# Seasonal variations in planktonic food web structure affect stability by shifting the distribution of energy fluxes

Simon Bazin, Isabelle Domaizon, Cécilia Barouillet, Victor Frossard, Arnaud Sentis

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Simon Bazin, Isabelle Domaizon, Cécilia Barouillet, Victor Frossard, Arnaud Sentis. Seasonal variations in planktonic food web structure affect stability by shifting the distribution of energy fluxes. Oikos, 2025, 10.1002/oik.11528. hal-05508607

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OIKOS

Research article

Seasonal variations in planktonic food web structure affect
stability by shifting the distribution of energy fluxes

Simon Bazin ID1,2, Isabelle Domaizon ID2,3, Cécilia Barouillet ID2,3, Victor Frossard ID2,3 and Arnaud Sentis ID1,2

Correspondence: Arnaud Sentis (arnaud sentis@inrae.fr)

Oikos

2025: e11528

doi: 10.1002/oik.11528

Subject Editor: Daniel Stouffer

Editor-in-Chief:

Paulo R. Guimaraes

Accepted 9 October 2025

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Seasonality in temperate ecosystems shapes species phenology, influencing interactions and food web structure. Variations in species richness and biomass affect trophic interaction strength, a crucial factor for community stability, which can be assessed through energy fluxes – an essential indicator of ecosystem function. Yet, we still have limited understanding on how energy fluxes and food web structure vary overtime and how the seasonal dynamics influence stability. We examined how the structure, energy fluxes and stability of a highly resolved multitrophic lake food web change across seasons. Two estimates representing different facets of stability were considered: resilience and reactivity. We found a strong seasonal pattern in food web structure, with higher complexity observed in summer and autumn. In addition, we found that more complex food webs are more reactive to perturbations, and consequently, less stable than simpler ones over short time-scale. This was influenced by a lower proportion of strong energy fluxes. Resilience was unaffected by seasonality, food web structure and energy fluxes, highlighting the need to account for multifaceted nature of stability. The seasonal variability of stability suggests that food webs may vary temporally in their vulnerability to environmental perturbations, which has important implications for ecosystem management.

Keywords: ecosystem function, energy fluxes, food web stability, food web structure, microzooplankton, seasonality

Introduction

Food webs provide essential insights into interspecific trophic interactions and biodi-
versity structure (Pimm and Lawton 1977, Thompson et al. 2012, Trøjelsgaard and
Olesen 2016, Pilosof et al. 2017). Trophic interactions are inherently dynamic over-
time, especially in temperate ecosystems where environmental seasonality leads to abi-
otic (temperature, precipitation, light, nutrients) and biotic (growth, reproduction,

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<footnote n="1">¹Aix Marseille Université, INRAE RECOVER, Aix en Provence, France</footnote>

<footnote n="2">²Pole R&D ECLA, Aix en Provence, France</footnote>

<footnote n="3">³Université Savoie Mont Blanc, INRAE CARRTEL, Thonon-les-Bains, France</footnote>
trophic interactions) fluctuations. Season-dependent species phenology directly shapes the complexity of food webs (McMeans et al. 2015), defined as the product of species richness and connectance (Dunne 2006). While some studies did not find significant correlations between the complexity and stability of food webs (Jacquet et al. 2016), others have shown that complexity can either increase (Dunne et al. 2002, Altena et al. 2016) or decrease (May 1972, Gross et al. 2009, Allesina and Tang 2012) stability. These discrepancies have prompted the search for other mechanisms and features of food web explaining their stability. Yet, most empirical studies on food webs and their stability are often limited to a limited number of consecutive time points (Jacquet et al. 2016, Ushio et al. 2018). As a result, there is very limited knowledge on how the complexity-stability relationship varies across seasons and if this could explain some of the discrepancies among empirical results. It is thus essential to deepen our understanding of the seasonal dynamics of food web structure and its link to their stability (McMeans et al. 2015).

A growing body of research suggests that fully understanding the relationship between food web complexity and stability requires accounting for the strength and distribution of trophic interactions rather than treating individual links through their simple occurrences (Brose et al. 2006, Thébault and Loreau 2006, Allesina and Tang 2012, Tang et al. 2014). For instance, Altena et al. (2016) found no significant relationship between complexity and food web stability when ignoring trophic interaction strength, while stability increased with complexity when trophic interaction strength was considered. This result showed that the more evenly trophic interactions strengths were distributed over links, the more stable the food webs were. In natural systems, distributions of the strength of trophic interactions dominated by many weak interactions relative to strong interactions tend to have a stabilizing effect, while those with a high proportion of strong interactions relative to weak ones are more likely to have a destabilizing effect (McCann et al. 1998, Emmerson and Yearsley 2004). However, our understanding of seasonal variations in the strength of trophic interactions and their impact on the structure and stability of food webs is still limited (Humphries et al. 2017). In this perspective, seasonal changes in species richness, biomass, or size distribution could play a substantial role in the temporal variations of strength of interactions (Rudolf 2012, Humphries et al. 2017) and explain the contrasting results in the complexity and stability relationship within food webs.

Historically, the strength of trophic interactions was often estimated by studying Holling's functional response (Holling 1966), which quantifies the per capita impact of the consumer on prey mortality (Berlow et al. 1999, 2004). Alternatively, recent approach consists in quantifying energy fluxes between prey and consumers, based on bioenergetic models (Rip and McCann 2011, Rooney and McCann 2012, McMeans et al. 2015). This approach enables linking community ecology, such as species richness and biomass, to the key ecosystem functions that are fluxes of energy and matter (Barnes et al. 2018). The links between biodiversity and ecosystem

functioning are often examined at a single trophic level or within simplified food chains (Duffy et al. 2007). However, the complex interplay between biodiversity structure and ecosystem functioning would likely necessitate accounting for the multitrophic nature of natural food webs and the distribution of trophic interaction strengths (Thébault and Loreau 2006, Cardinale et al. 2012, Soliveres et al. 2016, Wang and Brose 2018). In this context, quantifying energy fluxes within food webs was proposed as a powerful and universal tool for understanding ecosystem functioning across multiple trophic levels and ecological scales (Barnes et al. 2018). For instance, Kortsch et al. (2021) quantified energy fluxes within the food web of the Gulf of Riga over more than three decades and found that rising temperature during specific time periods led to decreases in detritivorous and increases in phyto- and zooplanktivorous fluxes, with consequent changes in ecosystem functions. However, there is limited knowledge on seasonal variations in energy fluxes within food webs, and even less is known on how these seasonal changes impact food web stability and ecosystem functioning (McMeans et al. 2015, Barnes et al. 2018).

In this study, the main objective is to determine the seasonal effects on food web structure (defined here as species richness, connectance and complexity), energy fluxes, and stability and identifying potential relationships among these key components of the food web. To do so, we investigated the seasonal dynamics of a highly resolved multitrophic lake planktonic food web from May 2014 to December 2016, with monthly or bimonthly sampling (i.e. 27 successive time points). To achieve an extensive high-resolution composition of planktonic food web, we complemented traditional observational data on phytoplankton, microzooplankton and metazooplankton (microscopy) with DNA-based data (metabarcoding) to monitor the ciliates community. Ciliates can form key trophic links in planktonic food webs (Novotny et al. 2021), shaping phytoplankton size structure (Stibor et al. 2004, Sommer and Sommer 2006, Wollrab et al. 2019) and its seasonal dynamics (Aberle et al. 2007, Berger et al. 2014). In addition, as bacteria are an important food source for many ciliates (Adl et al. 2019), we also included them in food webs by assessing their density from published literature. We did not include fish or higher order organisms as well as benthic organisms due to the lack of empirical data with high frequency sampling. Our data and analyses are thus representative of the pelagic microzooplanktonic community that is often underrepresented in food web studies. We inferred trophic relationships based on size differences between predators and prey (Gravel et al. 2013, Vagnon et al. 2021) and functional traits (feeding regime). We examined the relationships between seasonal changes in food web structure, stability and energy fluxes over time using matrix-based evaluations of stability. As food web stability is a multifaceted concept (Donohue et al. 2013, Domínguez-García et al. 2019), we used two complementary stability metrics: 1) asymptotic resilience representing the rate at which a community returns to equilibrium following a small disturbance. It is a measure of how quickly a system stabilizes over the long term, assuming that the disturbance is small enough not to push the system into a different regime. 2)
Reactivity representing how fast the system goes away from equilibrium after a perturbation. A system is reactive if some small disturbances initially amplify before ultimately decaying due to asymptotic stability. These two metrics are thus representative of the long and short-term effects of a perturbation, respectively. We expected 1) that the structure of the food web would be season-dependent, in response to changes in community composition derived from species phenology, and 2) that the season would indirectly affect the stability of the food webs by altering their complexity. We hypothesized 3) that energy fluxes would follow a seasonal pattern in response to seasonal changes in species relative abundance within the communities. As food web stability can be determined by the distribution of trophic interaction strengths (Thébault and Loreau 2006, Allesina and Tang 2012), we also hypothesized 4) that stability would be lower for food webs with high proportion of strong energy fluxes.

