Spatio-temporal disparities in phytoplankton dynamics and metabolite production depending on weather conditions

Spatio-temporal disparities in phytoplankton dynamics and metabolite production depending on weather conditions | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Spatio-temporal disparities in phytoplankton dynamics and metabolite production depending on weather conditions Fanny Noirmain, Benjamin Marie, Benjamin Legrand, Joël Baelen, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4880559/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Various studies suggest that global change is causing an increase in phytoplankton biomass, cyanobacteria prevalence and cyanotoxin production. However, there are conflicting reports regarding the response of cyanobacteria blooms to global warming and meteorological events, probably because of the lack of global approaches. Metabolomics approaches in natural system hold great promise in investigating the factors leading to variations in phytoplankton successions and subsequent cyanotoxin production. However, eco-metabolomics studies are still scares in literature and suffer to adequately unravel the biologically relevant variables under environmental changes. In this study, we investigate the temporal and spatial dynamics of phytoplankton community and the production of their primary and secondary untargeted metabolites in response to local meteorological events. Thus, we collected water samples in two points of the Aydat Lake (France): near the inflowing waters from Veyre River and at the middle of the lake during the 2021 summer. Untargeted intracellular metabolites were measured using ultra-high-performance liquid chromatography coupled with a high-resolution mass spectrometer, as well as phytoplankton biovolume and diversity and physicochemical lake’s parameters. Primarily, our results show the increase of the biovolume of diazotrophic cyanobacteria at the end of the drought and after rain events at both sites. During the drought, we observe a strong increase of intracellular lipid contents, probably in response to sudden nitrogen and phosphorus limitation. Differently, during the wet periods, we observe an increase of the phytoplankton glycerophospholipid content, especially at the middle of the lake, whereas significantly higher abundance of secondary metabolites was monitored at site near the wetland area. Since then, we report a strong correlation between the abundance of different cyanopeptides and the biovolume of Dolichospermum , which is present at both sites, we suggest acclimative responses to cope with the phytoplankton growing stimulation related with the increase of the nutritive ion influx following the rain events. The significant difference in the intra-cellular content in metabolites between the 2 sampling sites, separated by only 200m, while phytoplankton communities were similar suggests the existence of local metabolomic niches. phytoplankton cyanobacteria untargeted metabolites meteorological events Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 1. Introduction Concern is growing about the dynamics of phytoplankton biomass and toxic cyanobacteria bloom in face of global changes. It is dreaded that phytoplankton biomass and cyanobacteria blooms should increase in response to current climate changes (Plaas and Paerl, 2021 ; Jöhnk et al., 2008 ; Richardson et al., 2019 ; Bartosiewicz et al., 2019 ; Paerl and Otten, 2013 ). Except 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 drought period coupled with high intensity rain events (Pörtner et al., 2022 ). The influence of altered rainfall patterns on phytoplankton response is not extensively studied so far, leading to a still limited understanding of the 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. However, these studies may not accurately reflect the discontinuity and the intensity of rainfall observed in temperate climates (Stockwell et al., 2020 ; Znachor et al., 2008 ). 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 (Reichwaldt and Ghadouani, 2012 ; Richardson et al., 2019 ; Znachor et al., 2008 ). 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 contrasted (Winder and Sommer, 2012 ; Stockwell et al., 2020 ). Storms occurring after long dry period can induce an increase of the phytoplankton and cyanobacteria bloom due to high amounts of nutrients delivering to surface waters through run-off from the watershed. In contrast, depending on the intensity of the storms, de-stratification of water column 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 water column, rather favoring opportunistic fast-growing taxa (Reichwaldt and Ghadouani, 2012 ). Environmental changes can directly impact the cellular physiology of organisms, as demonstrated by studies that integrate metabarcoding and metabolomics to elucidate the responses of plant, fish, and algae holobionts face to environmental stressors (Sotton et al., 2019 ; Hirai et al., 2004 ; Paix et al., 2019 ; Marcellin-Gros et al., 2020 ). There is an increasing recognition that environmental factors exert a greater influence than phylogeny on the metabolomic profiles of green algae (Hughes et al., 2021 ). Indeed, metabolome composition can be altered under environmental conditions and biotic interactions, as previously suggested (Durham et al., 2022 ; Raina et al., 2022 ; Hughes et al., 2021 ; Marcellin-Gros et al., 2020 ). Thus, Sadler and von Elert ( 2014 ) identified secondary untargeted metabolites from a natural phytoplankton community and proved that this approach is useful for revealing seasonal chemotypes succession in a cyanobacterial community. Untargeted metabolomics approach can also be used to investigate the influence of abiotic variations on phytoplankton metabolomes. McNabney et al. ( 2023 ) identified 33 primary untargeted intracellular metabolites from phytoplankton communities 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 phytoplankton 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 hypotheses and the detection of a higher number of metabolites is necessary to elucidate the metabolic pathways involved in the response of environmental changes (Sardans et al., 2011 ). The number of eco-metabolomics studies in natural freshwater ecosystems remains very limited and critical information regarding the chemical profiles of phytoplankton in natural environments is still lacking (Sadler et al., 2014 ; McNabney et al., 2023 ). In the other hand, laboratory experiments often exhibit discrepancies in their results regarding variations in secondary metabolites from cyanobacteria, particularly microcystins, in response 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 experimental conditions and in the strains used in laboratory settings. Since laboratory experiments cannot fully replicate the complex interplay 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 phytoplankton successions and aiding the identification of environmental stressors that trigger the subsequent production of algal/cyanobacteria toxins. To improve our understanding of the metabolome of the phytoplankton community and their adaptive response to meteorological events, 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 specifically carried out before and after rain events. Monitoring of atmospheric variables, such as wind and rain events, allowed us to explore the biologically response shifts under environmental changes. Concurrently, we continuously monitored abiotic lake variables to capture variations associated with meteorological events. To investigate changes in the metabolome of phytoplankton and decipher seasonal succession patterns driven by abiotic variations, we thus performed multivariate statistical analysis using the annotated untargeted metabolites (primary and secondary), phytoplankton biovolumes, abiotic lake variables and atmospheric variables. 2. Material and methods 2.1. 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. 1 a-b). It is a natural lake that was formed when the Veyre River was dammed by a basaltic lava flow 7,500 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. 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 includes 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) are 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. 1 b). 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 is assessed based on Secchi disc transparency measurements (SD) by multiplying SD by 2.5 to estimate the euphotic depth at both sites. 2.2. Meteorological data The air temperature, wind speed, and relative humidity are continuously 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), approximately 7 km northeast of Aydat, at 893 m above sea level. In addition, we record the rainfall rain using a disdrometer (Parsivel 2 ), located 420 m from Lake Aydat at an elevation of approximately 10 m above the lake. The disdrometer is part of the instrumental suite deployed to characterize precipitation near the lake (Noirmain et al., 2022 ). 2.3. Lake sampling and analysis The monitoring of Lake Aydat has been conducted from August 05th, 2021, to September 27th, 2021. During this period, water samples are collected from the surface and at a depth of 1.5 meters in the middle of the lake, refer as middle point (MP) and near the wetland area, refer as wetland point (WP) (Fig. 1 b). 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 are taken both prior to and following precipitation events. At each depth and location, approximately 10 liters of lake water are pre-filtered through a 250-micrometer Nylon filter to remove larger particles and kept phytoplankton, and then transferred into 15-liter containers. The use of a high volume of water helps minimize temperature variations during transport from Lake Aydat to the laboratory in Clermont Ferrand, which takes less than 30 minutes. 2.3.1. Phytoplankton diversity The lake water is 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) is 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 are keeping at 4°C in the dark until a counting process carried out under a microscope following the European Standard NF15204 (AFNOR, 2006 ). 2.3.2. Chemical analysis For analysis of the major inorganic ions, 1 L of the fresh lake sample are filtrated on a 0·2-µm Nylon membrane, pre-rinsed with 500 mL of ultra-pure Milli-Q water to avoid contamination, the pH is measured on fresh samples and the remaining is stored at -20°C until analysis by ion chromatography. The concentrations of dissolved nutrients, including \(\:{\text{C}\text{a}}^{2+}\) , \(\:{\:\text{C}\text{l}}^{-}\) , \(\:{\text{K}}^{+}\) , \(\:{\text{M}\text{g}}^{2+}\) , \(\:{\text{N}\text{a}}^{+}\) , \(\:{\text{N}\text{H}}_{4}^{+}\) , \(\:{\text{N}\text{O}}_{3}^{-}\) , \(\:{\text{P}\text{O}}_{4}^{3-}\) , and \(\:{\text{S}\text{O}}_{4}^{2-}\) in the lake water are analyzed using ion chromatography (IC) on a Dionex ICS6000 system. An IonPac AG11-HC (guard-column 2 × 50 mm) and an IonPac AS11-HC 260 (analytical column 2 × 250 mm) are 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) are utilized for the analysis of cations. The elution is performed in gradient mode using KOH (1 mM to 60 mM in 35 minutes, 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 are recorded using a conductimetric cell detector and analyzed with Chromeleon 7.2 software. Three replicate measurements are conducted to determine the nutrient concentration. The limit of detection (LOD) is estimated with a signal-to-noise ratio of 3:1, while the limit of quantification (LOQ) is obtained using signal-to-noise ratio of 9:1 (Shrivastava and Gupta, 2011 ). Calibration curves are also generated for each ion to validate the limits of detection (LOD) and limits of quantification (LOQ) obtained through the signal-to-noise method. For anions, the LOD ranged from 0.7 µg.L − 1 for \(\:{\text{P}\text{O}}_{4}^{3-}\) to 2.3 µg.L − 1 for \(\:{\text{C}\text{l}}^{-}\) and the corresponding LOQ ranged from 2.1 to 6.8 µg.L − 1 , depending on the specific anions. For cations, the LOD ranges from 0.7 for \(\:{\text{K}}^{+}\:\) to 141 µg.L − 1 for \(\:{\text{M}\text{g}}^{2+}\) , the corresponding LOQ ranged from 2.1 to 425 µg.L − 1 , depending on the specific cations (Table S1 ). 