# Drought preceding rain events drives shifts in phytoplankton composition and metabolome in Lake Aydat (France) revealing molecular strategies

Fanny Noirmain, Benjamin Marie, Benjamin Legrand, Delphine Latour

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Fanny Noirmain, Benjamin Marie, Benjamin Legrand, Delphine Latour. Drought preceding rain events drives shifts in phytoplankton composition and metabolome in Lake Aydat (France) revealing molecular strategies. FEMS Microbiology Ecology, 2026, 102 (3), 10.1093/femsec/fiag015. hal-05530336

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# Drought preceding rain events drives shifts in phytoplankton composition and metabolome in Lake Aydat (France) revealing molecular strategies

Fanny Noirmain<sup>1,2,*</sup>, Benjamin Marie<sup>3</sup>, Benjamin Legrand<sup>4</sup>, Delphine Latour<sup>1</sup>

Editor: Martin W. Hahn

## Abstract

Global change is expected to increase cyanobacterial dominance and cyanotoxin production. However, cyanobacterial responses to meteorological events remain uncertain due to limited integrative approaches. Eco-metabolomics offers a promising tool to investigate drivers of phytoplankton successions and metabolite production. We investigated the spatio-temporal dynamics of phytoplankton and their metabolomes in relation to local meteorological conditions in Lake Aydat (France) during summer 2021, sampling both near the Veyre inflow and at the lake center. High-resolution mass spectrometry, combined with phytoplankton biovolumes and physicochemical parameters, revealed two distinct metabolomic fingerprints positively correlated with phytoplankton composition. The first metabolome is composed of mainly lipids and positively correlated with the presence of diatoms throughout the summer until the end of the drought. In contrast, the second metabolome is correlated with the presence of *Dolichospermum*, that occurs after a 22-day period without rain and when there is a low nitrate concentration ($NO_3^- \sim 0.4 \text{ mg} \cdot \text{l}^{-1}$). Interestingly, despite similar phytoplankton composition across sites, significant higher abundance of cyanopeptides are detected close to the river inflow during the second rain period, likely driven by higher nutrient inputs and warmer waters compared to the lake center. Overall, these findings demonstrate that fine-scale spatial monitoring can reveal distinct ecometabolomic niches, separated by only 200 m.

**Keywords** phytoplankton; cyanobacteria; secondary metabolites; cyanopeptides; meteorological events

## Introduction

There is growing concern about the dynamics of phytoplankton biomass and the increasing risks of toxic cyanobacterial blooms resulting from global changes (Jöhnk et al. 2008, Paerl and Otten 2013, Bartosiewicz et al. 2019, Richardson et al. 2019, Plaas and Paerl 2021). Apart from the increase in temperature, the greatest uncertainty regarding climate change lies in the precipitation pattern and its connection to cloud dynamics, with predictions suggesting that there will be an increase in the duration of droughts coupled with high-intensity rain events (Intergovernmental Panel on Climate Change, 2023). The influence of altered rainfall patterns on phytoplankton has not been extensively studied so far, leading to a limited understanding of phytoplankton adaptive mechanisms (Reichwaldt and Ghadouani 2012). Additionally, many studies have been conducted in subtropical climates, which are characterized by more frequent rains together with intense events such as tropical cyclones (Znachor et al. 2008, Stockwell et al. 2020). However, these studies may not accurately reflect the dis-

continuity and the intensity of rainfall observed in temperate climates (Znachor et al. 2008, Stockwell et al. 2020). Those rainfall events act as disturbances to water bodies, triggering important changes in environmental factors. In meso and eutrophic waterbodies, the impacts of climate change will act primarily through changing hydro-physical conditions which can indirectly influence the biomass of phytoplankton and especially the occurrence of cyanobacteria which are sensitive to stratification conditions (Znachor et al. 2008, Reichwaldt and Ghadouani 2012, Richardson et al. 2019). Depending on the intensity and frequency of storms and the biotic and abiotic local conditions, such as the phytoplankton community composition, the strength of the lake thermal stratification, and the nutrient availabilities, the response of the phytoplankton can be rather contrasting (Winder and Sommer 2012, Stockwell et al. 2020). Storms occurring after long dry periods can induce an increase of the phytoplankton and cyanobacteria bloom due to high amounts of nutrients delivered to surface waters through run-off from the watershed. In contrast, depending on the intensity of the storms, de-stratification of the water col-

<footnote n="1"><sup>1</sup>Université Clermont Auvergne, CNRS, Laboratoire Microorganismes: Genome, Environnement (LMGE), UMR6023, 63170 Aubière, France</footnote>

<footnote n="2"><sup>2</sup>INRAE - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Pathologie Végétale, F-84140 Montfavet, France</footnote>

<footnote n="3"><sup>3</sup>UMR7245 Molécules de Communication et Adaptation des Micro-organismes, Muséum National d'Histoire Naturelle, CNRS, 75005 Paris, France</footnote>

<footnote n="4"><sup>4</sup>ATHOS Environnement, 112 Avenue du Brézet, 63100 Clermont Ferrand, France</footnote>

<footnote>*Corresponding author. INRAE - Institut National de Recherche pour l'Agriculture, l'Alimentation et l'Environnement, Pathologie Végétale, F-84140 Montfavet, France. Email: fanny.noirmain@inrae.fr</footnote>
umn can lead to a destabilization of the blooming phytoplankton and cyanobacterial communities, which can be associated with an important re-setting of seasonal phytoplankton/cyanobacteria species succession to an earlier stage after mixing of the water column rather favoring opportunistic fast-growing taxa (Reichwaldt and Ghadouani 2012, Stockwell et al. 2020, Chorus et al. 2021).

Environmental changes can directly impact the cellular phys-
iology of organisms, as demonstrated by studies that integrate
metabarcoding and metabolomics to elucidate the responses of
plant, fish, and algae holobionts to environmental stressors (Hirai
et al. 2004, Paix et al. 2019, Sotton et al. 2019, Marcellin-Gros et al.
2020). Indeed, metabolome composition can be altered under en-
vironmental conditions and biotic interactions, as previously sug-
gested (Marcellin-Gros et al. 2020, Hughes et al. 2021, Durham et
al. 2022, Raina et al. 2022). Thus, Sadler and von Elert (2014) identi-
fied secondary untargeted metabolites from a natural phytoplank-
ton community and proved that this approach is useful for reveal-
ing seasonal chemotype succession in a cyanobacterial commu-
nity. An untargeted metabolomics approach can also be used to
investigate the influence of abiotic variations on phytoplankton
metabolomes. McNabney et al. (2023) identified 33 primary un-
targeted intracellular metabolites from phytoplankton communi-
ties in two eutrophic freshwater ecosystems, showing two distinct
patterns according to the sampling site, and suggested that theses
metabolic profiles could reflect the different communities of phy-
toplankton and be alternative indicators of algal bloom growth.
However, due to the limited number of identified metabolites
(only primary metabolites), the authors did not confirm their hy-
potheses and the detection of a higher number of metabolites is
necessary to elucidate the metabolic pathways involved in the re-
sponse of environmental changes (Sardans et al. 2011). The num-
ber of eco-metabolomics studies in natural freshwater ecosystems
remains very limited and critical information regarding the chem-
ical profiles of phytoplankton in natural environments is still lack-
ing (Sadler et al. 2014, McNabney et al. 2023). However, these
studies are essential as eco-metabolomic approaches aim to ex-
plore the metabolome and its shifts in response to environmental
changes (Sardans et al. 2011).

