td><td>12 ± 4<sup>a</sup></td><td>89 ± 20<sup>a</sup></td><td>463 ± 65<sup>a</sup></td></tr><tr><td rowspan="2">Mortality rate (%)</td><td>Open</td><td>26 ± 17<sup>a</sup></td><td>48 ± 22<sup>a</sup></td><td>10 ± 9<sup>a</sup></td><td>17 ± 19<sup>a</sup></td><td>—</td><td>—</td></tr><tr><td>Divided</td><td>11 ± 1<sup>a</sup></td><td>58 ± 8<sup>a</sup></td><td>6 ± 0<sup>a</sup></td><td>0 ± 0<sup>b</sup></td><td>—</td><td>—</td></tr></tbody></table>

Note: Different superscript letters indicate significant differences (p < 0.05) among fish species within each treatment and period.

to pikeperch by 1.92‰ and 1.19‰ in open and divided ponds in July, respectively, and common carp being ¹³C-enriched by 2.23‰ compared to pikeperch in divided ponds in October. Mean δ¹³C values did not differ significantly among the omnivorous species.

For each treatment and period, the mean δ¹⁵N value of pikeperch differed significantly from those of the omnivorous fish, with the pikeperch being ¹⁵N-enriched by 2.26‰ and 2.88‰ in July and October, respectively. Mean δ¹⁵N values did not differ significantly
<table><caption>**TABLE 4** | Differences in mean isotope values of carbon (δ¹³C) and nitrogen (δ¹⁵N) of fish between treatments, between periods and among treatment × period interactions. denDF, denominator degrees of freedom; R²m, marginal R²; R²c, conditional R².</caption><thead><tr><th rowspan="2">Species</th><th rowspan="2">Fixed-effect variables</th><th rowspan="2">denDF</th><th colspan="3">δ¹³C</th><th colspan="3">δ¹⁵N</th></tr><tr><th>p</th><th>R²m</th><th>R²c</th><th>p</th><th>R²m</th><th>R²c</th></tr></thead><tbody><tr><td rowspan="4">Pikeperch</td><td>Treatment</td><td>4</td><td>0.535</td><td>—</td><td>—</td><td>0.316</td><td>—</td><td>—</td></tr><tr><td>Period</td><td>43</td><td>&lt; 0.001</td><td>—</td><td>—</td><td>0.256</td><td>—</td><td>—</td></tr><tr><td>Treatment × period</td><td>43</td><td>0.088</td><td>—</td><td>—</td><td>0.893</td><td>—</td><td>—</td></tr><tr><td>Model fit</td><td>—</td><td>—</td><td>0.23</td><td>0.80</td><td>—</td><td>0.16</td><td>0.89</td></tr><tr><td rowspan="4">Common carp</td><td>Treatment</td><td>4</td><td>0.262</td><td>—</td><td>—</td><td>0.060</td><td>—</td><td>—</td></tr><tr><td>Period</td><td>44</td><td>0.052</td><td>—</td><td>—</td><td>&lt; 0.001</td><td>—</td><td>—</td></tr><tr><td>Treatment × period</td><td>44</td><td>0.032</td><td>—</td><td>—</td><td>0.616</td><td>—</td><td>—</td></tr><tr><td>Model fit</td><td>—</td><td>—</td><td>0.21</td><td>0.76</td><td>—</td><td>0.46</td><td>0.84</td></tr><tr><td rowspan="4">Roach</td><td>Treatment</td><td>4</td><td>0.478</td><td>—</td><td>—</td><td>0.298</td><td>—</td><td>—</td></tr><tr><td>Period</td><td>47</td><td>&lt; 0.001</td><td>—</td><td>—</td><td>0.967</td><td>—</td><td>—</td></tr><tr><td>Treatment × period</td><td>47</td><td>0.927</td><td>—</td><td>—</td><td>0.057</td><td>—</td><td>—</td></tr><tr><td>Model fit</td><td>—</td><td>—</td><td>0.28</td><td>0.54</td><td>—</td><td>0.13</td><td>0.49</td></tr><tr><td rowspan="4">Tench</td><td>Treatment</td><td>4</td><td>0.361</td><td>—</td><td>—</td><td>0.288</td><td>—</td><td>—</td></tr><tr><td>Period</td><td>31</td><td>&lt; 0.001</td><td>—</td><td>—</td><td>0.118</td><td>—</td><td>—</td></tr><tr><td>Treatment × period</td><td>31</td><td>0.789</td><td>—</td><td>—</td><td>0.132</td><td>—</td><td>—</td></tr><tr><td>Model fit</td><td>—</td><td>—</td><td>0.21</td><td>0.76</td><td>—</td><td>0.18</td><td>0.43</td></tr></tbody></table>