2.3.3. Metabolites extraction and analysis For each depth and location, one liter of lake water is filtered on 10-µm nylon membranes using ultra-fast filtration and then are immediately transferred into 15 mL sterile falcon tubes and quickly flash-frozen in liquid nitrogen to preserve the samples for metabolomics analysis. This process, known as quenching, is performed as quickly as possible to ensure a reliable method and minimize any disturbance to the endo-metabolism of cells (Sardans et al., 2011 ; Volmer et al., 2011 ). The filtration time for each sample is less than 30 seconds, and the Falcon tubes are kept at -20°C until the day of extraction. This procedure is applied four times to ensure four replicates of each sample per site and depth. Blanks are performed by filtering 1 liter of sterile ultra-pure water. On the day of extraction, we rinse the filter with 1 mL of sterile MQ water mixed with 0.1% NaCl and then vortex it. The filter is then centrifuged at 4°C (5,000 g, 5 min) with the Falcon tube cap attached, and we transfer the pellet to an Eppendorf tube. The Eppendorf tube is centrifuged at 4°C (15000 g, 10 min) and the supernatant is discarded, allowing us to weigh the pellet. We adjust 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 add 200 µL of the mixture. Next, we subject the samples to mechanical disruption through three cycles of freezing and thawing using liquid nitrogen, alternating with three cycles of sonication. Each sonication cycle lasts 15 seconds and is performed at a power of 40% using a Fisherbrand™ Model 120 Sonic Dismembrator. Throughout this process, we keep the samples in ice. After extraction, the samples are centrifuged at 4°C (15000 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 extract 224 samples through four serial extractions, with a blank sample added systematically in each serial. The extracts are 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 is done at a flow rate of 300 µL.min − 1 with a linear gradient of acetonitrile in 0.1% formic acid (5 to 90%) over a period of 21 minutes. Next, we analyze 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 is set at 2–4 Hz in the mass range of 50-1500 m/z. We generate a feature peak list from recalibrated mass spectra, with a calibration 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 term of intensity, retention time and mass drift of ions was carefully inspected and recalibration was automatic performed individually by the software on raw data of all samples according to standard molecules from Na formate calibrant solution. The resulting data are processed using MetaboScape software (Bruker), enabling the detection of 4,446 untargeted metabolites. The data is filtered for a minimum intensity of 5,000 counts, a minimum occurrence of at least 10% in all samples, and the charge states and related isotopic forms are combined. Finally, we perform 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 (Suppl. Figure 1). 2.4. Statistics To investigate if the concentrations of ions and relative abundance of untargeted metabolites vary among the sites and depths, we have performed statistical analysis using non-parametric permutation based MANOVA (PERMANOVA) with the adonis2() function (999 permutations), based on Euclidean distance metric. A Dunn test is performed to confirm the significant differences with adjusted p-value with holm method. Environmental variables (abiotic and biotic), and metabolomics datasets are both integrated using multiblock model (unsupervised and supervised) to assess the links between the variables, using mixOmics R package (Rohart et al., 2017 ). Additional pairwise models are realized to evaluate the correlation scores between each 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 are log10-transformed and mean-centered prior to analysis. The performance of the models is tested, and the optimal number of variables per dataset is 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 define 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. 3. Results and discussion 3.1. Different spatial responses of the chemical lake composition after rain events The analysis of the recorded rain rate at Aydat during the lake sampling period allows us to identify two wet periods: from August 05th, 2021, until August 12th, 2021, and from September 3rd, 2021, until September 27th, 2021, with a drought period of 22 days between them (rain rate < 0.1 mm.h − 1 ) (Fig. 2 a). The first wet period is characterized by a rainfall amount of 45 mm, while the second wet period is characterized by rain event with higher intensities, reaching 70 mm.h − 1 during a 15-min interval on 25th September at 21:30 UTC and a rainfall amount of 560 mm. On the other hand, high mean wind speed (> 6 m s − 1 ) occurs only during the first period, especially on August 6th, 2021, reaching 6.4 m.s − 1 in a 1-hour interval (Fig. 2 a). During the sampling season, both sites exhibit similar temporal variations in water temperature which vary following those of atmospheric temperature (Suppl. Figure 2). Thus, the water temperature increases from August 8th until the 16th, reaching 20.9°C and 21°C at MP and WP sites, respectively. It gradually decreases at the end of the drought period, on August 30th (17 & 18°C at MP and WP, respectively) and finally drops at 16.3°C and 17.1°C at MP and WP sites at the end of the lake sampling, on September 27th, 2021. We also observe a mean temperature difference of 0.8°C between the two sampling sites, with higher temperatures recorded at WP compared to MP (Fig. 2 b). This temperature difference is associated with a stronger thermal stratification at WP, where the mean depth of the thermocline is 5.7 m, compared to a mean thermocline depth of 7.3 m at MP. The higher temperature at WP is likely attributed to its shallower water column, which has a depth of 9 m, compared to 15 m at MP. Before the drought period, similar variations of the concentrations of ions are observed at both sites, with low concentrations of \(\:{\text{N}\text{H}}_{4}^{+}\) and \(\:{\text{P}\text{O}}_{4}^{3-}\) consistently remain below the detection limit (LOD<16 µg.L −1 and 0.7 µg.L − 1 , respectively) at both sites, while very high concentration of \(\:{\text{C}\text{a}}^{2+}\) , reaching 9.1 µg.L − 1 and 9.2 µg.L − 1 at MP and WP, respectively. During the drought, and especially after 18 days without rain, a general decrease in the concentrations of all ions is observed (on August 30th, 2021) (Fig. 2 c). Interestingly, strong disparities in the chemical water composition are reported across the sites following the rain events occurring after the drought, from September 3rd, 2021, until the end of the lake sampling. Indeed, we observed significant higher concentrations of \(\:{\text{N}\text{O}}_{3}^{-}\) , \(\:\:{\text{C}\text{a}}^{2+}\) , \(\:{\text{N}\text{a}}^{+}\) , \(\:{\text{C}\text{l}}^{-}\) , and \(\:{\text{S}\text{O}}_{4}^{2-}\) (p-value=4,5.10 −2 ; 3.10 − 2 ;1,2.10 − 2 ; 2,9.10 − 2 ; and 3.10 −3 , respectively) at WP compared to MP from August 30th 2021, until September 27th 2021. At WP, most