On the other hand, laboratory experiments often exhibit dis-
crepancies in their results regarding variations in secondary
metabolites from cyanobacteria, particularly microcystins, in re-
sponse to abiotic factors such as light and nutrient availability
(Long et al. 2001, Wiedner et al. 2003, Briand et al. 2016). These
discrepancies are frequently attributed to variations in experimen-
tal conditions and in the strains used in laboratory settings. Since
laboratory experiments cannot fully replicate the complex inter-
play of multiple factors present in natural systems (Sardans et
al. 2011), metabolomics approaches in natural system hold great
promise in investigating the factors leading to variations in phy-
toplankton successions and in aiding the identification of envi-
ronmental stressors that trigger the subsequent production of al-
gal/cyanobacteria toxins.

To improve our understanding of the metabolome of the phy-
toplankton community and their adaptive response to meteoro-
logical events, here we employed a specific eco-metabolomics-
based approach. Over an 8-week period, between August and
September 2021, we conducted samplings in a eutrophic lake at
two depths and two sites: one located in the middle of the lake
and the other near a wetland area. These samplings were specifi-

cally carried out before and after rain events. Monitoring of atmo-
spheric variables, such as wind and rain events, allowed us to ex-
plore the biological response shifts under environmental changes.
Concurrently, we continuously monitored abiotic lake variables to
capture variations associated with meteorological events. To in-
vestigate changes in the metabolome of phytoplankton and de-
cipher seasonal succession patterns driven by abiotic variations,
we thus performed multivariate statistical analyses using the an-
notated untargeted metabolites (primary and secondary), phy-
toplankton biovolumes, abiotic lake variables, and atmospheric
variables.

Material and methods

Lake site and instrumental setup

Lake Aydat (45.6°N; 2.9°E) is in the French Massif Central, around 15 km southwest of Clermont Ferrand, at 837 m above sea level (Fig. 1a–b). It is a natural lake that was formed when the Veyre River was dammed by a basaltic lava flow 7500 years ago. This small eutrophic dimictic lake has a total area of 0.6 km², a catchment area of 300 km², and a maximal depth of 15 m, and suffers recurrent cyanobacterial proliferations (Sabart et al. 2015). Lake Aydat receives 75% of its input from the Veyre River and 25% from lateral supply around the shores and via direct precipitation (Lavrieux et al. 2013).

The instrumental setup included a BBE FluoroProbe (FP; bbe Moldaenke GmbH, Kiel, Germany) instrument installed in the middle of the lake (referred to as “Middle Point”, MP) to ensure vertical profiles of water temperature, conductivity, and oxygen level. In addition, HOBO data loggers (Onset Computer Corp., Pocasset, MA) were positioned near the wetland area (referred to as “Wetland Point”, WP) to record temperature at intervals of 20 cm from the water surface to a depth of 2.8 m (Fig. 1b). We also utilized a YSI ProDSS Multiparameter Water Quality Meter instrument (YSI Incorporated, Ohio, USA) for intermittent *in situ* measurements of dissolved oxygen and temperature profiles in the middle of the lake at both sites (MP and WP). The estimation of turbidity was assessed based on Secchi disc transparency measurements (SD) by multiplying SD by 2.5 to estimate the euphotic depth at both sites.

Meteorological data

The air temperature, wind speed, and relative humidity were con-
tinuously monitored by a weather station operated by the French
meteorological network "Météo France" located at Saint-Genès-
Champanelle (45°43'22"N; 3°01'09"E), ~7 km northeast of Aydat,
at 893 m above sea level. In addition, rainfall was measured us-
ing a Parsivel² sensor (OTT Messtechnik, Germany), located 420 m
from Lake Aydat at an elevation of ~10 m above the lake. The dis-
drometer is part of the instrumental suite deployed to characterize
precipitation near the lake (Noirmain et al. 2022).

Lake sampling and analysis

The monitoring of Lake Aydat was conducted between 05 August
2021 and 27 September 2021. During this period, water samples
were collected from the surface and at a depth of 1.5 m in the
<figure><img src="image_2.png" /><figcaption>**Figure 1** (a) Localization of the town of Aydat in the Puy-de-Dôme department in the Auvergne-Rhône-Alpes region of central France, and (b) the map of Lake Aydat illustrating the locations of the two water collection points. The first is in the middle of the lake, referred to as the middle point ("MP") site. The second is situated near the wetland area and is referred to as the wetland point ("WP") site, close to the entrance of the Veyre river.</figcaption></figure>

middle of the lake, referred to the middle point (MP) and near the wetland area, referred to the wetland point (WP) (Fig. 1b). The MP site has a maximum depth of 15 m, while the WP site has a maximum depth of 9 m. Weekly and sporadic surface lake water samples were taken both prior to and following precipitation events. At each depth and location, ~10 l of lake water collected with a Van Dorn bottle were then pre-filtered through a 250-µm Nylon filter to remove larger particles and kept phytoplankton, and then transferred into 15-l containers. The use of a high volume of water helped to minimize temperature variations during transport from Lake Aydat to the laboratory in Clermont Ferrand, which takes less than 30 min.

## Phytoplankton diversity

The lake water was immediately filtered in the laboratory using a 150-µm Nylon membrane to avoid the presence of zooplankton in the lake samples. The filtrate (under 150 µm in size) was fixed in a neutral Lugol solution (Sigma-Aldrich) by adding 10 ml of Lugol's iodine stock solution to 150 ml of the filtrated lake sample, which were kept at 4°C in the dark until a counting process was carried out under an inverted microscope and in Utermol chambers following the European Standard NF15204 (AFNOR 2006). Although the Lugol's solution concentration used may induce cell shrinkage and lead to an underestimation of cell biovolume, all samples were processed simultaneously and shortly after collection, ensuring that this effect was consistent across samples and therefore had a negligible impact on comparative data analysis. For each taxa, the biovolume (in mm³.l⁻¹) was calculated with the Phytobs software (Laplace-Treyture and Feret 2016).