<table><caption>**TABLE 5** | Mean isotope values of carbon (δ¹³C) and nitrogen (δ¹⁵N) and mean Bayesian standard ellipse areas (SEA<sub>b</sub>), with 50% credible intervals around the mean, for pikeperch, common carp, roach and tench by treatment (Trt.) (open or divided pond) and period (July or October).</caption><thead><tr><th rowspan="2">Trt.</th><th rowspan="2">Species</th><th colspan="3">July</th><th colspan="3">October</th></tr><tr><th>δ¹³C (‰)</th><th>δ¹⁵N (‰)</th><th>SEA<sub>b</sub> (‰²)</th><th>δ¹³C (‰)</th><th>δ¹⁵N (‰)</th><th>SEA<sub>b</sub> (‰²)</th></tr></thead><tbody><tr><td rowspan="2">Open</td><td>Pikeperch</td><td>-26.64 ± 0.78<sup>b</sup></td><td>17.63 ± 1.02<sup>a</sup></td><td>1.96 (1.59-2.41)</td><td>-27.11 ± 0.90<sup>a</sup></td><td>18.11 ± 1.02<sup>a</sup></td><td>1.56 (1.34-1.92)</td></tr><tr><td>Common carp</td><td>-25.76 ± 1.27<sup>ab</sup></td><td>15.97 ± 1.28<sup>ab</sup></td><td>4.91 (4.11-5.81)</td><td>-26.80 ± 1.80<sup>a</sup></td><td>15.33 ± 0.63<sup>b</sup></td><td>2.8 (2.37-3.39)</td></tr><tr><td rowspan="2">Divided</td><td>Roach</td><td>-24.71 ± 0.90<sup>a</sup></td><td>14.63 ± 1.56<sup>b</sup></td><td>4.21 (3.45-5.09)</td><td>-26.01 ± 1.09<sup>a</sup></td><td>15.03 ± 1.09<sup>b</sup></td><td>3.51 (2.95-4.87)</td></tr><tr><td>Tench</td><td>-25.50 ± 1.34<sup>ab</sup></td><td>15.98 ± 0.93<sup>ab</sup></td><td>3.14 (2.25-4.77)</td><td>-26.79 ± 0.82<sup>a</sup></td><td>15.91 ± 1.14<sup>b</sup></td><td>2.31 (1.94-2.78)</td></tr><tr><td rowspan="2">Common carp</td><td>Pikeperch</td><td>-25.52 ± 0.51<sup>b</sup></td><td>17.19 ± 0.63<sup>a</sup></td><td>0.84 (0.66-1.04)</td><td>-26.77 ± 1.07<sup>b</sup></td><td>17.26 ± 0.24<sup>a</sup></td><td>0.77 (0.64-0.72)</td></tr><tr><td>Roach</td><td>-24.98 ± 1.37<sup>ab</sup></td><td>14.33 ± 0.47<sup>c</sup></td><td>1.78 (1.34-2.32)</td><td>-24.54 ± 1.62<sup>a</sup></td><td>13.81 ± 0.46<sup>c</sup></td><td>2.11 (1.76-2.50)</td></tr><tr><td rowspan="2">Tench</td><td>Roach</td><td>-24.33 ± 0.56<sup>a</sup></td><td>14.29 ± 1.01<sup>c</sup></td><td>1.66 (1.34-2.01)</td><td>-25.48 ± 1.42<sup>ab</sup></td><td>13.79 ± 0.79<sup>c</sup></td><td>2.68 (2.24-3.17)</td></tr><tr><td>Tench</td><td>-24.64 ± 0.96<sup>ab</sup></td><td>15.71 ± 0.77<sup>b</sup></td><td>1.85 (1.35-2.56)</td><td>-25.81 ± 1.76<sup>ab</sup></td><td>14.99 ± 0.55<sup>b</sup></td><td>2.87 (2.42-3.44)</td></tr></tbody></table>