of the ions show a recovery to pre-drought levels, especially at 1.5 m deep. However, the concentrations of \(\:{\text{N}\text{O}}_{3}^{-}\) not recover to pre-drought level; instead, they reached 88% and 55% of the pre-drought levels at the surface and at a depth of 1.5 meters on September 6th, 2021, respectively, at WP (Fig. 2 c). In contrast, at MP, we do not observe a recovery of the ions after rain events following the drought. Contrasting results have been reported in the literature regarding variations in ion concentrations following rain events, depending on factors such as rain intensities, the trophic state of the lake, and the geomorphology (Chorus et al., 2021 ). Huisman et al. ( 2018 ) suggested that intense rainfall enhances nutrient runoff, which can lead to profound nutrient enrichment of downstream waters, while Morabito et al. ( 2018 ) reported that pronounced rain events actually dilute nutrients rather than enrich them. Given the different mechanisms at play in various types of water bodies, the results are sensitive to the choice of water bodies and may not support generalizations (Chorus et al., 2021 ). Aydat Lake, seems to experience a significant influx of ions primarily from the inflowing water of the Veyre River near the wetland area. This inflow contributes mainly to the increase in ions concentrations following rain events at WP. In contrast, we observed a lower increase in ions concentrations in the middle of the lake (MP), indicating that some of the ions from the Veyre River may have been consumed before reaching that area, as also suggested by Ishikawa et al. ( 2022 ). 3.2. Temporal disparities in phytoplankton’ response following meteorological events The presence of Euglenophyta remained consistently low during the lake sampling period, while the biovolume of Charophyta, Chlorophyta (green algae), Bacillariophyta (diatoms), Cyanobacteria, Cryptophyta, and Ochrophyta (brown algae) exhibited variations during the lake campaign (Fig. 3 ). Specifically, from August 5th until August 23rd, we observed higher biovolume levels of diatoms ( Stephanodiscus, Cyclotella and Fragilaria ), green algae ( Sphaerocystis and Closterium ) and brown algae ( Uroglena ), while that of cyanobacteria was much lower (Fig. 3 ). Although this phytoplanktonic composition is not typically observed in August in this area, the presence of diatoms is not surprising as this group are known to thrive in habitats that are frequently stirred up (Padisák et al., 2006 ; Blottière et al., 2017 ; Pannard et al., 2007 ). These atypical conditions are likely attributed to the low water temperatures (below 19°C) and the frequent occurrence of rain events accompanied by wind. These factors may explain the limited presence of cyanobacteria, as these species typically have a low tolerance to mixing and low water temperature (Reynolds, 2006 ; Elliott, 2010 ; Padisák et al., 2009 ). In contrast, chlorophyceae like Sphaerocystis is known for being less sensitive to temperature variations compared to cyanobacteria (Reynolds et al., 2002 ), potentially explaning their prevalence in August (Fig. 3 a-c). Significant shifts in phytoplanktonic composition were reported at the end of the drought period, especially on August 30th, marked by the increase of water temperature and the decrease in \(\:{\text{N}\text{O}}_{3}^{-}\) concentration (Fig. 2 ). Specifically, we noticed the increase of the biovolume of diazotroph cyanobacteria, such as Aphanizomenon (Fig. 3 c). These observations aligns with previous studies indicating that diazotroph cyanobacteria thrive in eutrophic lakes with low nitrogen content, as they can fix atmospheric nitrogen (Wei et al., 2023 ; Reynolds et al., 2002 ). Conversely, at the end of the drought period, the biovolume of diatoms, ochrophytes and cryptophytes were low at both sampling sites (Fig. 3 b, d and e). Interestingly, the two rain events occurring following this drought period, from 03rd until 04th September and from 10th to 12th September, have shown contrasted impacts on cyanobacterial biovolume despite having similar characteristics in terms of rain intensity (less than 10 mm.h − 1 ): i) the first rain events following the drought, occurring from 3rd until 4th September, led to an important decrease of cyanobacterial biovolume on 6th September, especially of Aphanizomenon across all sampling points; ii) the second rain events after the drought, occurring from 10th to 12th September, had the opposite effect, resulting in an increase in the biovolume of the dominant cyanobacteria Aphanizomenon on the 13th September, reaching its maximum value in this study (Fig. 3 c). We observed a contrasting pattern with the cryptomonas genus (Fig. 3 e), exhibiting an initial increase following the first rain events from 03rd until 04th September, followed by a decrease in its abondance on the 13th of September after the second rain events following the drought, from 10th to 12th September. So, rain event with low intensity (< 10 mm.h − 1 ) is probably not the main driver explaining the dynamics of phytoplankton species in our case. Surprisingly, the significant increase in nutrients brought by the first rain event did not lead to the expected enhancement in the total biovolume of cyanobacteria. However, there was an exception with the biovolume of Dolichospermum , which increased at the lake surface (Fig. 3 c). In the same way, although the strength of thermal stratification is commonly recognized as a significant factor affecting cyanobacteria response to rain events (Chorus et al., 2021 ), our study found it to be less influential in our specific case as the first meters of the water column were already mixed before the rain events due to the unexpected decrease of water temperature from 21–22°C to 17–18°C at the end of August. This relatively low temperature for a month of August, could have induced a cold-stress response that appears to operate with a timescale of few days corresponding to the decrease of the cyanobacterial biovolume at the beginning of September, especially for Aphanizomenon which showed a higher decrease of its biovolume (Fig. 3 c). Such cold-stress have already been shown concerning microcystin biosynthesis when temperature reduce from 26°C to 19°C and where authors indicated that the processes involved operated on a different timescale (Martin et al., 2020 ). The timescale due to the cold stress might not exceed a few days as the