## Chemical analysis

For analysis of the major inorganic ions, 1 l of the fresh lake sample was filtrated on a 0.2-µm Nylon membrane, pre-rinsed with

500 ml of ultra-pure Milli-Q water to avoid contamination, the pH was measured on fresh samples and the remaining volume was stored at -20°C until analysis by ion chromatography. The concentrations of dissolved nutrients, including Ca²⁺, Cl⁻, K⁺, Mg²⁺, Na⁺, NH₄⁺, NO₃⁻, PO₄³⁻, and SO₄²⁻ in the lake water were analyzed using ion chromatography (IC) on a Dionex™ ICS-6000 HPIC™ System (Thermo Fisher Scientific, USA). A Dionex™ IonPac™ (Thermo Fisher Scientific, USA) AG11-HC (guard-column 2 × 50 mm) and AS11-HC 260 (analytical column 2 × 250 mm) were used for the analysis of anions, while an IonPac CG-16 (guard-column 2 × 50 mm) and an IonPac CS16 (analytical column 2 × 250 mm) were utilized for the analysis of cations. The elution was performed in gradient mode using KOH (1–60 mM in 35 min, flow rate of 0.36 ml.min⁻¹) for anions and isocratic mode with MSA (methanesulfonic acid at 30 mM, flow rate of 0.25 ml.min⁻¹) for cations. The chromatograms were recorded using a conductimetric cell detector and analyzed with Chromeleon 7.2 software. Three replicate measurements were conducted to determine the nutrient concentration. The limit of detection (LOD) was estimated with a signal-to-noise ratio of 3 : 1, while the limit of quantification (LOQ) was obtained using a signal-to-noise ratio of 9 : 1 (Shrivastava and Gupta 2011). Calibration curves were also generated for each ion to validate the LOD and LOQ obtained through the signal-to-noise method. For anions, the LOD ranged from 0.7 µg.l⁻¹ for PO₄³⁻ to 2.3 µg.l⁻¹ for Cl⁻ and the corresponding LOQ ranged from 2.1 to 6.8 µg.l⁻¹, depending on the specific anions. For cations, the LOD ranged from 0.7 for K⁺ to 141 µg.l⁻¹ for Mg²⁺, the corresponding LOQ ranged from 2.1 to 425 µg.l⁻¹, depending on the specific cations (Table S1).

## Metabolites extraction and analysis

We developed a method to maximize the extraction of intracellular metabolites by rapidly filtering the samples and then rinsing the filter before metabolite extraction. This procedure ensures
high sampling concentrations while minimizing cellular damage thanks to the fast filtration process (Volmer et al. 2011). This protocol is the result of antecedent testing, and we ultimately retained the procedure detailed below as the most effective. For each depth and location, 1 l of lake water was filtered on 10-µm nylon membranes using ultra-fast filtration and then were immediately transferred into 15 ml sterile falcon tubes and quickly flash-frozen in liquid nitrogen to preserve the samples for metabolomics analysis. This filter size allowed us to filtrate lake samples in 30 sec. Such rapid filtration is essential to prevent cellular stress and avoid the production of new metabolites before flash-freezing the samples in liquid nitrogen. Study from Volmer et al., (2011) indicate a filtration duration which must be completed in less than one minute to avoid stress-induced metabolic responses. The following step, i.e. the flash-freezing, known as quenching, was performed as quickly as possible to ensure a reliable method and minimize any disturbance to the endo-metabolism of cells (Sardans et al. 2011). The Falcon tubes were kept at -20°C until the day of extraction. This procedure was conducted four times to ensure four replicates of each sample per site and depth. Blanks were performed by filtering 1 l of sterile ultra-pure water.

On the day of extraction, we rinsed the filter with 1 ml of sterile MQ water mixed with 0.1% NaCl and then vortexed it. The filter was then centrifuged at 4°C (5000 g, 5 min) with the Falcon tube cap attached, and we transferred the pellet to an Eppendorf tube. The Eppendorf tube was centrifuged at 4°C (15 000 g, 10 min) and the supernatant was discarded, allowing us to weigh the pellet. We adjusted the volume of an ice-cold solvent mixture, composed of methanol, acetonitrile, and water in a 2 : 2 : 1 ratio, according to the weighed biomass: 100 µl of the ice-cold solvent mixture corresponds to 1 mg of biomass. For the blank, we systematically added 200 µl of the mixture. The solvents used offered a one-step generalist extraction for maximizing metabolite extraction (Bennett et al. 2008, Liu et al. 2023, Zhou et al. 2022), however nonpolar lipids may have resisted extraction.

Next, we subjected the samples to mechanical disruption through three cycles of freezing and thawing using liquid nitrogen, alternating with three cycles of sonication. Each sonication cycle lasted 15 sec and was performed at 40% power with a Fisherbrand™ Model 120 Sonic Dismembrator (Fisher Scientific, France). Throughout this process, we kept the samples in ice. After extraction, the samples were centrifuged at 4°C (15 000 g, 10 min) before being transferred into 40 µl vials (AR0-9973-13, Verex™ Vial Kit) and stored at -80°C until the detection of metabolites. In total, we extracted 224 samples through four serial extractions, with a blank sample added systematically in each series.

The extracts were further analyzed by injecting 2 µl of the solution onto a C18 column (Polar Advances II 2.5 pore, Thermo using an ultra-high-performance liquid chromatography (UHPLC) system (ELUTE, Bruker). The elution was conducted at a flow rate of 300 µl.min⁻¹ with a linear gradient of acetonitrile in 0.1% formic acid (5%–90%) over 21 min [phase A= water and 0.1% formic acid in water (volume/volume), phase B= 0.08% formic acid in acetonitrile (volume/volume)]. Next, we analyzed the individual metabolite content using a high-resolution electrospray ionization hybrid quadrupole time-of-flight (ESI-Qq-TOF) mass spectrometer (Compact, Bruker) operating in positive auto MS/MS mode. The scan rate was set at 2–4 Hz in the mass range of 50–1500 m/z. We generated a feature peak list from recalibrated mass spectra, with a cali-

bration accuracy of less than 0.5 ppm, by injecting an internal calibrant of sodium formate at the beginning of each sample analysis. Data quality in terms of intensity, retention time and mass drift of ions was carefully inspected and recalibration was automatically performed individually by the software on raw data of all samples according to standard molecules from Na formate calibrant solution.