# Material and methods

## Study site

Lake Geneva is the largest Alpine lake located on the border between France and Switzerland at an altitude of 372 m a.s.l. The lake is 72 km in length and up to 14 km wide, covering an area of 580 km² with a maximum depth of 310 m. Following various restoration programs, phosphorus inputs to the lake have decreased significantly since the 1980s, when the average annual phosphorus concentration was nearly 90 µg P l⁻¹. In 2023, the average phosphorus concentration in the 0–50 m layer was approximately 10 µg P l⁻¹ (CIPEL 2022) confirming the recent reversal in the trophic status commonly referred to as reoligotrophication.

## Environmental monitoring

We used in situ environmental and planktonic taxonomic data available through the OLA-IS database (Rimet et al. 2020). These data are collected as part of the environmental long-term monitoring coordinated by the Franco-Swiss International Commission for the Protection of the Waters of Lake Geneva (CIPEL), and performed by OLA (INRAE-Thonon, observatory on Lakes). The datasets used for Lake Geneva were obtained from a single sampling point (46°27′09.92″N, 06°36′52.70″E) located at the deepest part of the lake. Communities were sampled bi-monthly in the pelagic zone (from 0 to 50 m depth) where most plankton communities are present, except for the winter period, for which sampling was performed once a month. To get an extensive inventory of pelagic species, we also sampled water to monitor ciliates community once or twice a month, by coupling microscopy and eDNA analyses. This led to a comprehensive study of the high-resolution Lake Geneva meta-web and 27 time points food webs from May 2014 to December 2016.

## Biological inventories

Zooplanktonic crustaceans (copepods and cladocerans) and rotifers were sampled with a vertical tow from 50 m to the

surface using a 200-µm plankton net for crustaceans and a 64-µm plankton net for rotifers. These nets were 35 cm in diameter. Crustaceans were identified and counted under a light microscope at an 10× magnification, using Dussart (1967) and Amoros (1984) for taxonomic identification. Rotifers identification and count were carried out using an inverted microscope at an 16× magnification, using Koste (1978) and Stemberger (1979) for identification. Counts were expressed as ind. m⁻³.

Phytoplankton was sampled from 0 to 18 m in depth using a 5-l integrating water sampler (Hydro-Bios IWS). As pelagic phytoplankton grow within the euphotic zone, and the euphotic zone in Lake Geneva typically extends to less than 18 m (except during a few winter months, CIPEL 2014, 2015, 2016), very little phytoplankton is found beyond 18 m depth. Thus, the quantity sampled from 0 to 18 m was used to infer the phytoplankton density in the depth layer from 0 to 50 m. The phytoplankton data were analyzed using the classical Utermöhl method (Utermöhl 1958) and presented as a biovolume measured in µm³ ml⁻¹ (Rimet et al. 2020). All individuals, including crustaceans, rotifers and phytoplankton, were identified to at least the genus level.

Ciliates were sampled collecting 2 l of water using an Integrating Water Sampler Hydro-Bios IWS in the two strata 0–18 and 18–50 m. These samples were used for microscopic observations to estimate the total biovolume of ciliates, and for eDNA analyses (high throughput sequencing and metabarcoding) to obtain a taxonomic inventory of ciliates.

For the microscopic observations, one sub-sample (500 ml) for each date and for each strata was preserved with mercuric bichloride (25%) and processed (within 15 days after sampling) according to the method in Sime-Ngando et al. (1991). The sedimentation of samples was carried out over 48 h in an Utermöhl column, and the observation was recorded with an inverted microscope at an 1250× magnification. Ciliates were counted, measured and assigned to a geometric form to estimate their biovolume. The results were expressed as µm³ ml⁻¹.

For the eDNA analyses, a second sub-sample (500 ml) was filtered using glass microfiber filters (GFC 25mm of diameter) of 1.2 µm of porosity. The filters were stored at -80°C until DNA extraction.

## eDNA analysis: extraction, amplification and sequencing

DNA extractions from the filters were performed using NucleoMag DNA/RNA Water MagnetaPure 32 Nucleic Acid Purification System kits (MACHEREY-NAGEL). Each filter was cut into pieces and added to 550 µl of MAW1 lysis buffer. During the lysis step, the filters were shaken vertically in the buffer for 5 s and then horizontally for 5 min. The samples were subsequently transferred to 450 µl of lysis buffer as recommended in the kit instructions, and the DNA molecules were eluted in 65 µl of purified (RNase-free) water preheated to 56°C. The resulting DNA extracts were stored at -20°C.
The amplification of a portion of the V4 region of the 18S ribosomal gene was obtained as described in Barouillet et al. (2022), using a nested PCR. The primers and PCR protocols used for the preparation of amplicons are described in the Supporting information. The obtained PCR products were sent to the INSA/TBI Get-Biochips platform (Rangueil 31077 Toulouse Cedex) for amplicon purification and 2 × 250 bp sequencing on an Illumina MiSeq instrument using V2 chemistry. The demultiplexing step was realized by the sequencing platform.

The sequenced data were identified using the ‘dada2’ R package (Callahan et al. 2016). The quality scores indicate that the sequences were of good quality. The forward and reverse reads were then merged, sequences were truncated at the 230 and 220 bp for forward and reverse reads, respectively, to maximize overlap. Sequences containing ambiguities were removed. Chimera detection was carried out using the removeBimeraDenovo function specifying the ‘consensus’ method, which checks the sequence variants of each sample independently and then makes a consensus decision across the samples to determine whether the identified variants should be removed. The taxonomic assignment was performed using the PR2 database.

For amplicon sequence variants (ASVs), which are distinct DNA sequences inferred from high-throughput marker gene analysis, we aggregated their read counts at the genus level for those identified to the species level. This approach was chosen because 63.5% of the ASVs were identified at least to the genus level and covered 90.5% of the total number of reads (Supporting information), and allows us to work on the taxonomic level of genus. Additionally, the genus level allows a relevant assignment to functional traits. The association of identified ASVs to their functional traits was done through an exhaustive literature review. The range of size and biovolumes, as well as the feeding ecology (foraging traits categories) were constituted from a combination of several previously published databases and references (Ramond et al. 2019, Barouillet et al. 2022, Monjot et al. 2023). The table with the information about the functional traits is available on open access at: https://doi.org/10.5061/dryad.41ns1rnrr.

For each sampling date, we estimated the biovolume (µm³ ml⁻¹) for each genus by multiplying their relative proportions within the ciliate community (derived from the eDNA, i.e. % of total DNA reads) by the total biovolume of ciliates (obtained by microscopy). For zooplankton, the percentage of reads is generally consistent with the relative biomasses of species (Ershova et al. 2021) as for other biological groups such as fish (Pont et al. 2023). This conversion allows adjustments for between-sample quantification of species to track how species abundance varies over time (Luo et al. 2023).

## Individual body size/body mass and population biomass

We assigned an average adult body size (TL in µm) and individual fresh body mass (BM in g) to each genus to calculate population biomass, infer size-based food webs and estimate energy fluxes for each sampling date. Crustacean body size

was obtained from Vagnon et al. (2021). Dry body mass of crustacean was estimated using size-mass regressions from Bottrell et al. (1976) and Branstrator (2005), and converted to wet body mass using a dry-to-wet body mass conversion ratio of 0.25 (Yvon-Durocher et al. 2015). Rotifers body size was derived from previous researches (Pejler 1957, Berzins 1960, Chengalath et al. 1973, Nauwerck 1978, Sudzuki 1991, Ejsmont-Karabin 1998, Telesh et al. 1998). Dry body mass of rotifers was obtained from Pauli (1989), and converted to wet body mass using a dry-to-wet body mass conversion ratio of 0.07, except for the genus *Asplanchna* for which this ratio is 0.04 (Malley 1989). The fresh population biomasses (in g l⁻¹) of the crustacean and rotifer genera were then estimated by multiplying the number of individuals per litter by their respective individual fresh body mass.

Individual body size and biovolume of phytoplankton were estimated from Rimet and Druart (2018). Individual biovolumes were then converted to fresh body masses assuming a density of 1 g ml⁻¹ for planktonic algae. Ciliates body size was estimated from many references listed in the functional traits table. Each ciliate genus was assigned to a geometric shape (round or elongated) according to Monjot et al. (2023). For round ciliates, individual biovolume was calculated as the volume of a sphere: $V = \frac{4}{3} \times \pi \times (\frac{TL}{2})^3$. For elongated ciliates, biovolume was calculated as the volume of an ellipsoid assuming that width and height are each half the total length: $V = \frac{4}{3} (\pi \times \frac{TL}{2} \times \frac{TL}{4} \times \frac{TL}{4})$. Ciliates individual biovolume was then converted to fresh body mass assuming a density of 1 g ml⁻¹. The fresh population biomass of the phytoplankton and ciliates was estimated by converting the population biovolume in µm³ ml⁻¹ to population biomass in g l⁻¹ assuming a density of 1 g ml⁻¹.