cyanobacterial biovolume grown again on the 13th of September when water temperature rises again (19.5°C). Hence, it appears that the biovolume of Aphanizomenon is more influenced by water temperature rather than the volume of rainfall. Nevertheless, it's crucial to take into account the frequency and intensity of rain events: the rise in frequency towards the end of September led to a decrease in cyanobacteria at both sampling sites, even though the water temperature remained stable at the wetland point and slightly decreased at the middle point. These rain events were characterized by a high intensity up to 10 mm.h − 1 , which could impact the global dynamics of phytoplankton by limiting the biovolume of cyanobacteria and increasing those of diatoms ( Fragilaria and Navicula ) (Fig. 3 b-c), corresponding to the start of the increase in turbulence and autumnal conditions (Reynolds et al., 2002 ). Finally, on September 23rd, when the water temperature dropped below 19°C, the biovolume of all phyla was lower compared to the rest of the lake sampling period. 3.3. Intracellular metabolite profiles In our study, we detected 4,446 untargeted metabolites from phytoplankton biovolume and were able to annotate 260 of them thank to GNPS molecular network approach. The number of annotated metabolites remains very challenging as it depends on the design of the experiment, the biomass collected, the analytical techniques used, and the presence of reference molecules within public chemical databases. Therefore, high differences in the quantification and annotation of metabolites can be found across the different studies. For example, from previous studies using LC-MS-based untargeted metabolomics approaches carried out under lake systems, Sadler et al. ( 2014 ) identified approximately 100 secondary metabolites, some of them being known as chemotrypsine inhibitor; while McNabney et al. ( 2023 ) focused on primary metabolites and were able to annotate 33 metabolites. The chemical libraries we used in the present case are public and comprise both generalist and cyanobacterial-specific databases, such as HMDB, GNPS or CyanoMetDB (Jones et al., 2021 ), respectively. Therefore, we attempt to find here a high number of primary metabolites together with secondary metabolites derived from cyanobacteria and other phytoplankton taxa. A serious challenge for eco-metabolomic studies is to determine and quantify the maximum number of metabolites as possible to satisfy the need to disentangle the biologically relevant components and response shifts under environmental changes (Sardans et al., 2011 ). However, generally only 2–5% of the features detected in untargeted mass spectrometry analysis were matched with known metabolites in public libraries so far (da Silva et al., 2015 ) and molecular network approach offer great opportunity to deeper explore the chemical diversity of natural microbial ecosystems. Indeed, the exact number of metabolites in natural samples remains challenging, even in the case of microorganisms with relatively simple and well-understood metabolism (Sardans et al., 2011 ). We carry out multivariate analyses with either 4,446 untargeted metabolites and 260 annotated metabolites using PCA (Suppl. Figure 4) and confirm the similar patterns according to the dates observed with both datasets. Since regression models become infeasible when the number of metabolites exceeds the sample size (Antonelli et al., 2019 ), we carry out the subsequent statistical analyses using only the 260 annotated compounds to better explore the biological functions linked with environmental stressors. However, we keep in mind that our analysis focuses on annotated metabolites and thus does not reflect the entire metabolism pathways of the phytoplanktonic community. It is important to note that further work is required to annotate other unidentified metabolites, as various molecular clusters remain fully unannotated thank to automatic search with public database combining the already-described natural product part. Our statistical analysis shows that our sampling and extraction methods allow to characterize the eco-metabolomes during the lake campaign. Indeed, the intracellular untargeted metabolomics data are strongly correlated with the 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 exhibits strong correlations with the intracellular metabolomic data (R = 0.7) (Figs. 4 & 5 ). Interestingly, two distinct metabolome fingerprints are identified through the RDA (Fig. 4 ). The first metabolome fingerprint is explained by the biovolumes of species belonging to the phyla Bacillariophyta (Fig. 3 b) and Ochrophyta (Fig. 3 d) during dates characterized by high wind speed and pH, from August 2nd to August 23rd, 2021. Instead, the second metabolome fingerprint is explained by the biovolume of Cyanobacteria, from August 30th to September 27th, 2021 (Fig. 3 c), characterized by high rainfall amount and deeper depth of the euphotic zone (Fig. 4 ). These phyla are indeed associated with annotated metabolites, but it is important to note that other phyla may also produce additional unidentified metabolites during these dates. Hence, the correlations derived from subsequent statistical models, between phytoplankton biovolumes and the relative abundance of untargeted metabolites, may do not capture the entire chemical diversity related to phytoplanktonic composition. These correlations particularly highlight the dynamics of specific species that are positively or negatively correlated with the relative abundance of annotated metabolites. Consequently, we will concentrate on the period explained by these phyla and refer to the first period as the "diatoms' co-occurrence period" and the second period as the "cyanobacteria co-occurrence period". A previously study by McNabney et al, ( 2023 ) has already identified a distinct metabolome fingerprint based on the phytoplankton community and sampling sites, which is characterized by specific abiotic variables such as nitrate and conductivity. In our current study, we provide clear evidence of differences in the metabolome based on changes in the phytoplankton community following summer meteorological events. These changes are influenced by abiotic variations, such as fluctuations in ions concentrations, which exhibit a decay over several days following drought. Notably, the concentrations of ions begin to decrease after 11 days without rain, probably