# Interactions between green and brown food webs in aquatic ecosystems: a mesocosm experiment

Pierre Quévreux, Kejun Zou, Sébastien Barot, Élisa Thébault, Emma Rochelle-Newall, Ludwig Jardillier, Simon Agostini, Jacques Mériguet, Sarah Fiorini, Gérard Lacroix

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Pierre Quévreux, Kejun Zou, Sébastien Barot, Élisa Thébault, Emma Rochelle-Newall, et al. Interactions between green and brown food webs in aquatic ecosystems: a mesocosm experiment. 2024. [hal-04545691](https://hal.science/hal-04545691)

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# Combined effects of fish, light and allochthonous dissolved organic carbon on interactions between green and brown food webs: an aquatic mesocosm experiment

Pierre Quévreux, Kejun Zou, Sébastien Barot, Élisa Thébault, Emma Rochelle-Newall, Ludwig E Jardillier, Eric Edeline, Simon Agostini, Jacques Mériguet, Sarah Fiorini, et al.

<figure><img src="image_1.png" /></figure>

## To cite this version:

Pierre Quévreux, Kejun Zou, Sébastien Barot, Élisa Thébault, Emma Rochelle-Newall, et al. Combined effects of fish, light and allochthonous dissolved organic carbon on interactions between green and brown food webs: an aquatic mesocosm experiment. Freshwater Biology, 2025, 70 (8), pp.e70081. 10.1111/fwb.70081. hal-04545691v2

HAL Id: hal-04545691

https://hal.science/hal-04545691v2

Submitted on 29 Aug 2025

HAL is a multi-disciplinary open access archive for the deposit and dissemination of scientific research documents, whether they are published or not. The documents may come from teaching and research institutions in France or abroad, or from public or private research centers.

L'archive ouverte pluridisciplinaire HAL, est destinée au dépôt et à la diffusion de documents scientifiques de niveau recherche, publiés ou non, émanant des établissements d'enseignement et de recherche français ou étrangers, des laboratoires publics ou privés.

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Distributed under a Creative Commons Attribution 4.0 International License
# Interactions between green and brown food webs in aquatic ecosystems: a mesocosm experiment

Pierre Quévreux<fnref n="1" />, Kejun Zou<fnref n="1" />, Sébastien Barot<fnref n="1" />, Élisa Thébault<fnref n="1" />, Emma Rochelle-Newall<fnref n="1" />, Ludwig Jardillier<fnref n="2" />, Éric Edeline<fnref n="3" />, Simon Agostini<fnref n="4" />, Jacques Mériguet<fnref n="5" />, Sarah Fiorini<fnref n="4" />, and Gérard Lacroix<fnref n="1" />

**Corresponding author:** pierre.quevreux@cri-paris.org

**Current address:** Department of Physical Geography, Stockholm University, Stockholm SE-106 91, Sweden

## Key words

Nutrient cycling, microorganisms, zooplankton, fish, mixotrophy, light, allochthonous dissolved organic carbon, top-down, bottom up, trophic cascade

## Abstract

The classical concepts of top-down and bottom-up cascading effects in ecosystems are challenged by the interactions between green and brown food webs. The green food web relies on photosynthesis and the brown food web relies on the mineralisation of dead organic matter. Mutualistic interactions between these two food webs are crucial for ecosystem functioning because a major fraction of carbon is produced by the green food web while mineral nutrients are mainly recycled through the brown one. However, green and brown food webs can compete for mineral nutrients if decomposers are nutrient limited. Thus, bottom-up or top-down effects on one food web can have major cascading effects on the other one. In our freshwater mesocosm experiment, we tested cascading bottom-up effects targeting the green food web through sunlight filtering and the brown food web through organic carbon addition, and cascading top-down effects mediated by fish presence. Our main finding is a positive effect of fish on phytoplankton density in mesocosms where daylight was reduced. The absence of a significant decrease in zooplankton biomass in the presence of fish suggests that fish had a positive bottom-up effect on phytoplankton mediated by nutrient cycling, which would decrease the sequestration of nutrients and increase their availability for phytoplankton. This enhanced recycling by fish was visible through positive effects on both sediment production and DOC accumulation, which also had a positive effect on the brown food web with increased abundances of heterotrophic prokaryotes and microbial eukaryotes. We did not observe any cascading effect of light treatment on the brown food web, and of organic carbon treatment on the green food web. Our organic carbon treatment had nearly no effect on the brown food web, probably because of the moderate quantity of added carbon and of the low bioavailability of some of the added molecules. Our experiment shows that fish can strongly stimulate phytoplankton through bottom-up effects due to nutrient cycling, and not only through the classic trophic cascade due to the top-down control on large herbivorous zooplankton.

<footnote n="1">¹Sorbonne Université, UPEC, Université Paris Cité, CNRS, INRAE, IRD, Institut d'Écologie et des Sciences de l'Environnement - Paris, iEES-Paris, 4 place Jussieu, F-75252 Paris, France</footnote>

<footnote n="2">²Université Paris-Saclay, AgroParisTech, CNRS, Unité d'Écologie Systématique Évolution, Gif-sur-Yvette, France</footnote>

<footnote n="3">³INRA, Agrocampus Ouest, Unité d'Écologie et Santé des Écosystèmes, 65 rue de Saint-Brieuc, FR-35042, France</footnote>

<footnote n="4">⁴CNRS, ENS, Centre de Recherche en Écologie Expérimentale et Prédictive & Ecotron Ile-de-France, CEREEP - Ecotron IDF, 78 rue du Château, FR-77140 Saint-Pierre-Lès-Nemours, France</footnote>

<footnote n="5">⁵École Normale Supérieure, Institut de Biologie de l'École Normale Supérieure, 46 rue d'Ulm, Paris, France</footnote>
Combined effects of fish, light and allochthonous dissolved organic carbon on interactions between green and brown food webs: an aquatic mesocosm experiment

Pierre Quévreux<fnref n="1" />, Kejun Zou<fnref n="1" />, Sébastien Barot<fnref n="1" />, Élisa Thébault<fnref n="1" />, Emma Rochelle-Newall<fnref n="1" />, Ludwig Jardillier<fnref n="2" />, Éric Edeline<fnref n="3" />, Simon Agostini<fnref n="4" />, Jacques Mériguet<fnref n="5" />, Sarah Fiorini<fnref n="4" />, and Gérard Lacroix<fnref n="1" />

Abstract

1. The classical concepts of top-down and bottom-up cascading effects in ecosystems are challenged by the interactions between green and brown food webs. The green food web relies on photosynthesis and the brown food web relies on the mineralisation of dead organic matter. Mutualistic interactions between these two food webs are crucial for ecosystem functioning because a major fraction of organic carbon is produced by the green food web while mineral nutrients are mainly recycled through the brown one. However, green and brown food webs can compete for mineral nutrients if decomposers are nutrient limited. Thus, bottom-up or top-down effects on one food web can have major cascading effects on the other one.

2. In our freshwater mesocosm experiment, we tested cascading bottom-up effects targeting the green food web through sunlight filtering and the brown food web through organic carbon addition; cascading top-down effects mediated by fish presence.

3. Our main finding is a positive effect of fish on phytoplankton density in mesocosms where daylight was reduced. Fish also had a positive effect on the brown food web with increased abundances of heterotrophic prokaryotes and microbial eukaryotes. However, they had no significant effect on zooplankton biomass. We did not observe any cascading effect of light treatment on the brown food web, and of organic carbon treatment on the green food web. Our organic carbon treatment had nearly no effect on the brown food web.

4. The most likely explanation for the observed fish × light interaction is a bottom-up effect mediated by nutrient cycling embodied by the increased sediment production and DOC accumulation in the presence of fish. The lack of effect of our organic carbon treatment may be due to the moderate quantity of added carbon and of the low bioavailability of some of the added molecules.

5. Our experiment suggests that fish can strongly stimulate phytoplankton through bottom-up effects due to nutrient cycling, and not only through the classic trophic cascade due to the top-down control on large herbivorous zooplankton.

Key words

stoichiometry, phytoplankton, mixotrophy, zooplankton, trophic cascade

<footnote n="1">¹Sorbonne Université, UPEC, Université Paris Cité, CNRS, INRAE, IRD, Institut d'Écologie et des Sciences de l'Environnement - Paris, iEES-Paris, 4 place Jussieu, FR-75252 Paris, France</footnote>

<footnote n="2">²Université Paris-Saclay, AgroParisTech, CNRS, Unité d'Écologie Systématique Évolution, Gif-sur-Yvette, France</footnote>

<footnote n="3">³INRA, Agrocampus Ouest, Unité d'Écologie et Santé des Écosystèmes, 65 rue de Saint-Brieuc, FR-35042 Rennes, France</footnote>

<footnote n="4">⁴CNRS, ENS, Centre de Recherche en Écologie Expérimentale et Prédictive & Ecotron Ile-de-France, CEREEP - Ecotron IDF, 78 rue du Château, FR-77140 Saint-Pierre-lès-Nemours, France</footnote>

<footnote n="5">⁵École Normale Supérieure, Institut de Biologie de l'École Normale Supérieure, 46 rue d'Ulm, Paris, France</footnote>
Introduction

The relative importance of bottom-up and top-down controls on ecosystem functioning has received a lot of attention in food web ecology (e.g. Hunter and Price (1992) and Hulot et al. (2014)). However, most experimental and theoretical studies on this topic have ignored a fundamental aspect of ecosystem functioning which is the division of flows of matter and energy into two pathways : the pathway that relies on photosynthesis and constitutes the green food web, and the pathway that relies on detritus and constitutes the brown food web (Moore et al., 2004). The tight interactions between these two pathways are crucial for ecosystem functioning because a major fraction of carbon enters through the green pathway while mineral nutrients are mainly produced by the brown pathway.

Green and brown food webs have often been studied independently (Rosemond et al., 2001; Jones and Sayer. 2003; Jardillier et al., 2004), and most studies have focused on green food webs, in particular in aquatic ecosystems (Carpenter et al., 1985; Kagata and Ohgushi, 2006; Heath et al., 2014). However, green and brown food webs strongly interact and these complex interactions are expected to mediate top-down and bottom-up cascading effects.

First, green and brown food webs interact at the base of the food webs through mutualistic interactions via nutrient recycling or competition between decomposers and primary producers for nutrients. Thus, primary producers and decomposers can be either mutualists or competitors, depending on whether de-composers are limited by carbon or by nutrients such as nitrogen or phosphorus (Daufresne and Loreau, 2001; Danger et al., 2007b). For instance, Joint et al. (2002) observed a decrease in phytoplankton biomass and an increase in prokaryote (heterotrophic bacteria and archaea) biomass following glucose addition in a mesocosm experiment. The theoretical study of Zou et al. (2016) showed that the nutrient/carbon limitation of decomposers was likely a key determinant of the cascading effects of the green food web on decomposer production and associated bottom-up effects in the brown food web. When decomposers are carbon-limited, cascading effects of the green food web on decomposer production are expected to parallel those observed on primary production, while these effects can be opposite when decomposers are nutrient-limited (Zou et al., 2016). However, mixotrophic phytoplankton is able to bypass nutrient limi-tation by preying on prokaryotes (phagotrophic mixotrophy) or by consuming other organic compounds (osmotrophic mixotrophy). Mixotrophy is a versatile metabolism that allows some phytoplanktonic cells to shift metabolism triggered by environmental conditions (nutrient and light availability). Such a fea-ture is key for ecosystem stability because primary producers are at the base of trophic food webs. For instance, Faithfull et al. (2011) did not observe a reduction in phytoplankton abundance in response to glucose addition thanks to the dominance of mixotrophic species in the phytoplankton community of the lake in which they run their mesocosm experiment.

Second, green and brown food webs also interact at higher trophic levels via generalist predators that consume prey in both green and brown webs (Rooney et al., 2006; Wolkovich et al., 2014). For instance, crustacean zooplankton can feed both on phytoplankton in the green food web and on predators of the brown microbial food web such as ciliates (Urbano et al., 2022). A few theoretical studies have shown that the coupling of distinct energy pathways by predators can have strong consequences on ecosystem structure and stability (Rooney et al., 2006; Wollrab et al., 2012; Mougi, 2020). These predators can have a stabilising effect by preferentially grazing the most abundant prey and balancing biomass distribution among prey, however they might also lead to apparent competition between the coupled food webs (Vadeboncoeur et al., 2005). Decomposers and primary producers are expected to respond in the same or in opposite directions to enrichment depending on the way predators couple the green and brown energy pathways (Wollrab et al., 2012; Ward et al., 2015).

Finally, interactions between green and brown webs at both the base and top of food webs are not independent. For example, ecosystems dominated by planktivorous fish as top predators produce more degradable detritus than ecosystems dominated by large herbivorous zooplankton (Harrault et al., 2012). These differences in detritus degradability can have bottom-up cascading effects on the brown food web and consequently modify the type of interactions between primary producers and decomposers (Harrault et al., 2014). Predators also strongly modify the composition and size structure of food webs (Lazzaro et al., 2009; Gauzens et al., 2016), which could affect the interactions at the bottom of the food webs.

Despite the importance of these interactions for ecosystem functioning, very few experimental studies have investigated top-down and bottom-up effects in food webs by manipulating explicitly the green and the brown food webs at the same time (Daufresne et al., 2008; Faithfull et al., 2011; Degerman et al., 2018). The few existing studies have mostly focused either on interactions between decomposers and primary producers in response to nutrient enrichment (Danger et al., 2007a; Daufresne et al., 2008) or on consequences of allochthonous carbon inputs as a bottom-up perturbation of the brown food web (Faithfull et al., 2011; Degerman et al., 2018). The aim of this study is to investigate further the
# Introduction

The relative importance of bottom-up and top-down controls on ecosystem functioning has received a lot of attention in food web ecology (e.g. Hunter and Price (1992) and Hulot et al. (2014)). However, most experimental and theoretical studies on this topic have ignored a fundamental aspect of ecosystem functioning: matter and energy flow through distinct green and brown pathways (Moore et al., 2004). The green pathway relies on photosynthesis and produces organic matter *de novo* from CO₂ and mineral nutrients, while the brown pathway relies on decomposition and recycles nutrients contained in dead organic matter. Thus, they supply each other with essential resources, and this complementarity ensuring nutrient cycling is crucial for ecosystem functioning. Green and brown food webs have often been studied independently (Rosemond et al., 2001; Jones and Sayer, 2003; Jardillier et al., 2004), and most studies have focused on green food webs, in particular in aquatic ecosystems (Carpenter et al., 1985; Kagata and Ohgushi, 2006; Heath et al., 2014). However, green and brown food webs strongly interact via competition, predation and recycling, and these complex interactions are expected to mediate top-down and bottom-up cascading effects.

First, green and brown food webs interact at the base of the food webs through decomposers and primary producers. They can be mutualists through cross-feeding: primary producers supply decomposers with carbon, while decomposers mineralise nutrients contained in dead organic matter, which sustains primary production. However, if dead organic matter is nutrient-poor, decomposers need to take up mineral nutrients and then compete with primary producers. Thus, primary producers and decomposers can be either mutualists or competitors, depending on whether decomposers are limited by carbon or nutrients such as nitrogen or phosphorus (Daufresne and Loreau, 2001). For instance, in a microcosm experiment, Danger et al. (2007a) were able to encompass the entire competition-commensalism-mutualism interaction gradient by manipulating nitrogen and phosphorus availability. At a larger scale in a mesocosm experiment, Joint et al. (2002) observed a decrease in phytoplankton biomass and an increase in prokaryote (heterotrophic bacteria and archaea) biomass following a glucose addition that cancelled out the carbon limitation of bacteria and thus promoted competition for nutrients. The theoretical study of Zou et al. (2016) showed that the nutrient/carbon limitation of decomposers was likely a key determinant of the cascading effects of the green food web on decomposer production and associated bottom-up effects in the brown food web. When decomposers are carbon-limited, cascading effects of the green food web on decomposer production are expected to parallel those observed on primary pro-

duction, while these effects can be opposite when decomposers are nutrient-limited (Zou et al., 2016).

However, some phytoplankton species are able to thrive in nutrient and light-limited conditions by triggering mixotrophy, a versatile metabolism that allows them to shift metabolism between photoautotrophy and heterotrophy (Raven, 1997; Mitra et al., 2014). Thus, mixotrophic phytoplankton are able to bypass nutrient limitation by preying on prokaryotes (phagotrophic mixotrophy) or by consuming dissolved organic compounds (osmotrophic mixotrophy) (Raven, 1997; Mitra et al., 2014, 2016). This shift is predicted by mathematical models (Crane and Grover, 2010; Le Noac'h et al., 2025) and has been observed across nutrient availability gradients in lakes in North America for instance (Le Noac'h et al., 2024), which questions the previous theoretical (Daufresne and Loreau, 2001) and experimental (Joint et al., 2002; Danger et al., 2007b) results on phytoplankton-bacteria coexistence. For instance, Faithfull et al. (2011) did not observe a reduction in phytoplankton abundance in response to glucose addition due to the dominance of mixotrophic species in the phytoplankton community of the lake in which they ran their mesocosm experiment. In the same vein, Balzer et al. (2023) observed an increase in mixotrophic algae after leaf leachate addition.

Second, green and brown food webs also interact at higher trophic levels via generalist predators that consume prey in both green and brown food webs (Rooney et al., 2006; Wolkovich et al., 2014). For instance, crustacean zooplankton can feed both on phytoplankton in the green food web and on predators of the brown microbial food web such as ciliates (Urbano et al., 2022). A few theoretical studies have shown that the coupling of distinct energy pathways by predators can have strong consequences on ecosystem structure and stability (Rooney et al., 2006; Wollrab et al., 2012; Mougi, 2020). These predators can have a stabilising effect by preferentially grazing the most abundant prey and balancing biomass distribution among prey; however, they might also lead to apparent competition between the coupled food webs (Vadeboncoeur et al., 2005). Thus, decomposers and primary producers are expected to respond in the same or in opposite directions to enrichment depending on the way predators couple the green and brown energy pathways (Wollrab et al., 2012; Ward et al., 2015).

Finally, interactions between green and brown webs at both the base and top of food webs are not independent because animals strongly contribute to nutrient (Vanni, 2002; Schmitz et al., 2010) and carbon cycling (Schmitz et al., 2014). Mathematical models predict this bottom-up effect of predators to alter the trophic cascades generated by top-down control (Leroux and Loreau, 2010) and food web stability compared to systems without nutrient cycling (Quévreux et al., 2021; Theis et al., 2022). In aquatic ecosystems in
<figure><img src="image_2.png" /></figure>

Figure 1: **A)** Photo of the experimental setting during a sampling session. The six mesocosms at the front are uncovered to allow sampling. **B)** Experimental design consists of three crossed treatments: light filtration (Light+ = no light filtration), soluble organic carbon addition (OM+ = carbon addition) and fish addition (Fish+ = four Leuscicidae fish). Light filtration (−50% of light intensity) is expected to decrease the photosynthetic activity of phytoplankton to target the green food web. OM addition is expected to promote bacterial production and target the brown food web. Fish presence, which affects food-web structure, is expected to modify the coupling between green and brown food webs. Organisms in circles represent heterotrophic microbial eukaryotes and zooplankton, which are the consumers in freshwater planktonic food webs.

cascading effects between green and brown food webs and their consequences on ecosystem functioning by manipulating in a factorial design light availability (i.e. bottom-up perturbation on the green food web), level of added carbon organic matter (i.e. bottom-up perturbation on the brown food web), and fish presence (i.e. top-down control of the food web) in freshwater mesocosms. By combining these three treatments, we aim to highlight the complex interactions resulting from the interplay between bottom-up and top-down effects.

We expect the reduction of light availability to reduce primary production and to lead to a carbon limitation of the brown food web, thus decreasing the biomass of organisms from both green and brown food webs. Organic matter addition should promote the growth of heterotrophic prokaryotes and increase their limitation by nutrients such as nitrogen and phosphorus. As a consequence, we expect an increased competition between heterotrophic prokaryotes and phytoplankton for nutrients, which would lead to a decrease in the biomass of the organisms of the green food web. However, such a competition could be mitigated by the stimulation of the heterotrophic metabolism pathway of mixotrophic phytoplankton able to prey on heterotrophic prokaryotes. Therefore, mixotrophic phytoplankton should be more abundant when light is depleted or organic carbon is added. Finally, we expect fish to increase phytoplankton and heterotrophic prokaryotes biomass through the top-down control of zooplankton or a positive bottom-up effect triggered by easily degradable faeces and the release of mineral nutrients.

# Material and methods

## Experimentation site and experimental design

The experiment was set in the Experimental Pond Platform (EPP, N48°16′57″, E2°40′20″) of the PLANAQUA facility (“PLAteforme NAtionale expérimentale en écologie AQUAtique”) near to Paris, France. The EPP includes 16 artificial ponds with an individual volume of ~750 m³, in addition to a storage pond and an overflow lake of 4000 m³ each (Mougin et al., 2015). Ponds are isolated from the ground by two successive sets of geotextiles and two HDPE geomembranes. In summer 2014, they received 30 cm of washed sand originating from the Loire River and they were filled with a mixture of rainwater and drinking water. Subsequently, they were naturally colonised by aquatic organisms.

We installed a floating pontoon (27×5 m) in the water storage pond (125×15×3 m deep). The pontoon delimited two blocks of 12 × 3 m on which enclosures can be suspended (Fig. 1A). The mesocosm study was performed from the end of June 2015 to the beginning of November 2015 and thus began one year after the beginning of pond colonisation. Thirty-six translucent polyethylene enclosures (2 × 1 × 2.75 m, 5 m³), sealed at the bottom, were attached to the pontoon 750 cm above the lake surface. The pontoon was divided into two blocks of 3 × 6 enclosures. The different enclosures with the same combination of treatment were uniformly distributed to avoid potential confounding edge effects (Fig. S2-1 in the supporting information).
<table><caption>Table 1: Summary of experimental treatments in previous studies testing the effects of light reduction, fish presence, organic carbon addition and nutrient addition in aquatic mesocosms. In the OM column, "mixture" means a mixture of labile and recalcitrant DOC and "combination" means labile and/or recalcitrant DOC.</caption><thead><tr><th>Study</th><th>Light</th><th>Fish</th><th>OM (mg CL<sup>-1</sup>)</th><th>Nutrient</th><th>Days</th></tr></thead><tbody><tr><td>Our study</td><td>10-50%</td><td>rudds</td><td>5.4 (mixture)</td><td>-</td><td>105</td></tr><tr><td>Joint et al., 2002</td><td>-</td><td>-</td><td>4.5-9</td><td>NP</td><td>6</td></tr><tr><td>Faithfull et al., 2011</td><td>-</td><td>perch</td><td>4.2-21 (glucose)</td><td>-</td><td>45</td></tr><tr><td>Mette et al., 2011</td><td>4-40%</td><td>gizzard shad</td><td>-</td><td>NP</td><td>56</td></tr><tr><td>Hitchcock et al., 2016</td><td>-</td><td>-</td><td>1.5-16 (leachate)</td><td>-</td><td>22</td></tr><tr><td>Rock et al., 2016</td><td>35-90%</td><td>bluegill sunfish</td><td>-</td><td>NP</td><td>42</td></tr><tr><td>Degerman et al., 2018</td><td>-</td><td>stickle-back</td><td>120.5 (glucose)</td><td>NP</td><td>63</td></tr><tr><td>Brighenti et al., 2018</td><td>0-50%</td><td>-</td><td>0.6 (leachate)</td><td>NP</td><td>12</td></tr><tr><td>Garnier et al., 2023</td><td>~50%</td><td>young perch</td><td>-</td><td>NP</td><td>21</td></tr><tr><td>Balzer et al., 2023</td><td>-</td><td>-</td><td>(leachate)</td><td>-</td><td>34</td></tr><tr><td>Hébert et al., 2023</td><td>-</td><td>-</td><td>1.5 (reverse osmosis)</td><td>NP</td><td>61</td></tr><tr><td>Yalçın et al., 2024</td><td>-</td><td>-</td><td>1.5-9.5 (combination)</td><td>-</td><td>37</td></tr></tbody></table>

particular, fish excrete nutrients in their soluble form, which increases gross primary production and phytoplankton biomass (Vanni and Layne, 1997; Attayde and Hansson, 2001; Evangelista et al., 2017). Ecosystems dominated by planktivorous fish as top predators also produce more degradable detritus than ecosystems dominated by large herbivorous zooplankton (Harrault et al., 2012). These differences in detritus degradability can have bottom-up cascading effects on the brown food web and consequently modify the type of interactions between primary producers and decomposers (Harrault et al., 2014). Predators also strongly modify the composition and size structure of food webs (Lazzaro et al., 2009; Gauzens et al., 2016), which could affect the interactions at the bottom of food webs.