Note: Different superscript letters indicate significant differences (p < 0.05, Tukey's post hoc test) among fish species within each treatment and period.

among the omnivorous species in open ponds, but they did in divided ponds: tench were ¹⁵N-enriched by 1.40‰ in July and 1.19‰ in October compared to common carp and roach, whose mean δ¹⁵N values did not differ.

## 3.4 | Standard Ellipse Area of Fish

Pikeperch had the smallest mean SEA<sub>b</sub> for all treatments and periods (Figure 3, Table 5), with a probability no lower than 0.83 that the SEA<sub>b</sub> of pikeperch was the smallest (Figures S1 and S2).

The mean SEA<sub>b</sub> among common carp, roach and tench differed in each treatment and period. Among these three species, the probability ranged from 0.54-0.64 and from 0.31-0.62 in the open and divided ponds, respectively, in July (Figure S1). In October, it exceeded 0.72 in both treatments, except for the comparison between tench and roach in the divided pond, which was 0.59 (Figure S2). In July, the mean SEA<sub>b</sub> in open ponds was larger than that in divided ponds (Table 5) (probability > 0.82) (Figure S1). In October, the mean SEA<sub>b</sub> remained larger in open ponds for common carp and roach (probability > 0.78) but became larger in divided ponds for tench (probability = 0.29) (Figure S2). From
<figure><img src="image_5.png" /><figcaption>**FIGURE 2** | Mean isotope values of carbon (δ¹³C) and nitrogen (δ¹⁵N) (±1 standard deviation) of fish and their potential food resources by treatment (open or divided pond) and period (July or October). BMI, benthic macroinvertebrates; SOM, sediment organic matter.</figcaption></figure>

<figure><img src="image_6.png" /><figcaption>**FIGURE 3** | Isotope values of carbon (δ¹³C) and nitrogen (δ¹⁵N) of individual fish by species, treatment (open or divided pond) and period (July or October). Ellipses enclose the standard ellipse area for each species.</figcaption></figure>

July to October, in open ponds, the mean SEA<sub>b</sub> decreased for all fish stocked (Table 5). In divided ponds, however, the mean SEA<sub>b</sub> decreased for pikeperch but increased for common carp, tench and roach. The TL was highest for pikeperch (3.1–4.0) for all treatments and periods, followed by tench (2.7–3.3), while those of common carp and roach were similar (2.8–3.1) in October (Table 6).

Pikeperch had 0% trophic niche overlap with the other three fish species in the same treatment and period, except for a 2% overlap with common carp in open ponds in July (Figure 3, Table S5). The overlap between tench and common carp was much higher in open ponds (71% in July and 27% in October) than in divided ponds (0% in both periods). Similarly, the overlap between tench and roach was higher in open ponds (15% in July and 12% in October) than in divided ponds (4% in July and 2% in October). In contrast, the overlap between common carp and roach was higher in divided ponds (29% in July and 32% in October) than in open ponds (14% in July and 33% in October).