indicating the time taken for consumption of nutrients by phytoplankton. Conversely, an increase in ions concentrations occurs immediately after rain events. It is worth noting that our monitoring was conducted 3 days after the rain events, so it is possible that this increase occurred even earlier. To enhance our understanding of metabolome changes and unravel the biological functions and response shifts under environmental changes with a high temporal resolution, we employed multiblock sPLS models during both diatoms and cyanobacterial dominance periods (Fig. 5 ). Interestingly, the identification of metabolites during these two periods exhibits striking differences, as evidenced by the heatmap resulting from the models (Fig. 5 e-f), with a high lipid content observed during the diatoms co-occurrence period (Fig. 5 e) and a high cyanopeptides content observed during the cyanobacterial co-occurrence period (Fig. 5 f). Our findings reveal strong positive correlations among the abundance of certain untargeted metabolites, abiotic variables, and phytoplankton biovolumes, as demonstrated by correlation circle plots (Fig. 5 a-b & e-f). These plots depict the rapid changes in the metabolome during the sampling periods characterized by the co-occurrence of diatoms (Fig. 5 a) and cyanobacteria (Fig. 5 b). Consequently, we address these dynamics in two subsequent subsections, 3.3.1 and 3.3.2. in relation to meteorological events. 3.3.1. Physiological and molecular strategies of phytoplankton following the drought: insight on the lipid accumulation to cope with the decreased concentrations of ions. The analysis from the multiblock sPLS-model carried out during the “diatoms’ co-occurrence period” highlight different metabolomes according to the phytoplankton composition. Indeed, the biovolume of Chroococcus is strongly correlated at the onset of the lake campaign with amino acids, such as guanosine, tyrosine, and inosine (Fig. 5 e). Instead, the biovolumes of Closterium , Cyclotella and Scenedesmus are negatively correlated with amino acids but positively correlated during the drought with other kind of metabolites, such as sterols and glycerolipids, such as betaine lipids (lyso-diacylglyceryltrimethylhomoserine (LDGTS)) (Fig. 5 e). The strong correlation between these lipids and phytoplankton may potentially suggest a physiological response due to the low levels of \(\:{\text{P}\text{O}}_{4}^{3-}\) and \(\:{\text{N}\text{O}}_{3}^{-}\) . Indeed, it has been suggested that under nutrient limitations, such as phosphorus and nitrogen limitation, lipid can be accumulated by phytoplankton cells, which disrupt anabolic processes (Morales et al., 2021 ; Popko et al., 2016 ; Murakami et al., 2018 ). Furthermore, betaine lipids and sterol intracellular content increase from August 5th until 23rd, while the abundance of some phospholipids, such as glycerol-phosphatidylethanolamine (PE), and phosphatidylcholine (PC), decrease (Suppl. Figure 6a-c). Similar patterns have been previously reported by Giroud et al., ( 1988 ) suggested that betaine lipids are produced to complement the reduction in phospholipids occurring during phosphorus limitation. These authors suggested that accumulation of betaine lipids may aim to re-allocating phosphate use from membrane lipid synthesis to other metabolic pathways (Giroud et al., 1988 ). Interestingly, we also report that abundance of amino acids is high during the onset the period, from August 5th until 9th, but then decrease during the drought. The negative correlation between the amino acid contents and the biovolume of Cyanothece , Closterium , and Cyclotella , may suggested a downregulation of the production of amino acids, such as observed from culture of Phaeodactylum tricornutum (diatom) during nitrogen-starvation (Popko et al., 2016 ). In addition, the study of Feng et al. ( 2015 ) had also reported 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. The authors also reported a downregulation of energy metabolism, photosynthesis, amino acid, and nucleic acid metabolism (Feng et al., 2015 ). In addition, the study of Gargallo-Garriga et al., ( 2020 ) also observed an upregulation of the pathway for lipid metabolism in the dry season with regard with the wet season by comparing leaf metabolomic profiles of 54 species in two rainforests of French Guiana. Therefore, our results suggest that during the drought, when the concentrations of available ions decrease, Cyanothece , Closterium , and Cyclotella may develop acclimatized strategies to cope with the decrease in ions, as shown by the increase of betaine lipids and decrease of glycerophospholipids. Additionally, we observed the synthesis of antioxidant compounds, like polyphenols, which are recognized for their ability to scavenge reactive oxygen species (ROS) as a protective mechanism (Morales et al., 2021 ). Therefore, these molecules might be produced in reaction to oxidative stress induced by nitrogen limitation during drought, as observed in previous studies on microalgae cultures experiencing nitrogen starvation (Chokshi et al., 2017 ). Since our samples are obtained from a natural phytoplankton community, we cannot definitively conclude whether these lipids are specifically produced by all species or only by the dominant ones. Betaine lipids have been widely reported in organisms such as diatoms, microalgae, and cyanobacteria (Sato, 1992 ; Popko et al., 2016 ; Künzler and Eichenberger, 1997 ). In addition, previous study from Heal et al., ( 2021 ) demonstrated that microalgae produce diverse sets of metabolites, with 17% of the untargeted metabolites commonly found in marine phytoplankton and identified 44 metabolites that were observed in over 80% of the phytoplankton species, including amino acids, primary metabolites and nucleic acids. Based on all these findings, we can suppose that the production of lipids might be a common response of the phytoplankton community to environmental changes, indicating strategies of acclimatation employed by phytoplankton to cope with decreasing ion concentrations and potentially counteract the increasing levels of reactive oxygen species due to nitrogen limitation. 