Despite the importance of these interactions for ecosystem functioning, Degerman et al. (2018) are the only ones who have investigated top-down and bottom-up effects in food webs by manipulating green and brown food webs at the same time to our knowledge (Table 1). The other existing related studies have mostly focused either on interactions between decomposers and primary producers in response to nutrient enrichment (Danger et al., 2007a; Daufresne et al., 2008) or on the consequences of allochthonous carbon inputs as a bottom-up perturbation of the brown food web (Hitchcock et al., 2016; Balzer et al., 2023; Yalçın et al., 2024). Some studies considered both manipulations (Joint et al., 2002; Brighenti et al., 2018; Hébert et al., 2023) but never when testing the top-down effect of fish (Faithfull et al., 2011; Mette et al., 2011; Rock et al., 2016; Garnier et al., 2023; but see Degerman et al., 2018). Beyond their experimental design, they sampled a limited number of compartments and did not provide a broad picture of the ecosystem, thus preventing us from properly understanding

how both green and brown food webs respond to targeted perturbations.

The aim of this study is to investigate further the cascading effects between green and brown food webs and their consequences on ecosystem functioning by manipulating in a factorial design light availability (i.e. bottom-up perturbation of the green food web), level of added carbon organic matter (i.e. bottom-up perturbation of the brown food web) and fish presence (i.e. top-down control of the food web) in freshwater mesocosms. By combining these three treatments and sampling the main compartments of the green and brown food webs as well as measures related to functional diversity and phytoplankton nutrient limitation, we aim to highlight the complex interactions resulting from the interplay between bottom-up and top-down effects.

We expect the different compartments of the ecosystem to have contrasted responses to our three treatments as summarised in Table 2. We expect light availability to promote primary production (Mette et al., 2011; Rock et al., 2016) and detritus production for the brown food web (Daufresne et al., 2008), thus increasing the biomass of organisms from both green and brown food webs. Organic matter addition should promote the growth of heterotrophic prokaryotes (Hitchcock et al., 2016) and increase their limitation by nutrients such as nitrogen and phosphorus. As a consequence, we expect increased competition between heterotrophic prokaryotes and phytoplankton for nutrients, which would lead to an increase in nutrient limitation for phytoplankton with a resulting decrease in the biomass of the organisms of the green food web and an increase of those of the brown food web (Joint et al., 2002; Degerman et al., 2018; Yalçın et al., 2024). However, such competition could be mitigated by the stimulation of the heterotrophic metabolism pathway of mixotrophic phytoplankton, which is able to prey on heterotrophic prokaryotes. Therefore
The enclosures were filled with water from the storage pond hosting the pontoon on the 29th and 30th of June. All enclosures were successively filled for two minutes to avoid any bias due to variations in water composition during the day. Then, the water was enriched at the beginning of July with nitrogen (N) and phosphorus (P) to sustain the development of the community (nutrient concentrations in the storage water were initially very low). We twice added (3rd and 10th of July) 1.27 g NH₄NO₃, 0.65 g KH₂PO₄ and 0.39 g K₂HPO₄·3H₂O in each enclosure. Such addition corresponded to 140 µgN L⁻¹ and 56 µgP L⁻¹ (i.e. P:N ratio of 2.5). Zooplankton was collected with a net (140 µm mesh size) in the experimental ponds of the platform, pooled and introduced to the enclosures to initialise the community on the 6th of July. We noticed later that large cladocerans such as daphnia were absent in the collected community. Three treatments (with two modalities) were crossed in each enclosure: light, organic carbon and fish, leading to eight different combinations (Fig. 1B). Each combination with fishes had five replicates and each combination without fishes had four replicates. All treatments aimed to specifically target phytoplankton (green food web), heterotrophic prokaryotes (brown food web) and food web structure in order to alter the coupling between the two sub-food webs.

## Experimental treatments

### Light treatment

The filtration of daylight to limit photosynthesis was performed by two types of shade meshes used for crops, filtering 10% (Diatex FOM20B200CR) and 30% (Diatex F1038T-200BLANC) of daylight without changing the spectra quality. All enclosures were covered by shade meshes in order to avoid differential access by flying insects: the 10% shade mesh acted as a null treatment, while the light-depleted enclosures were covered by one layer of 30% and two layers of 10% shade mesh for a total reduction of 50% of light intensity in water (confirmed by a Li Cor light sensor). The shade mesh was attached to parallel aluminium bars, making a structure that could be rolled up for sampling (Fig. 1A).

### Organic matter treatment

Dissolved organic carbon (DOC) was added each week from the end of July to the beginning of November (for a total of 15 weeks) to stimulate bacterial activity. Each week, a total of 1.8 g C was added in half of the enclosures as a mixture of three organic molecules providing one third of the carbon each: glucose (C₆H₁₂O₆), cellobiose (C₁₂H₂₂O₁₁) and α-cyclodextrine (C₃₆H₆₀O₃₀) (Sigma Aldrich). These molecules are considered to vary in biodegradability from high to low, respectively, and are also present as substrates in the Biolog Ecoplate 96-well microplates (see the Functional diversity of bacteria section). A total of 5.4 mg C L⁻¹ was added to each treated enclosure, more than doubling the dissolved carbon stock of the system (initial DOC of the water: 3.96 mg C L⁻¹).

### Fish treatment

Fish were used as top predators for the last treatment and were added in the enclosures on the 16th of July. A set of randomly selected enclosures received four planktivorous fishes belonging to Leuscicidae, mainly rudds (*Scardinius erythrophthalmus*) and a few roach individuals (*Rutilus rutilus*) because all the fish we received did not belong to the same species, with an average length of 8 cm and an average weight of 6.76 ± 1.44 g. Fish were anaesthetised before manipulation with MS222 at 0.3 g mL⁻¹ and weighed to the nearest mg. Fish were marked individually using a nanotech pit-tag (Lutronic International, 1×6 mm) injected in the peritoneal cavityt. Three fish died during the experiment and were immediately replaced. At the end of the experiment, fish were again weighted to the nearest mg and euthanized with MS222. All the procedures were conducted in accordance with appropriate European (Directive, 2010/63/EU) and French national guidelines, permits, and regulations regarding animal care and experimental use (Agreement n° B77-431-1). Individual relative growth rate *g* (g g⁻¹ d⁻¹) was calculated assuming an exponential growth over the time spent in the enclosures Δ*t* (*mass*<sub>final</sub> = *mass*<sub>initial</sub>exp(*g*Δ*t*)). This implies that *g* was assumed to be constant and that fish biomass production at time *t* was proportional to fish biomass.

## Sampling and measurements

### Phytoplankton

Phytoplankton was sampled weekly from the end of July to November with a 2 L sampling bottle (Uwitec). 2 L of water were sampled at three different depths for a total of 6 L (0–50 cm, 50–100 cm, 100–150 cm) and mixed together. 3 mL were added to 150 µL of 20% paraformaldehyde and incubated
<table><caption>Table 2: Predicted direction of the effect of the three variables manipulated in our experiment: fish presence, light availability and allochthonous dissolved organic carbon input. "Chloro:phyto" refers to the ratio of chlorophyll mass to phytoplankton biomass. "Phyto." refers to phytoplankton, "Hetero." to heterotrophic and "Prokar." to prokaryotes.</caption><thead><tr><th></th><th>Compartment</th><th>Fish+</th><th>Light+</th><th>OM+</th></tr></thead><tbody><tr><td rowspan="5">Green food web</td><td>Phytoplankton</td><td>+</td><td>+</td><td>-</td></tr><tr><td>Zooplankton</td><td>-</td><td>+</td><td></td></tr><tr><td>Fish growth</td><td></td><td>+</td><td></td></tr><tr><td>Mixotrophs</td><td></td><td></td><td>+</td></tr><tr><td>Chloro:phyto</td><td></td><td>-</td><td>-</td></tr><tr><td></td><td>C:N, C:P phyto.</td><td>-</td><td>+</td><td>+</td></tr><tr><td rowspan="4">Brown fw</td><td>Hetero. prokar.</td><td>+</td><td>+</td><td>+</td></tr><tr><td>Hetero. protists</td><td>+</td><td>+</td><td>+</td></tr><tr><td>Sediments</td><td>+</td><td>+</td><td></td></tr><tr><td>DOC</td><td>+</td><td>+</td><td>+</td></tr></tbody></table>

fore, mixotrophic phytoplankton should be more abundant when light is depleted or organic carbon is added (Balzer et al., 2023). In addition to mixotrophy, we expect to observe plastic phenotypic responses of purely autotrophic phytoplankton to our treatment. Cells may compensate for the lack of light by producing more chlorophyll (Felip, 2000) and accumulate nutrients due to the lower total phytoplankton biomass. Conversely, the cell carbon to nutrient ratio is expected to increase when phytoplankton becomes nutrient limited because of the competition with heterotrophic prokaryotes following organic matter addition (Danger et al., 2007b; Hall et al., 2007; Mette et al., 2011). We expect fish to increase the biomass of phytoplankton and heterotrophic prokaryotes through the top-down control of zooplankton (Mette et al., 2011) or a positive bottom-up effect triggered by easily degradable faeces and the release of mineral nutrients (Attayde and Hansson, 2001; Harrault et al., 2014).

# Material and methods

## Experimentation site and experimental design

The experiment was set in the Experimental Pond Platform (EPP, N48°16′57″, E2°40′20″) of the PLANAQUA facility (“PLAteforme NAtionale expérimentale en écologie AQUAtique”) near Paris, France. The EPP includes 16 artificial ponds with an individual volume of ~750 m³, in addition to a storage pond and an overflow pond of 4000 m³ each (Mougin et al., 2015). All these water bodies are isolated from the ground by two successive sets of geotextiles and two HDPE geomembranes. In summer 2014, they received 30 cm of washed sand originating from the Loire River and they were

filled with a mixture of rainwater and drinking water. Subsequently, they were naturally colonised by aquatic organisms.

We installed a floating pontoon (27 × 5 m) in the water storage pond (125 × 15 × 3 m deep). The pontoon delimited two blocks of 12 × 3 m on which enclosures can be suspended (Fig. 1A). The mesocosm study was performed from the end of June 2015 to the beginning of November 2015 and thus began one year after the beginning of pond colonisation. Thirty-six translucent polyethylene enclosures (2 × 1 × 2.75 m, 5 m³), sealed at the bottom, were attached to the pontoon 25 cm above the water surface. The pontoon was divided into two blocks of 3 × 6 enclosures. The different enclosures with the same combination of treatments were uniformly distributed to avoid potential confounding edge effects (Fig. S2-1 in the supporting information).

The enclosures were filled with water from the storage pond hosting the pontoon on the 29th and 30th of June and did not receive sediments from the bottom of the storage pond. All enclosures were successively filled for two minutes to avoid any bias due to variations in water composition during the day. Then, the water was enriched at the beginning of July with nitrogen (N) and phosphorus (P) to sustain the development of the community (nutrient concentrations in the storage water were initially very low). We added 1.27 g NH₄NO₃, 0.65 g KH₂PO₄ and 0.39 g K₂HPO₄·3H₂O twice in each enclosure (3rd and 10th of July). Such addition corresponded to 140 µg N L⁻¹ and 56 µg P L⁻¹ (i.e. N:P ratio of 2.5). Zooplankton was collected with a net (140 µm mesh size) in the experimental ponds of the platform, pooled and introduced to the enclosures to initialise the community on the 6th of July. We noticed later that large cladocerans such as daphnia were absent in the collected community. Three treatments (with two modalities) were crossed in each enclosure: light, organic carbon and fish, leading to eight different combinations (Fig. 1B). Each combination with fish had five replicates and each combination without fish had four replicates. All treatments aimed to specifically target phytoplankton (green food web), heterotrophic prokaryotes (brown food web) and food web structure in order to alter the coupling between the two sub-food webs.

## Experimental treatments

### Light treatment

The filtering of daylight to limit photosynthesis was performed by two types of shade meshes used for crops, filtering 10% (Diatex FOM20B200CR, Saint-Genis-Laval, France) and 30% (Diatex F1038T-200BLANC) of daylight without altering spectral quality. All enclosures were covered by shade meshes in order to avoid differential access by flying insects: the 10% shade mesh
at 4°C for one hour before liquid nitrogen freezing, then samples were kept at -80°C for later FACS analysis (see the *Flow cytometry analysis of microorganisms* section). Moreover, 50 mL were directly analysed after sampling by a BBE fluoroprobe (Moldaenke, Kiel, Germany), which gives a proxy of the abundance of four phytoplankton groups: Chlorophyceae, Cyanobacteria, Bacillariophyceae/Dinophyceae and Cryptophyceae. At last, Lugol solution was added to 200 mL of water at the end of September for later identification of phytoplankton taxa (Table S2-11 in the supporting information) by an independent consultancy society (Bi-Eau, Angers, France). The Utermöhl (1958) method was used to quantify phytoplankton, with an inverted microscope. Phytoplankton biovolume was estimated on the basis of cell measurements and assignment of organism forms to geometric shapes. At the beginning of October, the same protocol was applied to collect the seston (particulate matter between 0.7 and 50 µm). Sampled water was first filtered through a 50 µm nylon filter to remove zooplankton and was then filtered through a pre-weighted and carbonised Whatman GF/F glass-fibre filter (nominal cut-off 0.7 µm). Filters were dried overnight at 60°C and weighed to determine seston mass.

## Stoichiometry

The filters were then used to quantify the percentage of carbon and nitrogen contained in the seston organic matter (i.e. phytoplankton and particulate organic matter (POM) < 50 µm) using a CHN elemental analyser (Carlo Erba NA2100, Thermo Quest CE International, Milan, Italy). Organic phosphorus content was quantified after sodium persulfate digestion and spectrophotometry. Subsamples were weighed to the nearest 0.001 mg (Perkin Elmer AD6 Autobalance). All elemental ratios are expressed in molar units.

## Zooplankton

Zooplankton was sampled weekly from the end of July to November with a 2 L sampling bottle (Uwitec). A total of 24 L was sampled at four different locations in the enclosures and at three different depths (0–50 cm, 50–100 cm, 100–150 cm). Then, the water was pooled and filtered through a 50 µm nylon filter and zooplankton was stored in 96% ethanol for later estimation of the main taxa abundance. At the beginning of October, the same protocol was applied to collect zooplankton that was then dried for 24 hours at 60°C just after sampling. The dry biomass of zooplankton was assessed by weighting the nylon filter before and after the sampling.

## Flow cytometry analysis of microorganisms

The frozen samples collected (see the Phytoplankton section) were analysed by Fluorescence Activated Cell Sorting (FACS) through flow cytometry (Accuri C6 Plus, Becton Dickinson). Pigmented cells were identified based on the autofluorescence of the chlorophyll a (orange fluorescence) (FL2, 585 ± 40 nm) vs red fluorescence (FL3, 670 nm). Cells containing chlorophyll a were distinguished using side scatter (SSC) vs FL3 and distinct size classes were defined to discriminate cyanobacteria from pico- and nanoeukaryotes. A subsample of each sample was stained with the nucleic acid stain SYBr Green I (Zubkov et al., 2007) to distinguish heterotrophic organisms using SSC vs green fluorescence (FL1, 533 ± 30 nm). Chlorophyll a-containing organisms were gated off plots of SSC vs FL1, having been identified in plots of FL3 vs FL1.

## Water physico-chemistry

The main water characteristics were measured weekly with a multiparameter probe (Kor Exo) at three different depths (0–50 cm, 50–100 cm, 100–150 cm). Chlorophyll a, pH, dissolved O₂ concentration, turbidity and temperature were thus measured. In addition, water filtered through pre-weighted and carbonised Whatman GF/F glass-fibre filters (nominal cut-off 0.7 µm) was used to determine water chemistry. Two 200 mL samples were collected at two different dates in October and were frozen at –20°C for later P and N analysis with a continuous segmented flow analyser (SEAL AA3 HR). Every two weeks, duplicate 30 mL samples for the measurement of DOC were collected. Samples were acidified with 35 µL of 85% phosphoric acid and stored in the dark at room temperature in carbonised glass tubes sealed with Teflon lids. Dissolved organic carbon (DOC) oncentration was determined using a total organic carbon analyser (TOCV-CSH; Shimadzu, Kyoto, Japan).

## Sediments

Sediments were collected in each enclosure by 5 cm diameter and 30 cm height PVC tubes suspended at 1.5 m below the water surface (see Danger et al. (2012)). Tubes were put in place in the middle of
<figure><img src="image_3.png" /></figure>

Figure 1: **A)** Photo of the experimental setting during a sampling session. The six mesocosms at the front are uncovered to allow sampling. **B)** Experimental design consists of three crossed treatments: light filtering (Light+ = no light filtering), soluble organic carbon addition (OM+ = carbon addition) and fish addition (Fish+ = four Leuscicidae fish). Light filtering (–50% of light intensity) is expected to decrease the photosynthetic activity of phytoplankton to target the green food web. OM addition is expected to promote bacterial production and target the brown food web. Fish presence, which affects food web structure, is expected to modify the coupling between green and brown food webs. Organisms in circles represent heterotrophic microbial eukaryotes and zooplankton, which are the consumers in freshwater planktonic food webs.

acted as the full light exposure treatment, while the
light-depleted enclosures were covered by one layer
of 30% and two layers of 10% shade mesh for a total
reduction of 50% of light intensity in water (con-
firmed by a Li Cor light sensor). The shade mesh
was attached to parallel aluminium bars, making
a structure that could be rolled up for sampling
(Fig. 1A). In the following, we consider the effect of
light exposure on the different compartments of the
ecosystem; therefore, Light- refers to light-depleted
enclosures while Light+ refers to fully exposed en-
closures.

Organic matter treatment

Dissolved organic carbon (DOC) was added each week from the end of July to the beginning of November (for a total of 15 weeks) to stimulate bacterial activity. Each week, a total of 1.8 g C was added in half of the enclosures as a mixture of three organic molecules providing one third of the carbon each: glucose (C₆H₁₂O₆), cellobiose (C₁₂H₂₂O₁₁) and α-cyclodextrine (C₃₆H₆₀O₃₀) (SigmaAldrich). These molecules are considered to vary in biodegradability from high to low, respectively, and are also present as substrates in the Biolog Ecoplate 96-well microplates (see the *Functional diversity of heterotrophic prokaryotes* section). A total of 5.4 mg C L⁻¹ was added to each treated enclosure, more than doubling the dissolved carbon stock of the system (initial DOC of the water: 3.96 mg C L⁻¹).

Fish treatment

Fish were used as top predators for the last treat-
ment and were added in the enclosures on the 16th

of July. The fish treatments consists of the presence
of four planktivorous fish belonging to Leuscicidae,
mainly rudds (*Scardinius erythrophthalmus*) and a
few roach individuals (*Rutilus rutilus*) because all
the fish we received did not belong to the same
species, with an average length of 8 cm and an aver-
age weight of 6.76 ± 1.44 g. Fish were anaesthetised
before manipulation with MS222 at 0.3 g mL⁻¹ and
weighed to the nearest mg. Fish were marked indi-
vidually using a Nanotech PIT-tag (Lutronic Inter-
national, Rodange, Luxembourg, 1 × 6 mm) injected
in the peritoneal cavity. Three fish died during the
experiment and were immediately replaced. At the
end of the experiment, fish were weighted to the
nearest mg and euthanized with MS222. All the
procedures were conducted in accordance with ap-
propriate European (Directive, 2010/63/EU) and
French national guidelines, permits and regulations
regarding animal care and experimental use (Agree-
ment n° B77-431-1). Individual monthly relative
growth rate G (mo⁻¹) was calculated from the ini-
tial mass Mᵢ and the final mass Mf assuming an
exponential growth over the time Δt spent in en-
closures (Mf = MᵢeGΔt). This implies that G was
assumed to be constant and that fish biomass pro-
duction at time t was proportional to fish biomass.

Sampling and measurements

We sampled the main compartments of the green
and brown food webs, with a specific focus on mea-
sures associated with the functional diversity and
physiological responses of phytoplankton and het-
erotrophic prokaryotes (Table 4).
<table><caption>Table 1: Summary of the statistical models. Our three treatments and their interactions are considered as fixed effects. The date column represents the number of dates at which each variable has been sampled.</caption><thead><tr><th>Variable</th><th>Dates</th><th>Additional effects</th><th>Random effects</th><th>Distribution</th><th>Test</th></tr></thead><tbody><tr><td>BBE data (e.g. chlorophyll a)</td><td>13</td><td>-</td><td>mesocosm+date</td><td>Gaussian</td><td>Chi<sup>2</sup></td></tr><tr><td>Multiparameter data (e.g. pH)</td><td>13</td><td>depth</td><td>mesocosm+date</td><td>Gaussian</td><td>Chi<sup>2</sup></td></tr><tr><td>Fish growth rate</td><td>-</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Seston</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>C:N and C:P of seston</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Zooplankton dry biomass</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Sediment</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Cytometry data (e.g. bacteria)</td><td>4</td><td>-</td><td>mesocosm+date</td><td>Poisson</td><td>Chi<sup>2</sup></td></tr><tr><td>DOC</td><td>7</td><td>-</td><td>mesocosm</td><td>Gaussian</td><td>Chi<sup>2</sup></td></tr><tr><td>Phytoplankton (count)</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Zooplankton (count)</td><td>2</td><td>date</td><td>mesocosm</td><td>quasi-Poisson</td><td>F</td></tr><tr><td>EcoPlates Biolog</td><td>2</td><td>substrate+date</td><td>-</td><td>Gaussian</td><td>F</td></tr></tbody></table>

July and were removed in the middle of November, after a total of four months. The supernatant water in the tube was removed and the sediments were collected in 200 mL plastic jars and dried at 60 °C for several days.

## Functional diversity of heterotrophic prokaryotes

The catabolic capacity of each aquatic microbial community was determined using Biolog Ecoplate™ 96-well microplates as performed by Pommier et al. (2014). Two different heterotrophic prokaryote communities were sampled: the pelagic community was sampled as was phytoplankton (see Phytoplankton section) at the end of September and at the end of October (with a total of two measures for each enclosure). The benthic community was sampled at the end of November in the supernatant of the sediment collection tubes (with two replicates per enclosure). The microplates include, in triplicate, 31 different carbon-based substrates and one water control. Each well, which contains growth media and a tetrazolium violet dye becoming purple when the substrate is oxidised, was inoculated with 150 µL of sample. The plates were incubated in the dark at lake temperature for seven days. Colour development (OD at 590 nm) was measured using a Bio-Rad Laboratories, Model 680 Microplate Reader every 24 h just after inoculation ($d_0$) and at $d_0+1, d_0+2, d_0+3, d_0+4$ and $d_0+7$. We calculated the average colour development for each group of substrates (Supporting Information) after subtraction of the appropriate water blanks.