The data were processed using MetaboScape software (Bruker), enabling the detection of 4446 untargeted metabolites. The data was filtered for a minimum intensity of 5000 counts, a minimum occurrence of at least 10% in all samples, and the charge states and related isotopic forms were combined. Finally, we performed additional molecular networking with the Metgem 1.3.6 freeware to identify 260 annotated metabolites based on cluster annotations against publicly available MS/MS libraries (Su et al. 2015).

## Statistics

To investigate if the concentrations of ions and relative abundance of untargeted metabolites correlated among the sites and depths, we performed statistical analysis using non-parametric permutation based MANOVA (PERMANOVA) with the *adonis2*() function (999 permutations), based on the Euclidean distance metric (Oksanen et al. 2022). A Dunn test was performed to confirm the significant differences of the relative abundance of ions and metabolites across sites and depths, using the dunn.test package (adjusted P-value with the Holm stepwise method) (Dinno 2015).

Environmental variables (abiotic and biotic), and metabolomics datasets were both integrated using the multiblock model (unsupervised and supervised) to assess the links between the variables, using the mixOmics R package (Rohart et al. 2017). Additional pairwise models were realized to evaluate the correlation scores between the data sets.

The supervised multiblock sPLS-DA, known as DIABLO, aims to identify correlated or co-expressed variables measured on heterogeneous datasets. It also aims to explain the differences observed across the sampling sites and depths (MP and WP at surface and 1.5 m deep). The metabolomics and environmental datasets were log10-transformed and mean-centered prior to analysis. The performance of the models was tested, and the optimal number of variables per dataset was selected for DIABLO to ensure a sufficient number of variables for downstream validation and interpretation (Singh et al. 2019). For both the unsupervised sPLS and supervised sPLS-DA models, we defined a full design value of 0.5, which allows for a compromise between the strength of correlations with all datasets and the predictive ability of the models.

## Results

### First rain period (5–12 August, 2021): wind-driven conditions favoring a diatom-dominated phytoplankton community

The first wet period, lasting 8 days, was characterized by a moderate cumulative rainfall of 45 mm and high mean wind speed
(>6 m.s⁻¹) especially on 6 August 2021, reaching 6.4 m.s⁻¹ in a 1-h interval (Fig. 2a). During this period, both sampling sites (MP and WP) exhibited similar temporal variations in water temperature following atmospheric temperature variations, with a maximum during mid-August (20.7°C–21.8°C) and a mean water temperature of 17.6°C and 18.65 °C at MP and WP, respectively. Water temperature at WP site remained consistently warmer than MP, likely due to its shallower depth (9 vs 15 m), with a mean temperature difference between site of 1°C. The punctual measurements of thermocline depth, derived from vertical water temperature profiles, confirmed these differences, showing a deeper thermocline at MP than at WP (Table S2).

Regarding ionic composition, both sites are characterized by low concentrations of $NH_4^+$ and $PO_4^{3-}$, with values systematically below the detection limit (LOD <16 µg.l⁻¹ and 0.7 µg.l⁻¹, respectively), and remaining below these limits throughout the entire lake campaign. The mean concentrations of $NO_3^-$, 1.3 and 1.4 mg.l⁻¹ at MP and WP sites respectively, also appeared low compared with the average nitrate ($NO_3^-$) concentration in EU groundwater, reaching 21 mg l⁻¹ from 2000 to 2022 (waterbase and wise statistics from European Environment Agency). This low nitrate concentration during the first rain period is not surprising as $NO_3^-$ level can be markedly reduced during the peak growing season due to phytoplankton uptake, especially in stratified lakes (Bhataria and Jain 2016). In contrast, the concentrations of $Ca^{2+}$, which oscillated around 11.36 and 10.73 mg.l⁻¹ at MP and WP respectively (Table S2), were slightly higher than the global median concentration of freshwater from 57 countries worldwide (4 mg.l⁻¹) (Weyhenmeyer et al. 2019).

Regarding the phytoplankton community composition, we noted a dominance of diatoms (e.g. *Stephanodiscus*, *Cyclotella*, *Fragilaria*), green algae (e.g. *Sphaerocystis*, *Closterium*), and brown algae (e.g. *Uroglena*), whereas cyanobacteria biovolumes remained low (Fig. 3).

## Drought period (13 August–2 September, 2021): strong nutrient depletion after 18 days without rainfall promotes the emergence of diazotrophic cyanobacteria

This period extending over 22 days until 2 September 2021 (rain rate < 0.1 mm.h⁻¹), was characterized by a mean water temperature of 19.13 and 20.13 °C at MP and WP, respectively, marked by a gradual decrease, reaching 17.22 °C at MP and 18.14 °C at WP. However, the mean water temperature was higher than those reported during the first rain period. As observed during the first rain period, water temperatures remained higher at WP than at MP, with an average difference of 1 °C. In contrast, ion concentrations showed a marked and significant decline, particularly after 18 consecutive days without rainfall, on 30 August (Fig. 2c). For example, the concentration of some ions rapidly decreased, especially after 23 August (Fig. 2c). For both sites, the concentration of $SO_4^{2-}$, $Mg^{2+}$ and $NO_3^-$ were significantly lower during the drought than during the first rain periods (p-values at MP=1.9×10⁻², 1.76×10⁻² and 9.94×10⁻⁶; p-values at WP: 1.3×10⁻³, 8.2×10⁻³ and 2.27×10⁻⁸).

Interestingly, on 30 August, the biovolume of diazotrophic cyanobacteria increased suddenly, notably of *Aphanizomenon*,

while diatoms, ochrophytes and cryptophytes were low at both sampling sites (Fig. 3b, d, and e).

## Second rain period (3-27 September 2021): no disruption of water stratification but nutrient inputs that promote cyanobacterial growth

A second rain period occurred between 3 and 27 September 2021, characterized by rain event with higher intensities, reaching 70 mm.h⁻¹ during a 15-min interval on 25 September at 21:30 UTC, and higher cumulative rainfall, 560 mm than during the first rain period (Fig. 2a). During this period, the mean water temperatures (19.19°C and 20.13°C at MP and WP, respectively) remained similar to those obtained during the drought, although they declined markedly at the end of the lake campaign, on 27 September, reaching 16.5°C and 17°C at MP and WP, respectively. The water temperature difference between sites averaged 0.62°C, slightly lower than the 1°C difference reported for the two preceding periods. In contrast, the water temperature differences across the depths (surface vs 1.5 m depth) differed by 0.28°C at MP and 0.13°C at WP, while during the drought they were only 0.11°C at MP and 0.02°C at WP. This suggests that the post-drought warming likely enhanced water column stability, particularly at the WP site, despite the repeated rain events (Fig. 2b).