Heterotrophic bacteria were taken into account in the food web model without considering their taxonomic composition. They were considered as a unique biological group composed of cells with a diameter of 2 µm and a fresh body mass of $8 \times 10^{-14}$ g (corresponding to an individual biovolume of 0.08 µm³, Domaizon et al. 2003). The bacterial density was estimated using data from a 3-year monthly monitoring conducted in Lake Geneva to evaluate the temporal dynamic of bacterial density in the 0–50 m water strata (Personnic et al. 2009). The available data (monthly sampling from 2004 to 2006) showed that bacterial density exhibited a strong seasonal pattern, which remained consistent across the years. Accordingly, we assigned a bacterial density of $5 \times 10^6$ cells ml⁻¹ for the months of April through October and $2 \times 10^6$ cells ml⁻¹ for the months of November through March. The bacterial biomass (in g l⁻¹) was then calculated by multiplying this density by the individual biovolume of 0.08 µm³, and then converting this value to biomass per liter assuming a density of 1 g ml⁻¹. Numerous bacterial genera are present in Lake Geneva (Ezzedine et al. 2020), and this approach led to underrepresent the number of nodes bacteria should occupy in the food web. However, given that the allometric niche
model is used to construct the food web, and that the bacteria in the pelagic zones of Lakes Geneva, Bourget, and Annecy are predominantly cocci of uniform size, it seemed relevant to represent bacteria as a single node in the trophic network, even though this morphological uniformity may conceal diverse functional roles played by bacteria. In addition, preliminary analyses without accounting for bacteria indicated an overestimation of the energy fluxes from phytoplankton to ciliate taxa and bacteria are an important food source for many ciliates (Adl et al. 2019). Thus, integrating them into the food web is important to produce reliable flux estimates.

## Food web inference

Species trophic interactions were inferred by fitting an allo-metric niche model (Vagnon et al. 2021) to the extensive bio-diversity inventory present over the whole sampling period enabling to generate the Lake Geneva planktonic metaweb (i.e. all trophic interactions between all sampled taxa). This allometric niche model considers body size as a niche trait for predicting trophic interactions using body size of consumer *i* to define its feeding range (*r*<sub>i</sub>). This range extends from the minimum (*r*<sub>imin</sub>) to the maximum (*r*<sub>imax</sub>) prey body sizes, calculated as the 5% and 95% quantiles of regressions between log<sub>10</sub>(consumer body size) as the explanatory variable and log<sub>10</sub>(prey body size) as the response variable. Any species with a body size falling within the feeding range of the consumer *r*<sub>i</sub> is considered as a potential prey, and was set at 1 in the metaweb, 0 otherwise. We refined the binary matrix *M* to ensure that strict herbivores only consumed phytoplankton while strict carnivores consumed all except phytoplankton. We determined whether a genus is a strict herbivore, omnivore, or strict carnivore based on Adl et al. (2019) for ciliates, Gilbert (2022) for rotifers, and Vincent et al. (2020) for crustaceans (Supporting information). Our taxonomic list includes mixotrophic phytoplankton genera, which represent about 20% of the phytoplankton genera, as well as ciliates that host symbiotic algae, providing them with mixotrophic capabilities. However, estimating the relative contributions of autotrophy and heterotrophy to the biomass production of an organism is challenging. Consequently, we did not account for mixotrophy in our food web models. We subsampled this metaweb for each sampling date, resulting in 27 time points of food web. From these, we extracted food web structure metrics, only referring here to species richness (*S*), connectance (*C*), and complexity (*Cx* = *S* × *C*), using the ‘NetIndices’ R package (Kones et al. 2009). Species not detected at a given time point are not necessarily extinct from the lake but may simply be very rare at this moment. As a result, the food web structure reflects only the detectable fraction of the community, representing the most abundant species and thus likely capturing the main energy fluxes pathways.

## Energy fluxes

Energy fluxes between species of each food web were estimated using a bioenergetic model computed with the ‘fluxweb’ R package (Gauzens et al. 2019). This model assumes a

steady state system which implies that $L_j$, the total amount of energy lost by a species *j*, is compensated by the metabolized energy it gains from consumption $G_j$ ($L_j = G_j$). Energy losses ($L_j$, Eq. 1) are calculated as the sum of metabolic losses ($X_j$) for the population plus the flux from species *j* to its consumer *k* at the higher trophic level ($F_{jk}$).

$$L_j = X_j + \sum_k F_{jk} \quad (1)$$

Energy gains ($G_j$, Eq. 2) are calculated as the sum of ingoing fluxes from the resource *i* to the species *j* ($F_{ij}$) multiplied with the resource assimilation efficiency ($e_{ij}$).

$$G_j = \sum_i F_{ij} e_{ij} \quad (2)$$

Fluxes from prey to each consumer were estimated scaling *M* using a linear scaling with prey population biomass (setting ‘bioms.pref’ option of *fluxing* function to *TRUE*). Any flux $F_{ij}$ can be expressed as $F_{ij} = W_{ij}F_j$, where $F_j$ represents the total incoming flux to consumer *j*, and $W_{ij}$ specifies the fraction of $F_j$ that comes from resource *i* (with $\sum_i W_{ij} = 1$). Consequently, the energy flux from resource *i* to consumer *j* was calculated as:

$$\sum_i W_{ij} F_j e_{ij} = X_j + \sum_k W_{jk} F_k \quad (3)$$

With:

$$W_{ij} = \frac{w_{ij} B_i}{\sum_k w_{kj} B_k} \quad (4)$$

Where $B_i$ is the population biomass (in g l<sup>-1</sup>) of resource *i* and $w_{ij}$ is the value (0 or 1) from the resource *i* to consumer *j* in the binary matrix *M*.

Mass-specific metabolic rate ($X_j$, Eq. 5, in J s<sup>-1</sup> g<sup>-1</sup>, used to calculate the population energetic losses setting ‘bioms.losses’ option of *fluxing* function to *TRUE*) was estimated for each genus at each sampling date using the allometric equation from Brown et al. (2004):

$$X_j = e^{i0} \text{BM}^{-0.29} e^{\frac{-E}{kT}} \quad (5)$$

Where $i_0$ is a normalization constant independent of body size and temperature (17.17 J s<sup>-1</sup> g<sup>-0.71</sup> for invertebrates), BM the fresh individual body mass (in g), *E* the activation energy (0.69 eV), *k* the Boltzmann’s constant (8.617343 × 10<sup>-5</sup> eV K<sup>-1</sup>), and *T* the absolute temperature in Kelvin (Boltzmann 1970, Arrhenius 1889).

Temperature-dependant assimilation efficiency ($\epsilon$, Eq. 6, dimensionless, used to define the efficiency with which the genus is assimilated as prey setting ‘ef.level’ option of fluxing
function to 'prey') was calculated for each genus at each sampling date deriving a formula from Lang et al. (2017):

$$ \varepsilon = \frac{e^{0} e^{E_{\varepsilon}(T-T_{0})/kT T_{0}}}{1 + e^{0} e^{E_{\varepsilon}(T-T_{0})/kT T_{0}}} \quad (6) $$

where $\dot{e}_0$ is the normalization constant for assimilation efficiency (0.179 for herbivory and 2.260 for carnivory), $E_\varepsilon$ the activation energy of assimilation efficiency (0.164 eV) and $T_0$ the temperature in Kelvin normalized to 20°C. As we lack information on how bacterial assimilation efficiency depends on temperature, we have set it to 0.6 (as in Romera et al. 2024). As fluxes were determined from the population level metabolism, they were estimated as joules per second and then converted into joules per year.

## Food web resilience and reactivity

The long-term stability of each food web was assessed as the real part of the maximum eigenvalue of the Jacobian matrix $J$ ($\lambda_{fmax}$), which is derived from the matrix of fluxes in a Lotka-Volterra-like system of equations using the modified create. jacob function (simplifying the equations used to derive the Jacobian matrix) from the 'fluxweb' R package (Gauzens et al. 2019). The $\lambda_{fmax}$ value represents the resilience of the food web, indicating its ability to return to an equilibrium state following a small disturbance. If $\lambda_{fmax}$ is positive, the equilibrium is unstable, implying that minor perturbations in population abundances can amplify and destabilize the system. Conversely, if $\lambda_{fmax}$ is negative, the system can absorb disturbances and return to equilibrium. Consequently, the more positive the real part of $\lambda_{fmax}$, is, the more unstable the system will be.

The short-term stability of each food web was assessed as the maximum eigenvalue of the Hermitian part $H$ of this Jacobian matrix ($\lambda_{Hmax}$) which is calculated as $H = (J + J^T)/2$ (Neubert and Caswell 1997) where $J^T$ is the conjugate transpose of the Jacobian matrix. The $\lambda_{Hmax}$ value represents the reactivity of the food web, indicating how the reaction to perturbations will initially grow in magnitude. The greater the $\lambda_{Hmax}$, the further the perturbation initially pushes the system away from equilibrium.