The catabolic capacity of each aquatic microbial community was determined using Biolog Ecoplate™ 96-well microplates as performed by Pommier et al. (2014). Two different bacterial communities were sampled: the pelagic community was sampled as was phytoplankton (see Phytoplankton section) at the end of September and at the end of October (with a total of two measures for each enclosure), and the benthic community was sampled at the end of November in the supernatant of the tubes collecting the sediments (with two replicates per enclosure). The microplates include 31 different carbon-based substrates and one water control in triplicates. Each well, which contained growth media and a tetrazolium violet dye becoming purple when the substrate is oxidised, was inoculated with 150 µL of sample. The plates were incubated in the dark at lake temperature for seven days. Colour development (OD at 590 nm) was measured using a Bio-Rad Laboratories, model 680 Microplate Reader every 24 h just after inoculation ($d_0$) and at $d_0+1, d_0+2, d_0+3, d_0+4$ and $d_0+7$. We calculated the average colour development for each group of substrates after subtraction of the appropriate water blanks.

## Statistical analysis

Statistical analyses were performed with R version 4.3.1 (R Core Team, 2023) and are summarised in Table 1. We log-transformed response variables when necessary to guarantee the normality of residuals. Our three treatments were considered as qualitative variables, with two modalities each, whose interactions were considered in the model. Additional qualitative variables were considered to account for repeated measurements or depth (see *Additional effects* column in Table 1). We used linear mixed effect models with the function *lmer()* of the package *nlme* to set mesocosm and sampling date as random effects when the response variable was repeatedly measured in time (more than two repeated measures). Sampling date was converted into a continuous variable to model DOC because of its linear accumulation
## Phytoplankton

Phytoplankton was sampled weekly from the end of July to November with a 2 L sampling bottle (Uwitec, Mondsee, Austria). 2 L of water were sampled at three different depths for a total of 6 L (0–50 cm, 50–100 cm, 100–150 cm) and mixed together. 3 mL were added to 150 µL of 20% paraformaldehyde and incubated at 4 °C for one hour before liquid nitrogen freezing, then samples were kept at –80 °C for later Fluorescence Activated Cell Sorting (FACS) analysis (see the Flow cytometry analysis of microorganisms section). Moreover, 50 mL were directly analysed after sampling by a BBE fluoroprobe (Moldaenke, Kiel, Germany), which gives a proxy of the abundance of four phytoplankton groups: Chlorophyceae, Cyanobacteria, Bacillariophyceae/Dinophyceae and Cryptophyceae. At last, Lugol solution was added to 200 mL of water at the end of September for later identification of phytoplankton taxa (Table S2-1 in the supporting information) by an independent consultancy society (Bi-Eau, Angers, France). The Utermöhl (1958) method was used to quantify phytoplankton, with an inverted microscope. Phytoplankton biovolume was estimated on the basis of cell measurements and assignment of organism forms to geometric shapes.

## Stoichiometry of phytoplankton

At the beginning of October, the same protocol as that used for phytoplankton sampling was applied to collect the seston (particulate matter between 0.7 and 50 µm). Sampled water was first filtered through a 50 µm nylon filter to remove zooplankton and was then filtered through a pre-weighted and carbonised Whatman GF/F glass-fibre filter (nominal cut-off 0.7 µm). Filters were dried overnight at 60 °C and weighed to determine seston mass. The filters were then used to quantify the percentage of carbon and nitrogen contained in the seston organic matter (i.e. phytoplankton and particulate organic matter (POM) < 50 µm) using a CHN elemental analyser (Carlo Erba NA2100, Thermo Quest CE International, Milan, Italy). Organic phosphorus content was quantified after sodium persulfate digestion by spectrophotometry. Subsamples were weighed to the nearest 0.001 mg (Perkin Elmer AD6 Autobalance, Waltham, USA). All elemental ratios are expressed in molar units.

## Zooplankton

Zooplankton was sampled weekly from the end of July to November with a 2 L sampling bottle (Uwitec). A total of 24 L was sampled at four different locations in the enclosures and three different depths (0–50 cm, 50–100 cm, 100–150 cm). Then, the water was pooled and filtered through a 50 µm nylon filter and zooplankton was stored in 96%

ethanol for later estimation of the main taxa abundance. At the beginning of October, the same protocol was applied to collect zooplankton that was then dried for 24 h at 60 °C just after sampling. The dry biomass of zooplankton was assessed by weighing the nylon filter before and after the sampling.

## Flow cytometry analysis of micro-organisms

The frozen samples collected (see the *Phytoplankton* section) were analysed by Fluorescence Activated Cell Sorting (FACS) through flow cytometry (Accuri C6 Plus, Becton Dickinson, Franklin Lakes, USA). Pigmented cells were identified based on the autofluorescence of chlorophyll *a* (orange fluorescence) (FL2, 585 ± 40 nm) vs red fluorescence (FL3, 670 nm). Cells containing chlorophyll *a* were distinguished using side scatter (SSC) vs FL3 and distinct size classes were defined to discriminate cyanobacteria from pico- and nano-eukaryotes. A subsample of each sample was stained with nucleic acid stain SYBr Green I (Zubkov et al., 2007) to distinguish heterotrophic organisms using SSC vs green fluorescence (FL1, 533 ± 30 nm). Chlorophyll *a*-containing organisms were gated off plots of SSC vs FL1, having been identified in plots of FL3 vs FL1.

## Water physico-chemistry

The main water characteristics were measured weekly with a multiparameter probe (Kor Exo, YSI, Yellow Springs, USA) at three different depths (0–50 cm, 50–100 cm and 100–150 cm). Chlorophyll *a*, pH, dissolved O₂ concentration, turbidity and temperature were thus measured. In addition, water filtered through pre-weighed and carbonised Whatman GF/F glass-fibre filters (nominal cut-off 0.7 µm) was used to determine water chemistry. Two 200 mL samples were collected at two different dates in October and were frozen at –20 °C for later P and N analysis with a continuous segmented flow analyser (SEAL AA3 HR, Mequon, USA). Every two weeks, duplicate 30 mL samples for the measurement of DOC were collected since they represent resources for heterotrophic prokaryotes and the brown food web. Samples were acidified with 35 µL of 85% phosphoric acid and stored in the dark at room temperature in carbonised glass tubes sealed with Teflon lids. Dissolved organic carbon (DOC) concentration was determined using a total organic carbon analyser (TOCV-CSH; Shimadzu, Kyoto, Japan).

## Sediments

To assess the effect of our treatments on the abundance of the resources of the brown food web, sediments accumulated during the experiment were collected in each enclosure by 5 cm diameter and 30 cm height PVC tubes suspended at 1.5 m below the water surface (see Danger et al., 2012). Tubes
in water over time. We used generalised linear models with random effects with the function *glmer()* of the package *lme4* for flow cytometry data with a Poisson distribution (count data) and offset equal to the logarithm of the analysed volume to account for variation in the analysed volume with this method across samples. Zooplankton counting data were modelled with the function *glmmPQL()* of the *MASS* package to implement a quasi-Poisson distribution of residuals to account for the over-representation of zeros in data because of the absence of several taxa in some enclosures. The statistical significance of our three crossed treatments and their interactions, and corresponding p-values, were obtained with type II ANOVA implemented by the function *Anova()* of the package *car*, with Chi² tests for Poisson distributions and mixed models, and F tests for Gaussian and quasi-Poisson distributions. *Post hoc* Tukey's tests were performed by the function *emmeans()* of the package *emmeans* to compare the different modalities of treatment interactions, which were extracted with the *cld()* function of the package *lsmeans*. A Principal Components Analysis (PCA) of the functional diversity zooplankton was performed with the *PCA()* function of the package *FactoMineR*, then, the significance of our treatments on functional diversity was tested with a PERMANOVA implemented by the function *adonis2()* of the package *vegan*, permutations being restricted to data points with the same sampling date. The data and *R* code used to perform these statistical analyses and plot figures are available on GitHub at the following link: https://github.com/PierreQuevreux/stat_mesocosm_green_brown_CEREEP.

# Results

## Overall response of the food web

Chlorophyll *a* concentration (Fig. 2A) (and dry concentration of seston, see Fig. S1-4A in the supporting information) responded positively to fish presence and light exposure. We also noticed a significant effect of the interaction between fish and light treatment (Table 2: shading significantly reduced chlorophyll *a* and seston concentration only when fish were absent. The total dry mass concentration of zooplankton (Fig. 2B) did not respond significantly to any treatment. Fish in enclosures fully exposed to daylight had a significantly higher growth rate (+0.0011 ± 0.0003 g g⁻¹ d⁻¹, (mean+SE)) than fishes in light depleted enclosures (-0.0006 ± 0.0002 g g⁻¹ d⁻¹, Fig. 2C). Heterotrophic protists (Fig. 2D) and heterotrophic prokaryotes (Fig. 2E) were significantly more abundant in enclosures with fish. Sediment mass (Fig. 2F) and DOC accumulation (Fig. 2G) were significantly higher in enclosures with fish (respectively +0.508 ± 0.122 mg and +0.0030 ± 0.0002 mg L⁻¹ d⁻¹). DOC accumulation also responded positively to full daylight exposition (+0.0020 ± 0.0002 mg L⁻¹ d⁻¹) and to organic carbon addition (+0.0011 ± 0.0002 mg L⁻¹ d⁻¹), and displayed complex responses to interactions between treatments. On the one hand, daylight exposition and organic carbon addition had positive independent effects on DOC accumulation in the absence of fish. On the other hand, in the presence of fish, light exposure only had a positive effect when organic carbon was added.

## Functional diversity and community composition

The different taxonomic classes of phytoplankton had contrasted responses to our treatments (Fig. 3A, Table 2). Chlorophyceae were not abundant compared to the two other taxa and only responded significantly to a fish × light interaction (Fig. 3B). Fish had a negative effect on Dinophyceae abundance (Fig. 3C), while Zygnematophyceae were affected by both fish and light treatments, and their interaction (Fig. 3D). Zygnematophyceae had a response similar to chlorophyll *a* concentration (2A) and were clearly the taxonomic group responsible for its variations. Although fish did not impact zooplankton biomass (2B), they strongly altered their community composition (Fig. 3E and Fig. S1-8 and Table S1-7 in the supporting information for more details on the PCA) with a positive effect on small cladocerans (large cladocerans such as *Daphnia* being absent) and copepods, and a negative effect on *Chaoborus*. Fish did not affect significantly rotifers (see Fig. S1-9, S1-11 and S1-12 and Table S1-8 for the detailed response of the different taxa). Finally, fish presence significantly decreased the degradation capacity of the benthic community for all substrate families present in the EcoPlates™ Biolog except polymers (Fig. 3F and statistics summarised in Table S1-9 in the supporting information), but not in the pelagic community. The light and the organic carbon addition treatments significantly affected a few substrate families but no general trend could be identified (detailed results per substrate family are given in Fig. S1-13 and S1-14 in the supporting information).

## Physiological response of phytoplankton

The chlorophyll *a* concentration per seston mass unit did not respond significantly to any treatment (Fig. 4A, Table 2), which indicates that the production of chlorophyll *a* by phytoplankton did not respond
were put in place in the middle of July and were removed in the middle of November, after a total of 4 months. The supernatant water in the tube was removed and the sediments were collected in 200 mL plastic jars and dried at 60 °C for several days.

## Functional diversity of heterotrophic prokaryotes

The catabolic capacity of aquatic microbial community was determined using Biolog Ecoplate™ 96-well (Hayward, USA) microplates as performed by Pommier et al. (2014). Two different heterotrophic prokaryote communities were sampled: the pelagic community was sampled as was phytoplankton (see *Phytoplankton* section) at the end of September and at the end of October (with a total of two measures for each enclosure). The benthic community was sampled at the end of November in the supernatant of the sediment collection tubes (with two replicates per enclosure). The microplates include, in triplicate, 31 different carbon-based substrates and one water control. Each well, which contains growth media and a tetrazolium violet dye becoming purple when the substrate is oxidised, was inoculated with 150 µL of sample. The plates were incubated in the dark at pond temperature for 7 days. Colour development (OD at 590 nm) was measured using a microplate reader (Model 680, Bio-Rad, Hercules, USA) every 24 h just after inoculation ($d_0$) and at $d_0 + 1$, $d_0 + 2$, $d_0 + 3$, $d_0 + 4$ and $d_0 + 7$. We calculated the average colour development for each group of substrates after subtraction of the appropriate water blanks (supporting information S1-4).

## Statistical analysis

Statistical analyses were performed with R version 4.3.1 (R Core Team, 2023) and are summarised in Table 3. We log-transformed response variables when necessary to alleviate deviation from normality of residuals. Our three treatments were considered as qualitative variables, with two modalities

each, whose interactions were considered in the model. Additional qualitative variables were considered to account for repeated measurements or depth (see *Additional effects* column in Table 3). We used linear mixed effect models with the function *lmer()* of the *lme4* package (Bates et al., 2003) to set mesocosm and sampling date as random effects when the response variable was repeatedly measured in time (more than two repeated measures). The normality of residuals and random effects was visually tested in a Q-Q plot. Sampling date was converted into a continuous variable to model DOC because of its linear accumulation in water over time. We used generalised linear models with random effects with the function *glmer()* of the *lme4* package (Bates et al., 2003) for flow cytometry data with a Poisson distribution (count data) and an offset equal to the logarithm of the analysed volume to account for variations in the analysed volume across samples. Zooplankton counting data were modelled with the function *glmmPQL()* of the *MASS* package (Ripley and Venables, 2009) to implement a quasi-Poisson distribution of residuals to account for the overrepresentation of zeros in data because of the absence of several taxa in some enclosures. The statistical significance of our three crossed treatments and their interactions, and corresponding p-values, were obtained with type II ANOVA implemented by the function *Anova()* of the *car* package (Fox et al., 2001), with chi-square tests for Poisson distributions and mixed models, and F tests for Gaussian and quasi-Poisson distributions. *Post hoc* Tukey's tests were performed by the function *emmeans()* of the *emmeans* package (Lenth, 2017) to compare the different modalities of treatment interactions, which were extracted with the *cld()* function of the *lsmeans* package (Lenth, 2012).

A Principal Components Analysis (PCA) of the functional diversity zooplankton was performed with the PCA() function of the *FactoMineR* package (Husson et al., 2006), then, the significance of

<table><caption>Table 3: Summary of the statistical models. Our three treatments and their interactions are considered as fixed effects. The date column represents the number of dates at which each variable has been sampled.</caption><thead><tr><th>Variable</th><th>Dates</th><th>Additional effects</th><th>Random effects</th><th>Distribution</th><th>Test</th></tr></thead><tbody><tr><td>BBE data (e.g. chlorophyll a)</td><td>13</td><td>-</td><td>mesocosm+date</td><td>Gaussian</td><td>Chi<sup>2</sup></td></tr><tr><td>Multiparameter data (e.g. pH)</td><td>13</td><td>depth</td><td>mesocosm+date</td><td>Gaussian</td><td>Chi<sup>2</sup></td></tr><tr><td>Fish growth rate</td><td>-</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Seston</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>C:N and C:P of seston</td><td>1</td><td>chlorophyll a</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Zooplankton dry biomass</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Sediment</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Cytometry data (e.g. bacteria)</td><td>4</td><td>-</td><td>mesocosm+date</td><td>Poisson</td><td>Chi<sup>2</sup></td></tr><tr><td>DOC</td><td>7</td><td>-</td><td>mesocosm</td><td>Gaussian</td><td>Chi<sup>2</sup></td></tr><tr><td>Phytoplankton (count)</td><td>1</td><td>-</td><td>-</td><td>Gaussian</td><td>F</td></tr><tr><td>Zooplankton (count)</td><td>2</td><td>date</td><td>mesocosm</td><td>quasi-Poisson</td><td>F</td></tr><tr><td>EcoPlates Biolog</td><td>2</td><td>substrate+date</td><td>-</td><td>Gaussian</td><td>F</td></tr></tbody></table>
<figure><img src="image_3.png" /><figcaption>Figure 2: Response of the main compartments of the food web to the eight combinations of our three treatments. A) Chlorophyll *a* concentration (µg L⁻¹) measured by the BBE probe. B) Zooplankton dry biomass concentration (mg L⁻¹). C) Individual growth rate of fish (g g⁻¹ d⁻¹) calculated over 133 days on average. D) Heterotrophic protist (heterotrophic microbial eukaryote) concentration measured by FACS (cell µL⁻¹). E) Heterotrophic prokaryotes (bacteria and archaea) concentration measured by FACS (cell µL⁻¹). F) Sediment dry mass collected at the end of the experiment (mg). G) Dissolved organic carbon (DOC) concentration over time (mg L⁻¹). Statistically significant treatments and interactions are notified on each graph and letters represent treatment combinations leading to significantly different responses of measured variables according to *post hoc* Tukey's tests.</figcaption></figure>

to our treatments. Mixotrophic phytoplankton abundance, which was determined by identification at the species level, was negatively affected by fish (Fig. 4B) and had a response similar to Dinophyceae (Fig. 3D). Dinophyceae species present in our mesocosm are known to be mixotrophic phytoplankton contrary to Zygnematophyceae (according to the current knowledge) and, given their high abundance, were clearly the main mixotrophic taxa. The C:N ratio of seston was higher in mesocosms receiving less daylight (Fig. 4C), while the C:P ratio was significantly higher in enclosures with fish (+187 ± 48) than in enclosures without fish (Fig. 4D).

Discussion

The results of our experiment can be summarised in a few major points (Fig. 5). First, we observed consistent top-down effects of fish presence on components of the green food web (phytoplankton abundance and composition), the brown food web (planktonic heterotrophic prokaryotes and protists abundance and catabolic activity of their benthic counterparts), zooplankton composition and on water physico-chemistry variables (DOC). This top-down effect of fish also interacts with the bottom-up effect of light
our treatments on functional diversity was tested
with a PERMANOVA implemented by the func-
tion adonis2() of the vegan package (Oksanen et
al., 2001), permutations being restricted to data
points with the same sampling date. Standardised
effect sizes were represented by Cohen's d and cal-
culated by the t_to_d() function of the effectsize
package (Ben-Shachar et al., 2020) by passing in
arguments the t-values and degrees of freedom re-
turned by summary(). Those values were obtained
from simple linear models only including significant
effects and applied to untransformed data following
Nakagawa and Cuthill (2007). However, we were
not able to calculate the confidence interval of ef-

fect sizes because the normality of residuals was not
always verified in such simple models. The Fish-
/Light-/OM- treatment was considered as reference
(no fish, light depleted, no OM addition) for the
calculation of effect sizes and for ANOVAs. The
data and *R* code used to perform these statistical
analyses and plot figures are available on Zenodo
(10.5281/zenodo.15788788).

<figure><img src="image_4.png" /><figcaption>Figure 2: Response of the main compartments of the food web to the eight combinations of our three treatments. A) Chlorophyll *a* concentration (µg L⁻¹) measured by the BBE probe. B) Zooplankton dry biomass concentration (mg L⁻¹). C) Individual growth rate of fish (mo⁻¹) calculated over 133 days on average. D) Heterotrophic protist (heterotrophic microbial eukaryote) concentration measured by FACS (cell µL⁻¹). Note that the *post hoc* Tukey's test was not able to identify the OM×light interaction and letters only render the fish effect. E) Heterotrophic prokaryotes (bacteria and archaea) concentration measured by FACS (cell µL⁻¹). F) Sediment dry mass collected at the end of the experiment (mg). G) Dissolved organic carbon (DOC) concentration over time (mg L⁻¹). Statistically significant treatments and interactions are notified on each graph and letters represent treatment combinations leading to significantly different responses of measured variables according to *post hoc* Tukey's tests.</figcaption></figure>
<table><caption>Table 2: Significance of treatments (p-values). Each sub-part of the table corresponds to a figure panel. We only provide the significance of the treatments on the slope of the linear regression model between DOC concentration and time, time being considered as a quantitative variable here. Similarly, we provide the significance of the intercept of the regression between C:N and C:P ratios, and chlorophyll a concentration.</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F:L</th><th>F:OM</th><th>L:OM</th><th>F:L:OM</th></tr></thead><tbody><tr><td rowspan="3">Chlorophyll</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.581</td><td><strong>0.001</strong></td><td>0.128</td><td>0.972</td><td>0.775</td></tr><tr><td>χ<sup>2</sup></td><td>32.98</td><td>15.83</td><td>0.305</td><td>11.01</td><td>2.322</td><td>0.001</td><td>0.082</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Zooplankton</td><td>p-value</td><td>0.424</td><td>0.287</td><td>0.554</td><td>0.733</td><td>0.529</td><td>0.295</td><td>0.174</td></tr><tr><td>F</td><td>0.658</td><td>1.179</td><td>0.359</td><td>0.118</td><td>0.406</td><td>1.137</td><td>1.942</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Fish growth</td><td>p-value</td><td>-</td><td><strong>&lt;.001</strong></td><td>0.85</td><td>-</td><td>-</td><td>0.239</td><td>-</td></tr><tr><td>F</td><td>-</td><td>14.6</td><td>0.036</td><td>-</td><td>-</td><td>1.408</td><td>-</td></tr><tr><td>Df</td><td>-</td><td>1</td><td>1</td><td>-</td><td>-</td><td>1</td><td>-</td></tr><tr><td rowspan="3">Heterotrophic protists</td><td>p-value</td><td><strong>0.024</strong></td><td>0.328</td><td>0.556</td><td>0.84</td><td>0.213</td><td>0.044</td><td>0.354</td></tr><tr><td>χ<sup>2</sup></td><td>5.118</td><td>0.957</td><td>0.346</td><td>0.041</td><td>1.553</td><td>4.052</td><td>0.86</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Heterotrophic procaryotes</td><td>p-value</td><td><strong>0.006</strong></td><td>0.064</td><td>0.949</td><td>0.322</td><td>0.876</td><td>0.415</td><td>0.959</td></tr><tr><td>χ<sup>2</sup></td><td>7.633</td><td>3.438</td><td>0.004</td><td>0.979</td><td>0.024</td><td>0.665</td><td>0.003</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Sediments</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.303</td><td>0.323</td><td>0.125</td><td>0.975</td><td>0.589</td><td>0.884</td></tr><tr><td>F</td><td>16.89</td><td>1.101</td><td>1.012</td><td>2.496</td><td>0.001</td><td>0.299</td><td>0.022</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">DOC</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.015</strong></td><td><strong>0.047</strong></td><td><strong>0.002</strong></td></tr><tr><td>χ<sup>2</sup></td><td>195.69</td><td>69.1</td><td>27.97</td><td>36.63</td><td>5.883</td><td>3.959</td><td>9.32</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Chlorophyceae</td><td>p-value</td><td>0.9</td><td>0.262</td><td>0.165</td><td>0.023</td><td>0.923</td><td>0.728</td><td>0.176</td></tr><tr><td>F</td><td>0.016</td><td>1.311</td><td>2.029</td><td>5.749</td><td>0.009</td><td>0.123</td><td>1.923</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Zygnematophyceae</td><td>p-value</td><td><strong>0.022</strong></td><td><strong>0.022</strong></td><td>0.924</td><td><strong>0.012</strong></td><td>0.451</td><td>0.794</td><td>0.975</td></tr><tr><td>F</td><td>5.894</td><td>5.897</td><td>0.009</td><td>7.287</td><td>0.584</td><td>0.069</td><td>0.001</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Dinophyceae</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.059</td><td>0.756</td><td>0.456</td><td>0.192</td><td>0.888</td><td>0.327</td></tr><tr><td>F</td><td>19.71</td><td>3.881</td><td>0.099</td><td>0.572</td><td>1.79</td><td>0.02</td><td>0.996</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Diversity zoo</td><td>p-value</td><td><strong>0.001</strong></td><td>0.502</td><td>0.792</td><td>0.934</td><td>0.671</td><td>0.906</td><td>0.817</td></tr><tr><td>F</td><td>4.938</td><td>0.792</td><td>0.541</td><td>0.386</td><td>0.629</td><td>0.412</td><td>0.518</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Chloro:seston</td><td>p-value</td><td>0.826</td><td>0.296</td><td>0.304</td><td>0.912</td><td>0.841</td><td>0.73</td><td>0.813</td></tr><tr><td>F</td><td>0.049</td><td>1.136</td><td>1.1</td><td>0.012</td><td>0.041</td><td>0.122</td><td>0.057</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Mixotrophs</td><td>p-value</td><td><strong>0.025</strong></td><td>0.122</td><td>0.552</td><td>0.282</td><td>0.814</td><td>0.798</td><td>0.901</td></tr><tr><td>F</td><td>5.61</td><td>2.539</td><td>0.362</td><td>1.204</td><td>0.056</td><td>0.067</td><td>0.016</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">C:N-chloro</td><td>p-value</td><td>0.713</td><td><strong>0.041</strong></td><td>0.78</td><td>0.81</td><td>0.99</td><td>0.502</td><td>0.692</td></tr><tr><td>F</td><td>0.139</td><td>4.747</td><td>0.08</td><td>0.059</td><td>0</td><td>0.467</td><td>0.162</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">C:P-chloro</td><td>p-value</td><td><strong>0.029</strong></td><td>0.555</td><td>0.934</td><td>0.906</td><td>0.924</td><td>0.437</td><td>0.346</td></tr><tr><td>F</td><td>5.522</td><td>0.361</td><td>0.007</td><td>0.014</td><td>0.009</td><td>0.628</td><td>0.931</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>
# Results

## Overall response of the food web

Overall, fish presence had a significant effect on the size of all sampled compartments, either alone or in interaction with other treatments, except zooplankton that did not respond significantly. In the brown food web, fish presence increased significantly the abundance of heterotrophic prokaryotes and protists (Fig. 2D-E) and sediment mass (Fig. 2F). While DOC accumulation always increased with fish presence, such increase also depended on both light exposure and OM addition (significant triple interaction, Fig. 2G, Table 4).