Regarding the ion's composition, their concentration significantly increased during the post-drought period associated with repeated rain events. By comparing the concentrations of ions between the drought and the second rain period, we noted that $Na^+$, $Cl^-$, $Mg^{2+}$, $Ca^{2+}$, and $SO_4^{2-}$ were significantly higher at MP during the second rain period than during drought (P-values=1.96×10⁻⁴, 6.3×10⁻³, 4.6×10⁻², 2.49×10⁻⁴, and 1.89×10⁻³). Instead, at WP only $NO_3^-$ and $K^+$ had a concentration significantly higher during rain period two than during drought period (P-values=6.84×10⁻³ and 1.95×10⁻²). These differences are likely linked to differences in concentration across depths, higher at the lake surface than at 1.5 m depth; and to the delay between the onset of drought and the subsequent decline in nutrient availability, which occurred 18 days after drought began. Nevertheless, a majority of ions had significant higher concentrations at WP than MP, especially for $NO_3^-$, $Ca^{2+}$, $K^+$, $Mg^{2+}$, $Na^+$, $Cl^-$, and $SO_4^{2-}$ (P-values=1.14×10⁻³; 2.3×10⁻⁴; 1.9×10⁻²; 9.5×10⁻⁵; 2.2×10⁻⁴; 2×10⁻⁴; and 1×10⁻⁴, respectively). In addition, WP is the site where all the ions, except $NO_3^-$, showed a recovery to pre-drought levels, especially at 1.5 m deep (Fig. 2c). In contrast, at MP, we did not observe a recovery of the ions after rain events following the drought.

At the beginning of the second rain period, on 6 September, we reported an important decrease in cyanobacterial biovolume, especially of *Aphanizomenon* across all sampling points; while its biovolume increased to be the dominant genus among cyanobacteria on 13 September, after the second rain events following the drought, between 10 and 12 September (Fig. 3c). We observed a contrasting pattern with the *cryptomonas* genus (Fig. 3e), exhibiting an initial increase following the first rain events from 03 until 04 September, followed by a decrease in its abundance on 13 September. Finally, on 23 September, when the water temperature dropped below 19°C, the biovolume of all phyla was lower compared to the rest of the lake sampling period.
<figure><img src="image_3.png" /><figcaption>**Figure 2** Mean meteorological variables (a), including rainfall intensities recorded from the Parsivel² sensor measured at Aydat instrumental site and wind speed recorded at Saint-Genès-Champanelle. Mean abiotic lake factors, (b) water temperature and (c) concentrations of inorganic ions measured during the lake campaign within lake water for each site, middle point (MP) and wetland point (WP) colored in black and red, respectively, and represented by a continuous line and a dotted line for the sampling conducted at the surface and at 1.5 m deeper, respectively. Concentrations are expressed in mg.L⁻¹.</figcaption></figure>
<figure><img src="image_4.png" /></figure>

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<figure><img src="image_6.png" /></figure>

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**Figure 3** Biovolumes of the dominant species monitored during the lake campaign, categorized based on their phyla: Charophyta and Chlorophyta (a), Bacillariophyta (b), Cyanobacteria (c), Ochrophyta (d), Cryptophyta (e), and Euglenozoa (f). These values were determined from lake samples collected at the middle point (MP) and wetland point (WP), both at the surface and 1.5 m from the lake surface. Meteorological events (rain and drought periods) are visually distinguished on the graph.
## Intracellular metabolite profiles: two distinct patterns linked to phytoplankton shifts and local environmental drivers

In our study, we detected 4446 untargeted metabolites from phytoplankton biovolume and were able to annotate 260 of them thanks to the GNPS molecular network approach. We carried out multivariate analyses with 4446 untargeted metabolites and 260 annotated metabolites using PCA (Suppl. Fig. 4) and confirmed the similar patterns according to the dates observed with both datasets. Hence, we carried out the subsequent statistical analyses using only the 260 annotated compounds to better explore the biological functions linked with environmental stressors (Figs. 6 & 7). The statistical analysis showed that our sampling and extraction methods allowed us to characterize the eco-metabolomes during the lake campaign. Indeed, the intracellular untargeted metabolomics data were strongly correlated with the total biovolume of phytoplankton (R=0.82) and with abiotic variables such as ion concentrations (R=0.71). Additionally, the “environmental variables” group, which includes atmospheric parameters (wind speed and rain rate) as well as lake variables (water temperature, pH, and euphotic zone depth), also exhibited strong correlations with the intracellular metabolomic data (R=0.7) (Figs 4 & 5).

From the RDA analysis, two distinct metabolite fingerprints were identified: the first corresponds to metabolites produced between 8 and 23 August, while the second reflects metabolites produced from 30 August until the end of the lake campaign (Fig. 4). Interestingly, these two metabolite fingerprints are not directly aligned with the timing of meteorological events but instead closely track the shift in phytoplankton composition. This pattern is expected, as we observed a strong correlation between intracellular metabolites and phytoplankton structure (R = 0.82). Regarding the first metabolite fingerprint, numerous lipids, dominated by polyphenols and glycerophospholipids are positively correlated with diatoms (Bacillariophyta) and brown algae (Ochrophyta) biovolumes (Figs 5 and 6). Interestingly, the relative abundance of betaine lipids and sterols increased especially between 05 and 23 August (Fig. 5c). In contrast, the abundance of some phospholipids, such as Glycerophospho-N-palmitoyl ethanolamine, and phosphatidylcholine (PC) decreased (Fig. 5a-b). Hence, to better understand these dynamics, an sPLS analyses was performed only with the dates corresponding to the first metabolite fingerprint. This analysis showed that during the first rain period, the biovolume of *Chroococcus* is strongly correlated with amino acids, such as guanosine, tyrosine, and inosine (Fig. 6a, c, and e); while we observed a negative correlation between the amino acid contents and the biovolume of *Closterium*, *Cyclotella*, and *Scenedesmus* during the drought (Fig. 6a, c, and e). In addition, during the drought period, the biovolumes of *Closterium*, *Cyclotella*, and *Scenedesmus* were positively correlated with other kinds of metabolites, sterols and glycerolipids, such as betaine lipids (lyso-diacylglyceryltrimethylhomoserine (LDGTS)) (Fig. 6e).

On the other hand, the second metabolome fingerprint on the RDA plot, is explained by the biovolumes of cyanobacteria, between 30 August and 27 September 2021, characterized by higher rainfall and deeper depth of the euphotic zone (Fig. 4). These dates correspond to the last days of drought (30 August), until the end of the lake campaign. The metabolome profile was marked by a high content of cyanopeptides, with a small proportion of lipids, such

as Glycerophospho-N-palmitoyl ethanolamine and especially detected at MP (Fig. 5a). By performing sPLS analysis only focused on the second metabolomic fingerprint (Fig. 6b, d, and f), we noted that most of cyanopeptides were positively correlated with the biovolume of *Dolichospermum* during dates where cyanobacteria biovolume reached its maximum value (around 13 September). For example, the relative abundance of microsytin LR increase from 06 September and reached its maximum value on 17 September and then decreased (Fig. 5d). The biovolumes of *Aphanizomenon*, which were higher during this period, were not correlated with other cyanopeptides.