<table><caption>**TABLE 6** | Mean trophic level of pikeperch, common carp, roach and tench by treatment (open or divided pond) and period (July or October).</caption><thead><tr><th>Treatment</th><th>Species</th><th>July</th><th>October</th></tr></thead><tbody><tr><td rowspan="4">Open</td><td>Pikeperch</td><td>3.2</td><td>4.0</td></tr><tr><td>Common carp</td><td>2.7</td><td>3.1</td></tr><tr><td>Roach</td><td>2.3</td><td>3.1</td></tr><tr><td>Tench</td><td>2.7</td><td>3.3</td></tr><tr><td rowspan="4">Divided</td><td>Pikeperch</td><td>3.1</td><td>3.8</td></tr><tr><td>Common carp</td><td>2.3</td><td>2.8</td></tr><tr><td>Roach</td><td>2.3</td><td>2.8</td></tr><tr><td>Tench</td><td>2.7</td><td>3.2</td></tr></tbody></table>

## 3.5 | Fish Diet Composition Estimated by Stable-Isotope Mixing Models

The mixing model indicated that pikeperch fed mainly on fry (≥ 39%) (Figure 4, Tables S2 and S3). Its diet composition was similar for both treatments for a given period, but differed between periods for a given treatment. The fry percentage increased from 39%–47% in July to 83%–85% in October, depending on the treatment. In July, pikeperch diet was supplemented with tadpoles (25%–26% depending on the treatment), BMI (17%–23% depending on the treatment) and zooplankton (11% in both treatments).

Common carp, roach and tench had similar diet compositions, with some differences between periods and treatments (Figure 4). Their diets contained macrophytes (15%–25% for common carp and tench and 29%–36% for roach), fry (10%–20% in July and 21%–30% in October for common carp and roach, and up to 44% for tench), zooplankton (7%–15%), phytoplankton (6%–13%), BMI (11%–15%) and SOM (10%–15% for tench and 7%–30% for common carp and roach, with higher percentages in divided ponds than in open ponds, and which increased in October). Their diets were supplemented with tadpoles (13%–17%) in July.

# 4 | Discussion

## 4.1 | Effects of Period on Fish Diet Composition, Food-Resource Biomasses and Stable Isotopes

Pikeperch had higher δ¹⁵N values than the omnivorous fish species (Figure 2) because its diet consisted mainly of fry and BMI (Figure 4). Bousseba et al. (2024) reported that pikeperch is an opportunistic predator that adapts its diet to the availability of food resources. This supports the potential to use pikeperch to control fish reproduction in polyculture systems (Aubin et al. 2021; Nahon et al. 2024). It can also be used to increase the consumption of food resources that exist in ponds temporarily, particularly tadpoles. Common carp, roach and tench had similar diet compositions, with food resources contributing approximately similar percentages (Figure 4). However, tench tended to consume more fry in October, highlighting its higher TL (3.7) than those of common carp (3.1) and roach (3.0), according to the FishBase database (Froese and Pauly 2025). A similar trend
<figure><img src="image_7.png" /><figcaption>**FIGURE 4** | Estimated contributions of food resources to the diets of fish using Bayesian stable-isotope mixing models by treatment (open or divided pond) and period (July or October). BMI, benthic macroinvertebrates; SOM, sediment organic matter.</figcaption></figure>

in TLs was estimated in the present study (Table 6). These species are also considered opportunistic feeders that can adjust their diets to the availability of food resources (Giles et al. 1990; Nahon et al. 2020). However, the biomasses of BMI, zooplankton and phytoplankton did not differ significantly between July and October (Table 2), likely due to their high turnover rates and the ability of fish in polyculture systems to regulate their production (Šetlíková et al. 2016; Toruan et al. 2022). For example, in French pond polyculture systems, BMI and zooplankton can turn over 12–16 and 70–88 times per year, respectively (Roucaute et al. 2024). A study of polyculture systems with different stocking biomasses in the same experimental setup also reported similar BMI biomasses, even when the stocking biomass was doubled (Roucaute et al. 2024).