Indeed, fish positive effects on DOC accumulation were amplified when light was shaded and in absence of OM addition as shown by the negative effect sizes of the fish×light and fish×OM interactions offsetting the positive simple effect of fish presence. In the green food web, the effect of fish presence also significantly interacted with that of light exposure: chlorophyll *a* (and dry concentration of seston, see Fig. S1-4A and Table S1-2 in the supporting information) only responded positively to fish presence when light was shaded (Fig. 2A). This is shown by the negative effect size of the fish×light interaction offsetting the additive simple effects of fish presence and light exposure (Table 4). Light exposure had significant effects on the size or growth of sev-

<figure><img src="image_5.png" /></figure>

Figure 3: Response of the composition of phytoplankton (including cyanobacteria), zooplankton and functional diversity of heterotrophic prokaryotes. A) Biomass distribution (assessed by the biovolume with the Utermöhl method) among the main phytoplankton taxonomic classes. Biovolume concentration of the three dominant taxa: B) Chlorophyceae, C) Zygnematophyceae and D) Dinophyceae. Statistically significant treatments and interactions are notified on each graph and letters represent treatment combinations leading to significantly different responses of measured variables according to *post hoc* Tukey's tests. E) Biplot of the principal components analysis (PCA) showing the response of the main zooplankton groups to treatments. Additional details on the PCA are given in the supporting information. The statistical analysis (PERMANOVA, Table 4) has been performed at the lowest taxonomic levels (species and genus levels) but we did the PCA at higher levels for the sake of readability. Significant effects of treatments and interactions on species composition of phytoplankton and zooplankton are notified in each panel. F) Metabolic profile of the heterotrophic prokaryote community in the pelagic and benthic compartments (measured by EcoPlates™ Biolog). The position on each of the six axes indicates the average absorbance (or optical density OD) for that group of substrates after 7 days of incubation.
<figure><img src="image_4.png" /><figcaption>Figure 3: Response of the composition of phytoplankton (including cyanobacteria), zooplankton and functional diversity of heterotrophic prokaryotes. A) Biomass distribution (assessed by the biovolume) among the main phytoplankton taxonomic classes. Biovolume concentration of the three dominant taxa: B) Chlorophyceae, C) Zygnematophyceae and D) Dinophyceae. Statistically significant treatments and interactions are notified on each graph and letters represent treatment combinations leading to significantly different responses of measured variables according to *post hoc* Tukey's tests. E) Biplot of the principal components analysis (PCA) showing the response of the main zooplankton groups to treatments. Additional details on the PCA are given in the supporting information. The statistical analysis (PERMANOVA, Table 2) has been performed at the lowest taxonomic levels (species and genus levels) but we did the PCA at higher levels for the sake of readability. Significant effects of treatments and interactions on species composition of phytoplankton and zooplankton are notified in each panel. F) Metabolic profile of the heterotrophic prokaryote community in the pelagic and benthic compartments (measured by EcoPlates™ Biolog). The position on each of the six axes indicates the average absorbance (or optical density OD) for that group of substrates after 7 days of incubation.</figcaption></figure>

to change phytoplankton abundance and composition, suggesting a reduction of nutrient limitation of phytoplankton by fish. Second, our results suggest only weak cascading effects between the green and the brown food webs, since light filtration did not clearly affect measured components of the brown food web (abundance and catabolic activity of microbial communities) and the addition of organic carbon had no clear effect on the main components of the green food web (no effect on fish growth, zooplankton and phytoplankton).

## Top-down effects of fish on green food web: importance of direct nutrient cycling through fish excretion?

Fish occurrence induced a positive effect on phytoplankton abundance, but only under the low-light treatment (Light-). This fish×light effect was reflected by estimates of total chlorophyll *a* (BBE probe in Fig. 2A and multi-parameter probe in Fig. S1-3B and C in the supporting information), seston biomass (Fig. S1-4A in the supporting information), and turbidity (S1-2D in the supporting information). A positive effect of fish on phytoplankton has been very frequently observed and has been interpreted as
<figure><img src="image_6.png" /><figcaption>Figure 4: Physiological response of phytoplankton. A) Ratio chlorophyll *a* concentration to seston concentration (µg mg⁻¹). B) Concentration of mixotrophic algae (from the taxa identification) (mm³ L⁻¹). C) C:N ratio of seston and D) C:P ratio of seston depending on chlorophyll *a* concentration. Significant effects of treatments and interactions are notified in each panel.</figcaption></figure>

eral the compartments of the green and brown food webs, but mainly through interactions with other treatments except for fish growth rate that consistently increased with light exposure (Fig. 2C). Indeed chlorophyll *a* only increased with light exposure in absence of fish (Fig. 2A), and light exposure significantly increased DOC accumulation mostly when fish were absent (Fig. 2G). Zooplankton, as well as other compartments of the brown food web, were not significantly affected by light exposure, except for a very weak significant interaction between light exposure and OM addition for protists abundance (Table 4). Apart from clear increased DOC accumulation when OM was added, except in presence of fish with light shading (Fig. 2G), OM addition did not significantly affect the abundance or biomass of other food web compartments.

## Functional diversity and community composition

Fish were the stronger drivers of phytoplankton and zooplankton community composition and microbial functional diversity. Light exposure had a significant positive effect on only two phytoplankton taxa and OM addition had no effect (Table 4). More precisely, the different taxonomic classes of phy-

tooplankton had contrasted responses to our treatments (Fig. 3A). Chlorophyceae were not abundant compared to the two other dominant taxa and only responded significantly to a fish×light interaction (Fig. 3B). Fish had a negative effect on Dinophyceae abundance (Fig. 3C), while Zygnemato phyceae were affected by both fish and light treatments, and their interaction (Fig. 3D). Zygnemato phyceae had a response similar to chlorophyll *a* concentration (Fig. 2A and Table 4) and were clearly the taxonomic group responsible for its variations. Although fish did not impact zooplankton biomass (Fig. 2B), they strongly altered their community composition (Fig. 3E and Fig. S1-8 and Table S1-4 in the supporting information for more details on the PCA) with a positive effect on small cladocerans (large cladocerans such as *Daphnia* being absent since the beginning of the experiment) and copepods, and a negative effect on *Chaoborus*. Fish did not significantly affect rotifers (see Fig. S1-9, S1-11 and S1-12 and Table S1-5 for the detailed response of the different taxa). Finally, fish presence significantly decreased the degradation capacity of the benthic community for all substrate families present in the EcoPlates™ Biolog except polymers (Fig. 3F and statistics summarised in Table S1-6 in the supporting information), but not in the pelagic
<figure><img src="image_5.png" /><figcaption>Figure 4: Physiological response of phytoplankton. A) Ratio chlorophyll *a* concentration to seston concentration (µg mg⁻¹). B) Concentration of mixotrophic algae (from the taxa identification) (µm³ mL⁻¹). C) C:N ratio of seston and D) C:P ratio of seston depending on chlorophyll *a* concentration. Significant effects of treatments and interactions are notified in each panel.</figcaption></figure>

a classical cascading effect of top-predators on primary producers, mediated by a reduction of grazing pressure by zooplankton (see the meta-analysis of Hulot et al. (2014)). This reduction of phytoplankton grazing has not always been associated to a decrease in zooplankton biomass, Cyprinidae or Leuciscidae fish tending to shift zooplankton composition by suppressing large Cladocerans, which are the most efficient grazers (Bertolo et al., 1999a; Danger et al., 2008). In our study, zooplankton biomass was not affected by fish presence (Fig. 2B), nor was the relative abundance of large zooplanktonic organisms. Instead, zooplankton biomass remained 3-5 times lower in all treatments (close to 100 µgDW L⁻¹) compared to similar mesocosm experiments in temperate systems (Bertolo et al., 1999b; Danger et al., 2012). Furthermore, large zooplankton organisms, such as Daphnidae and Calanoida, were absent in our mesocosms, which were dominated by small herbivorous and detritivorous species (Chydoridae and small rotifers such as Lecane) and omnivorous Cyclopidae as in Garnier et al. (2023). Clearly, the very low zooplankton biomass, the absence of efficient grazers, and the very high level of chlorophyll *a* in the enclosures strongly suggest that primary producers were not strongly top-down controlled by herbivores in our experiment. This has also been reported in some previous studies (Leibold, 1989; Pace et al., 1998; Faithfull et al., 2011; Garnier et al., 2023).

An alternative, and more probable, explanation for the observed positive effect of fish on phytoplankton is associated with nutrient excretion by fish. Considering the rapid and important growth of primary producers in the first weeks of the experiment (Fig. S1-3A in the supporting information), phytoplankton probably became rapidly nutrient-limited (Fig. S1-1 in the supporting information). This nutrient demand was likely partly satisfied by consumer-driven recycling, and in particular by fish excretion as zooplankton represented less than 2% of fish biomass in our experiment. Indeed, consumer-driven recycling has been proposed to play a key role in determining nutrient availability for phytoplankton (Vanni and Layne, 1997; Vanni et al., 1997; Vanni, 2002). This explanation is also supported by the higher abundance of mixotrophic phytoplankton in the absence of fish (Fig. 3D and Fig. 4B), which was probably able to overcome nutrient limitation by consuming heterotrophic prokaryotes or organic matter.

The significant interaction between the fish and light treatments is also in agreement with effects asso-
<table><caption>Table 4: Significance of treatments (p-values) and effect sizes (ES). Each sub-part of the table corresponds to a figure panel. Effect sizes are calculated with the Fish-/Light-/OM- treatment set as reference. We only provide the effect size of the Light-/OM- treatment for heterotrophic protists and Fish-/Light+ treatment for Chlorophyceae because they were driving the effect of interactions between treatments and corresponding simple effects were not significant. We only provide the significance of the treatments on the slope of the linear regression model between DOC concentration and time, time being considered as a quantitative variable here. Similarly, we provide the significance of the intercept of the regression between C:N and C:P ratios, and chlorophyll a concentration. The degree of freedom associated to the test of each effect was 1.</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F×L</th><th>F×OM</th><th>L×OM</th><th>F×L×OM</th></tr></thead><tbody><tr><td rowspan="3">Chlorophyll</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.581</td><td><strong>0.001</strong></td><td>0.128</td><td>0.972</td><td>0.775</td></tr><tr><td>χ<sup>2</sup></td><td>32.98</td><td>15.83</td><td>0.305</td><td>11.01</td><td>2.322</td><td>0.001</td><td>0.082</td></tr><tr><td>ES</td><td>0.52</td><td>0.4</td><td>-</td><td>-0.28</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Zooplankton</td><td>p-value</td><td>0.424</td><td>0.287</td><td>0.554</td><td>0.733</td><td>0.529</td><td>0.295</td><td>0.174</td></tr><tr><td>F</td><td>0.658</td><td>1.179</td><td>0.359</td><td>0.118</td><td>0.406</td><td>1.137</td><td>1.942</td></tr><tr><td>ES</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Fish growth</td><td>p-value</td><td>-</td><td><strong>&lt;.001</strong></td><td>0.85</td><td>-</td><td>-</td><td>0.239</td><td>-</td></tr><tr><td>F</td><td>-</td><td>14.6</td><td>0.036</td><td>-</td><td>-</td><td>1.408</td><td>-</td></tr><tr><td>ES</td><td>-</td><td>0.87</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Heterotrophic protists</td><td>p-value</td><td><strong>0.024</strong></td><td>0.328</td><td>0.557</td><td>0.84</td><td>0.213</td><td><strong>0.044</strong></td><td>0.354</td></tr><tr><td>χ<sup>2</sup></td><td>5.118</td><td>0.957</td><td>0.346</td><td>0.041</td><td>1.552</td><td>4.05</td><td>0.858</td></tr><tr><td>ES</td><td>-0.1</td><td>-</td><td>-</td><td>-</td><td>-</td><td>0.3</td><td>-</td></tr><tr><td rowspan="3">Heterotrophic procaryotes</td><td>p-value</td><td><strong>0.006</strong></td><td>0.064</td><td>0.949</td><td>0.322</td><td>0.876</td><td>0.415</td><td>0.959</td></tr><tr><td>χ<sup>2</sup></td><td>7.634</td><td>3.44</td><td>0.004</td><td>0.98</td><td>0.024</td><td>0.665</td><td>0.003</td></tr><tr><td>ES</td><td>0.36</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Sediments</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.303</td><td>0.323</td><td>0.125</td><td>0.975</td><td>0.589</td><td>0.884</td></tr><tr><td>F</td><td>16.89</td><td>1.101</td><td>1.012</td><td>2.496</td><td>0.001</td><td>0.299</td><td>0.022</td></tr><tr><td>ES</td><td>1.49</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">DOC</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td><strong>0.015</strong></td><td><strong>0.047</strong></td><td><strong>0.002</strong></td></tr><tr><td>χ<sup>2</sup></td><td>195.69</td><td>69.1</td><td>27.97</td><td>36.63</td><td>5.883</td><td>3.959</td><td>9.32</td></tr><tr><td>ES</td><td>0.55</td><td>0.32</td><td>0.18</td><td>-0.24</td><td>-0.15</td><td>0.01</td><td>0.11</td></tr><tr><td rowspan="3">Chlorophyceae</td><td>p-value</td><td>0.9</td><td>0.262</td><td>0.165</td><td><strong>0.023</strong></td><td>0.923</td><td>0.728</td><td>0.176</td></tr><tr><td>F</td><td>0.016</td><td>1.311</td><td>2.029</td><td>5.749</td><td>0.009</td><td>0.123</td><td>1.923</td></tr><tr><td>ES</td><td>-</td><td>-</td><td>-</td><td>0.78</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Zygnematophyceae</td><td>p-value</td><td><strong>0.022</strong></td><td><strong>0.022</strong></td><td>0.924</td><td><strong>0.012</strong></td><td>0.451</td><td>0.794</td><td>0.975</td></tr><tr><td>F</td><td>5.894</td><td>5.897</td><td>0.009</td><td>7.287</td><td>0.584</td><td>0.069</td><td>0.001</td></tr><tr><td>ES</td><td>1.11</td><td>1.43</td><td>-</td><td>-1.09</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Dinophyceae</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.059</td><td>0.756</td><td>0.456</td><td>0.192</td><td>0.888</td><td>0.327</td></tr><tr><td>F</td><td>19.71</td><td>3.881</td><td>0.099</td><td>0.572</td><td>1.79</td><td>0.02</td><td>0.996</td></tr><tr><td>ES</td><td>-0.81</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Diversity zoo</td><td>p-value</td><td><strong>0.001</strong></td><td>0.454</td><td>0.771</td><td>0.927</td><td>0.683</td><td>0.915</td><td>0.832</td></tr><tr><td>F</td><td>4.938</td><td>0.792</td><td>0.541</td><td>0.386</td><td>0.629</td><td>0.412</td><td>0.518</td></tr><tr><td>ES</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Chloro:seston</td><td>p-value</td><td>0.826</td><td>0.296</td><td>0.304</td><td>0.912</td><td>0.841</td><td>0.73</td><td>0.813</td></tr><tr><td>F</td><td>0.049</td><td>1.136</td><td>1.1</td><td>0.012</td><td>0.041</td><td>0.122</td><td>0.057</td></tr><tr><td>ES</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">Mixotrophs</td><td>p-value</td><td><strong>0.025</strong></td><td>0.122</td><td>0.552</td><td>0.282</td><td>0.814</td><td>0.798</td><td>0.901</td></tr><tr><td>F</td><td>5.61</td><td>2.539</td><td>0.362</td><td>1.204</td><td>0.056</td><td>0.067</td><td>0.016</td></tr><tr><td>ES</td><td>-0.22</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">C:N-chloro</td><td>p-value</td><td>0.713</td><td><strong>0.041</strong></td><td>0.78</td><td>0.81</td><td>0.99</td><td>0.502</td><td>0.692</td></tr><tr><td>F</td><td>0.139</td><td>4.747</td><td>0.08</td><td>0.059</td><td>0</td><td>0.467</td><td>0.162</td></tr><tr><td>ES</td><td>-</td><td>-0.09</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr><tr><td rowspan="3">C:P-chloro</td><td>p-value</td><td><strong>0.029</strong></td><td>0.555</td><td>0.934</td><td>0.906</td><td>0.924</td><td>0.437</td><td>0.346</td></tr><tr><td>F</td><td>5.522</td><td>0.361</td><td>0.007</td><td>0.014</td><td>0.009</td><td>0.628</td><td>0.931</td></tr><tr><td>ES</td><td>1.33</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td><td>-</td></tr></tbody></table>
ciated with nutrient excretion by fish (Fig. 5). Phytoplankton is known to have highly plastic C:nutrient ratios depending on nutrient availability and daylight exposure (Sterner et al., 1997, 2002; Dickman et al., 2006; Hall et al., 2007; Mette et al., 2011). Indeed, when light exposure is reduced, phytoplankton cells compensate the lack of energy by an increased demand of nitrogen or phosphorus to produce more proteins, RNA (Rhee and Gotham, 1981; Healey, 1985) or chlorophyll *a* (Felip, 2000), which are involved in photosynthetic structures and could explain the positive effect of nutrient excretion by fish on chlorophyll *a* in our experiment. However, our results did not support this compensation hypothesis since the chlorophyll:seston ratio did not respond to our treatments (Fig. 4A) and the C:N ratio of the seston was even higher when light was depleted (Fig. 4C). In addition, the C:P ratio of the seston was significantly higher in enclosures with fish (Fig. 4D), which contradicts the hypothesis of a higher nutrient content in phytoplankton and is probably due to the immobilisation of P in fish biomass (Rock et al., 2016). Instead, we observed a similar increase in the C:N and C:P ratios when phytoplankton biomass increased, while a limitation by N or P would have led to a constant C:N or C:P as in (Danger et al., 2007b). This change in phytoplankton stoichiometry can neither be explained by a shift in the species composition since one species of Zygnematophyceae, *Cosmarium laeve*, drove the chlorophyll *a* response to treatments. Therefore, we conclude that fish had a positive bottom-up effect on phytoplankton, which was probably mediated by the limitation by nutrients other than N and P.

## Top-down effects of fish on brown food web: importance of the quality of settling organic matter?

Fish had a positive bottom-up effect on the brown food web represented by an increase in the density of heterotrophic protists and microbial eukaryotes (Fig. 2D and E), through the higher accumulation of DOC in the pelagic compartment (Fig. 2G) and sediments in the benthic compartment (Fig. 2F). This observation is related to previous experiments in which the presence of Leuciscidae increased the production of labile organic matter, in particular exopolysaccharides such as transparent exopolymer particles (TEP) that aggregated with particulate matter and were transported downward (Danger et al., 2012; Harrault et al., 2012). However, this increased excretion would be due to fish directly because of the absence of difference in phytoplankton abundance when light was not depleted. This was paralleled with a significant fish effect on the catabolic activity of the free microbial communities collected in the sediment traps (Fig. 3F). The utilisation patterns of the 31 substrates of the EcoPlates™ Biolog suggest that the relative oxidation of amines, amino acids, carbohydrates, phenolic acids and carboxylic acids was lower in the sediment of fish enclosures. The relative metabolic potential of the microbial community on a particular category of substrate has been interpreted as dependent upon its availability (Leflaive et al., 2008) or upon its relative concentration within the bulk of dissolved organic matter Pommier et al. (2014). Our results on the benthic microbial communities are in total agreement with those of Pommier et al. (2014), who observed that the addition of highly bioavailable organic matter to aquatic microbial communities favoured more specialised communities and reduced their metabolic potential. Interestingly, this specialisation trend was not observed within pelagic microbial communities. This might be due to the fact that a large part of fresh organic matter settled, probably associated to TEP, at the bottom of enclosures as shown by the positive effect of fish on sediment accumulation. All things being equal, pelagic microbial communities should sustain on less abundant and more diverse resources, and thus remain more generalist than benthic microbial communities.

## Effects of light and organic matter addition: weak cascades between the green and the brown food web?

Light had clear bottom-up effects on the green food web, in particular on total chlorophyll *a* and green algae, with clear bottom-up repercussions on fish growth rate in accordance with Rock et al. (2016) who had a similar experimental set-up with light reduction performed by shade mesh (Table 3). Our results also showed that an increase in light exposure resulted in an increase in DOC concentration, which is produced during active photosynthesis by phytoplankton. First, this increase in DOC concentration might be simply an effect of phytoplankton biomass (more primary producers implies more exudation). Indeed, the significant interaction between light filtering and fish presence on DOC concentration could be related to weaker light effects on phytoplankton concentration in enclosures with fish. Although primary production is considered as an important autochthonous source of DOC for heterotrophic microbial communities in aquatic ecosystems (e.g. Baines and Pace, 1991 and Morana et al., 2014), light filtering did not significantly affect the abundance of heterotrophic microbial communities. It only marginally affected microbial metabolic activities despite its negative effect on DOC concentration. Similarly, the addition of organic matter appeared to have only limited effects on heterotrophic prokaryote community functioning. The relative metabolic potential of the microbial community was only marginally affected
community. The light and the organic carbon addition treatments significantly affected a few substrate families but no general trend could be identified (detailed results per substrate family are given in Fig. S1-13 and S1-14 in the supporting information).