Interestingly, intracellular metabolites differed statistically across sites during the second wet period (P-value = 0.014) while phytoplankton composition remain similar (Suppl. Fig. 3 & Suppl. Fig. 5). Therefore, to better understand the differences across the sampling sites, we carried out a supervised multiblock sPLS-DA model (Fig. 7 & Suppl. Fig. 4). This analysis showed, thanks to hierarchical clustering, that the endo-metabolome differed strongly across the sites, but only after 30 August (Fig. 7). Interestingly, most cyanopeptides had significant higher relative abundance at WP than at MP, such as anabaenopeptin, microginins, shinorine, and micropeptin B, as well as cyanotoxins, such as microcystin-LR (Fig. 7). In contrast, more lipids were detected at MP than at WP, such as glycerophospholipids.

## Discussion

Understanding the effects of climate change—particularly drought and precipitation—on phytoplankton responses requires the integration of diverse methodological approaches to produce robust and comprehensive data with high-resolution monitoring (Machado et al. 2023). Such approaches are essential for improving the prediction and management of harmful algal bloom risks. To date, most studies have assessed phytoplankton responses at broad temporal scales, often focusing on seasonal patterns following precipitation events (Beale et al. 2018). However, this perspective tends to overlook rapid and short-lived shifts in community composition and toxin production that can occur immediately after meteorological disturbances. These rapid dynamics present major challenges for effective risk assessment and water management (Winder and Sommer 2012, Stockwell et al. 2020). To address this knowledge gap, we implemented a high frequency ecometabolomics approach to explore the molecular strategies employed by phytoplankton in response to meteorological events. By sampling the lake at short intervals (every 2–3 days) before and after rainfall, and combining metabolomic profiling with *in situ* measurements of phytoplankton biovolume, diversity, and physicochemical parameters, we can capture fine-scale adaptive responses controlled by both biotic and abiotic drivers. Our results reveal that phytoplankton metabolic strategies are strongly modulated by environmental drivers associated with combination of meteorological events, offering new insight into ecosystem functioning after short-term dry and wet periods (Fig. 2).

## Phytoplankton response to drought-induced nutrient limitations

A marked shift in phytoplankton composition was observed during drought, especially after 23 August, coinciding with a signifi-
<figure><img src="image_10.png" /><figcaption>**Figure 4** Explanatory variables from redundancy analysis (RDA) conducted with metabolomics data (260 annotated metabolites). Predictors, environmental variables were used as explanatory variables (showing in the plot with arrows colored in black). The metabolite samples are color-coded based on the date and represented by symbols according to the site and the depths of sampling: The middle point (MP) site is represented by circle and triangle symbols for lake water samplings collected at surface and 1.5 m deeper, respectively. The wetland point (WP) site is represented by plus (+) and cross (X) symbols indicating water samples taken at surface and 1.5 m, respectively. The biovolumes of phytoplankton are expressed in mm³.l⁻¹.</figcaption></figure>

cant decline in ion concentrations at the end of an 18-day drought. This nutrient depletion likely created favorable conditions for the proliferation of diazotrophic cyanobacteria, as previously reported in similar contexts (Reynolds et al. 2002, Wei et al. 2023). The significant decrease in $NO_3^-$ concentration (Fig. 2) is probably one of the main drivers explaining this shift in the phytoplankton composition. Similar results were reported in literature, where nutrient availability was also cited as a critical stressor to explain shift in bacterial community structure and metabolic function (Beale et al. 2018).

Interestingly, we also observed a shift in metabolomic composition after 23 August, strongly correlated with the phytoplankton composition variation. Indeed, the beginning of the drought is marked by the high abundance of lipids, particularly betaine lipids, glycerol-phospholipids, and sterols (Fig. 5), positively correlated with the biovolumes of *Cyclotella*, *Scenedesmus*, *Elakatothrix*, and *Closterium* between 16 and 23 August (Fig. 6a, c-e). In contrast, after 30 August, we reported the decrease in the abundance of specific phospholipids, such as glycerol-phosphatidylethanolamine (PE) and phosphatidylcholine (PC) while the abundance of cyanopeptides increased (Figs. 5 & 6a, c-e). These results may suggest that during the drought, marked by low concentrations of $PO_4^{3-}$ (<0.7 µg.l⁻¹) and $NO_3^-$ (mean around 0.6 ml.l⁻¹) (Fig. 5a-c), phytoplankton species, such as diatoms, accumulate lipids to mitigate the effects of reduced ion

availability. These findings align with previous laboratory studies demonstrating that phytoplankton cells accumulate lipids under nutrient-limited conditions, such as phosphorus and nitrogen scarcity, which disrupts anabolic processes (Popko et al. 2016, Murakami et al. 2018, Morales et al. 2021). Additionally, Giroud et al., (1988) proposed that betaine lipids may function as a compensatory mechanism, reallocating phosphate from membrane lipid synthesis to other essential metabolic pathways during phosphorus limitation. Therefore, we can suggest that low concentrations of $PO_4^{3-}$ and $NO_3^-$ are potential drivers for lipid metabolism.

In addition to the lipid accumulation, our results may also suggest a downregulation of the production of amino acids under nutrient limitation. Indeed, the intracellular level of amino acid decreased while those of betaine lipid increased during the drought (Fig. 5) and a negative correlation was reported between the amino acid contents and the biovolume of *Closterium*, *Scenedesmus*, and *Cyclotella* (Fig. 6e). A similar hypothesis was made from laboratory experiments, by Popko et al., (2016), from the culture of *Phaeodactylum tricornutum* (diatom) during nitrogen-starvation. Feng et al. (2015) also showed a downregulation of amino acid production under nutrient limitation, with higher production of glycolipids compared to phospholipids, as well as an upregulation of protein degradation, lipid accumulation, and photorespiration.
<figure><img src="image_11.png" /><figcaption>**Figure 5** Metabolite spatial and temporal variations exemplified with (a) glycero-phospholipids, identified as Glycerophospho-N-palmitoyl ethanolamine, (b) phosphatidylcholine identified as Lysophosphatidylcholine 14:0 PC (14 : 0/0 : 0), (c) betaine lipid identified as lyso-diacylglyceryltrimethylhomoserine (LDGTs 18:2d), and (d) Microcystin LR, within samples collected at the middle of the lake, (MP) and close to the wetland area (WP), at two depths, surface and 1.5 m deeper.</figcaption></figure>

The lipid accumulation and downregulation of amino acids, is accompanied by the synthesis of antioxidant compounds, like polyphenols, which are recognized for their ability to scavenge re-active oxygen species (ROS) as a protective mechanism (Morales et al. 2021). Hence, we can suggest that these molecules may be produced in response to oxidative stress induced by nitrogen limitation during drought conditions, consistent with previous studies on microalgae cultures subjected to nitrogen starvation (Chokshi et al. 2017).