3.3.2. Temporal shifts in metabolites production according to the sites The sPLS model carry out during the “cyanobacterial co-occurrence” period reveal two distinct metabolome fingerprints according to the sites (Fig. 5 d). Most of the metabolites annotated correspond to cyanopeptides and are positively correlated with the biovolumes of Dolichospermum , thriving at both sites following rain events. In contrast, Aphanizomenon , the dominant genus among cyanobacteria, exhibits a low correlation between its biovolume and most of the metabolites present during the rainy period. One possible explanation is that metabolites associated with Aphanizomenon are not identified in the databases we used as only a small part of metabolites are currently known and annotated. Moreover, Aphanizomenon exhibited contrasting dynamics in response to rain events (section 3.2), unlike Dolichospermum (Fig. 3 c & Suppl. Figure 5). Therefore, correlations between the relative abundance of untargeted metabolites and the abundance of Dolichospermum are more robust compared to those observed with Aphanizomenon . These correlations need to surpass the correlation cutoff threshold (set at 0.4 for correlation circle plots and at 0.7 for the heatmaps) to be reported in the present statistical results (Fig. 5 & Suppl. Figure 5). On the contrary, these annotated metabolites are negatively correlated with the biovolumes of Closterium and Sphaerocystis (Fig. 5 f). We do not find positive correlations between their biovolumes and any annotated metabolites, which is not surprising considering that we primarily utilized available generalist and specialist databases such as, respectively, HMDB and CyanoMetDB, which predominantly contain metabolites produced by cyanobacteria (Jones et al., 2021 ). However, this does not imply the absence of metabolites produced from Closterium and Sphaerocystis as, during the drought, the biovolume of Closterium have been observed to be also positively correlated with certain lipids (Fig. 5 e). In addition, previous studies have reported that cyanopeptides can be rather produced during the growth phase of cyanobacteria. Therefore, this could explain the high proportion of identified cyanopeptides during this period (Chorus et al., 2021 ), especially on the 13th of September, when the cyanobacteria biovolume rich their maximum value (Fig. 3 c). Interestingly, we observed significant variations in the intra-cellular content in metabolites between the sites during the wet period ( p -value = 0.014) while phytoplankton communities were similar (Fig. 6 & Suppl. Figure 5). To our knowledge, the only study that has demonstrated shifts in metabolome fingerprints in aquatic ecosystems is the recent work of McNabney et al. ( 2023 ). Their study showed contrasting metabolomes associated with the phytoplankton community, as well as the concentration of nitrate and conductivity across two sites separated by 200 km. In our study, the sites are only 200 m apart (Fig. 1 b), highlighting a divergence in their respective metabolomic niches. This concept have been recently proposed by the study of Gargallo-Garriga et al. ( 2020 ) when demonstrating that trees at different location exhibit different functional niches. Therefore, to better understand the differences across the sampling sites, we carry out a supervised multiblock sPLS-DA model (Fig. 6 & Suppl. Figure 4), since the metabolites appear significantly different across the sites ( p -value = 0.04) (Suppl. Figure 4). This analysis shows, thank to hierarchical clustering, that endo-metabolome differs strongly across the sites, but only after August 30th (Fig. 6 ). Interestingly, most cyanopeptides have significant higher relative abundance at WP rather than at MP, such as anabaenopeptin, microginins, shinorine, and micropeptin B, as well as cyanotoxins, such as microcystin LR (Fig. 6 ). Most of these secondary metabolites are referred as bioactive compounds (Demay et al., 2019 ). In our study, since the relative abundances of cyanopeptides are strongly correlated with the concentrations of inorganic ions (Fig. 6 ), we suggest that the synthesis of these peptides is primarily favored by nutrients availability and may be related to biotic interaction function ( e.g. allelopathy), very likely providing competitive advantage to the producing cyanobacteria. In addition, these components may also act as potential as nitrogen reservoir, as previously suggested by several studies for microcystin-LR, aeruginosins, cyanopeptolins, and shinorine (Agrawal et al., 2005 ; Davis et al., 2010 ; Sadler and von Elert, 2014 ; Harke and Gobler, 2013 ; Peinado et al., 2004 ; Horst et al., 2014 ). Indeed, during nutrient limited conditions, it was hypothesized that the biosynthesis of arginine should be used by dinoflagellates and cyanobacteria as a nitrogen reservoir molecule. After rain events, according to the geomorphology of the lake, spatial changes of abiotic conditions can be observed. Indeed, we recorded a higher water temperature and a stronger lake thermal stratification at the wetland area (WP) compared to the middle of the lake (MP). In this study, we report that during the wet period a higher abundance of polyphenol compounds and glycerophospholipids are detected at MP, while most cyanopeptides have been identified at WP (Fig. 6 & Suppl. Figure 6). This result seems also to indicate a distinctive molecular response of this cyanobacteria between the two sites, distant of only 200 meters. Therefore, the untargeted metabolomic approach is well-suited for highlighting ecological acclimatation by providing a measurable method to identify the corresponding responses of the phytoplankton community to abiotic variations triggered by meteorological events. Consequently, eco-metabolomic approaches can be utilized to study the metabolomic niches, as previously proposed by Gargallo-Garriga et al. ( 2020 ) in the context of trees located at different locations. To the best of our knowledge, this is the first time that we demonstrate the acclimatized response of phytoplankton in lake ecosystems following meteorological events, revealing a strong heterogeneity based on sampling dates and sites. 4. Conclusion The study on changes in the phytoplankton metabolome following meteorological events in natural systems has been sparsely investigated and was still lacking of 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 secondary untargeted metabolites, form a total of 4,446 detected metabolites whom 260 could have been thus annotated. The investigation of these metabolites allows us to confirm that eco-metabolome fingerprints reflect the phytoplankton community, driven by abiotic variations following meteorological events. We observe rapid changes in the metabolomes of phytoplankton in response to meteorological events, resulting in variations in abiotic conditions triggered by drought and rain events. For example, during the drought, we observe 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 higher relative abundance at WP than at MP, suggested their potential role in biotic interactions and/or in nitrogen reservoir since WP is characterized by significantly higher concentrations of ions following the rain events. 