## Statistical analyses

We carried out likelihood ratio tests to compare all the simple linear models to linear mixed-effects models that included year as a random effect, while maintaining the overall structure of the models. The simple linear models always better fitted the data. Consequently, we proceeded only the simple linear models.

We computed linear models to assess the effect of season (as a fixed factor) on the metrics relative to food web structure (i.e. species richness, connectance and complexity), energy fluxes (i.e. total energy fluxes and distribution of energy fluxes) and stability (i.e. resilience and reactivity). In this study, winter covered December to February, spring March to May, summer June to August, and autumn September

to November. The distribution of energy fluxes within each food web was determined by calculating the proportion of fluxes exceeding 0.2% of the total energy fluxes (defined as strong fluxes). We set a threshold of 0.2% to discriminate between strong and weak energy fluxes because this threshold efficiently divides the energy fluxes distribution of each food web into two classes: a very large number of fluxes close to zero and a few much stronger fluxes (Supporting information).

We computed linear models to examine how energy fluxes metrics (total energy fluxes and distribution of energy fluxes), as well as stability metrics (i.e. resilience and reactivity) were influenced by complexity. In addition, as stability is predicted to be influenced by the distribution of energy fluxes (Thébault and Loreau 2006), we calculated linear models with resilience and reactivity depending on the proportion of strong to weak energy fluxes as a fixed factor.

In order to investigate how energy fluxes were distributed among consumer groups according to the season, we summed the incoming fluxes for each consumer group on each sampling date, except for molluscs Dreisseina that are found on only three sampling dates. We then calculated a linear model having the incoming fluxes as response variable depending on consumer group, season, and their interaction as fixed factors.

When it was required, the numerical variables were log-transformed to verify the model assumptions. For each model, we calculated the Fisher statistic for the fixed factors using the Anova function from the 'car' R package (Fox and Weisberg 2019) to assess the significance of the fixed effects. When a fixed factor showed a significant effect, we conducted Tukey post hoc tests to perform pairwise comparisons using the emmeans function from the 'emmeans' R package (Lenth and Lenth 2018). Residuals were inspected to verify the assumptions of each model using the check_model function from the 'performance' R package (Lüdecke et al. 2021). All statistical analyses were performed using R ver. 4.2.2 (www.r-project.org). Data and R scripts are available from the Dryad Repository: http://doi.org/10.5061/dryad.41ns1rnrr.

## Results

### The planktonic metaweb of Lake Geneva

Coupling traditional and molecular surveys led to include in the metaweb 114 taxa, including 58 primary producers, 33 ciliates, 15 rotifers, 1 mollusc (veliger larvae of dreissenids), 6 crustaceans and 1 node of bacteria (Supporting information). Using the allometric niche model and information on consumer feeding types, we found 2317 trophic links out of the 12 996 potential ones, corresponding to a connectance of 0.18 and a complexity of 20 (0.18 × 114).

The large crustaceans *Leptodora* and *Bythotrephes* were identified as the top predators within this planktonic food web, with a trophic level (TL) of 3.19. In addition, we identified seven genera of ciliates (*Phialina*, *Didinium*, *Epispathidium*, *Monodinium*, *Acineria*, *Cyclotrichium* and *Pseudoholophrya*), which occupied high trophic levels (TL between 2.83 and 3.06) due to their carnivorous diet. The six omnivorous
consumers with a trophic level around 2.5 (Daphnia sp., Eubosmina, Cyclops, Eudiapтомus, Asplanchna and Ploesoma) were the most connected nodes (Supporting information), as they consume phytoplankton, small ciliates and rotifers, but they are also preyed upon by the top predators *Leptodora* and *Bythotrephes*. Many ciliates and rotifers have a trophic level of 2, feeding on phytoplankton. Bacteria are exclusively consumed by ciliates and veliger larvae of dreissenids.

## Seasonal changes in food web structure

The minimum and maximum number of species simultaneously present in food webs were 41 and 63, respectively. Species richness strongly depended on the season (Fig. 1A, df=3, F=11.7, p < 0.001), and was significantly lower in spring than in summer and autumn. Though the food web connectance also significantly varied across seasons (Fig. 1B, df=3, F=4.5, p=0.013), with a higher connectance in spring than in autumn, these variations were weaker than the seasonal differences observed for the species richness. The variation in the connectance values were indeed limited, ranging from 0.18 to 0.22. Consequently, the complexity was positively correlated with species richness. Thus, complexity

had a significant seasonal pattern (Fig. 1C, df=3, F=7.6, p=0.001), with lower complexity in spring as compared to summer and autumn.

## Seasonal changes in food web energy fluxes and stability

The total energy fluxes within food webs ranged from 2.4 to 44.0 J year⁻¹. No significant seasonal variations were observed neither on the total energy fluxes (Fig. 2A, df=3, F=1.9, p=0.152) nor on the proportion of strong energy fluxes (Fig. 2B, df=3, F=2.4, p=0.098).

All food webs exhibited positive resilience values indicating they were unstable. We found no significant differences of resilience across seasons (Fig. 2C, df=3, F=0.8, p=0.509). In addition, all food webs also exhibited positive values of reactivity, indicating that the reaction to perturbation should initially grow in magnitude. In contrast to the resilience, reactivity significantly varied across seasons (Fig. 2D, df=3, F=11.5, p < 0.001), with lower reactivity observed in winter and spring compared to summer and autumn.

<figure><img src="image_5.png" /><figcaption>Figure 1. Seasonal variations of food web structure metrics, specifically species richness (A), connectance (B), and complexity (C). Blue, green, red and orange points represent the winter, spring, summer and autumn seasons, respectively.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Figure 2. The blue panel represent the seasonal variations of energy fluxes metrics, specifically total energy fluxes (A) and proportion of strong energy fluxes (i.e. exceeding 0.2% of the total energy fluxes) (B). The orange panel represent the seasonal variations of food web stability metrics, specifically resilience (C) and reactivity (D). Blue, green, red and orange points represent the winter, spring, summer and autumn seasons, respectively.</figcaption></figure>

## The links between food web structure and energy fluxes

We found no significant effect of food web complexity on total energy fluxes (Fig. 3A, df=1, F=0.1, p=0.731). However, the proportion of strong energy fluxes decreased with increasing food web complexity (Fig. 3B, df=1, F=16.4, p < 0.001).

## The links between food web complexity, energy fluxes distribution and stability

Food web resilience was not significantly affected by food web complexity (Fig. 4A, df=1, F=0.1, p=0.801) or by the distribution of energy fluxes (Fig. 4B, df=1, F=0.6, p=0.461). In contrast to resilience, reactivity significantly increased with increasing food web complexity (Fig. 4C,

df=1, F=19.7, p < 0.001) and decreased with increasing proportion of strong energy fluxes within food web (Fig. 4D, df=1, F=6.8, p=0.015).

## Detailed analysis of total energy fluxes by feeding mode and consumer group

Herbivorous fluxes contributed substantially to the total energy fluxes, accounting for 48–80% depending on the sampling date (Supporting information). Bacterivorous fluxes made up 14–50% of total energy fluxes, driven almost entirely by the consumption of bacteria by ciliates. The remaining energy fluxes were carnivorous fluxes, generated by consumers eating organisms other than phytoplankton and bacteria within the food web.
<figure><img src="image_7.png" /><figcaption>Figure 3. Food web complexity effect on total energy fluxes (A) and proportion of strong energy fluxes (B). Blue, green, red and orange points represent the winter, spring, summer and autumn seasons, respectively.</figcaption></figure>

We found a significant effect of consumer group (Fig. 5, df=2, F=117.2, p < 0.001), season (df=3, F=3.3, p=0.027) and the interaction between these two factors (df=6, F=3.2, p=0.008) on the sum of incoming fluxes. In winter, the fluxes incoming to ciliates were significantly higher than those incoming to rotifers. Although the differences were not statistically significant, the incoming fluxes to crustaceans appeared lower than those incoming to ciliates and higher than those incoming to rotifers. In spring and autumn, incoming fluxes to ciliates were significantly higher than those incoming to crustaceans, which were in turn higher than those incoming to rotifers. In contrast, in summer, no significant differences in incoming fluxes were observed between the consumer groups.

Discussion

Our understanding of how energy fluxes and food web
structures change across seasons – and how these seasonal

dynamics influence food web stability – remains limited. To
address this knowledge gap, we investigated the effects of sea-
sonal variations on the structure, energy fluxes, and stabil-
ity of a highly resolved multitrophic planktonic food web,
allowing capturing the complex interplay between biodiver-
sity and ecosystem functioning (Thébault and Loreau 2006,
Soliveres et al. 2016). To achieve this, we combined tradi-
tional and molecular approaches to reconstruct the highly
resolved pelagic food web (Roslin and Majaneva 2016).