These relatively stable BMI, zooplankton and phytoplankton biomasses contrasted with the seasonal shifts observed in fish diets. The diet composition of each fish species changed during the production cycle (Figure 4). Mean $\delta^{13}$C values decreased significantly from July to October for pikeperch, roach and tench, which was not observed for their mean $\delta^{15}$N values (Table 4). These differences likely reflect seasonal shifts in resource availability in fish diets (Flood et al. 2023). In particular, the presence of tadpoles in July may have increased the mean $\delta^{13}$C value of all food resources and influenced the isotope values of tadpole consumers (Altig et al. 2007; Montaña et al. 2019). Furthermore, BMI and tadpoles had similar $\delta^{13}$C and $\delta^{15}$N values (Figure 2), which may have made it difficult to discriminate between them based on these isotope values. As tadpoles complete metamorphosis and leave ponds by August or are eaten by fish, they become unavailable as a food resource, which decreases $\delta^{13}$C values and may explain the decrease in the mean $\delta^{15}$N values of fish between periods was due to fish consuming prey of similar TLs, despite seasonal differences in resource availability. In contrast, mean $\delta^{15}$N values of common carp decreased significantly from July to October, perhaps due to the slight increase in SOM in their diet (Figure 4).

Mean $\delta^{13}$C values also decreased significantly from July to October for macrophytes and BMI (each composed of multiple species) (Figure 2), perhaps due to differences in the species sampled each period and, for macrophytes, due to autumn senescence. Additionally, BMI may have shifted their diets due to changes

in food resources. Mean $\delta^{15}$N values decreased significantly from July to October for fry (Figure 2), perhaps due to fry diets, which include mainly lower-TL food resources such as phytoplankton, macrophytes and SOM, along with zooplankton (Nahon et al. 2024), which may have become increasingly scarce as fry biomass increased over time. The decrease in $\delta^{13}$C values for BMI in October may have also influenced the mean $\delta^{13}$C values of their consumers: pikeperch, roach and tench. For common carp, the increase in the percentage of SOM, which has low $\delta^{15}$N values, in its diet of up to 30%, may have influenced its $\delta^{15}$N values.

## 4.2 | Effects of Divided Ponds on Fish Productivity, Competition, Food-Resource Biomasses and Isotope Values Before Opening the Fence

The SGR of each fish species did not differ significantly between the treatments in July (Table 3), perhaps because the treatments had similar SOM characteristics (C and N concentrations and C:N ratio) and biomasses of food resources (e.g., BMI, zooplankton, phytoplankton) (Table 2). The halves of divided ponds in which adult fish had been stocked had food resources similar to those in open ponds, which could support high fish productivity. In divided ponds, the biomasses of phytoplankton and zooplankton did not differ significantly between the halves with or without adult fish (Table 2), due to their distribution in the water column, which tends to become homogeneous due to mixing caused by wind and fish movement (Moreno-Ostos et al. 2009). The lack of significant difference in the biomass of BMI between treatments can be explained by the presence of macrophytes, which serve as refuges for fry and BMI (Gonzalez Sagrario and Balseiro 2010; Gonzalez Sagrario et al. 2009) and likely played a role in the open ponds, similar to the physical protection of the fence in the divided ponds. Additionally, the natural productivity of BMI, zooplankton and phytoplankton in these ponds seemed sufficiently high to sustain an extensive stocking biomass. Moreover, the stocking biomass may have been too low to limit food-resource availability greatly, which may explain the lack of significant differences in the biomasses of BMI, zooplankton or phytoplankton between the halves with or without adult fish in divided ponds, as well as between the treatments. The stocked fish likely did not consume enough food resources to cause a measurable decrease, especially as such small ponds tend to
have higher food-resource productivity than larger ponds (Aubin et al. 2021). Fish stocking biomass could be increased to attempt to generate treatment effects. An interesting complementary experiment would be to stock two ponds of different sizes with the same fish biomass: one small open pond and one large divided pond in which the fish have access only to an area equivalent to that of the small pond. Alternatively, both treatments may have had similar predation pressure, as the fry could feed on BMI, zooplankton, phytoplankton and SOM in the area protected by the fence, which may have kept the total biomass of these resources similar to that in open ponds. Dividing ponds appears relatively simple and cost-effective to implement in aquaculture systems, and it has the potential to increase fish productivity at a low cost. However, further studies are needed to assess its effectiveness.