## Physiological response of phytoplankton

Consistently with the results presented above, fish and light exposure were the only treatments altering phytoplankton physiology. The chlorophyll *a* concentration per seston mass unit did not respond significantly to any treatment (Fig. 4A, Table 4), which indicates that the production of chlorophyll *a* by phytoplankton did not respond to our treatments. Mixotrophic phytoplankton abundance, which was determined by identification at the species level, was negatively affected by fish (Fig. 4B) and had a response similar to Dinophyceae (Fig. 3D). Dinophyceae species present in our mesocosm are known to be mixotrophic phytoplankton contrary to Zygnematophyceae (accord-

ing to the current knowledge) and, given their high abundance, were clearly the main mixotrophic taxa. The C:N ratio of seston was higher in mesocosms receiving less daylight (Fig. 4C), while the C:P ratio was significantly higher in enclosures with fish than in enclosures without fish (Fig. 4D).

## Discussion

The results of our experiment can be summarised in a few major points (Fig. 5). First, we observed consistent top-down effects of fish presence on components of the green food web (phytoplankton abundance and composition), the brown food web (planktonic heterotrophic prokaryotes and protists abundance and catabolic activity of their ben-thic counterparts), zooplankton composition and on water physico-chemistry variables (DOC). This top-down effect of fish also interacted with the bottom-up effect of light to change phytoplankton abundance and composition, suggesting a reduction of nutrient limitation of phytoplankton by fish. Second, contrary to our expectations, our results suggest only weak cascading effects between the green

<figure><img src="image_7.png" /></figure>

Figure 5: Scheme summarising the main direct and indirect effects of the three treatments on the different compartments of the ecosystem present in our mesocosms. The light treatment represents the effect of full daylight exposure (Light+ in the previous figures). Yellow (resp. red or black) arrows pointing on a compartment indicate that the light (resp. fish or OM) treatment has a clear significant effect on the abundance or composition of this compartment (sometime depending on an interaction with another treatment). Arrows also rather delineate the different direct and indirect pathways in the food web, by which these effects might originate, than the visually summarising effect sizes. The bottom-left box represents the fish×light interaction in which the positive bottom-up effect of fish compensates for the negative effect of light depletion on phytoplankton, whose abundance is represented by the size of the silhouettes.
by DOC addition, in contrast with the results of Pommier et al. (2014).

Concerning the cascading effects from brown to green food webs, DOC addition neither affected the green algae concentration nor the algal taxa composition (Fig. 3A-D). This absence of effect is in contradiction with previous experiments showing reduced phytoplankton biomass in response to addition of labile DOC because of the immobilisation of nutrients by heterotrophic prokaryotes (Joint et al., 2002; Degerman et al., 2018). Faithfull et al. (2011) did not find such effects because the phytoplankton community was dominated by mixotrophic taxa in their experiment, which was not the case in our experiment. Rather, the contrast between our results and those of Joint et al. (2002) and Degerman et al. (2018) might be partly explained by the strong differences in experiment duration and levels of organic carbon added in the two studies. We added 5.4 mg$_{C}$ L$^{-1}$ over a period of 15 weeks while Joint et al. (2002) added between 4.5 and 9 mg$_{C}$ L$^{-1}$ over a period of 6 days only (Table 3). Although our organic carbon addition was lower than in previous experiments, it was representative of carbon transfers from terrestrial ecosystems to lakes reported by Gounand et al. (2018) (between 1.2 and 465 (median 6.4) g C m$^{-2}$ year$^{-1}$, 46.8 g C m$^{-2}$ year$^{-1}$ in our experiment). In addition, our organic matter addition relied on a mixture of three molecules but the low degradation capacity of two of them (Fig. S1-14A and B in the supporting information) might have made this treatment less efficient than pure glucose addition as done in previous studies, although it is more realistic than a massive input of glucose. However, this must be considered carefully because phytoplankton is osmo-mixotrophic for glucose (Gérard Lacroix's personal observations) and glucose carbon enrichment may not target heterotrophic prokaryotes and the brown food web. Joint et al. (2002) and Degerman et al. (2018) also found an increase in prokaryote production following DOC addition.

<table><caption>Table 3: Summary of experimental treatments in previous studies testing the effects of light reduction, fish presence, organic carbon addition and nutrient addition in aquatic mesocosms.</caption><thead><tr><th>Study</th><th>Light</th><th>Fish</th><th>OM (mg<sub>C</sub> L<sup>-1</sup>)</th><th>Nutrient</th><th>Days</th></tr></thead><tbody><tr><td>Our study</td><td>10-50%</td><td>rudds</td><td>5.4 (mixture)</td><td>-</td><td>105</td></tr><tr><td>Joint et al., 2002</td><td>-</td><td>-</td><td>4.5-9</td><td>NP</td><td>6</td></tr><tr><td>Faithfull et al., 2011</td><td>-</td><td>perch</td><td>4.2-21 (glucose)</td><td>-</td><td>45</td></tr><tr><td>Mette et al., 2011</td><td>4-40%</td><td>gizzard shad</td><td>-</td><td>NP</td><td>56</td></tr><tr><td>Hitchcock et al., 2016</td><td>-</td><td>-</td><td>1.5-16 (leachate)</td><td>-</td><td>22</td></tr><tr><td>Rock et al., 2016</td><td>35-90%</td><td>bluegill sunfish</td><td>-</td><td>NP</td><td>42</td></tr><tr><td>Degerman et al., 2018</td><td>-</td><td>stickle-back</td><td>120.5 (glucose)</td><td>NP</td><td>63</td></tr><tr><td>Garnier et al., 2023</td><td>~50%</td><td>young perch</td><td>-</td><td>NP</td><td>21</td></tr></tbody></table>

## Conclusion and perspectives

Our results showed some bottom-up effects of light and organic matter addition on green and brown food webs, but the cascading effects from bottom-up effects in one web to the other seemed to be weak. Fish presence had a strong effect on both green and brown food webs: it changed phytoplankton abundance in the green web, increased the abundance of heterotrophic prokaryotes, altered their metabolic activity and increased the abundance of their protist predators in the brown web, and changed zoo-plankton community composition. Our results suggest that fish effects were mostly mediated by their impact on detritus decomposition and nutrient recycling in this experiment, which would reduce the sequestration of nutrients in poorly degradable sediments and increase their availability for phytoplankton. This contrasts with other studies in which phytoplankton was strongly controlled by large Cladocerans in absence of fish. This led to our most important observation, which concerns the interaction between fish and light treatments where light depletion only reduced phytoplankton abundance in the absence of fish. To our knowledge, such an interaction has not been reported in previous experiments (Mette et al., 2011; Rock et al., 2016; Garnier et al., 2023). Thus, our results highlight the importance of the interactions between bottom-up and top-down effects for ecosystem functioning mediated by the physiological response of organisms. We advocate for the development of mathematical models in line with those of Zou et al. (2016) and Quévreux et al. (2021) including the functional traits of organisms to establish detailed theoretical predictions of the functioning of aquatic ecosystem (Dutkiewicz et al., 2021; Wickman et al., 2024). In addition, future studies should pay attention to two major features of aquatic ecosystems highlighted by our results: First, the bottom-up effect of fish should be better analysed by identifying in detail their effect on nutrient cycling and phytoplankton stoichiometry. Second, the composition of the
and the brown food webs, since light exposure did not clearly affect the measured components of the brown food web (abundance and catabolic activity of microbial communities) and the addition of organic carbon had no clear effect on the main components of the green food web (no effect on fish growth, zooplankton and phytoplankton).

## Top-down effects of fish on the green food web: importance of direct nutrient cycling through fish excretion?

According to our expectations, fish presence and light exposure induced a positive effect on phytoplankton abundance (Table 4). However, the positive effect of fish was only visible under the low-light treatment (Light-). This fish×light effect was reflected by estimates of total chlorophyll *a* (BBE probe in Fig. 2A and multi-parameter probe in Fig. S1-3B and C in the supporting information), seston biomass (Fig. S1-4A in the supporting information) and turbidity (Fig. S1-2D in the supporting information). A positive effect of fish on phytoplankton has been very frequently observed and has been interpreted as a classical cascading effect of top-predators on primary producers, mediated by a reduction of grazing pressure by zooplankton (see the meta-analysis of Hulot et al., 2014). This reduction of phytoplankton grazing has not always been associated to a decrease in zooplankton biomass, Cyprinidae or Leuscicidae fish tending to shift zooplankton composition by suppressing large Cladocerans, which are the most efficient grazers (Bertolo et al., 1999a; Danger et al., 2008). In our study and contrary to our expectation (Table 4), zooplankton biomass was not affected by fish presence (Fig. 2B), nor was the relative abundance of large zooplanktonic organisms. Instead, zooplankton biomass remained three to five times lower in all treatments (close to $100 \mu g_{DW} L^{-1}$) compared to similar mesocosm experiments in temperate systems (Bertolo et al., 1999b; Danger et al., 2012). Furthermore, large zooplankton organisms, such as Daphnidae and Calanoida, were absent in our mesocosms, which were dominated by small herbivorous and detritivorous species (Chydoridae and small rotifers such as Lecane) and omnivorous Cyclopidae as in Garnier et al. (2023). Clearly, the very low zooplankton biomass, the absence of efficient grazers, and the very high level of chlorophyll *a* in the enclosures strongly suggest that primary producers were not strongly top-down controlled by herbivores in our experiment. This has also been reported in some previous studies (Leibold, 1989; Pace et al., 1998; Faithfull et al., 2011; Garnier et al., 2023).

Alternatively, we propose that the observed positive effect of fish on phytoplankton is associated with nutrient excretion by fish. Considering the

rapid and important growth of primary producers in the first weeks of the experiment (Fig. S1-3A in the supporting information), phytoplankton probably became rapidly nutrient-limited (Fig. S1-1 in the supporting information). This nutrient demand was likely partly satisfied by consumer-driven recycling, and in particular by fish excretion as zooplankton represented less than 2% of fish biomass in our experiment. Indeed, consumer-driven recycling has been proposed to play a key role in determining nutrient availability for phytoplankton (Vanni et al., 1997; Vanni, 2002). In particular, mesocosm experiments have been able to disentangle the top-down and bottom-up effects of fish to demonstrate the positive effect of fish-mediated nutrient cycling on phytoplankton abundance (Vanni and Layne, 1997; Attayde and Hansson, 2001). Although we did not use a similar setup with nutrient-permeable chambers to protect phytoplankton from grazer zooplankton, we can reasonably assume that fish mostly had a bottom-up effect due to nutrient excretion because of the lack of top-down control of phytoplankton by zooplankton in enclosure without fish and the absence of other non-consumptive effects such as water mixing (Fig. S1-3C in the supporting information). This explanation is also supported by the higher abundance of mixotrophic phytoplankton in the absence of fish (Fig. 3D and Fig. 4B), which was probably able to overcome nutrient limitation by consuming heterotrophic prokaryotes or organic matter. Nevertheless, other mechanisms, as yet unidentified given the scope of the experiment, may be considered and explored in future experiments.

The significant interaction between the fish and light treatments is also in agreement with effects associated with nutrient excretion by fish (Fig. 5). Phytoplankton is known to have highly plastic C:nutrient ratios depending on nutrient availability and daylight exposure (Sterner et al., 1997, 2002; Dickman et al., 2006; Hall et al., 2007; Mette et al., 2011). Indeed, when light exposure is reduced, phytoplankton cells compensate the lack of energy by an increased demand of nitrogen or phosphorus to produce more proteins, RNA (Rhee and Gotham, 1981; Healey, 1985) or chlorophyll *a* (Felip, 2000), which are involved in photosynthetic structures and could explain the positive effect of nutrient excretion by fish on chlorophyll *a* in our experiment. However, our results did not support this compensation hypothesis since the chlorophyll:seston ratio did not respond to our treatments (Fig. 4A) and the C:N ratio of the seston was even higher when light was depleted (Fig. 4C). In addition, the C:P ratio of the seston was significantly higher in enclosures with fish (Fig. 4D), which contradicts the hypothesis of a higher nutrient content in phytoplankton and is probably due to the immobilisation of P in fish biomass (Rock et al., 2016). Instead, we observed a similar increase in the C:N and C:P ra-
<figure><img src="image_6.png" /></figure>

Figure 5: Summary of the effects of the three treatments on the different compartments of the ecosystem present in our mesocosms. The light treatment represents the effect of full daylight exposure (Light+ in the previous figures). The bottom-left box represents the fish×light interaction in which the positive bottom-up effect of fish compensates for the negative effect of light depletion on phytoplankton, whose abundance is represented by the size of the silhouettes.

initial zooplankton community and the presence or absence of large Cladocerans, in particular, should
be documented since it seems to condition the top-down effect of fish.

Acknowledgement

We thank the PhD program "Ecole Doctorale Frontières du Vivant (FdV) – Programme Bettencourt" for its financial support. This experiment was funded by the EC2CO (CNRS) project "CASCADE". It benefited from technical and human resources provided by IEES-Paris (CNRS/IRD/INRAE UMR 7618) and CEREEP-Ecotron IleDeFrance (CNRS/ENS UAR 3194). The CEREEP-Ecotron IleDeFrance benefited from the support by CNRS and ENS, by the "Conseil régional d'Île-de-France" (DIM R2DS Program "I-05-098/R", "2011-11017735" & "2015-1657"), and by European Union (FEDER 2007-2013). The infrastructure PLANAQUA received support under the program "Investissements d'Avenir" launched by the French government and implemented by ANR with the references ANR-10-EQPX-13-01 PLANAQUA and ANR-11-INBS-0001 AnaEE France. We thank Alexis Millot, David Carmignac, Kévin Tambosco and Yoan Marcangeli for their technical support and all the team of the CEREEP Ecotron IleDeFrance. We also thank Victor Moinard, Melissa Fontenille, Florence Hulot, Rémy Langou and Michaël Danger for their help during sampling and sample analysis.

Author contribution

Conceptualisation: PQ, KZ, SB, ÉT, GL. Developing methods: PQ, KZ, SB, ÉT, ERN, LJ, ÉE, GL.
Conducting the research: PQ, KZ, ÉT, SA, JM, SF, GL. Data analysis: PQ, SB, ÉT, ÉE, GL. Data
interpretation: PQ, KZ, SB, ÉT, ERN, LJ, ÉE, GL. Preparation of figures and tables: PQ. Writing: PQ,
KZ, SB, ÉT, ERN, LJ, ÉE, SF, GL.
tios when phytoplankton biomass increased, while a limitation by N or P would have led to a constant C:N or C:P as in Danger et al. (2007b). This change in phytoplankton stoichiometry can neither be explained by a shift in the species composition since one species of Zygnematophyceae, *Cosmarium laeve*, drove the chlorophyll a response to treatments. Therefore, we conclude that fish had a positive bottom-up effect on phytoplankton, which was probably mediated by the limitation by nutrients other than N and P.

## Top-down effects of fish on the brown food web: importance of the quality of settling organic matter?

As expected (Table 2), fish had a positive bottom-up effect on the brown food web represented by an increase in the density of heterotrophic protists and prokaryotes (Fig. 2D and E), through the higher accumulation of DOC in the pelagic compartment (Fig. 2G) and sediments in the benthic compartment (Fig. 2F). This observation is in agreement with previous experiments in which the presence of Leuciscidae fish increased the production of labile organic matter, in particular opolysaccharides such as transparent exopolymer particles (TEP) that aggregated with particulate matter and were transported downward (Danger et al., 2012; Harrault et al., 2012). However, this increased excretion would be directly due to fish because of the absence of difference in phytoplankton abundance when light was not depleted. This was paralleled with a significant fish effect on the catabolic activity of the free microbial communities collected in the sediment traps (Fig. 3F). The utilisation patterns of the 31 substrates of the EcoPlates™ Biolog suggest that the relative oxidation of amines, amino acids, carbohydrates, phenolic acids and carboxylic acids was lower in the sediment of fish enclosures. The relative metabolic potential of the microbial community on a particular category of substrate has been interpreted as dependent upon its availability (Leflaive et al., 2008) or upon its relative concentration within the bulk of dissolved organic matter (Pommier et al., 2014). Our results on the benthic microbial communities are in total agreement with those of Pommier et al. (2014), who observed that the addition of highly bioavailable organic matter to aquatic microbial communities favoured more specialised communities and reduced their metabolic potential. Interestingly, this specialisation trend was not observed within pelagic microbial communities. This might be due to the fact that a large part of fresh organic matter settled, probably associated with TEP, at the bottom of enclosures as shown by the positive effect of fish on sediment accumulation. All things being equal, pelagic microbial communities should sustain on less abundant and more diverse

resources, and thus remain more generalist than benthic microbial communities.

## Effects of light and organic matter addition: weak cascades between the green and the brown food web?

As expected (Table 2), light exposure had clear positive bottom-up effects on the green food web, in particular on total chlorophyll a and green algae but only in absence of fish. Light exposure also had a clear positive bottom-up repercussions on fish growth rate in accordance with Rock et al. (2016) who had a similar experimental set-up with light reduction performed by shade mesh (Table 1). Our results also showed that an increase in light exposure resulted in an increase in DOC concentration, which is produced during active photosynthesis by phytoplankton. First, this increase in DOC concentration might be simply an effect of phytoplankton biomass (more primary producers implies more exudation). Indeed, the significant interaction between light exposure and fish presence on DOC concentration could be related to weaker light effects on phytoplankton concentration in enclosures with fish. Although primary production is considered as an important autochthonous source of DOC for heterotrophic microbial communities in aquatic ecosystems (e.g. Baines and Pace, 1991 and Morana et al., 2014), light exposure did not significantly affect the abundance of heterotrophic microbial communities contrary to our expectations (Table 2). It only marginally affected microbial metabolic activities despite its negative effect on DOC concentration. Similarly, the addition of organic matter appeared to have only limited effects on heterotrophic prokaryote community functioning. The relative metabolic potential of the microbial community was only marginally affected by DOC addition, in contrast with the results of Pommier et al. (2014).

Concerning the cascading effects from brown to green food webs, DOC addition neither affected green algae concentration and algal taxa composition (Fig. 3A-D), nor the abundance of mixotrophic taxa contrary to our expectations (Table 2). However, OM addition did not affect strongly some components of the brown food web such as heterotrophic procaryotes and protists abundance contrary to Joint et al. (2002) and Degerman et al. (2018) (Fig. 2D and E) or functional diversity (Fig. 3F). This absence of effect is in contradiction with previous experiments showing reduced phytoplankton biomass in response to addition of labile DOC because of the immobilisation of nutrients by heterotrophic prokaryotes (Joint et al., 2002; Degerman et al., 2018). Faithfull et al. (2011) did not find such effects because the phytoplankton community was dominated by mixotrophic taxa in
## Data availability

The data and *R* code used to perform the statistical analyses and plot figures are available on GitHub at the following link: https://github.com/PierreQuevreux/stat_mesocosm_green_brown_CEREEP.

## Ethical statement

All the procedures were conducted in accordance with appropriate European (Directive, 2010/63/EU) and French national guidelines, permits, and regulations regarding animal care and experimental use (Agreement n° B77-431-1).

## Conflict of interest

We have no conflict of interest to disclose.

## References

Baines, S. B., & Pace, M. L. (1991). The production of dissolved organic matter by phytoplankton and its importance to bacteria: Patterns across marine and freshwater systems. *Limnology and Oceanography*, *36*(6), 1078-1090. https://doi.org/10.4319/lo.1991.36.6.1078

Bertolo, A., Lacroix, G., Lescher-Moutous, F., & Sala, S. (1999a). Effects of physical refuges on fish-plankton interactions. *Freshwater Biology*, *41*(4), 795-808. https://doi.org/10.1046/j.1365-2427.1999.00424.x

Bertolo, A., Lacroix, G., & Lescher-Moutous, F. (1999b). Scaling food chains in aquatic mesocosms: Do the effects of depth override the effects of planktivory? *Oecologia*, *121*(1), 55-65. https://doi.org/10.1007/s004420050907

Carpenter, S. R., Kitchell, J. F., & Hodgson, J. R. (1985). Cascading trophic interactions and lake productivity. *BioScience*, *35*(10), 634-639. https://doi.org/10.2307/1309989

Danger, M., Allard, B., Arnous, M. B., Carrias, J.-F., Mériguet, J., Ten-Hage, L., & Lacroix, G. (2012). Effects of food-web structure on the quantity and the elemental quality of sedimenting material in shallow lakes. *Hydrobiologia*, *679*(1), 251-266. https://doi.org/10.1007/s10750-011-0890-2

Danger, M., Lacroix, G., Oumarou, C., Benest, D., & MéRiguet, J. (2008). Effects of food-web structure on periphyton stoichiometry in eutrophic lakes: A mesocosm study. *Freshwater Biology*, *53*(10), 2089-2100. https://doi.org/10.1111/j.1365-2427.2008.02031.x

Danger, M., Leflaive, J., Oumarou, C., Ten-Hage, L., & Lacroix, G. (2007a). Control of phytoplankton-bacteria interactions by stoichiometric constraints. *Oikos*, *116*(7), 1079-1086. https://doi.org/10.1111/j.0030-1299.2007.15424.x

Danger, M., Oumarou, C., Benest, D., & Lacroix, G. (2007b). Bacteria can control stoichiometry and nutrient limitation of phytoplankton. *Functional Ecology*, *21*(2), 202-210. https://doi.org/10.1111/j.1365-2435.2006.01222.x

Daufresne, T., Lacroix, G., Benhaim, D., & Loreau, M. (2008). Coexistence of algae and bacteria: A test of the carbon hypothesis. *Aquatic Microbial Ecology*, *53*, 323-332. https://doi.org/10.3354/ame01254

Daufresne, T., & Loreau, M. (2001). Ecological stoichiometry, primary producer-decomposer interactions, and ecosystem persistence. *Ecology*, *82*(11), 3069-3082. https://doi.org/10.1890/0012-9658(2001)082[3069:ESPPDI]2.0.CO;2

Degerman, R., Lefébure, R., Byström, P., Båmstedt, U., Larsson, S., & Andersson, A. (2018). Food web interactions determine energy transfer efficiency and top consumer responses to inputs of dissolved organic carbon. *Hydrobiologia*, *805*(1), 131-146. https://doi.org/10.1007/s10750-017-3298-9

Dickman, E. M., Vanni, M. J., & Horgan, M. J. (2006). Interactive effects of light and nutrients on phytoplankton stoichiometry. *Oecologia*, *149*(4), 676-689. https://doi.org/10.1007/s00442-006-0473-5

Dutkiewicz, S., Boyd, P. W., & Riebesell, U. (2021). Exploring biogeochemical and ecological redundancy in phytoplankton communities in the global ocean. *Global Change Biology*, *27*(6), 1196-1213. https://doi.org/10.1111/gcb.15493

Faithfull, C. L., Huss, M., Vrede, T., & Bergström, A.-K. (2011). Bottom-up carbon subsidies and top-down predation pressure interact to affect aquatic food web structure. *Oikos*, *120*(2), 311-320. https://doi.org/10.1111/j.1600-0706.2010.18683.x
their experiment, which was not the case in our ex-
periment. Contrary to our expectations (Table 2)
and Balzer et al. (2023), OM addition did not trig-
ger an increase in the abundance of mixotrophic
taxa. Rather, the contrast between our results and
those of Joint et al. (2002) and Degerman et al.
(2018) might be partly explained by the strong dif-
ferences in experiment duration and levels of or-
ganic carbon added in the two studies. We added
5.4 mg C L⁻¹ over a period of 15 weeks while Joint
et al. (2002) added between 4.5 and 9 mg C L⁻¹ over
a period of 6 days only (Table 1). Although our or-
ganic carbon addition was lower than in previous
experiments, it was representative of carbon trans-
fers from terrestrial ecosystems to lakes reported
by Gounand et al. (2018): between 1.2 and 465
(median 6.4) g C m⁻² year⁻¹, 46.8 g C m⁻² year⁻¹
in our experiment. In addition, our organic mat-
ter addition relied on a mixture of three molecules
but the low degradation capacity of two of them
(Fig. S1-14A and B in the supporting information)
might have made this treatment less efficient than
pure glucose addition as done in previous studies,
although it is more realistic than a massive input of
glucose. However, this must be considered carefully
because phytoplankton is osmo-mixotrophic for glu-
cose (Gérard Lacroix's personal observations) and
carbon enrichment based on glucose may not target
heterotrophic prokaryotes and the brown food web
in general.