Our finding confirmed that environmental seasonal-
ity has a substantial impact on food web structure. Species
richness and complexity were significantly higher in sum-
mer and autumn compared to spring. Seasonal changes in
the composition of primary producer communities, as well
as other groups like zooplankton, are well documented and
are described in the seminal work of the Plankton Ecology
Group (PEG) model (Sommer et al. 1986, 2012). Although
the effects of seasonal changes on community composition
are widely recognized, taxonomic data are often aggregated
over time scales to create a food web that overlooks temporal
<figure><img src="image_8.png" /><figcaption>Figure 4. Food web complexity effect on resilience (A) and reactivity (C). Energy fluxes distribution effect on resilience (B) and reactivity (D). Blue, green, red and orange points represent the winter, spring, summer and autumn seasons, respectively.</figcaption></figure>

variability. This temporal aggregation may result in an overestimation of species richness and food web complexity actually at play in ecosystems, as species or trophic links vary over time. For instance, in our study, the maximum species richness observed in a food web on a single time point was 63, whereas the species richness of the metaweb (i.e. when aggregating all successive time points) almost double to reach 114 species. Consequently, by incorporating food web temporal dynamics, we can deepen our understanding of the relationship between food web structure and ecosystem functions, shedding light on the mechanisms that govern ecosystem responses to perturbations and ecosystem adaptive capacity.

We expected that seasonality would significantly influence the biomass of primary producers and consumers, and that these seasonal fluctuations in biomass would drive seasonal patterns in total energy flux and distribution of energy fluxes within food webs. Contrary to our expectations, we did not detect any significant effect of season on total energy flux and the proportion of strong energy fluxes. Additional analyses revealed that only ciliate biomass exhibited a significant

seasonal variation (Supporting information), with higher biomass observed in spring compared to summer and autumn. Although our model could be refined, for instance to capture fine-scale seasonal variations in bacterial density (instead of using a fixed bacterial density for two periods as we did here), or to integrate other metazooplanktonic taxa as bacterivores even though they are able to graze a minor part of the bacterioplankton on comparison to ciliates (Zingel et al. 2007), our data confirmed the crucial role of ciliates in energy fluxes within aquatic food webs (Novotny et al. 2021). Ciliates indeed represented the group in which the sum of incoming energy fluxes was highest. However, although ciliate biomass strongly correlates with total energy flux (Supporting information), its seasonal variations alone were insufficient to lead significant seasonal differences in total energy flux. This lack of a seasonal pattern in total energy flux contrasts with previous studies that have reported strong seasonal patterns in energy fluxes within food webs. For instance, the Kluane food web has been shown to shift from summer – characterised by many weak interactions between generalist consumers,
<figure><img src="image_9.png" /><figcaption>Figure 5. Distribution of incoming fluxes among the consumer groups depending on the seasons.</figcaption></figure>

herbivores and primary producers – to winter, when there are fewer but stronger interactions between carnivores, herbivores and remaining plant resources (Humphries et al. 2017). This suggests that variations in energy fluxes are more closely linked to phenological shifts and changes in the biomass of interacting taxa - which themselves may be seasonally dependent - rather than to the direct effects of seasonality.

Contrary to our expectations, we found no direct link between seasons and resilience, nor any indirect links through changes in complexity. This lack of correlation between food web complexity and long-term stability aligns with a recent study that also reported no relationship between complexity and long-term stability across 116 empirical food webs worldwide (Jacquet et al. 2016). We also found no significant effect of the distribution of energy fluxes on resilience. This result contrasts with theoretical predictions suggesting that stability is strongly affected by the distribution of trophic interactions strength, with strong fluxes having a destabilizing effect and weak fluxes a stabilizing one (Allesina and Tang 2012, Tang et al. 2014). However, examining long-term responses alone does not fully capture the multi-faceted nature of food web stability (Donohue et al. 2013, 2016, Domínguez-García et al. 2019, Yang et al. 2023). It is noteworthy that short-term responses, measured as reactivity, can diverge from long-term responses and can serve as better predictors of population fluctuations and extinction risk within food webs (Yang et al. 2023).

Our results indicate that reactivity was season-dependent, as it was significantly higher in summer and autumn than in winter and spring. This suggests that the planktonic food web could be less stable and more reactive to perturbations in summer and autumn, making it more vulnerable to external perturbations during these seasons. Ushio et al. (2018) also

pointed out the seasonal dependence of food web stability, as they observed that higher fish species diversity in summer was associated with increased stability and reduced fish population fluctuations in Maizuru Bay, Japan. However, this latter study investigated another aspect of stability (local Lyapunov stability) and includes fish in the food web, highlighting that comparing stability among studies may be limited by the use of different stability metrics and differences in food web complexity.

Our findings indicate that the observed increase in reactivity during summer and autumn is primarily an indirect consequence of seasonal changes in food web complexity. In addition, we observed a significant decrease in the proportion of strong energy fluxes as complexity increases, which resulted in reduced reactivity. We could speculate from the results that increasing complexity, driven by seasonality, may amplify reactivity by lowering the proportion of strong energy fluxes within food webs. For instance, in low-complexity food webs – characterized by fewer consumer and prey species – consumer energy needs are often met through a limited number of dominant trophic links, resulting in a higher proportion of strong energy fluxes, even if total energy throughput remains low. In contrast, in more complex food webs, energy is more evenly distributed across a greater number of interactions, leading to a lower proportion of strong fluxes and a more buffered response to perturbations. Consequently, our results support theoretical studies suggesting that food web stability is influenced by both complexity and the distribution of trophic interactions strength (Thébault and Loreau 2006, Allesina and Tang 2012, Altena et al. 2016). However, contrary to our expectations, our results indicate that an increase in the proportion of strong energy fluxes decreases food web reactivity and, as a result, enhances short-term stability. This
finding diverges from previous theoretical and empirical
studies, which have often suggested that a high proportion of
strong interactions relative to weak ones is destabilizing while
many weak interactions relative to strong ones contribute
to food web stability (McCann et al. 1998, Emmerson and
Yearsley 2004, Wootton and Stouffer 2016). Nevertheless,
these studies focused on the effects of the distribution of the
trophic interaction strength on long-term stability, assessed
using the dominant eigenvalue of the Jacobian community
matrix. To our knowledge, our study is the only one to have
examined how the distribution of the trophic interaction
strength affects short-term stability, and to have pointed out
that short-term stability can react differently from long-term
stability to trophic interaction strength distribution.

Our analysis focused exclusively on the pelagic plank-
tonic food web, excluding both fish and benthic organisms.
While this provides valuable insights into upper-layer trophic
dynamics, it introduces important limitations. In particular,
omitting fish likely leads to an underestimation of upward
energy fluxes, as a portion of the energy transferred to zoo-
plankton may instead be diverted toward fish through preda-
tion. Additionally, fish can exert strong per capita effects on
their prey, potentially contributing to further destabilization
of food web dynamics. In addition, we estimated energy fluxes
using the fluxweb package, which infers fluxes from standing
stock biomasses, metabolic theory, and the assumption of
steady-state equilibrium. This method provides a static view
of energy transfer, suitable for comparing relative changes
across environmental gradients such as seasonal or thermal
variation. However, as recently emphasized by Wenhui et al.
(2025), such steady-state approaches differ from dynamic
species interaction models, which simulate energy fluxes as
emergent properties of time-dependent population dynam-
ics. While our method does not account for transient feed-
backs or temporal variation in species interactions, it remains
a tractable and ecologically grounded approach for evaluat-
ing structural and functional differences across experimental
treatments. As such, our absolute estimates of energy fluxes,
food web complexity, and stability should be interpreted with
caution, as they do not fully capture the entire community's
dynamics. Nevertheless, these limitations do not compro-
mise our ability to assess relative seasonal differences, which
is the primary objective of this study. Future work should
aim to incorporate additional taxonomic groups, particularly
fish and benthic organisms, to more accurately represent the
complexity of aquatic food webs.

Conclusion

Overall, we found important variations in food web struc-
ture, energy fluxes and reactivity across season. We thus argue
that food web structure and stability should not be inferred
from data lumped over multiple sampling dates. This tem-
poral aggregation risks overestimating species richness and
food web complexity, potentially leading to misleading con-
clusions about the relationships among food web structure,

stability and ecosystem functioning. Our results also demon-
strate that environmental seasonality significantly influences
food web structure and stability, with important implications
for understanding ecosystem dynamics. Increased complex-
ity, driven by seasonality, was associated with a decrease in
the proportion of strong energy fluxes, which amplified reac-
tivity and thus, contrary to theoretical predictions, reduced
stability. These results challenge the long-standing hypothesis
that a higher proportion of strong interactions destabilizes
food webs. Instead, our results suggest that the distribution
of trophic interaction strengths may affect stability in dif-
ferent ways according to the specific facet of stability being
considered. Our study also highlighted the key functional
role of micro-consumers, particularly ciliates, in linking
food webs based on primary production (green food webs)
and food webs based on decomposition of organic matter
by bacteria (brown food webs) (Zou et al. 2016). The cur-
rent view emphasizes the coupling of green and brown food
webs at higher trophic levels (e.g. fish, Wolkovich et al. 2014,
Sitvarin et al. 2016), but they are also linked at lower levels
by micro-consumers within the planktonic microbial loop –
connections that are already well-documented yet still seldom
integrated into ecological models.