Each fish species had a similar diet composition in the open ponds and the halves of divided ponds with adult fish (Figure 4), as the biomass of food resources in them (e.g., SOM, BMI, zooplankton, phytoplankton) did not differ significantly (Table 2). However, all four fish species had a larger mean SEA<sub>b</sub> in open ponds than in divided ponds (Table 5), perhaps due to specialising their diets in the latter because they occupied less area at a higher density, which likely decreased niche overlap. Diet specialisation in high-density populations has been observed for perch (Svanbäck and Persson 2004). In contrast, the larger mean SEA<sub>b</sub> in open ponds indicated more generalist feeding strategies, which resulted in niche overlap. This difference in mean SEA<sub>b</sub> was not completely reflected in diet compositions, as the mixing models aggregated multiple resources into a single group.

## 4.3 | Effects of Divided Ponds on Fish Productivity, Competition, Food-Resource Biomasses and Isotope Values at Harvest

The SGR, net yield and mortality rate of each fish species did not differ significantly between treatments in October (Table 3). Like in July, this can be explained by the similarity of most of the food resources (i.e., C and N in SOM; biomass of BMI, zooplankton and phytoplankton) in the two treatments (Table 2). Common carp had the highest percentage of total net yield (> 75%) in all treatments (Table 3). The biomass of pikeperch, common carp, roach and tench increased by a factor of ca. 6, 40, 2 and 2 over the 225 day production cycle, respectively. The net fish yield of 461–488 kg ha<sup>-1</sup> was similar to those reported for highly productive extensive earthen ponds in the EU (Girard et al. 2024; Zdenek et al. 2025). Furthermore, common carp had a high mortality rate of up to 52% depending on the treatment (Table 3), likely due to predation, as they were stocked at a small size. At stocking, their length was 54% that of pikeperch (Table 1), which may have caused a high risk of predation, as reported by Dörner and Wagner (2003). For pikeperch, the mortality rate ranged from 11% to 26% depending on the treatment (Table 3), which is similar to the 3%–22% reported by Policar et al. (2013). However, some of this mortality may have been due to cannibalism (Kopp et al. 2009; Livne et al. 2020).

In October, mean δ<sup>13</sup>C and δ<sup>15</sup>N values of fish (Table 4) and potential food resources (Table S4) did not differ significantly between the treatments, which suggests that the fish consumed similar

food resources regardless of the treatment (Vander Zanden and Rasmussen 1999) and may explain why each fish species had a similar diet composition in both treatments during each period (Figure 4). However, for tench in October, the mean SEA<sub>b</sub> was larger in divided ponds than in open ponds (Table 5). As a solitary species (Vanovac et al. 2021), the tench likely consumed more diverse resources after the fences were removed, which explains why its mean SEA<sub>b</sub> was also larger than those of other fish in divided ponds in October. Overall, the mean SEA<sub>b</sub> ranged from 0.77 to 4.91 in this extensive polyculture system, which was larger than those in a polyculture system of black bass, common carp and roach and that received additional feed (0.27–1.94) (Nahon et al. 2024). This result suggests that the fish assemblage in the present study was able to consume a wider range of resources, indicating dietary plasticity of its species. The low niche overlap of fish species in October (< 33%) indicated relatively little trophic competition among the fish species stocked and confirms that this fish assemblage is suitable for earthen-pond polyculture in the EU.