Conclusion and perspectives

Our results showed some bottom-up effects of
light and organic matter addition on green and
brown food webs, but the cascading effects from
bottom-up effects in one web to the other seemed
to be weak. Fish presence had a strong effect on
both green and brown food webs: it changed phy-
toplankton abundance in the green web, increased
the abundance of heterotrophic prokaryotes, altered
their metabolic activity and increased the abun-
dance of their protist predators in the brown web,
and changed zooplankton community composition.
Our results suggest that fish effects were mostly
mediated by their impact on detritus decompo-
sition and nutrient recycling in this experiment,
which would reduce the sequestration of nutrients
in poorly degradable sediments and increase their
availability for phytoplankton. This contrasts with
other studies in which phytoplankton was strongly
controlled by large Cladocerans in the absence of
fish. This led to our most important observation,
which concerns the interaction between fish and
light treatments where light depletion only reduced
phytoplankton abundance in the absence of fish. To
our knowledge, such an interaction has not been re-
ported in previous experiments (Mette et al., 2011;
Rock et al., 2016; Garnier et al., 2023). Thus,
our results highlight the importance of the inter-

actions between bottom-up and top-down effects
for ecosystem functioning mediated by the physi-
ological response of organisms. We advocate for
the development of mathematical models in line
with those of Zou et al. (2016) and Quévreux et
al. (2021) including the functional traits of organ-
isms to establish detailed theoretical predictions of
the functioning of aquatic ecosystems (Dutkiewicz
et al., 2021; Wickman et al., 2024). In addition,
future studies should pay attention to two major
features of aquatic ecosystems highlighted by our
results. First, the bottom-up effect of fish should
be better analysed by identifying in detail their
effect on nutrient cycling and phytoplankton stoi-
chiometry, for example, by incubating phytoplank-
ton in nutrient-permeable chambers to disentan-
gle this bottom-up effect from top-down control of
zooplankton (Vanni and Layne, 1997; Attayde and
Hansson, 2001). Second, the composition of the
initial zooplankton community and the presence or
absence of large Cladocerans, in particular, should
be documented since it seems to condition the top-
down effect of fish.

Acknowledgements

We thank the PhD program "Ecole Doctorale
Frontières du Vivant (FdV) – Programme Bet-
tencourt" for its financial support. This work
was supported by the Initiative Structurante
Ecosphère Continentale et Côtière (EC2CO,
CNRS) project "CASCADE". It benefited from
technical and human resources provided by
iEES-Paris (CNRS/IRD/INRAE/Sorbonne Uni-
versité/Université Paris-Est Créteil/Université
Paris Cité UMR 7618) and CEREEP-Ecotron
IleDeFrance (CNRS/ENS UAR 3194). The
CEREEP-Ecotron IleDeFrance benefited from
the support by CNRS and ENS, by the "Conseil
régional d'Ile-de-France" (DIM R2DS Program
"I-05-098/R", "2011-11017735" & "2015-1657"), and
by European Union (FEDER 2007-2013). The
infrastructure PLANAQUA received support under
the program "Investissements d'Avenir" launched
by the French government and implemented by
ANR with the references ANR-10-EQPX-13-01
PLANAQUA and ANR-11-INBS-0001 AnaEE
France. We thank Alexis Millot, David Carmignac,
Kévin Tambosco and Yoan Marcangeli for their
technical support and all the team of the CEREEP
Ecotron IleDeFrance. We also thank Victor
Moinard, Melissa Fontenille, Florence Hulot, Rémy
Langou and Michaël Danger for their help during
sampling and sample analysis.

Author contributions

Conceptualisation: PQ, KZ, SB, ÉT, GL. Devel-
oping methods: PQ, KZ, SB, ÉT, ERN, LJ, ÉE,
GL. Conducting the research: PQ, KZ, ÉT, SA,
Felip, M. (2000). The relationship between phytoplankton biovolume and chlorophyll in a deep oligotrophic lake: Decoupling in their spatial and temporal maxima. *Journal of Plankton Research*, **22**(1), 91–106. https://doi.org/10.1093/plankt/22.1.91

Garnier, A., Östman, Ö., Ask, J., Bell, O., Berggren, M., Rulli, M. P. D., Younes, H., & Huss, M. (2023). Coastal darkening exacerbates eutrophication symptoms through bottom-up and top-down control modification. *Limnology and Oceanography, 68*(3), 678–691. https://doi.org/10.1002/lno.12302

Gauzens, B., Legendre, S., Lazzaro, X., & Lacroix, G. (2016). Intermediate predation pressure leads to maximal complexity in food webs. *Oikos*, **125**(4), 595–603. https://doi.org/10.1111/oik.02627

Gounand, I., Little, C. J., Harvey, E., & Altermatt, F. (2018). Cross-ecosystem carbon flows connecting ecosystems worldwide. *Nature Communications*, **9**(1), 4825. https://doi.org/10.1038/s41467-018-07238-2

Hall, S. R., Leibold, M. A., Lytle, D. A., & Smith, V. H. (2007). Grazers, producer stoichiometry, and the light: Nutrient hypothesis revisited. *Ecology*, **88**(5), 1142–1152. https://doi.org/10.1890/06-0923

Harrault, L., Allard, B., Danger, M., Maunoury-Danger, F., Guilpart, A., & Lacroix, G. (2012). Influence of food-web structure on the biodegradability of lake sediment. *Freshwater Biology*, **57**(11), 2390–2400. https://doi.org/10.1111/fwb.12020

Harrault, L., Allard, B., Mériguet, J., Carmignac, D., Huon, S., Gauzens, B., & Lacroix, G. (2014). Bottom-up effects of lake sediment on pelagic food-web compartments: A mesocosm study. *Freshwater Biology*, **59**(8), 1695–1709. https://doi.org/10.1111/fwb.12375

Healey, F. P. (1985). Interacting effects of light and nutrient limitation on the growth rate of Synechococcus linearis (cyanophyceae). *Journal of Phycology*, **21**(1), 134–146. https://doi.org/10.1111/j.0022-3646.1985.00134.x

Heath, M. R., Speirs, D. C., & Steele, J. H. (2014). Understanding patterns and processes in models of trophic cascades (K. Lafferty, Ed.). *Ecology Letters*, **17**(1), 101–114. https://doi.org/10.1111/ele.12200

Hitchcock, J. N., Mitrovic, S. M., Hadwen, W. L., Roelke, D. L., Growns, I. O., & Rohlfs, A.-M. (2016). Terrestrial dissolved organic carbon subsidizes estuarine zooplankton: An in situ mesocosm study: DOC additions subsidize zooplankton. *Limnology and Oceanography*, **61**(1), 254–267. https://doi.org/10.1002/lno.10207

Hulot, F. D., Lacroix, G., & Loreau, M. (2014). Differential responses of size-based functional groups to bottom-up and top-down perturbations in pelagic food webs: A meta-analysis. *Oikos*, **123**(11), 1291–1300. https://doi.org/10.1111/oik.01116

Hunter, M., & Price, P. (1992). Playing chutes and ladders heterogeneity and the relative roles of bottom-up and top-down forces in natural communities. *Ecology*, **73**(3). https://doi.org/10.2307/1940152

Jardillier, L., Basset, M., Domaizon, I., Belan, A., Amblard, C., Richardot, M., & Debroas, D. (2004). Bottom-up and top-down control of bacterial community composition in the euphotic zone of a reservoir. *Aquatic Microbial Ecology*, **35**, 259–273. https://doi.org/10.3354/ame035259

Joint, I., Henriksen, P., Fonnes, G., Bourne, D., Thingstad, T., & Riemann, B. (2002). Competition for inorganic nutrients between phytoplankton and bacterioplankton in nutrient manipulated mesocosms. *Aquatic Microbial Ecology*, **29**, 145–159. https://doi.org/10.3354/ame029145

Jones, J. I., & Sayer, C. D. (2003). Does the fish-invertebrateperiphyton cascade precipitate plant loss in shallow lakes? *Ecology*, **84**(8), 2155–2167. https://doi.org/10.1890/02-0422

Kagata, H., & Ohgushi, T. (2006). Bottom-up trophic cascades and material transfer in terrestrial food webs. *Ecological Research*, **21**(1), 26–34. https://doi.org/10.1007/s11284-005-0124-z

Lazzaro, X., Lacroix, G., Gauzens, B., Gignoux, J., & Legendre, S. (2009). Predator foraging behaviour drives food-web topological structure. *Journal of Animal Ecology*, **78**(6), 1307–1317. https://doi.org/10.1111/j.1365-2656.2009.01588.x

Leflaive, J., Danger, M., Lacroix, G., Lyautey, E., Oumarou, C., & Ten-Hage, L. (2008). Nutrient effects on the genetic and functional diversity of aquatic bacterial communities. *FEMS Microbiology Ecology*, **66**(2), 379–390. https://doi.org/10.1111/j.1574-6941.2008.00593.x

Leibold, M. A. (1989). Resource edibility and the effects of predators and productivity on the outcome of trophic interactions. *The American Naturalist*, **134**(6), 922–949. https://doi.org/10.1086/285022

Mette, E. M., Vanni, M. J., Newell, J. M., & Gonzàlez, M. J. (2011). Phytoplankton communities and stoichiometry are interactively affected by light, nutrients, and fish. *Limnology and Oceanography*, **56**(6), 1959–1975. https://doi.org/10.4319/lo.2011.56.6.1959

Moore, J. C., Berlow, E. L., Coleman, D. C., Ruiter, P. C., Dong, Q., Hastings, A., Johnson, N. C., McCann, K. S., Melville, K., Morin, P. J., Nadelhoffer, K., Rosemond, A. D., Post, D. M., Sabo, J. L., Scow, K. M., Vanni, M. J., & Wall, D. H. (2004). Detritus, trophic dynamics and biodiversity. *Ecology Letters*, **7**(7), 584–600. https://doi.org/10.1111/j.1461-0248.2004.00606.x
JM, SF, GL. Data analysis: PQ, SB, ÉT, ÉE, GL. Data interpretation: PQ, KZ, SB, ÉT, ERN, LJ, ÉE, GL. Preparation of figures and tables: PQ. Writing: PQ, KZ, SB, ÉT, ERN, LJ, ÉE, SA, JM, SF, GL.

## Data availability

The data and *R* code used to perform the statistical analyses and plot figures are available on Zenodo (10.5281/zenodo.15788788).

## Ethical statement

All the procedures were conducted in accordance with appropriate European (Directive, 2010/63/EU) and French national guidelines, permits, and regulations regarding animal care and experimental use (Agreement n° B77-431-1).

## Conflict of interest

We have no conflict of interest to disclose.

## References

Attayde, J. L., & Hansson, L.-A. (2001). Fish-mediated nutrient recycling and the trophic cascade in lakes. *Canadian Journal of Fisheries and Aquatic Sciences*, *58*(10), 1924-1931. https://doi.org/10.1139/f01-128

Baines, S. B., & Pace, M. L. (1991). The production of dissolved organic matter by phytoplankton and its importance to bacteria: Patterns across marine and freshwater systems. *Limnology and Oceanography*, *36*(6), 1078-1090. https://doi.org/10.4319/lo.1991.36.6.1078

Balzer, M. J., Hitchcock, J. N., Hadwen, W. L., Kobayashi, T., Westhorpe, D. P., Boys, C., & Mitrovic, S. M. (2023). Experimental additions of allochthonous dissolved organic matter reveal multiple trophic pathways to stimulate planktonic food webs. *Freshwater Biology*, *68*(5), 821-836. https://doi.org/10.1111/fwb.14066

Bates, D., Maechler, M., Bolker, B., & Walker, S. (2003). Lme4: Linear Mixed-Effects Models using 'Eigen' and S4 [Institution: Comprehensive R Archive Network Pages: 1.1-35.5]. https://doi.org/10.32614/CRAN/package.lme4

Ben-Shachar, M., Lüdecke, D., & Makowski, D. (2020). Effectsize: Estimation of effect size indices and standardized parameters. *Journal of Open Source Software*, *5*(56), 2815. https://doi.org/10.21105/joss.02815

Bertolo, A., Lacroix, G., Lescher-Moutous, F., & Sala, S. (1999a). Effects of physical refuges on fish-plankton interactions. *Freshwater Biology*, *41*(4), 795-808. https://doi.org/10.1046/j.1365-2427.1999.00424.x

Bertolo, A., Lacroix, G., & Lescher-Moutous, F. (1999b). Scaling food chains in aquatic mesocosms: Do the effects of depth override the effects of planktivory? *Oecologia*, *121*(1), 55-65. https://doi.org/10.1007/s004420050907

Brighenti, L. S., Staehr, P. A., M. Brandão, L. P., Barbosa, F. A. R., & Bezerra-Neto, J. F. (2018). Importance of nutrients, organic matter and light availability on epilimnetic metabolic rates in a mesotrophic tropical lake. *Freshwater Biology*, *63*(9), 1143-1160. https://doi.org/10.1111/fwb.13123

Carpenter, S. R., Kitchell, J. F., & Hodgson, J. R. (1985). Cascading trophic interactions and lake productivity. *BioScience*, *35*(10), 634-639. https://doi.org/10.2307/1309989

Crane, K. W., & Grover, J. P. (2010). Coexistence of mixotrophs, autotrophs, and heterotrophs in planktonic microbial communities. *Journal of Theoretical Biology*, *262*(3), 517-527. https://doi.org/10.1016/j.jtbi.2009.10.027

Danger, M., Allard, B., Arnous, M. B., Carrias, J.-F., Mériguet, J., Ten-Hage, L., & Lacroix, G. (2012). Effects of food-web structure on the quantity and the elemental quality of sedimenting material in shallow lakes. *Hydrobiologia*, *679*(1), 251-266. https://doi.org/10.1007/s10750-011-0890-2

Danger, M., Lacroix, G., Oumarou, C., Benest, D., & MéRiguet, J. (2008). Effects of food-web structure on periphyton stoichiometry in eutrophic lakes: A mesocosm study. *Freshwater Biology*, *53*(10), 2089-2100. https://doi.org/10.1111/j.1365-2427.2008.02031.x

Danger, M., Leflaive, J., Oumarou, C., Ten-Hage, L., & Lacroix, G. (2007a). Control of phytoplankton-bacteria interactions by stoichiometric constraints. *Oikos*, *116*(7), 1079-1086. https://doi.org/10.1111/j.0030-1299.2007.15424.x

Danger, M., Oumarou, C., Benest, D., & Lacroix, G. (2007b). Bacteria can control stoichiometry and nutrient limitation of phytoplankton. *Functional Ecology*, *21*(2), 202-210. https://doi.org/10.1111/j.1365-2435.2006.01222.x

Daufresne, T., Lacroix, G., Benhaim, D., & Loreau, M. (2008). Coexistence of algae and bacteria: A test of the carbon hypothesis. *Aquatic Microbial Ecology*, *53*, 323-332. https://doi.org/10.3354/ame01254

Daufresne, T., & Loreau, M. (2001). Ecological stoichiometry, primary producer-
Morana, C., Sarmento, H., Descy, J.-P., Gasol, J. M., Borges, A. V., Bouillon, S., & Darchambeau, F. (2014). Production of dissolved organic matter by phytoplankton and its uptake by heterotrophic prokaryotes in large tropical lakes. *Limnology and Oceanography, 59*(4), 1364-1375. https://doi.org/10.4319/lo.2014.59.4.1364

Mougi, A. (2020). Coupling of green and brown food webs and ecosystem stability. *Ecology and Evolution*, ece3.6586. https://doi.org/10.1002/ece3.6586

Mougin, C., Azam, D., Caquet, T., Cheviron, N., Dequiedt, S., Le Galliard, J.-F., Guillaume, O., Houot, S., Lacroix, G., Lafolie, F., Maron, P.-A., Michniewicz, R., Pichot, C., Ranjard, L., Roy, J., Zeller, B., Clobert, J., & Chanzy, A. (2015). A coordinated set of ecosystem research platforms open to international research in ecotoxicology, AnaEE-France. *Environmental Science and Pollution Research*, 22(20), 16215-16228. https://doi.org/10.1007/s11356-015-5233-9

Pace, M. L., Cole, J. J., & Carpenter, S. R. (1998). Trophic cascades and compensation: Differential responses of microzooplankton in whole-lake experiments. *Ecology*, 79(1), 138-152. https://doi.org/10.1890/0012-9658(1998)079[0138:TCACDR]2.0.CO;2

Pommier, T., Merroune, A., Bettarel, Y., Got, P., Janeau, J.-L., Jouquet, P., Thu, T. D., Toan, T. D., & Rochelle-Newall, E. (2014). Off-site impacts of agricultural composting: Role of terrestrially derived organic matter in structuring aquatic microbial communities and their metabolic potential. *FEMS Microbiology Ecology*, 90(3), 622-632. https://doi.org/10.1111/1574-6941.12421

Quévreux, P., Barot, S., & Thébault, É. (2021). Interplay between the paradox of enrichment and nutrient cycling in food webs. *Oikos*, 130(1), 95-109. https://doi.org/10.1111/oik.07937

R Core Team. (2023). R: A Language and Environment for Statistical Computing. https://www.R-project.org/

Rhee, G.--u., & Gotham, I. J. (1981). The effect of environmental factors on phytoplankton growth: Light and the interactions of light with nitrate limitation. *Limnology and Oceanography*, 26(4), 649-659. https://doi.org/10.4319/lo.1981.26.4.0649

Rock, A. M., Hall, M. R., Vanni, M. J., & González, M. J. (2016). Carnivore identity mediates the effects of light and nutrients on aquatic food-chain efficiency. *Freshwater Biology*, 61(9), 1492-1508. https://doi.org/10.1111/fwb.12790

Rooney, N., McCann, K. S., Gellner, G., & Moore, J. C. (2006). Structural asymmetry and the stability of diverse food webs. *Nature*, 442(7100), 265-269. https://doi.org/10.1038/nature04887

Rosemond, A. D., Pringle, C. M., Ramirez, A., & Paul, M. J. (2001). A test of top-down and bottom-up control in a detritus-based food web. *Ecology*, 82(8), 2279. https://doi.org/10.2307/2680231

Sterner, R. W., Elser, J. J., Fee, E. J., Guildford, S. J., & Chrzanowski, T. H. (1997). The light:nutrient ratio in lakes: The balance of energy and materials affects ecosystem structure and process. *The American Naturalist*, 150(6), 663-684. https://doi.org/10.1086/286088

Sterner, R. W., Elser, J. J., & Vitousek, P. M. (2002). *Ecological stoichiometry: The biology of elements from molecules to the biosphere* [OCLC: 248866756]. Princeton University Press.

Urbano, V. D. A., Delanira-Santos, D., & Benedito, E. (2022). The role of cladocerans in green and brown food web coupling. *Iheringia. Série Zoologia*, 112, e2022022. https://doi.org/10.1590/1678-4766e2022022

Vadeboncoeur, Y., McCann, K. S., Zanden, M. J. V., & Rasmussen, J. B. (2005). Effects of multi-chain omnivory on the strength of trophic control in lakes. *Ecosystems*, 8(6), 682-693. https://doi.org/10.1007/s10021-003-0149-5

Vanni, M. J. (2002). Nutrient cycling by animals in freshwater ecosystems. *Annual Review of Ecology and Systematics*, 33(1), 341-370. https://doi.org/10.1146/annurev.ecolsys.33.010802.150519

Vanni, M. J., & Layne, C. D. (1997). Nutrient recycling and herbivory as mechanisms in the "top-down" effect of fish on algae in lakes. *Ecology*, 78(1), 21-40. https://doi.org/10.1890/0012-9658(1997)078[0021:NRAHAM]2.0.CO;2

Vanni, M. J., Layne, C. D., & Arnott, S. E. (1997). "Top-down" trophic interactions in lakes: Effects of fish on nutrient dynamics. *Ecology*, 78(1), 1-20. https://doi.org/10.1890/0012-9658(1997)078[0001:TDTIIL]2.0.CO;2

Ward, C. L., McCann, K. S., & Rooney, N. (2015). HSS revisited: Multi-channel processes mediate trophic control across a productivity gradient (J. Chase, Ed.). *Ecology Letters*, 18(11), 1190-1197. https://doi.org/10.1111/ele.12498

Wickman, J., Litchman, E., & Klausmeier, C. A. (2024). Eco-evolutionary emergence of macroecological scaling in plankton communities. *Science*, 383(6684), 777-782. https://doi.org/10.1126/science. adk6901

Wolkovich, E. M., Allesina, S., Cottingham, K. L., Moore, J. C., Sandin, S. A., & De Mazancourt, C. (2014). Linking the green and brown worlds: The prevalence and effect of multichannel feeding in food webs. *Ecology*, 95(12), 3376-3386. https://doi.org/10.1890/13-1721.1
decomposer interactions, and ecosystem
persistence. *Ecology*, **82**(11), 3069–3082.
https://doi.org/10.1890/0012-9658(2001)
082[3069:ESPPDI]2.0.CO;2

Degerman, R., Lefébure, R., Byström, P., Båmstedt, U., Larsson, S., & Andersson, A. (2018). Food web interactions determine energy transfer efficiency and top consumer responses to inputs of dissolved organic carbon. *Hydrobiologia*, **805**(1), 131–146. https://doi.org/10.1007/s10750-017-3298-9

Dickman, E. M., Vanni, M. J., & Horgan, M. J. (2006). Interactive effects of light and nutrients on phytoplankton stoichiometry. *Oecologia*, **149**(4), 676–689. https://doi.org/10.1007/s00442-006-0473-5

Dutkiewicz, S., Boyd, P. W., & Riebesell, U. (2021). Exploring biogeochemical and ecological redundancy in phytoplankton communities in the global ocean. *Global Change Biology*, **27**(6), 1196–1213. https://doi.org/10.1111/gcb.15493

Evangelista, C., Lecerf, A., Britton, J. R., & Cucherousset, J. (2017). Resource composition mediates the effects of intraspecific variability in nutrient recycling on ecosystem processes. *Oikos*, **126**(10), 1439–1450. https://doi.org/10.1111/oik.03787

Faithfull, C. L., Huss, M., Vrede, T., & Bergström, A.-K. (2011). Bottom-up carbon subsidies and top-down predation pressure interact to affect aquatic food web structure. *Oikos*, **120**(2), 311–320. https://doi.org/10.1111/j.1600-0706.2010.18683.x

Felip, M. (2000). The relationship between phyto-
plankton biovolume and chlorophyll in a
deep oligotrophic lake: Decoupling in their
spatial and temporal maxima. *Journal of
Plankton Research*, **22**(1), 91–106. https:
//doi.org/10.1093/plankt/22.1.91

Fox, J., Weisberg, S., & Price, B. (2001). Car: Companion to Applied Regression [Institution: Comprehensive R Archive Network Pages: 3.1-3]. https://doi.org/10.32614/CRAN/package.car

Garnier, A., Östman, Ö., Ask, J., Bell, O., Berggren, M., Rulli, M. P. D., Younes, H., & Huss, M. (2023). Coastal darkening exacerbates eutrophication symptoms through bottom-up and top-down control modification. *Limnology and Oceanography*, *68*(3), 678–691. https://doi.org/10.1002/lno.12302