Given that our samples were collected from a natural phytoplankton community and that only cells larger than 10 µm were retained, it remains uncertain whether these metabolites are produced by all species (potentially including a minor fraction of non-phytoplanktonic cells), predominantly by the dominant taxa, or potentially by species that were not identified through microscopy. However, betaine lipids have been widely reported in various micro-organisms, including diatoms, microalgae, and cyanobacteria (Sato 1992, Künzler and Eichenberger 1997, Popko et al. 2016). Based on all these findings, it suggests that the production of lipids could be a common response of the phytoplankton community to environmental changes by accumulating specific lipids to cope with declining ion concentrations and potentially mitigate oxidative stress caused by nitrogen limitation. Therefore, our results demonstrate that eco-metabolomics represents a

promising approach for gaining deeper insights into the adaptive strategies of phytoplankton in response to environmental stressors and better understanding the influence of climate change on micro-organisms.

**Phytoplankton composition and metabolic responses differ between the two rainfall events due to antecedent drought event and environmental drivers**

During both rain period, phytoplankton community composition differs markedly between the two rainfall periods: while diatoms dominate during the first period, cyanobacteria exhibit higher bio-volumes during the second. However, these community remained stables during each rain periods, suggesting that moderate rain intensities did not affect directly the phytoplankton composition. This pattern contrasts with expectations from the literature, which often reports that rainfall can disrupt water column stratification and phytoplankton succession (Chorus et al. 2021). In our case, however, the lake exhibited strong water vertical stratification, which likely maintained sufficiently stable conditions to prevent major changes in community composition. However, at the end
<figure><img src="image_12.png" /><figcaption>**Figure 6** Unsupervised multiblock sPLS model using lake samples collected (a, c, & e) between 02 and 23 August 2021 (diatom co-occurrence period) and (b, d, & f) between 30 August and 27 September 2021 (cyanobacterial co-occurrence period). Panels (a–b) show the correlation circle plots between components 1 and 2, integrating environmental variables, ion concentrations, phytoplankton biovolume, and untargeted annotated metabolites. Panels (c–d) display the sample plots based on metabolites, phytoplankton biovolume, ions, and environmental variables, with samples distinguished according to sampling dates. Panels (e–f) present the heatmaps resulting from both models for the two temporal periods. Ion concentrations are expressed in mg.l⁻¹, the biovolumes of phytoplankton are expressed in mm³.l⁻¹, the metabolites are expressed in relative unit abundance. The correlation cutoff threshold is set at 0.4 for correlation circle plots.</figcaption></figure>
<figure><img src="image_13.png" /><figcaption>**Figure 7** Heatmap from supervised mutiblock sPLS-DA models (DIABLO). Color-coded Clustered Image Maps (CIMs) represent high-dimensional datasets analyzed with the DIABLO framework—cimDiablo(). The color gradient represents the deviation of each feature value from its mean, measured in standard deviations. By default, values exceeding ±3 standard deviations are trimmed. Input data were centered and scaled prior to analysis. For visualization purposes, the displayed output values were additionally trimmed to the range [-2, 2] to improve readability of the heatmap.</figcaption></figure>

of the second rain period, after repeated rain events with higher intensities, especially after 13 September, we report different pattern among cyanobacteria biovolumes. Indeed, the biovolume of *Aphanizomenon* decrease while that of *Dolichospermum* is always unaffected, likely suggesting different sensitivity to rain events, not yet reported in literature.

Each rain period was also characterized by different metabolome fingerprints: major lipids are produced during the first rain period, whereas cyanopeptides are detected in high abundance during the second rain period. However, the RDA analysis show similar metabolome fingerprint from the first rain

period until the end of the drought and a second metabolome fingerprint associated to dates corresponding to the second rain period. Hence, no major differences in the metabolome are found between the first rain period and the beginning the drought, while it shifts after the drought until the end of the lake campaign. These results indicate that the metabolome is closely linked to phytoplankton composition, which itself is shaped by environmental conditions. Indeed, after 18 consecutive days without rain, $NO_3^-$ concentration dropped to 0.41 and 0.48 mg.l$^{-1}$ at WP and MP, respectively; creating conditions favorable for the proliferation of diazotrophic cyanobacteria at both sites. After 30 August, the new
metabolome fingerprint is mostly explained by the presence of *Dolichospermum*. This pattern aligns with previous studies suggesting that rain events following drought periods can enhance cyanobacterial growth when nutrient input is combined with prolonged stratification and high evaporation rates (Coumou and Rahmstorf 2012, Reichwaldt and Ghadouani 2012). Therefore, rain events following drought appear to have a more pronounced effect on phytoplankton composition than rain events occurring under nutrient-replete conditions, where no significant changes in phytoplankton composition and metabolomic abundance were reported.

Interestingly, differences in metabolomic profiles are much more pronounced at WP than at MP during the second rain periods. Indeed, we report that some lipids are detected at MP during each rain periods, such as for glycerophospho-N-palmitoyl ethanolamine; while the metabolome at WP differed markedly between periods, with cyanopeptides produced during the second rain events and glycerophospholipids prevailing during the first rain period (Figs. 5a and 7). These results suggest that consequences of drought on intracellular metabolome may differ according to the sites due to the lake geomorphology and environmental drivers. Indeed, during the second rain periods a similar phytoplankton composition is reported across sites while ion concentrations and water temperature differ according to the sites. Overall, these findings demonstrate that fine-scale spatial monitoring can reveal distinct ecometabolomic niches, separated by only 200 m. The differences in metabolomic profiles between sites, despite similar taxonomic composition, suggest that metabolomic shifts are not solely driven by phytoplankton community structure. Instead, they are strongly influenced by local abiotic factors such as nutrient availability, temperature, site geomorphology, and preceding climatic conditions. This spatial differentiation underscores the potential of eco-metabolomics to unravel localized environmental drivers shaping phytoplankton adaptive strategies. The untargeted metabolomics approach proved particularly effective in capturing ecological adaptations by providing insights into phytoplankton responses to meteorologically induced abiotic fluctuations. Our results align with those of McNabney et al. (2023), who identified site-specific metabolomic profiles in phytoplankton communities across a 200-km transect, emphasizing that metabolomic niches can emerge even at relatively small spatial scales. However, fine temporal monitoring is essential for tracking shifts in metabolomic function, especially given the rapid changes we observed in intracellular cyanopeptide content, including cyanotoxins, within just a few days following post-drought rain events.