## 4.4 | Limitations of the Study

It is challenging to combine zootechnical and ecological approaches to optimise management practices in environments as complex as extensive ponds. Despite efforts to standardise pond size and fish stocking composition, in the absence of additional feed to drive production, each pond evolves differently, making it difficult to obtain significant treatment effects. When included as a random effect in the mixed-effect models, the pond contributed up to 73% of the variance, which may have obscured the effects of the two treatments. While the treatment effect was not statistically significant overall, it may have been stronger or weaker for certain ponds. Despite its relevance, the stable-isotope method has strong limitations, particularly the difficulty in consistently including all potential food resources in mixing models (Fry and Sherr 1989; Parnell et al. 2013). Discriminating certain food resources depends on the trophic discrimination factors used, which are influenced by several factors, including individual metabolism and the environment (Britton and Busst 2018). Additionally, some food resources can have a similar isotope value, which makes them difficult to discriminate. Furthermore, food resources consumed in low quantities or intermittently may not be detected accurately. The diet composition estimated by mixing models depends greatly on the food resources included, and some may have been missed, especially if they were no longer present at the time of sampling. To address these limitations, complementary methods such as DNA metabarcoding (Amundsen and Sánchez-Hernández 2019) can provide a more comprehensive overview of food resources. A complementary method would be stomach-content analysis, which can identify recently ingested food resources more precisely, but provides information only for a specific moment. It may also underrepresent certain food resources, especially small organisms or those that are digested rapidly. Finally, it usually requires killing the fish, which limits its use, especially when working with relatively few fish, as in this study. We recommend increasing stocking biomass and further investigating trophic discrimination factors to better understand fish diet composition and feeding behaviour. The biomasses of food resources such as macrophytes and tadpoles were not estimated, but it would be
useful to do so, as this information may help explain the diet compositions of their consumers.

## 5 | Conclusion and Perspectives

Each fish species' diet composition did not differ significantly between treatments, likely because the biomasses of their main food resources did not differ significantly either. In July, the biomass of these food resources could support high fish productivity in the halves of divided ponds with adult fish, likely due to the low stocking biomass of fish. The SGR, net yield and mortality rate did not differ significantly between treatments. However, diet composition differed between periods, likely due to the presence of tadpoles, which represented ca. 25% of the pikeperch diet and 13%-17% of omnivorous fish diets. The lower niche overlap in divided ponds than in open ponds indicates less interspecific competition. The low niche overlap among the omnivorous fish species suggests that they had dietary plasticity and little interspecific competition. This implies that the consumption of the fish assemblage studied may have equalled the productivity of food resources, making this assemblage (and its species' initial percentages of total biomass) promising for polyculture systems. Finally, to better understand long-term effects and assess the growth of fish to marketable size, we recommend continuing the study for at least 2 years. Additionally, a more in-depth approach would involve increasing the stocking biomass or testing more contrasting conditions, which would explore species' adaptive capacities and interspecific competition in polyculture systems more deeply. Longer-term monitoring with more frequent sampling would provide higher temporal resolution of the dynamics of fish diet composition. Combining these experimental data with dynamic modelling of trophic networks could refine the understanding of diet composition. This knowledge could also help optimise species percentages in earthen-pond polycultures, thus increasing the efficiency with which food resources are used.

### Author Contributions

**Purco Ralaiarison Ralien:** conceptualization, data curation, formal analysis, methodology, visualization, writing – original draft, writing – review and editing. **Michael S. Corson:** conceptualization, data curation, supervision, validation, writing – review and editing. **Marc Roucaute:** conceptualization, data curation, methodology, supervision, writing – review and editing. **Christophe Menniti:** Data curation. **Kazi Ahmed Kabir:** supervision, validation, writing – review and editing. **Joël Aubin:** conceptualization, data curation, funding acquisition, project administration, supervision, validation, writing – review and editing. **Sarah Nahon:** conceptualization, data curation, supervision, validation, writing – review and editing.

### Acknowledgements

We thank the team of the INRAE-U3E experimental site (especially Antoine Gallard, Bernard Joseph, Maira Coke, Romain Leblanc and Yoann Bennevault) and Celine Bouillis, Laurence Carteaux, Jan-Mickael Busnel, Kilian Paquet, Tanguy Nardon, Angela Larivain, Andrés Casanova, Lorenzo Rossi and Michele Zoli for collecting data from the ponds. We also thank Alexandrine Pannard and Stephanie Llopis for collecting data from the ponds and identifying zooplankton, as well as Anne-Laure Besnard for grinding samples for isotope analysis.

### Funding

The authors have nothing to report.

### Conflicts of Interest

The authors declare no conflicts of interest.

### Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

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**Supporting Information**

Additional supporting information can be found online in the Supporting Information section.

**Supplementary material:** aff270161-sup-0001-

SuppMat.docx **Supplementary material:** aff270161-sup-0002-

SuppMat.docx