Speculations and alternative viewpoints

Our results raise broader questions about how we define
and measure stability in ecological networks. Traditional
approaches have largely relied on asymptotic resilience (i.e.
the largest eigenvalue of the Jacobian matrix) as a proxy for
long-term stability. Yet, stability is a multi-faceted concept,
and alternative perspectives such as network viability (the
ability of all species to maintain positive abundances over
time) may provide complementary or even more ecologically
meaningful insights (Rohr et al. 2025). Recent advances in
computational methods now make it feasible to assess viabil-
ity in empirical food webs, an approach that could transform
how we evaluate the persistence of complex, temporally vary-
ing ecological networks. If applied to our data, one might
even speculate that the food web of Lake Geneva could
appear more viable than suggested by asymptotic stability
metrics alone.

Another dimension worth considering is that food webs
are rarely static entities. While ecological networks are often
studied as snapshots aggregated across space or time, in real-
ity they are continuously reshaped by phenology, diel activ-
ity rhythms, and short-term environmental fluctuations.
The strong seasonal dynamics highlighted in our study may
represent only part of the story: food webs could shift sub-
stantially between day and night, as different taxa dominate
energy fluxes and trophic interactions at different times of
the diel cycle. Such fine-scale temporal restructuring may
have profound consequences for both stability and viability.
This raises the provocative possibility that the same ecosystem
could alternate between more or less stable (or viable) states
within a single 24-h period, in addition to seasonal cycles.
Together, these considerations point to a potential shift in perspective: from viewing food webs as relatively static structures whose stability is assessed via a narrow set of metrics, to recognizing them as highly dynamic, multi-scale systems whose persistence depends on both their temporal variability and the dimension of stability under consideration. Exploring stability through the lens of viability, and across temporal resolutions ranging from hours to seasons, could yield a richer and perhaps more realistic understanding of how ecological communities respond to perturbations. For this purpose, fine-scale ecological (e.g. Lexplore plateform, https://lexplor.e.info/fr/accueil/) and new generation (eDNA) monitoring programs are critical and necessarily imply a convergence between stakeholder and scientific goals.

**Acknowledgements** – We thank Benoit Gauzens for his helpful assistance in fitting the fluxweb model to our empirical data and interpreting the model outputs. We thank OLÀ (Observatory on Lakes), the information system OLA Data, the research infrastructure AnaEE-France (Analysis and Experimentation on Ecosystems) for providing limnological data. We thank all the UMR CARRTEL technicians involved in the collection and processing of biological samples.

**Funding** – This work was funded by Pole R&D ECLA, OFB, INRAE, USMB.

**Conflict of interest** – The authors declare no conflict of interest.

## Author contributions

**Simon Bazin:** Formal analysis (lead); Investigation (lead); Methodology (lead); Writing – original draft (lead); Writing – review and editing (lead). **Isabelle Domaizon:** Conceptualization (equal); Data curation (equal); Formal analysis (supporting), Funding acquisition (equal); Investigation (supporting); Methodology (equal); Project administration (equal); Supervision (supporting), Validation (supporting), Writing – original draft (supporting), Writing – review and editing (supporting). **Cécilia Barouillet:** Conceptualization (supporting), Data curation (lead); Formal analysis (supporting), Investigation (supporting).

**Victor Frossard:** Conceptualization (equal); Funding acquisition (equal); Investigation (supporting), Methodology (supporting), Project administration (equal); Supervision (equal); Validation (equal); Writing – original draft (supporting), Writing – review and editing (supporting).

**Arnaud Sentis:** Conceptualization (equal); Funding acquisition (equal); Investigation (supporting), Methodology (supporting), Project administration (equal); Supervision (equal); Validation (equal); Writing – original draft (supporting), Writing – review and editing (supporting).

## Data availability statement

Data are available from the Dryad Digital Repository: https://doi.org/10.5061/dryad.41ns1rnrr (Bazin et al. 2025).

## Supporting information

The Supporting information associated with this article is available with the online version.

## References

Aberle, N., Lengfellner, K. and Sommer, U. 2007. Spring bloom succession, grazing impact and herbivore selectivity of ciliate communities in response to winter warming. – Oecologia 150: 668–681.

Adl, S. M. et al. 2019. Revisions to the classification, nomenclature and diversity of Eukaryotes. – J. Eukaryot. Microbiol. 66: 4–119.

Allesina, S. and Tang, S. 2012. Stability criteria for complex ecosystems. – Nature 483: 205–208.

Altena, C., Hemerik, L. and de Ruiter, P. C. 2016. Food web stability and weighted connectance: the complexity-stability debate revisited. – Theor. Ecol. 9: 49–58.

Amoros, C. 1984. Introduction pratique à la systématique des organismes des eaux continentales Françaises-5. Crustacés Cladocères. – Publ. Soc. Linn. Lyon 53: 72–107.

Arrhenius, S. 1889. Über die Reaktionsgeschwindigkeit bei der Inversion von Rohrzucker durch Säuren. – Z. Phys. Chem. 4U: 226–248.

Barnes, A. D., Jochum, M., Lefcheck, J. S., Eisenhauer, N., Scherber, C., O’Connor, M. I., De Ruiter, P. and Brose, U. 2018. Energy flux: the link between multitrophic biodiversity and ecosystem functioning. – Trends Ecol. Evol. 33: 186–197.

Barouillet, C., Vasselon, V., Keck, F., Millet, L., Etienne, D., Galop, D., Rius, D. and Domaizon, I. 2022. Paleoreconstructions of ciliate communities reveal long-term ecological changes in temperate lakes. – Sci. Rep. 12: 7899.

Bazin, S., Domaizon, I., Barouillet, C., Frossard, V. and Sentis, A. 2025. Data from: Seasonal variations in planktonic food web structure affect stability by shifting the distribution of energy fluxes. – Dryad Digital Repository https://doi.org/10.5061/dryad.41ns1rnrr.

Berger, S. A., Diehl, S., Stibor, H., Sebastian, P. and Scherz, A. 2014. Separating effects of climatic drivers and biotic feedbacks on seasonal plankton dynamics: no sign of trophic mismatch. – Freshw. Biol. 59: 2204–2220.

Berlow, E. L., Navarrete, S. A., Briggs, C. J., Power, M. E. and Menge, B. A. 1999. Quantifying variation in the strengths of species interactions. – Ecology 80: 2206–2224.

Berlow, E. L., Neutel, A. M., Cohen, J. E., De Ruiter, P. C., Ebenman, B., Emmerson, M., Fox, J. W., Jansen, V. A. A., Iwan Jones, J., Kokkoris, G. D., Logofet, D. O., McKane, A. J., Montoya, J. M. and Petchey, O. 2004. Interaction strengths in food webs: issues and opportunities. – J. Anim. Ecol. 73: 585–598.

Berzins, B. 1960. Zooplankton sheets. – Rotatoria I-VI 86: 84–89.

Boltzmann, L. 1970. Weitere Studien über das Wärmegleichgewicht unter Gasmolekülen. – Kinetische Theor. II: 115–225.

Bottrell, H. H. et al. 1976. Review of some problems in zooplankton production studies. – Norw. J. Zool. 21: 477–483.

Branstrator, D. K. 2005. Contrasting life histories of the predatory cladocerans *Leptodora kindtii* and *Bythotrephes longimanus*. – J. Plankton Res. 27: 569–585.

Brose, U., Williams, R. J. and Martinez, N. D. 2006. Allometric scaling enhances stability in complex food webs. – Ecol. Lett. 9: 1228–1236.

Brown, J. H., Gillooly, J. F., Allen, A. P., Savage, V. M. and West, G. B. 2004. Toward a metabolic theory of ecology. – Ecology 85: 1771–1789.

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W., Johnson, A. J. A. and Holmes, S. P. 2016. DADA2: high-resolution sample inference from Illumina amplicon data. – Nat. Methods 13: 581–583.
Cardinale, B. J., Duffy, J. E., Gonzalez, A., Hooper, D. U., Per-
rings, C., Venail, P., Narwani, A., Mace, G. M., Tilman, D.,
Wardle, D. A., Kinzig, A. P., Daily, G. C., Loreau, M., Grace,
J. B., Larigauderie, A., Srivastava, D. S. and Naeem, S. 2012.
Biodiversity loss and its impact on humanity. – Nature 486:
59–67.

Chengalath, R., Fernando, C. H. and Koste, W. 1973. Rotifera from Sri Lanka (Ceylon) 2 further studies on the Eurotatoria including new records. - Freshw. Fauna and Fish. Shi LANMA 293: 29-62.