Interestingly, microcystin-LR reached its maximum abundance on 17 September, i.e. 14 days after the first rainfall on 3 September. This peak coincided with the period during which *Dolichospermum* also exhibited their highest biovolumes. This cyanobacteria is known to produce cyanotoxins, such as microcytin, and can lead to harmful algal bloom and health danger for human and animals (Chorus et al., 2021; Dreher et al. 2018). Such results may confirm literature hypothesis, suggesting that cyanopeptids are produced during the growth of cyanobacteria (Long et al. 2001). However, this is probably not the case concerning all metabolites as Natumi and Janssen (2020) shown by laboratory experiments that relative cyanopeptide profiles were unchanged throughout the growth cycle. Cyanopeptids, and especially cyanotoxins, have strong ecological impact of higher trophic level and on the func-

tioning of lake ecosystems (Chorus et al. 2021). The ecological role of cyanopeptide synthesis, including cyanotoxins, remains unclear. However, several studies propose that cyanobacteria produce secondary metabolites such as aeruginosins, cyanopeptolins, microginins, anabaenopeptins, and microcystins as part of adaptive strategies to mitigate environmental stress, including damage induced by UV radiation (Jacinavicius et al. 2021). Furthermore, Arruda et al., (2024), reported that factors like dissolved oxygen, nitrate, and total phosphorus promote microcystin and microginin production, while elevated water temperatures favor aeruginosin synthesis. In our study, the influx of ions following rain events appears to have triggered intracellular production of cyanopeptides, including known cyanotoxins, with significantly higher levels observed at WP compared to MP. These metabolites, which include microcystin-LR, microginins, oscillaginin, and anabaenopeptins (Fig. 7), showed strong correlations with ion concentrations during the second post-drought rain period. Notably, microcystin-LR abundance peaked four days after ion concentrations reached their maximum and coincided with a sharp increase in *Dolichospermum* biovolume. This temporal alignment supports previous observations that cyanotoxins accumulate intracellularly during the exponential growth phase of cyanobacteria (Romanis et al. 2021).

Our findings reinforce the idea that cyanopeptide production is closely linked to nutrient availability, as suggested by previous culture-based studies (Chia et al. 2018). Moreover, these compounds may play key roles in biotic interactions, such as allelopathy, offering a competitive edge to the producing organisms. Interestingly, Beale et al., (2018) observed that during the wet season, microbial communities in marine sediments shifted toward organisms utilizing fast-kinetic metabolic pathways, such as aerobic respiration and inorganic nutrient assimilation. Similarly, our data suggest that such rapid microbial and functional shifts can also occur at fine spatial and temporal scales in freshwater systems, within days following drought and subsequent rain events. This highlights the potential for rapid changes in cyanotoxin production in response to meteorological variability.

To further elucidate the link between environmental stressors and cyanopeptide production, complementary metagenomic analyses could be integrated with metabolomics. As demonstrated in marine sediments by Beale et al. (2018), such approaches can reveal ecologically relevant functional responses to environmental change. Metagenomics would allow for the characterization of cyanobacterial communities and identification of toxin-producing taxa before, during, and after meteorological events. This could help determine whether specific species harbor genes related to cyanotoxin biosynthesis and enable the identification of site-specific indicator species associated with toxin production (Romanis et al. 2021).

## Conclusion

Changes in the phytoplankton metabolome following meteorological events in natural systems has been sparsely investigated and has still been lacking a sufficient number of identified metabolites to adequately unravel the biologically relevant functions induced by environmental changes considering the whole phytoplankton chemical diversity (Sadler et al. 2014, McNabney et al. 2023). In our study, we identify a set of both primary and sec-
ondary untargeted metabolites, from a total of 4446 detected metabolites of which 260 could have been thus annotated. The investigation of these metabolites allows us to confirm that eco-metabolome fingerprints are associated with shifts in phytoplankton composition, driven by abiotic variations following meteorological events. We observed rapid changes in the metabolomes of phytoplankton in response to meteorological events, as a result of variations in abiotic conditions triggered by drought and rain events. For example, during the drought, we observed a strong lipid accumulation, such as betaine lipids, probably in response to nitrogen and phosphorus limitation.

In contrast, during the wet periods, we observe an increase in the production of glycerophospholipids, especially at the MP site and high abundance of cyanopeptides at WP, associated with the biovolume of *Dolichospermum*. These cyanopeptides have a significantly spatial difference of relative abundance, suggesting the importance to include spatial monitoring of cyanopeptides during toxic cyanobacterial harmful bloom. Further studies are needed to confirm the high heterogeneity of metabolome fingerprints across different lake samplings by considering the dynamics of abiotic variables in high temporal resolution since they show quick variations following the meteorological events. To confirm the theory of the existence of local metabolomic niches, extended monitoring across different locations would be required, considering the heterogeneity across the sampling locations at different spatiotemporal scales.

## Acknowledgements

We thank the CPER, FEDER, and CAP 20-25, which funded the atmospheric instruments. This work was made possible thanks to the OPGC technical staff who managed the atmospheric instruments installation (Frédéric Peyrin and Claude Hervier), and operation (Philippe Cacault). We thank Frédéric Tridon and Jean-Luc Baray which provide software for MRR and Parsivel data processing. We thank the members of Team IRTA for their help with the lake sampling. The MS spectra were acquired at the Plateau technique de spectrométrie de masse bio-organique, Muséum National d'Histoire Naturelle, Paris, France. Finally, we thank “Volcans vacances” lodging, where the atmospheric instruments were hosted. We thank Cindy Morris for proofreading the manuscript.

## Author Contributions

Fanny Noirmain (Conceptualization, Formal Analysis, Investigation, Methodology, Validation, Writing – original draft), Benjamin Marie (Formal Analysis, Methodology, Software, Writing – review & editing), Benjamin Legrand (Formal Analysis, Resources), Delphine Latour (Conceptualization, Supervision, Writing – review & editing).

## Supplementary material

Supplementary material are available at FEMSEC online.

## Conflicts of interest

None declared.

## Funding

Fédération des Recherches en Environnement, France.

## Data availability

The metabolomics dataset is available on www.metabolomicsworkbench.org, a public repository for metabolomics metadata (Sud et al. 2016) under study ID ST004041 and can be accessed via the following link: https://www.metabolomicsworkbench.org/data/D RCCMetadata.php?Mode=Study&StudyID=ST004041

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