Gauzens, B., Legendre, S., Lazzaro, X., & Lacroix, G. (2016). Intermediate predation pressure leads to maximal complexity in food webs. *Oikos*, **125**(4), 595–603. https://doi.org/10.1111/oik.02627

Gounand, I., Little, C. J., Harvey, E., & Altermatt, F. (2018). Cross-ecosystem carbon flows connecting ecosystems worldwide. *Nature*

Communications, 9(1), 4825. https://doi.
org/10.1038/s41467-018-07238-2

Hall, S. R., Leibold, M. A., Lytle, D. A., & Smith, V. H. (2007). Grazers, producer stoichiometry, and the light: Nutrient hypothesis revisited. *Ecology*, **88*(5), 1142–1152. https://doi.org/10.1890/06-0923

Harrault, L., Allard, B., Danger, M., Maunoury-Danger, F., Guilpart, A., & Lacroix, G. (2012). Influence of food-web structure on the biodegradability of lake sediment. *Freshwater Biology*, **57**(11), 2390–2400. https://doi.org/10.1111/fwb.12020

Harrault, L., Allard, B., Mériguet, J., Carmignac, D., Huon, S., Gauzens, B., & Lacroix, G. (2014). Bottom-up effects of lake sediment on pelagic food-web compartments: A mesocosm study. *Freshwater Biology*, **59**(8), 1695–1709. https://doi.org/10.1111/fwb.12375

Healey, F. P. (1985). Interacting effects of light and nutrient limitation on the growth rate of Synechococcus linearis (cyanophyceae). *Journal of Phycology*, **21**(1), 134–146. https://doi.org/10.1111/j.0022-3646.1985.00134.x

Heath, M. R., Speirs, D. C., & Steele, J. H. (2014). Understanding patterns and processes in models of trophic cascades (K. Lafferty, Ed.). *Ecology Letters*, **17**(1), 101–114. https://doi.org/10.1111/ele.12200

Hébert, M.-P., Soued, C., Fussmann, G. F., & Beiser, B. E. (2023). Dissolved organic matter mediates the effects of warming and inorganic nutrients on a lake planktonic food web. *Limnology and Oceanography*, *68*(S1), S23–S38. https://doi.org/10.1002/lno.12177

Hitchcock, J. N., Mitrovic, S. M., Hadwen, W. L., Roelke, D. L., Growns, I. O., & Rohlfss, A.-M. (2016). Terrestrial dissolved organic carbon subsidizes estuarine zooplankton: An in situ mesocosm study: DOC additions subsidize zooplankton. *Limnology and Oceanography*, *61*(1), 254–267. https://doi.org/10.1002/lno.10207

Hulot, F. D., Lacroix, G., & Loreau, M. (2014). Differential responses of size-based functional groups to bottom-up and top-down perturbations in pelagic food webs: A meta-analysis. *Oikos*, **123**(11), 1291–1300. https://doi.org/10.1111/oik.01116

Hunter, M., & Price, P. (1992). Playing chutes and ladders heterogeneity and the relative roles of bottom-up and top-down forces in natural communities. *Ecology*, **73**(3), 1230–1246. https://doi.org/10.2307/1940152

Husson, F., Josse, J., Le, S., & Mazet, J. (2006). FactoMineR: Multivariate Exploratory Data Analysis and Data Mining [Institution: Comprehensive R Archive
Wollrab, S., Diehl, S., & De Roos, A. M. (2012). Simple rules describe bottom-up and top-down control in food webs with alternative energy pathways (A. Beckerman, Ed.). *Ecology Letters*, *15*(9), 935–946. https://doi.org/10.1111/j.1461-0248.2012.01823.x

Zou, K., Thébault, É., Lacroix, G., & Barot, S. (2016). Interactions between the green and brown food web determine ecosystem functioning. *Functional Ecology*, *30*(8), 1454–1465. https://doi.org/10.1111/1365-2435.12626

Zubkov, M. V., Burkill, P. H., & Topping, J. N. (2007). Flow cytometric enumeration of DNA-stained oceanic planktonic protists. *Journal of Plankton Research*, *29*(1), 79–86. https://doi.org/10.1093/plankt/fbl059
Network Pages: 2.11]. https://doi.org/10.32614/CRAN/package.FactoMineR

Jardillier, L., Basset, M., Domaizon, I., Belan, A., Amblard, C., Richardot, M., & Debroas, D. (2004). Bottom-up and top-down control of bacterial community composition in the eutrophic zone of a reservoir. *Aquatic Microbial Ecology, 35*, 259-273. https://doi.org/10.3354/ame035259

Joint, I., Henriksen, P., Fonnes, G., Bourne, D., Thingstad, T., & Riemann, B. (2002). Competition for inorganic nutrients between phytoplankton and bacterioplankton in nutrient manipulated mesocosms. *Aquatic Microbial Ecology, 29*, 145-159. https://doi.org/10.3354/ame029145

Jones, J. I., & Sayer, C. D. (2003). Does the fish-invertebrate periphyton cascade precipitate plant loss in shallow lakes? *Ecology, 84*(8), 2155-2167. https://doi.org/10.1890/02-0422

Kagata, H., & Ohgushi, T. (2006). Bottom-up trophic cascades and material transfer in terrestrial food webs. *Ecological Research, 21*(1), 26-34. https://doi.org/10.1007/s11284-005-0124-z

Lazzaro, X., Lacroix, G., Gauzens, B., Gignoux, J., & Legendre, S. (2009). Predator foraging behaviour drives food-web topological structure. *Journal of Animal Ecology, 78*(6), 1307-1317. https://doi.org/10.1111/j.1365-2656.2009.01588.x

Le Noac'h, P., Cremella, B., Kim, J., Soria-Píriz, S., Del Giorgio, P. A., Pollard, A. I., Huot, Y., & Beisner, B. E. (2024). Nutrient availability is the main driver of nanophytoplankton phago-mixotrophy in North American lake surface waters (J. Dolan, Ed.). *Journal of Plankton Research, 46*(1), 9-24. https://doi.org/10.1093/plankt/fbad054

Le Noac'h, P., Diehl, S., & Beisner, B. E. (2025). Vertical niche partitioning and the performance of mixotrophic generalists against autotrophic and heterotrophic specialists under contrasting light-nutrient supply regimes. *The American Naturalist, 205*(4), 435-450. https://doi.org/10.1086/734553

Leflaive, J., Danger, M., Lacroix, G., Lyautey, E., Oumarou, C., & Ten-Hage, L. (2008). Nutrient effects on the genetic and functional diversity of aquatic bacterial communities. *FEMS Microbiology Ecology, 66*(2), 379-390. https://doi.org/10.1111/j.1574-6941.2008.00593.x

Leibold, M. A. (1989). Resource edibility and the effects of predators and productivity on the outcome of trophic interactions. *The American Naturalist, 134*(6), 922-949. https://doi.org/10.1086/285022

Lenth, R. (2012). Lsmeans: Least-Squares Means [Institution: Comprehensive R Archive

Network Pages: 2.30-0]. https://doi.org/10.32614/CRAN/package.lsmeans

Lenth, R. V. (2017). Emmeans: Estimated Marginal Means, aka Least-Squares Means [Institution: Comprehensive R Archive Network Pages: 1.10.6]. https://doi.org/10.32614/CRAN/package.emmeans

Leroux, S. J., & Loreau, M. (2010). Consumer-mediated recycling and cascading trophic interactions. *Ecology, 91*(7), 2162-2171. https://doi.org/10.1890/09-0133.1

Mette, E. M., Vanni, M. J., Newell, J. M., & Gonzàlez, M. J. (2011). Phytoplankton communities and stoichiometry are interactively affected by light, nutrients, and fish. *Limnology and Oceanography, 56*(6), 1959-1975. https://doi.org/10.4319/lo.2011.56.6.1959

Mitra, A., Flynn, K. J., Burkholder, J. M., Berge, T., Calbet, A., Raven, J. A., Granéli, E., Glibert, P. M., Hansen, P. J., Stoecker, D. K., Thingstad, F., Tillmann, U., Våge, S., Wilken, S., & Zubkov, M. V. (2014). The role of mixotrophic protists in the biological carbon pump. *Biogeosciences, 11*(4), 995-1005. https://doi.org/10.5194/bg-11-995-2014

Mitra, A., Flynn, K. J., Tillmann, U., Raven, J. A., Caron, D., Stoecker, D. K., Not, F., Hansen, P. J., Hallegraeff, G., Sanders, R., Wilken, S., McManus, G., Johnson, M., Pitta, P., Våge, S., Berge, T., Calbet, A., Thingstad, F., Jeong, H. J., ... Lundgren, V. (2016). Defining planktonic protist functional groups on mechanisms for energy and nutrient acquisition: Incorporation of diverse mixotrophic strategies. *Protoprotist, 167*(2), 106-120. https://doi.org/10.1016/j.protis.2016.01.003

Moore, J. C., Berlow, E. L., Coleman, D. C., Ruiter, P. C., Dong, Q., Hastings, A., Johnson, N. C., McCann, K. S., Melville, K., Morin, P. J., Nadelhoffer, K., Rosemond, A. D., Post, D. M., Sabo, J. L., Scow, K. M., Vanni, M. J., & Wall, D. H. (2004). Detritus, trophic dynamics and biodiversity. *Ecology Letters, 7*(7), 584-600. https://doi.org/10.1111/j.1461-0248.2004.00606.x

Morana, C., Sarmento, H., Descy, J.-P., Gasol, J. M., Borges, A. V., Bouillon, S., & Darchambeau, F. (2014). Production of dissolved organic matter by phytoplankton and its uptake by heterotrophic prokaryotes in large tropical lakes. *Limnology and Oceanography, 59*(4), 1364-1375. https://doi.org/10.4319/lo.2014.59.4.1364

Mougi, A. (2020). Coupling of green and brown food webs and ecosystem stability. *Ecology and Evolution, ece3.6586*. https://doi.org/10.1002/ece3.6586
# S1 Supplementary results

## S1-1 Water physico-chemistry

<figure><img src="image_7.png" /></figure>

Figure S1-1: Mineral nutrient concentration. Total A) nitrogen and B) phosphorus concentrations µg L⁻¹. C) N:P ratio in mesocosms (mass ratio). The average N:P is equal to 330 at the beginning of the experiment and equal to 40 at the end of the experiment.

The water column was stratified for all parameters (but the difference between 50 cm and 150 cm is on average equal to 0.5 °C for temperature, Fig. S1-2A). Oxygen concentration (Fig. S1-2B) and pH (Fig. S1-2C) were positively affected by fish but not by light, while turbidity responded to our treatments similarly to chlorophyll *a* (Fig. S1-2D).

<figure><img src="image_8.png" /></figure>

Figure S1-2: Physico-chemistry of the water measured the multiparameter probe and aggregated from July to November. A) Temperature (°C) at three depths. B) Oxygen concentration (mg L⁻¹). C) pH. D) Turbidity (log scale).
Mougin, C., Azam, D., Caquet, T., Cheviron, N., Dequiedt, S., Le Galliard, J.-F., Guillaume, O., Houot, S., Lacroix, G., Lafolie, F., Maron, P.-A., Michniewicz, R., Pichot, C., Ranjard, L., Roy, J., Zeller, B., Clobert, J., & Chanzy, A. (2015). A coordinated set of ecosystem research platforms open to international research in ecotoxicology, AnaEE-France. *Environmental Science and Pollution Research, 22*(20), 16215-16228. https://doi.org/10.1007/s11356-015-5233-9

Nakagawa, S., & Cuthill, I. C. (2007). Effect size, confidence interval and statistical significance: A practical guide for biologists. *Biological Reviews, 82*(4), 591-605. https://doi.org/10.1111/j.1469-185X.2007.00027.x

Oksanen, J., Simpson, G. L., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R., O'Hara, R., Solymos, P., Stevens, M. H. H., Szoecs, E., Wagner, H., Barbour, M., Bedward, M., Bolker, B., Borcard, D., Carvalho, G., Chirico, M., De Caceres, M., Durand, S., ... Weedon, J. (2001). Vegan: Community Ecology Package [Institution: Comprehensive R Archive Network Pages: 2.6-8]. https://doi.org/10.32614/CRAN/package.vegan

Pace, M. L., Cole, J. J., & Carpenter, S. R. (1998). Trophic cascades and compensation: Differential responses of microzooplankton in whole-lake experiments. *Ecology, 79*(1), 138-152. https://doi.org/10.1890/0012-9658(1998)079[0138:TCACDR]2.0.CO;2

Pommier, T., Merroune, A., Bettarel, Y., Got, P., Janeau, J.-L., Jouquet, P., Thu, T. D., Toan, T. D., & Rochelle-Newall, E. (2014). Off-site impacts of agricultural composting: Role of terrestrially derived organic matter in structuring aquatic microbial communities and their metabolic potential. *FEMS Microbiology Ecology, 90*(3), 622-632. https://doi.org/10.1111/1574-6941.12421

Quévreux, P., Barot, S., & Thébault, É. (2021). Interplay between the paradox of enrichment and nutrient cycling in food webs. *Oikos, 130*(1), 95-109. https://doi.org/10.1111/oik.07937

R Core Team. (2023). R: A Language and Environment for Statistical Computing. https://www.R-project.org/

Raven, J. A. (1997). Phagotrophy in phototrophs. *Limnology and Oceanography, 42*(1), 198-205. https://doi.org/10.4319/lo.1997.42.1.0198

Rhee, G.-u., & Gotham, I. J. (1981). The effect of environmental factors on phytoplankton growth: Light and the interactions of light with nitrate limitation. *Limnology and

Oceanography, 26*(4), 649-659. https://doi.org/10.4319/lo.1981.26.4.0649

Ripley, B., & Venables, B. (2009). MASS: Support Functions and Datasets for Venables and Ripley's MASS [Institution: Comprehensive R Archive Network Pages: 7.3-64]. https://doi.org/10.32614/CRAN/package.MASS

Rock, A. M., Hall, M. R., Vanni, M. J., & González, M. J. (2016). Carnivore identity mediates the effects of light and nutrients on aquatic food-chain efficiency. *Freshwater Biology, 61*(9), 1492-1508. https://doi.org/10.1111/fwb.12790

Rooney, N., McCann, K. S., Gellner, G., & Moore, J. C. (2006). Structural asymmetry and the stability of diverse food webs. *Nature, 442*(7100), 265-269. https://doi.org/10.1038/nature04887

Rosemond, A. D., Pringle, C. M., Ramirez, A., & Paul, M. J. (2001). A test of top-down and bottom-up control in a detritus-based food web. *Ecology, 82*(8), 2279. https://doi.org/10.2307/2680231

Schmitz, O. J., Hawlena, D., & Trussell, G. C. (2010). Predator control of ecosystem nutrient dynamics. *Ecology Letters, 13*(10), 1199-1209. https://doi.org/10.1111/j.1461-0248.2010.01511.x

Schmitz, O. J., Raymond, P. A., Estes, J. A., Kurz, W. A., Holtgrieve, G. W., Ritchie, M. E., Schindler, D. E., Spivak, A. C., Wilson, R. W., Bradford, M. A., Christensen, V., Deegan, L., Smetacek, V., Vanni, M. J., & Wilmers, C. C. (2014). Animating the carbon cycle. *Ecosystems, 17*(2), 344-359. https://doi.org/10.1007/s10021-013-9715-7

Sterner, R. W., Elser, J. J., Fee, E. J., Guildford, S. J., & Chrzanowski, T. H. (1997). The light:nutrient ratio in lakes: The balance of energy and materials affects ecosystem structure and process. *The American Naturalist, 150*(6), 663-684. https://doi.org/10.1086/286088

Sterner, R. W., Elser, J. J., & Vitousek, P. M. (2002). *Ecological stoichiometry: The biology of elements from molecules to the biosphere* [OCLC: 248866756]. Princeton University Press.

Theis, K., Quévreux, P., & Loreau, M. (2022). Nutrient cycling and self-regulation determine food web stability. *Functional Ecology, 36*(1), 202-213. https://doi.org/10.1111/1365-2435.13961

Urbano, V. D. A., Delanira-Santos, D., & Benedito, E. (2022). The role of cladocerans in green and brown food web coupling. *Iheringia. Série Zoologia, 112*, e2022022. https://doi.org/10.1590/1678-4766e2022022
<table><caption>Table S1-4: Significance (p-values) of treatments on water physico-chemistry measured the multiparam- eter probe.</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F:L</th><th>F:OM</th><th>L:OM</th><th>F:L:OM</th><th>Depth</th></tr></thead><tbody><tr><td rowspan="3">Chlorophyll</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.759</td><td>0.012</td><td>0.392</td><td>0.701</td><td>0.834</td><td><strong>&lt;.001</strong></td></tr><tr><td>χ<sup>2</sup></td><td>30.83</td><td>21.26</td><td>0.094</td><td>6.382</td><td>0.733</td><td>0.147</td><td>0.044</td><td>421.65</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">Temperature</td><td>p-value</td><td>0.956</td><td>0.056</td><td>0.682</td><td>0.574</td><td>0.23</td><td>0.202</td><td>0.554</td><td><strong>0.003</strong></td></tr><tr><td>χ<sup>2</sup></td><td>0.003</td><td>3.654</td><td>0.168</td><td>0.316</td><td>1.443</td><td>1.627</td><td>0.35</td><td>11.89</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">Oxygen</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.693</td><td>0.052</td><td>0.205</td><td>0.144</td><td>0.813</td><td>0.595</td><td><strong>&lt;.001</strong></td></tr><tr><td>χ<sup>2</sup></td><td>34.01</td><td>0.156</td><td>3.773</td><td>1.606</td><td>2.13</td><td>0.056</td><td>0.283</td><td>35.44</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">pH</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.445</td><td>0.174</td><td>0.277</td><td>0.435</td><td>0.896</td><td>0.85</td><td><strong>0.035</strong></td></tr><tr><td>χ<sup>2</sup></td><td>17.5</td><td>0.584</td><td>1.847</td><td>1.18</td><td>0.61</td><td>0.017</td><td>0.036</td><td>6.71</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">Turbidity</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.91</td><td>0.008</td><td>0.149</td><td>0.941</td><td>0.859</td><td><strong>&lt;.001</strong></td></tr><tr><td>χ<sup>2</sup></td><td>43.6</td><td>15.19</td><td>0.013</td><td>6.966</td><td>2.086</td><td>0.006</td><td>0.031</td><td>45.27</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr></tbody></table>

## S1-2 Phytoplankton

<table><caption>Table S1-5: Significance of treatments on the response of phytoplankton (p-values).</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F:L</th><th>F:OM</th><th>L:OM</th><th>F:L:OM</th></tr></thead><tbody><tr><td rowspan="3">Seston</td><td>p-value</td><td><strong>0.012</strong></td><td><strong>0.027</strong></td><td>0.493</td><td><strong>0.026</strong></td><td>0.101</td><td>0.977</td><td>0.974</td></tr><tr><td>F</td><td>7.218</td><td>5.491</td><td>0.483</td><td>5.523</td><td>2.88</td><td>0.001</td><td>0.001</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">C:N seston</td><td>p-value</td><td>0.15</td><td>0.796</td><td>0.461</td><td>0.094</td><td>0.451</td><td>0.525</td><td>0.794</td></tr><tr><td>F</td><td>2.196</td><td>0.068</td><td>0.56</td><td>3.002</td><td>0.584</td><td>0.415</td><td>0.069</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">C:P seston</td><td>p-value</td><td><strong>0.001</strong></td><td>0.401</td><td>0.802</td><td>0.179</td><td>0.222</td><td>0.773</td><td>0.902</td></tr><tr><td>F</td><td>14.41</td><td>0.728</td><td>0.064</td><td>1.899</td><td>1.559</td><td>0.085</td><td>0.016</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Cyanobacteria (BBE)</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.143</td><td>0.922</td><td>0.278</td><td>0.671</td><td>0.219</td><td>0.645</td></tr><tr><td>χ<sup>2</sup></td><td>21.13</td><td>2.15</td><td>0.01</td><td>1.177</td><td>0.181</td><td>1.514</td><td>0.212</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Diatoms (BBE)</td><td>p-value</td><td><strong>0.02</strong></td><td><strong>0.035</strong></td><td>0.517</td><td>0.874</td><td>0.073</td><td>0.249</td><td>0.453</td></tr><tr><td>χ<sup>2</sup></td><td>5.437</td><td>4.469</td><td>0.421</td><td>0.025</td><td>3.209</td><td>1.329</td><td>0.562</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Pico-phytoplankton</td><td>p-value</td><td>0.441</td><td>0.907</td><td>0.386</td><td>0.695</td><td>0.64</td><td>0.558</td><td>0.824</td></tr><tr><td>χ<sup>2</sup></td><td>0.594</td><td>0.014</td><td>0.751</td><td>0.154</td><td>0.219</td><td>0.343</td><td>0.049</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Nano-phytoplankton</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.745</td><td><strong>0.001</strong></td><td>0.068</td><td>0.634</td><td>0.594</td></tr><tr><td>χ<sup>2</sup></td><td>12.93</td><td>24.45</td><td>0.106</td><td>11.22</td><td>3.327</td><td>0.227</td><td>0.284</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>
Utermöhl, H. (1958). Zur Vervollkommnung der quantitativen Phytoplankton-Methodik. *Internationale Vereinigung für Theoretische und Angewandte Limnologie: Mitteilungen*, 9(1), 1–38.