CIPEL 2014. Rapports sur les études et recherches entreprises sur le bassin lémanique. – https://www.cipel.org/catalogue/rapport-scientifique/

CIPEL 2015. Rapports sur les études et recherches entreprises sur le bassin lémanique. – https://www.cipel.org/catalogue/rapport-scientifique/

CIPEL 2016. Rapports sur les études et recherches entreprises sur le bassin lémanique. – https://www.cipel.org/catalogue/rapport-scientifique/

CIPEL 2022. Rapports sur les études et recherches entreprises sur le bassin lémanique. – https://www.cipel.org/catalogue/rapport-scientifique/

Domaizon, I., Viboud, S. and Fontvieille, D. 2003. Taxon-specific and seasonal variations in flagellates grazing on heterotrophic bacteria in the oligotrophic Lake Annecy - importance of mixotrophy. - FEMS Microbiol. Ecol. 46: 317-329.

Domínguez-García, V., Dakos, V. and Kéfi, S. 2019. Unveiling dimensions of stability in complex ecological networks. – Proc. Natl Acad. Sci. USA 116: 25714-25720.

Donohue, I., Petchey, O. L., Montoya, J. M., Jackson, A. L., McNally, L., Viana, M., Healy, K., Lurgi, M., O'Connor, N. E. and Emmerson, M. C. 2013. On the dimensionality of ecological stability. – Ecol. Lett. 16: 421-429.

Donohue, I., Hillebrand, H., Montoya, J. M., Petchey, O. L., Pimm, S. L., Fowler, M. S., Healy, K., Jackson, A. L., Lurgi, M., McClean, D., O'Connor, N. E., O'Gorman, E. J. and Yang, Q. 2016. Navigating the complexity of ecological stability. – Ecol. Lett. 19: 1172-1185.

Duffy, J. E., Cardinale, B. J., France, K. E., McIntyre, P. B., Thébault, E. and Loreau, M. 2007. The functional role of biodiversity in ecosystems: incorporating trophic complexity. – Ecol. Lett. 10: 522–538.

Dunne, J. A. 2006. The network structure of food webs. – In: Pascual, M. and Dunne, J. A. (eds), Ecological networks: linking structure to dynamics in food webs. Oxford Univ. Press, pp. 27–86.

Dunne, J. A., Williams, R. J. and Martinez, N. D. 2002. Network topology and biodiversity loss in food webs: robustness increases with connectance. – Ecol. Lett. 5: 558–567.

Dussart, B. 1967. Les Copepodes des eaux continentales. – Cala-
noides Harpacticoides I: 500.

Ejsmont-Karabin, J. 1998. Empirical equations for biomass calcula-
tion of planktonic rotifers. - Pol. Arch. Hydrobiol. 45: 513-522.

Emmerson, M. and Yearsley, J. M. 2004. Weak interactions, omnivory and emergent food-web properties. – Proc. R. Soc. B 271: 397–405.

Ershova, E. A., Wangensteen, O. S., Descoteaux, R., Barth-Jensen, C. and Præbel, K. 2021. Metabarcoding as a quantitative tool for estimating biodiversity and relative biomass of marine zoo-plankton. - ICES J. Mar. Sci. 78: 3342-3355.

Ezzedine, J., Desdevises, Y. and Jacquet, S. 2020. Exploring archaeal and bacterial diversity and co-occurrence in Lake Geneva. – Adv. Oceanogr. Limnol. 1: 45–58.

Fox, J. and Weisberg, S. 2019. An R companion to applied regression, 3rd edn. – Sage, Thousand Oaks. https://www.john-fox.ca/Companion/.

Gauzens, B., Barnes, A., Giling, D. P., Hines, J., Jochum, M., Lef-check, J. S., Rosenbaum, B., Wang, S. and Brose, U. 2019. Fluxweb: an R package to easily estimate energy fluxes in food webs. – Methods Ecol. Evol. 10: 270–279.

Gilbert, J. J. 2022. Food niches of planktonic rotifers: diversification and implications. – Limnol. Oceanogr. 67: 2218–2251.

Gravel, D., Poisot, T., Albouy, C., Velez, L. and Mouillot, D. 2013. Inferring food web structure from predator–prey body size relationships. – Methods Ecol. Evol. 4: 1083–1090.

Gross, T., Rudolf, L., Levin, S. A. and Dieckmann, U. 2009. Generalized models reveal stabilizing factors in food webs. – Science 325: 747–750.

Holling, C. S. 1966. The functional response of invertebrate predators to prey density. – Mem. Entomol. Soc. Can. 98: 5–86.

Humphries, M. M., Studd, E. K., Menzies, A. K. and Boutin, S. 2017. To everything there is a season: summer-to-winter food webs and the functional traits of keystone species. – Integr. Comp. Biol. 57: 961–976.

Jacquet, C., Moritz, C., Morissette, L., Legagneux, P., Massol, F., Archambault, P. and Gravel, D. 2016. No complexity-stability relationship in empirical ecosystems. – Nat. Commun. 7: 12573.

Kones, J. K., Soetaert, K., van Oevelen, D. and Owino, J. O. 2009. Are network indices robust indicators of food web functioning? A Monte Carlo approach. – Ecol. Modell. 220: 370–382.

Kortsch, S., Frelat, R., Pecuchet, L., Olivier, P., Putnis, I., Bons-
dorff, E., Ojaveer, H., Jurgensone, I., Stråke, S., Rubene, G.,
Krüze, Æ. and Nordström, M. C. 2021. Disentangling tempo-
ral food web dynamics facilitates understanding of ecosystem
functioning. – J. Anim. Ecol. 90: 1205–1216.

Koste, W. 1978. Rotatoria. Die Rädertiere Mitteleuropas, Begrün-
det von Max Voigt. Überordnung Monogononta. Gebrüder
Borntraeger, Berlin, Stuttgart. I. Text (673p) u. II. Tafeld.

Lang, B., Ehnes, R. B., Brose, U. and Rall, B. C. 2017. Temperature and consumer type dependencies of energy flows in natural communities. – Oikos 126: 1717–1725.

Lenth, R. and Lenth, M. R. 2018. Package “Lsmeans”. – Am. Stat. 34: 216–221.

Lüdecke, D., Ben-Shachar, M. S., Patil, I., Waggoner, P. and Makowski, D. 2021. Performance: an R package for assessment, comparison and testing of statistical models. – J. Open Source Softw. 6.

Luo, M., Ji, Y., Warton, D. and Yu, D. W. 2023. Extracting abundance information from DNA-based data. – Mol. Ecol. Resour. 23: 174–189.

Malley, D. F. 1989. Range of variation in estimates of dry weight for planktonic Crustacea and Rotifera from temperate North American lakes. - Fisheries and Oceans.

May, R. M. 1972. Will a large complex system be stable? – Nature 238: 413–414.

McCann, K., Hastings, A. and Huxel, G. R. 1998. Weak trophic interactions and the balance of nature. – Nature 395: 794–798.

McMeans, B. C., McCann, K. S., Humphries, M., Rooney, N. and Fisk, A. T. 2015. Food web structure in temporally-forced ecosystems. – Trends Ecol. Evol. 30: 662–672.

Monjot, A., Bronner, G., Courtine, D., Cruaud, C., Da Silva, C., Aury, J. M., Gavory, F., Moné, A., Vellet, A., Wawrzyniak, I., Colombet, J., Billard, H., Debroas, D. and Lepère, C. 2023. Functional diversity of microbial eukaryotes in a meromictic
lake: coupling between metatranscriptomic and a trait-based approach. – *Environ. Microbiol.* 25: 3406–3422.

Nauwerck, A. 1978. Notes on the planktonic rotifers of Lake Ontario. – *Arch. Hydrobiol.* 84: 269–301.

Neubert, M. G. and Caswell, H. 1997. Alternatives to resilience for measuring the responses of ecological systems to perturbations. – *Ecology* 78: 653–665.

Novotny, A., Zamora-Terol, S. and Winder, M. 2021. DNA metabarcoding reveals trophic niche diversity of micro and mesozooplankton species. – *Proc. R. Soc. B* 288: 20210908.

Pauli, H. R. 1989. A new method to estimate individual dry weights of rotifers. – *Hydrobiologia* 186–187: 355–361.

Pejler, B. 1957. Taxonomical and ecological studies on planktonic rotatoria from northern Swedish lapland. – *Almqvist Wiksell*.

Personnic, S., Domaizon, I., Dorigo, U., Berdjeb, L. and Jacquet, S. 2009. Seasonal and spatial variability of virio-, bacterio- and Picophytoplanktonic abundances in three peri-alpine lakes. – *Hydrobiologia* 627: 99–116.

Pilosof, S., Porter, M. A., Pascual, M. and Kéfi, S. 2017. The multilayer nature of ecological networks. – *Nat. Ecol. Evol.* 1: 101.