Vadeboncoeur, Y., McCann, K. S., Zanden, M. J. V., & Rasmussen, J. B. (2005). Effects of multi-chain omnivory on the strength of trophic control in lakes. *Ecosystems*, 8(6), 682–693. https://doi.org/10.1007/s10021-003-0149-5

Vanni, M. J. (2002). Nutrient cycling by animals in freshwater ecosystems. *Annual Review of Ecology and Systematics*, 33(1), 341–370. https://doi.org/10.1146/annurev.ecolsys.33.010802.150519

Vanni, M. J., & Layne, C. D. (1997). Nutrient recycling and herbivory as mechanisms in the “top-down” effect of fish on algae in lakes. *Ecology*, 78(1), 21–40. https://doi.org/10.1890/0012-9658(1997)078[0021: NRAHAM]2.0.CO;2

Vanni, M. J., Layne, C. D., & Arnott, S. E. (1997). “Top-down” trophic interactions in lakes: Effects of fish on nutrient dynamics. *Ecology*, 78(1), 1–20. https://doi.org/10.1890/0012-9658(1997)078[0001:TDTIIL]2.0.CO;2

Ward, C. L., McCann, K. S., & Rooney, N. (2015). HSS revisited: Multi-channel processes mediate trophic control across a productivity gradient (J. Chase, Ed.). *Ecology Letters*, 18(11), 1190–1197. https://doi.org/10.1111/ele.12498

Wickman, J., Litchman, E., & Klausmeier, C. A. (2024). Eco-evolutionary emergence of macroecological scaling in plankton communities. *Science*, 383(6684), 777–782. https://doi.org/10.1126/science.adk6901

Wolkovich, E. M., Allesina, S., Cottingham, K. L., Moore, J. C., Sandin, S. A., & De Mazancourt, C. (2014). Linking the green and brown worlds: The prevalence and effect of multichannel feeding in food webs. *Ecology*, 95(12), 3376–3386. https://doi.org/10.1890/13-1721.1

Wollrab, S., Diehl, S., & De Roos, A. M. (2012). Simple rules describe bottom-up and top-down control in food webs with alternative energy pathways (A. Beckerman, Ed.). *Ecology Letters*, 15(9), 935–946. https://doi.org/10.1111/j.1461-0248.2012.01823.x

Yalçın, G., Yıldız, D., Calderó-Pascual, M., Yetim, S., Şahin, Y., Parakatselaki, M.-E., Avcı, F., Karakaya, N., Ladoukakis, E. D., Berger, S. A., Ger, K. A., Jeppesen, E., & Beklioğlu, M. (2024). Quality matters: Response of bacteria and ciliates to different allochthonous dissolved organic matter sources as a pulsed disturbance in shallow lakes. *Science of The Total Environment*,

916, 170140. https://doi.org/10.1016/j.scitotenv.2024.170140

Zou, K., Thébault, É., Lacroix, G., & Barot, S. (2016). Interactions between the green and brown food web determine ecosystem functioning. *Functional Ecology*, 30(8), 1454–1465. https://doi.org/10.1111/1365-2435.12626

Zubkov, M. V., Burkill, P. H., & Topping, J. N. (2007). Flow cytometric enumeration of DNA-stained oceanic planktonic protists. *Journal of Plankton Research*, 29(1), 79–86. https://doi.org/10.1093/plankt/fbl059
<table><caption>Table S1-6: Significance of treatments on the response of phytoplankton taxa (p-values).</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F:L</th><th>F:OM</th><th>L:OM</th><th>F:L:OM</th></tr></thead><tbody><tr><td rowspan="3">Chlorophyceae</td><td>p-value</td><td>0.574</td><td>0.562</td><td>0.172</td><td>0.157</td><td>0.906</td><td>0.573</td><td>0.297</td></tr><tr><td>F</td><td>0.324</td><td>0.344</td><td>1.961</td><td>2.112</td><td>0.014</td><td>0.326</td><td>1.128</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Cyanobacteria</td><td>p-value</td><td>0.315</td><td>0.156</td><td>0.998</td><td>0.09</td><td>0.624</td><td>0.54</td><td>0.48</td></tr><tr><td>F</td><td>1.047</td><td>2.123</td><td>0</td><td>3.092</td><td>0.246</td><td>0.384</td><td>0.513</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Dinophyceae</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.065</td><td>0.769</td><td>0.439</td><td>0.186</td><td>0.896</td><td>0.348</td></tr><tr><td>F</td><td>18.2</td><td>3.676</td><td>0.088</td><td>0.617</td><td>1.84</td><td>0.017</td><td>0.912</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Trebouxiophyceae</td><td>p-value</td><td>0.08</td><td>0.272</td><td>0.696</td><td><strong>0.031</strong></td><td>0.092</td><td>0.886</td><td>0.233</td></tr><tr><td>F</td><td>3.293</td><td>1.258</td><td>0.156</td><td>5.161</td><td>3.048</td><td>0.021</td><td>1.488</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Zygnematophyceae</td><td>p-value</td><td><strong>0.022</strong></td><td><strong>0.022</strong></td><td>0.91</td><td><strong>0.012</strong></td><td>0.444</td><td>0.796</td><td>0.968</td></tr><tr><td>F</td><td>5.856</td><td>5.895</td><td>0.013</td><td>7.275</td><td>0.603</td><td>0.068</td><td>0.002</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Shannon index</td><td>p-value</td><td><strong>0.026</strong></td><td>0.239</td><td>0.936</td><td>0.258</td><td>0.406</td><td>0.928</td><td>0.978</td></tr><tr><td>F</td><td>5.492</td><td>1.445</td><td>0.007</td><td>1.335</td><td>0.712</td><td>0.008</td><td>0.001</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>

Chlorophyll *a* strongly varied during the experiment (Fig. S1-3A), its concentration being maximum at the beginning because of the initial nutrient enrichment (Fig. S1-1). Chlorophyll *a* concentration measured by the multiparameter probe (Fig. S1-3B) had the same response to our treatments as the chlorophyll *a* concentration measured by the BBE probe (see Fig. 2A in the main text) with positive effects of light and fish and a fish×light interaction (Table S1-4). It was also stratified with a larger concentration at greater depth (Fig. S1-3C). However, fish presence did not decrease chlorophyll *a* concentration stratification, thus fish did not affect phytoplankton through water mixing and resuspension.

Seston concentration displays a response to treatments similar to the response of chlorophyll *a* (Fig. S1-4A and Table S1-5), thus confirming the robustness of our measure of the phytoplankton compartment. Our treatments did not affect the C:N of the seston (Fig. S1-4B) if we do not account for the dilution effect of phytoplankton abundance (as done in Fig. 4C in the main text). Fish presence has a positive effect on the C:P ratio (Fig. S1-4C), interpreted as a P sink effect of fish (Rock et al., 2016).

Pico-phytoplankton abundance (Fig. S1-5A) did not respond to treatments but nano-phytoplankton was significantly affected by the interaction between fish and light treatments (Fig. S1-5B and Table S1-5): light had a positive effect only in enclosures without fish and fish had an effect only in enclosures where daylight was filtered, which is similar to the response of chlorophyll *a* (see Fig. 2A in the main text and Fig. S1-3B). Given the contrast between taxa cell abundance (Fig. S1-7A) and taxa biovolume (Fig. 3A in the main text), we can identify the pico-phytoplankton as Chlorophyceae (Fig. 3B) and the nano-phytoplankton as Zygnematophyceae (Fig. 3C) and Dinophyceae (Fig. 3D).

The different taxa forming the phytoplankton community inhabiting our mesocosms responded more or less to treatments (Fig. S1-7A and Table S1-6) and we present the response of the most abundant taxa in the following. Chlorophyceae (Fig. S1-7B), cyanobacteria (Fig. S1-7C) and trebouxiophyceae (Fig. S1-7E) did not respond to treatments. Dinophyceae (Fig. S1-7D) were more abundant in the absence of fish, while Zygnematophyceae (Fig. S1-7F) responded positively to fish and light treatments, and their interaction, as for chlorophyll *a* (see Fig. 2A in the main text). The Shannon index was higher in the absence of fish (Fig. S1-7G), thus indicating a negative effect of fish on phytoplankton taxonomic diversity.
# S1 Supplementary results

## S1-1 Water physico-chemistry

<figure><img src="image_8.png" /></figure>

Figure S1-1: Mineral nutrient concentration. Total A) nitrogen and B) phosphorus concentrations (µg/L). C) N:P ratio in mesocosms (mass ratio). The average N:P is equal to 330 at the beginning of the experiment and equal to 40 at the end of the experiment.

The water column was stratified for all parameters (but the difference between 50 cm and 150 cm is on average equal to 0.5 °C for temperature, Fig. S1-2A). Oxygen concentration (Fig. S1-2B) and pH (Fig. S1-2C) were positively affected by fish but not by light, while turbidity responded to our treatments similarly to chlorophyll a (Fig. S1-2D).

<figure><img src="image_9.png" /></figure>

Figure S1-2: Physico-chemistry of the water measured the multiparameter probe and aggregated from July to November. A) Temperature (°C) at three depths. B) Oxygen concentration (mg L⁻¹). C) pH. D) Turbidity (log scale).
<figure><img src="image_9.png" /><figcaption>Figure S1-3: Detailed response of chlorophyll a concentration (µg L⁻¹). A) Temporal dynamics of chlorophyll a (BBE probe data). B) Average chlorophyll concentration (multiparameter probe data). C) Chlorophyll a concentration at three depths (multiparameter probe data).</figcaption></figure>

<figure><img src="image_10.png" /><figcaption>Figure S1-4: response of the seston. A) Seston dry mass concentration (mg L⁻¹). B) C:N ratio of the seston. C) C:P ratio of the seston.</figcaption></figure>
<table><caption>Table S1-1: Significance (p-values) of treatments on water physico-chemistry measured the multiparameter probe. The effects of interactions between depth and our treatments are not present because they were not significant.</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F:L</th><th>F:OM</th><th>L:OM</th><th>F:L:OM</th><th>Depth</th></tr></thead><tbody><tr><td rowspan="3">Chlorophyll</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.759</td><td>0.012</td><td>0.392</td><td>0.701</td><td>0.834</td><td><strong>&lt;.001</strong></td></tr><tr><td>χ<sup>2</sup></td><td>30.83</td><td>21.26</td><td>0.094</td><td>6.382</td><td>0.733</td><td>0.147</td><td>0.044</td><td>421.65</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">Temperature</td><td>p-value</td><td>0.956</td><td>0.056</td><td>0.682</td><td>0.574</td><td>0.23</td><td>0.202</td><td>0.554</td><td><strong>0.003</strong></td></tr><tr><td>χ<sup>2</sup></td><td>0.003</td><td>3.654</td><td>0.168</td><td>0.316</td><td>1.443</td><td>1.627</td><td>0.35</td><td>11.89</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">Oxygen</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.693</td><td>0.052</td><td>0.205</td><td>0.144</td><td>0.813</td><td>0.595</td><td><strong>&lt;.001</strong></td></tr><tr><td>χ<sup>2</sup></td><td>34.01</td><td>0.156</td><td>3.773</td><td>1.606</td><td>2.13</td><td>0.056</td><td>0.283</td><td>35.44</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">pH</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.445</td><td>0.174</td><td>0.277</td><td>0.435</td><td>0.896</td><td>0.85</td><td><strong>0.035</strong></td></tr><tr><td>χ<sup>2</sup></td><td>17.5</td><td>0.584</td><td>1.847</td><td>1.18</td><td>0.61</td><td>0.017</td><td>0.036</td><td>6.71</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr><tr><td rowspan="3">Turbidity</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.91</td><td>0.008</td><td>0.149</td><td>0.941</td><td>0.859</td><td><strong>&lt;.001</strong></td></tr><tr><td>χ<sup>2</sup></td><td>43.6</td><td>15.19</td><td>0.013</td><td>6.966</td><td>2.086</td><td>0.006</td><td>0.031</td><td>45.27</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>2</td></tr></tbody></table>

## S1-2 Phytoplankton

<table><caption>Table S1-2: Significance of treatments on the response of phytoplankton (p-values).</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F:L</th><th>F:OM</th><th>L:OM</th><th>F:L:OM</th></tr></thead><tbody><tr><td rowspan="3">Seston</td><td>p-value</td><td><strong>0.012</strong></td><td><strong>0.027</strong></td><td>0.493</td><td><strong>0.026</strong></td><td>0.101</td><td>0.977</td><td>0.974</td></tr><tr><td>F</td><td>7.218</td><td>5.491</td><td>0.483</td><td>5.523</td><td>2.88</td><td>0.001</td><td>0.001</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">C:N seston</td><td>p-value</td><td>0.15</td><td>0.796</td><td>0.461</td><td>0.094</td><td>0.451</td><td>0.525</td><td>0.794</td></tr><tr><td>F</td><td>2.196</td><td>0.068</td><td>0.56</td><td>3.002</td><td>0.584</td><td>0.415</td><td>0.069</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">C:P seston</td><td>p-value</td><td><strong>0.001</strong></td><td>0.401</td><td>0.802</td><td>0.179</td><td>0.222</td><td>0.773</td><td>0.902</td></tr><tr><td>F</td><td>14.41</td><td>0.728</td><td>0.064</td><td>1.899</td><td>1.559</td><td>0.085</td><td>0.016</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Cyanobacteria (BBE)</td><td>p-value</td><td><strong>&lt;.001</strong></td><td>0.143</td><td>0.922</td><td>0.278</td><td>0.671</td><td>0.219</td><td>0.645</td></tr><tr><td>χ<sup>2</sup></td><td>21.13</td><td>2.15</td><td>0.01</td><td>1.177</td><td>0.181</td><td>1.514</td><td>0.212</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Diatoms (BBE)</td><td>p-value</td><td><strong>0.02</strong></td><td><strong>0.035</strong></td><td>0.517</td><td>0.874</td><td>0.073</td><td>0.249</td><td>0.453</td></tr><tr><td>χ<sup>2</sup></td><td>5.437</td><td>4.469</td><td>0.421</td><td>0.025</td><td>3.209</td><td>1.329</td><td>0.562</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Pico-phytoplankton</td><td>p-value</td><td>0.441</td><td>0.907</td><td>0.386</td><td>0.695</td><td>0.64</td><td>0.558</td><td>0.824</td></tr><tr><td>χ<sup>2</sup></td><td>0.594</td><td>0.014</td><td>0.751</td><td>0.154</td><td>0.219</td><td>0.343</td><td>0.049</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Nano-phytoplankton</td><td>p-value</td><td><strong>&lt;.001</strong></td><td><strong>&lt;.001</strong></td><td>0.745</td><td><strong>0.001</strong></td><td>0.068</td><td>0.634</td><td>0.594</td></tr><tr><td>χ<sup>2</sup></td><td>12.93</td><td>24.45</td><td>0.106</td><td>11.22</td><td>3.327</td><td>0.227</td><td>0.284</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>
<figure><img src="image_11.png" /><figcaption>Figure S1-5: Response of phytoplankton concentration measured by FACS (cell µL⁻¹). **A)** Nano-phytoplankton. **B)** Pico-phytoplankton.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Figure S1-6: Response of **A)** cyanobacteria and **B)** diatoms measured by the concentration of their pigments (µg L⁻¹) by the BBE probe.</figcaption></figure>
<table><caption>Table S1-3: Significance of treatments on the response of phytoplankton taxa (p-values).</caption><thead><tr><th>Variable</th><th></th><th>Fish (F)</th><th>Light (L)</th><th>OM</th><th>F:L</th><th>F:OM</th><th>L:OM</th><th>F:L:OM</th></tr></thead><tbody><tr><td rowspan="3">Chlorophyceae</td><td>p-value</td><td>0.574</td><td>0.562</td><td>0.172</td><td>0.157</td><td>0.906</td><td>0.573</td><td>0.297</td></tr><tr><td>F</td><td>0.324</td><td>0.344</td><td>1.961</td><td>2.112</td><td>0.014</td><td>0.326</td><td>1.128</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Cyanobacteria</td><td>p-value</td><td>0.315</td><td>0.156</td><td>0.998</td><td>0.09</td><td>0.624</td><td>0.54</td><td>0.48</td></tr><tr><td>F</td><td>1.047</td><td>2.123</td><td>0</td><td>3.092</td><td>0.246</td><td>0.384</td><td>0.513</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Dinophyceae</td><td>p-value</td><td>&lt;.001</td><td>0.065</td><td>0.769</td><td>0.439</td><td>0.186</td><td>0.896</td><td>0.348</td></tr><tr><td>F</td><td>18.2</td><td>3.676</td><td>0.088</td><td>0.617</td><td>1.84</td><td>0.017</td><td>0.912</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Trebouxiophyceae</td><td>p-value</td><td>0.08</td><td>0.272</td><td>0.696</td><td><strong>0.031</strong></td><td>0.092</td><td>0.886</td><td>0.233</td></tr><tr><td>F</td><td>3.293</td><td>1.258</td><td>0.156</td><td>5.161</td><td>3.048</td><td>0.021</td><td>1.488</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Zygnematophyceae</td><td>p-value</td><td><strong>0.022</strong></td><td><strong>0.022</strong></td><td>0.91</td><td><strong>0.012</strong></td><td>0.444</td><td>0.796</td><td>0.968</td></tr><tr><td>F</td><td>5.856</td><td>5.895</td><td>0.013</td><td>7.275</td><td>0.603</td><td>0.068</td><td>0.002</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr><tr><td rowspan="3">Shannon index</td><td>p-value</td><td><strong>0.026</strong></td><td>0.239</td><td>0.936</td><td>0.258</td><td>0.406</td><td>0.928</td><td>0.978</td></tr><tr><td>F</td><td>5.492</td><td>1.445</td><td>0.007</td><td>1.335</td><td>0.712</td><td>0.008</td><td>0.001</td></tr><tr><td>Df</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td><td>1</td></tr></tbody></table>

<figure><img src="image_10.png" /><figcaption>Figure S1-3: Detailed response of chlorophyll *a* concentration (µg L⁻¹). **A)** Temporal dynamics of chlorophyll *a* (BBE probe data). **B)** Average chlorophyll *a* concentration (multiparameter probe data). **C)** Chlorophyll *a* concentration at three depths (multiparameter probe data).</figcaption></figure>
<figure><img src="image_13.png" /></figure>

Figure S1-7: Response of the main phytoplankton taxa (cell mL⁻¹). A) Average abundance of the main taxa. B) Chlorophyceae. C) Cyanobacteria. D) Dinophyceae. E) Trebouxiophyceae. F) Zygnematophyceae. G) Shannon index.

S1-3 Zooplankton

<figure><img src="image_14.png" /></figure>

Figure S1-8: Percentage of variance explained by each eigenvector of the PCA of the zooplankton composition.
<figure><img src="image_11.png" /><figcaption>Figure S1-4: Response of the seston. A) Seston dry mass concentration (mg L⁻¹). B) C:N ratio of the seston. C) C:P ratio of the seston.</figcaption></figure>

<figure><img src="image_12.png" /><figcaption>Figure S1-5: Response of phytoplankton concentration measured by FACS (cell µL⁻¹). A) Nano-phytoplankton. B) Pico-phytoplankton.</figcaption></figure>

<figure><img src="image_13.png" /><figcaption>Figure S1-6: Response of A) cyanobacteria and B) diatoms measured by the concentration of their pigments (µg L⁻¹) by the BBE probe.</figcaption></figure>

Chlorophyll *a* strongly varied during the experiment (Fig. S1-3A), its concentration being maximum at the beginning because of the initial nutrient enrichment (Fig. S1-1). Chlorophyll *a* concentration measured by the multiparameter probe (Fig. S1-3B) had the same response to our treatments as the chlorophyll *a* concentration measured by the BBE probe (see Fig. 2A in the main text) with positive effects of light and
<table><caption>Table S1-7: Contributions of zooplankton taxa (%) to the main eigenvectors.</caption><thead><tr><th></th><th>Dim 1</th><th>Dim 2</th><th>Dim 3</th><th>Dim 4</th></tr></thead><tbody><tr><td>Small cladocerans</td><td>41</td><td>12.4</td><td>10.3</td><td>36.3</td></tr><tr><td>Copepods</td><td>5.5</td><td>49.3</td><td>26.3</td><td>18.9</td></tr><tr><td>Chaoborus</td><td>36.3</td><td>17.3</td><td>12.3</td><td>34.1</td></tr><tr><td>Rotifers</td><td>17.2</td><td>21</td><td>51.1</td><td>10.7</td></tr></tbody></table>

In the following, we present zooplankton counting at the genus level. We aggregated the two dates or treated them as independent data sets to test the robustness of the effects of our treatments over time. When dates were aggregated, sampling date was included in the model.

Copepods were positively affected by light when Cyclopidae and nauplii were aggregated over the two sampling dates (+0.55 ± 0.27 (Log individuals per 24L), Table S1-8). However, we only see a positive effect of fish in August sampling (Fig. S1-9A and Fig. S1-10A and B) for aggregated and non aggregated data and light effect is not significant. Thus, fish had a positive effect on Copepoda at the beginning of the experiment but this effect vanished later, while the positive effect of fish on Chydoridae (the only Cladocera in our experiments, except in one mesocosm) held at the two sampling dates (+2.26 ± 0.7 (Log individuals per 24L) Fig. S1-9B and Fig. S1-10C). Chaoborus (large Diptera larvae) were only present in enclosures without fish (Fig. S1-9C).

Rotifers were not affected by our treatments when the two sampling dates were aggregated (Fig. S1-9D). However, a significant effect of the addition of organic matter was observed in September. This effect was observed in September for Bdelloid (Fig. S1-11C), Polyarthra (negative effect, Fig. S1-12A) and Brachionus (positive effect, Fig. S1-12C). Polyarthra were negatively affected by organic matter addition while full light exposure had a positive effect. Thus Bdelloid and Brachionus can be associated to the brown food web while Polyarthra would be assimilated to the green food web. Fish had a negative effect on Bdelloid (Fig. S1-11C) and Polyarthra (Fig. S1-12A) and a positive effect on Keratella (Fig. S1-12B) in August. In September, the effect of fish on Bdelloid (Fig. S1-11C) and Keratella (Fig. S1-12B) was still visible. Anuraeopsis (Fig. S1-12D) and Lecane (Fig. S1-11A) were negatively affected by fish. Thus, except for Keratella, fish had a negative effect on many rotifer species.
fish and a fish × light interaction (Table S1-1). It was also stratified with a larger concentration at greater depth (Fig. S1-3C). However, fish presence did not decrease chlorophyll *a* concentration stratification, thus fish did not affect phytoplankton through water mixing and resuspension. More generally, we did not observe any significant interaction effects between depth and our treatments (Table S1-1), thus probably discarding behavioural responses of organisms, and fish in particular, altering their position in the water column in response to light intensity for instance.

Seston concentration displays a response to treatments similar to the response of chlorophyll *a* (Fig. S1-4A and Table S1-2), thus confirming the robustness of our measure of the phytoplankton compartment. Our treatments did not affect the C:N of the seston (Fig. S1-4B) if we do not account for the dilution effect of phytoplankton abundance (as done in Fig. 4C in the main text). Fish presence has a positive effect on the C:P ratio (Fig. S1-4C), interpreted as a P sink effect of fish (Rock et al., 2016).

Pico-phytoplankton abundance (Fig. S1-5A) did not respond to treatments but nano-phytoplankton was significantly affected by the interaction between fish and light treatments (Fig. S1-5B and Table S1-2): light exposure had a positive effect only in enclosures without fish and fish had an effect only in enclosures in which daylight was filtered, which is similar to the response of chlorophyll *a* (see Fig. 2A in the main text and Fig. S1-3B). Given the contrast between taxa cell abundance (Fig. S1-7A) and taxa biovolume (Fig. 3A in the main text), we can identify the pico-phytoplankton as Chlorophyceae (Fig. 3B) and the nano-phytoplankton as Zygnematophyceae (Fig. 3C) and Dinophyceae (Fig. 3D).

The different taxa forming the phytoplankton community inhabiting our mesocosms responded more or less to treatments (Fig. S1-7A and Table S1-3) and we present the response of the most abundant taxa in the following. Chlorophyceae (Fig. S1-7B), cyanobacteria (Fig. S1-7C) and Trebouxiophyceae (Fig. S1-7E) did not respond to treatments. Dinophyceae (Fig. S1-7D) were more abundant in the absence of fish, while Zygnematophyceae (Fig. S1-7F) responded positively to fish and light exposure, and their interaction, as for chlorophyll *a* (see Fig. 2A in the main text). The Shannon index was higher in the absence of fish (Fig. S1-7G), thus indicating a negative effect of fish on phytoplankton taxonomic diversity.

<figure><img src="image_14.png" /><figcaption>Figure S1-7: Response of the main phytoplankton taxa assessed by cell concentration (cell mL⁻¹). A) Average abundance of the main taxa. B) Chlorophyceae. C) Cyanobacteria. D) Dinophyceae. E) Trebouxiophyceae. F) Zygnematophyceae. G) Taxonomic diversity (Shannon index).</figcaption></figure>
<figure><img src="image_15.png" /><figcaption>Figure S1-9: Response of zooplankton from three main aggregated groups. A) Cladocera (Chydoridae and Bosminiae), B) Copepods (adults and nauplii), C) Chaoborus (large Diptera larvae) and D) rotifers (Lecanes, Lepadella, Bdelloid, Polyarthra, Brachionus, Hexarthra and Anuraepsis), Keratella were not included because of their strong variance.</figcaption></figure>

<figure><img src="image_16.png" /><figcaption>Figure S1-10: Zooplankton numeration of large zooplankton from 24 L samples A) Cycloidea (large Copepoda) B) Nauplii (Copepoda juveniles) and C) Chydoridae (small Cladocera).</figcaption></figure>