Pimm, S. L. and Lawton, J. H. 1977. Number of trophic levels in ecological communities. – *Nature* 268: 329–331.

Pont, D., Meulenbroek, P., Bammer, V., Dejean, T., Erős, T., Jean, P., Lenhardt, M., Nagel, C., Pekarik, L., Schabuss, M., Stoeckle, B. C., Stoica, E., Zornig, H., Weigand, A. and Valentini, A. 2023. Quantitative monitoring of diverse fish communities on a large scale combining eDNA metabarcoding and qPCR. – *Mol. Ecol. Resour.* 23: 396–409.

Ramond, P., Sourisseau, M., Simon, N., Romac, S., Schmitt, S., Rigaut-Jalabert, F., Henry, N., De Vargas, C. and Siano, R. 2019. Coupling between taxonomic and functional diversity in protistan coastal communities. – *Environ. Microbiol.* 21: 730–749.

Rimet, F. and Druart, J. C. 2018. A trait database for phytoplankton of temperate lakes. – *Ann. Limnol. Int. J. Limnol.* 54: 18.

Rimet, F. et al. 2020. The observatory on LAkes (OLA) database: sixty years of environmental data accessible to the public. – *J. Limnol.* 79: 164–178.

Rip, J. M. K. and McCann, K. S. 2011. Cross-ecosystem differences in stability and the principle of energy flux. – *Ecol. Lett.* 14: 733–740.

Rohr, R. P., Bersier, L. F. and Arditi, R. 2025. Will a large complex model ecosystem be viable? The essential role of positive interactions. – *Ecology* 106: e70064.

Romera, P. J. et al. 2024. Detection of energetic equivalence depends on food web architecture and estimators of energy use. – *Preprint. Eco.Evo.Rxiv.* https://ecoevorxiv.org/repository/view/7362/.

Rooney, N. and McCann, K. S. 2012. Integrating food web diversity, structure and stability. – *Trends Ecol. Evol.* 27: 40–46.

Roslin, T. and Majaneva, S. 2016. The use of DNA barcodes in food web construction – terrestrial and aquatic ecologists unite! – *Genome* 59: 603–628.

Rudolf, V. H. W. 2012. Seasonal shifts in predator body size diversity and trophic interactions in size-structured predator-prey systems. – *J. Anim. Ecol.* 81: 524–532.

Sime-Ngande, T., Bourdier, G., Amblard, C. and Pinel-Alloul, B. 1991. Short-term variations in specific biovolumes of different bacterial forms in aquatic ecosystems. – *Microb. Ecol.* 21: 211–226.

Sitvarin, M. I., Rypstra, A. L. and Harwood, J. D. 2016. Linking the green and Brown worlds through nonconsumptive predator effects. – *Oikos* 125: 1057–1068.

Soliveres, S. et al. 2016. Biodiversity at multiple trophic levels is needed for ecosystem multifunctionality. – *Nature* 536: 456–459.

Sommer, U. and Sommer, F. 2006. Cladocerans versus Copepods: the cause of contrasting top-down controls on freshwater and marine phytoplankton. – *Oecologia* 147: 183–194.

Sommer, U., Gliwicz, Z. M., Lampert, W. and Duncan, A. 1986. The PEG-model of seasonal succession of planktonic events in fresh waters. – *Arch. Hydrobiol.* 106: 433–471.

Sommer, U., Adrian, R., De Senerpont Domis, L., Elser, J. J., Gae-dke, U., Ibelings, B., Jeppesen, E., Lürling, M., Molinero, J. C., Mooij, W. M., Van Donk, E. and Winder, M. 2012. Beyond the plankton ecology group (PEG) model: mechanisms driving plankton succession. – *Annu. Rev. Ecol. Evol. Syst.* 43: 429–448.

Stemberger, R. S. 1979. A guide to rotifers of the Laurentian Great Lakes. – *Environmental Monitoring and Support Laboratory, Office of Research and Development, United States Environmental Protection Agency*.

Stibor, H., Vadstein, O., Diehl, S., Gelzleichter, A., Hansen, T., Hantzsche, F., Katechakis, A., Lippert, B., Løseth, K., Peters, C., Roederer, W., Sandow, M., Sundt-Hansen, L. and Olsen, Y. 2004. Copepods act as a switch between alternative trophic cascades in marine pelagic food webs. – *Ecol. Lett.* 7: 321–328.

Sudzuki, M. 1991. The Rotifera from Singapore and Taiwan. – *Proc. Jpn Soc. Syst. Zool.* 43: 1–83.

Tang, S., Pawar, S. and Allesina, S. 2014. Correlation between interaction strengths drives stability in large ecological networks. – *Ecol. Lett.* 17: 1094–1100.

Telesh, I. V., Rahkola, M. and Viljanen, M. 1998. Carbon content of some Freshwater rotifers. – *In: Wurdak, E., Wallace, R., Segers, H. (eds.), Rotifera VIII: a comparative approach. Springer*.

Thébault, E. and Loreau, M. 2006. The relationship between biodiversity and ecosystem functioning in food webs. – *Ecol. Res.* 21: 17–25.

Thompson, R. M., Brose, U., Dunne, J. A., Hall, R. O., Hladyz, S., Kitching, R. L., Martinez, N. D., Rantala, H., Romanuk, T. N., Stouffer, D. B. and Tylianakis, J. M. 2012. Food webs: reconciling the structure and function of biodiversity. – *Trends Ecol. Evol.* 27: 689–697.

Trojelsgaard, K. and Olesen, J. M. 2016. Ecological networks in motion: micro- and macroscopic variability across scales. – *Funct. Ecol.* 30: 1926–1935.

Ushio, M., Hsieh, C. H., Masuda, R., Deyle, E. R., Ye, H., Chang, C. W., Sugihara, G. and Kondoh, M. 2018. Fluctuating interaction network and time-varying stability of a natural fish community. – *Nature* 554: 360–363.

Utermöhl, H. 1958. Zur Vervollkommnung der Quantitativen phytoplankton-Methodik: mit 1 Tabelle und 15 Abbildungen im Text und auf 1 Tafel. – *Int. Ver. Theor. Angew. Limnol. Mitt.* 9: 1–38.

Vagnon, C., Cattanéo, F., Goulon, C., Grimardias, D., Guillard, J. and Frossard, V. 2021. An allometric niche model for species interactions in temperate freshwater ecosystems. – *Ecosphere* 12: e03420.

Vincent, F., Bertolo, A., Lacroix, G., Mouchet, M. and Edeline, E. 2020. Trait-dependency of trophic interactions in zooplankton food webs. – *Oikos* 129: 891–902.
Wang, S. and Brose, U. 2018. Biodiversity and ecosystem functioning in food webs: the vertical diversity hypothesis. – *Ecol. Lett.* 21: 9–20.

Wenhui, C., Bingqing, X., Xiuqiang, D., Jisong, Y., Min, L., Depu, Z., Shengfu, L., Zhenbo, L., Fan, L. and Zhonghua, R. 2025. Comparative study of the characteristics of the energy flow and food web structure in the Laizhou Bay ecosystem based on the ecopath and LIM-MCMC models. – *Front. Mar. Sci.* 12: 1572355.

Wolkovich, E. M., Allesina, S., Cottingham, K. L., Moore, J. C., Sandin, S. A. and de Mazancourt, C. 2014. Linking the green and Brown worlds: the prevalence and effect of multichannel feeding in food webs. – *Ecology* 95: 3376–3386.

Wollrab, S., Pondaven, P., Behl, S., Beker, B. and Stibor, H. 2019. Differences in size distribution of marine phytoplankton in presence versus absence of jellyfish support theoretical predictions on top-down control patterns along alternative energy pathways. – *Mar. Biol.* 167: 9.

Wootton, K. L. and Stouffer, D. B. 2016. Many weak interactions and few strong; food-web feasibility depends on the combination of the strength of species' interactions and their correct arrangement. – *Theor. Ecol.* 9: 185–195.

Yang, Y., Coyte, K. Z., Foster, K. R. and Li, A. 2023. Reactivity of complex communities can be more important than stability. – *Nat. Commun.* 14: 7204.

Yvon-Durocher, G., Allen, A. P., Cellamare, M., Dossena, M., Gaston, K. J., Leitao, M., Montoya, J. M., Reuman, D. C., Woodward, G. and Trimmer, M. 2015. Five years of experimental warming increases the biodiversity and productivity of phytoplankton. – *PLoS Biol.* 13: e1002324.

Zingel, P., Agasild, H., Nõges, T. and Kisand, V. 2007. Ciliates are the dominant grazers on pico- and nanoplankton in a shallow, naturally highly eutrophic lake. – *Microb. Ecol.* 53: 134–142.

Zou, K., Thébault, E., Lacroix, G. and Barot, S. 2016. Interactions between the green and Brown food web determine ecosystem functioning. – *Funct. Ecol.* 30: 1454–1465.