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 meaningful, considering the heterogeneity across the sampling locations at different spatio-temporal scales. Declarations Author Contribution FN was responsible for writing the original draft, methodology, conceptualization, investigation, and visualization.BM provided expertise in metabolomics, conceptualization, review & editing. BL provided technical expertise in phytoplankton enumeration.JVB provided expertise in atmospheric measurements, supervision.DL was responsible for conceptualization, supervision, review and editing. Acknowledgement We thank the FRE (Fédération des Recherches en Environnement), 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. 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S. and Ghadouani, A.: Effects of rainfall patterns on toxic cyanobacterial blooms in a changing climate: Between simplistic scenarios and complex dynamics, Water Res., 46, 1372–1393, https://doi.org/10.1016/j.watres.2011.11.052, 2012. Reynolds, C. S.: The Ecology of Phytoplankton, Cambridge University Press, 437 pp., 2006. Reynolds, C. S., Huszar, V., Kruk, C., Naselli-Flores, L., and Melo, S.: Towards a functional classification of the freshwater phytoplankton, J. Plankton Res., 24, 417–428, https://doi.org/10.1093/plankt/24.5.417, 2002. Richardson, J., Feuchtmayr, H., Miller, C., Hunter, P. D., Maberly, S. C., and Carvalho, L.: Response of cyanobacteria and phytoplankton abundance to warming, extreme rainfall events and nutrient enrichment, Glob. Change Biol., 25, 3365–3380, https://doi.org/10.1111/gcb.14701, 2019. Rohart, F., Gautier, B., Singh, A., and Cao, K.-A. L.: mixOmics: An R package for ‘omics feature selection and multiple data integration, PLOS Comput. 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M., Llames, M. E., Matsuzaki, S. S., Nodine, E. R., Nõges, P., Patil, V. P., Pomati, F., Rinke, K., Rudstam, L. G., Rusak, J. A., Salmaso, N., Seltmann, C. T., Straile, D., Thackeray, S. J., Thiery, W., Urrutia‐Cordero, P., Venail, P., Verburg, P., Woolway, R. I., Zohary, T., Andersen, M. R., Bhattacharya, R., Hejzlar, J., Janatian, N., Kpodonu, A. T. N. K., Williamson, T. J., and Wilson, H. L.: Storm impacts on phytoplankton community dynamics in lakes, Glob. Change Biol., 26, 2756–2784, https://doi.org/10.1111/gcb.15033, 2020. Volmer, M., Northoff, S., Scholz, S., Thüte, T., Büntemeyer, H., and Noll, T.: Fast filtration for metabolome sampling of suspended animal cells, Biotechnol. Lett., 33, 495–502, https://doi.org/10.1007/s10529-010-0466-7, 2011. Wei, J., Li, Q., Liu, W., Zhang, S., Xu, H., and Pei, H.: Changes of phytoplankton and water environment in a highly urbanized subtropical lake during the past ten years, Sci. Total Environ., 879, 162985, https://doi.org/10.1016/j.scitotenv.2023.162985, 2023. Wiedner, C., Visser, P. M., Fastner, J., Metcalf, J. S., Codd, G. A., and Mur, L. R.: Effects of Light on the Microcystin Content of Microcystis Strain PCC 7806, Appl. Environ. Microbiol., 69, 1475–1481, https://doi.org/10.1128/AEM.69.3.1475-1481.2003, 2003. Winder, M. and Sommer, U.: Phytoplankton response to a changing climate, Hydrobiologia, 698, 5–16, https://doi.org/10.1007/s10750-012-1149-2, 2012. Znachor, P., Zapomělová, E., Řeháková, K., Nedoma, J., and Šimek, K.: The effect of extreme rainfall on summer succession and vertical distribution of phytoplankton in a lacustrine part of a eutrophic reservoir, Aquat. Sci., 70, 77–86, https://doi.org/10.1007/s00027-007-7033-x, 2008. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4880559","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":355318827,"identity":"ce5309f2-61ab-42ef-97f3-0680db3894f3","order_by":0,"name":"Fanny Noirmain","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABAklEQVRIiWNgGAWjYBAC9gYogw1EJDDYwGX4cWnhOYCqJQ3KYmCQbMCqAUkLFBwmQgv72WOPeRjuyPFJn0788HDHeTmD+w1sEj9qGCTMcejh4clLN+ZheGbMxpe7WSLxzG1jyTYGNsmeYwwSMgewa7FnyDGT5mE4nNjGw7tBIrHtdmI/GwObNNB1dRK4HMb/Bq5l84/EtnP1bWAt/xgkcGqRQNiyDWjLgQR+kBbGNnxa3phJzjE4bMwG1GKR2JZsOLMtsdmyt08Ctxb+HDOJNxWH5eR7eDff/NlmJ29w+PDBGz++2eDUAgJMPAYofMYGIIFPA1DJD7zSo2AUjIJRMOIBAC4MSQ4TOHyBAAAAAElFTkSuQmCC","orcid":"","institution":"University of Clermont Auvergne","correspondingAuthor":true,"prefix":"","firstName":"Fanny","middleName":"","lastName":"Noirmain","suffix":""},{"id":355318828,"identity":"b045979b-5585-49c7-98ef-d80f501945b5","order_by":1,"name":"Benjamin Marie","email":"","orcid":"","institution":"UMR7245 Molécules de Communication et Adaptation des Micro-organismes, Muséum National d’Histoire Naturelle, CNRS","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Marie","suffix":""},{"id":355318830,"identity":"53591873-0309-4723-8f77-4082dbe9c2fc","order_by":2,"name":"Benjamin Legrand","email":"","orcid":"","institution":"ATHOS Environnement","correspondingAuthor":false,"prefix":"","firstName":"Benjamin","middleName":"","lastName":"Legrand","suffix":""},{"id":355318833,"identity":"2b685d6f-5d36-4348-92a3-9cfadd438ac1","order_by":3,"name":"Joël Baelen","email":"","orcid":"","institution":"Université de la Réunion, CNRS, Météo-France, UMR8105","correspondingAuthor":false,"prefix":"","firstName":"Joël","middleName":"","lastName":"Baelen","suffix":""},{"id":355318835,"identity":"6bb01734-e2bf-464a-94d4-72b6b713533d","order_by":4,"name":"Delphine Latour","email":"","orcid":"","institution":"University of Clermont Auvergne","correspondingAuthor":false,"prefix":"","firstName":"Delphine","middleName":"","lastName":"Latour","suffix":""}],"badges":[],"createdAt":"2024-08-08 11:10:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4880559/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4880559/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":64746450,"identity":"5ee8b4f7-bc83-4063-a894-98088e08130c","added_by":"auto","created_at":"2024-09-18 09:45:39","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":139183,"visible":true,"origin":"","legend":"\u003cp\u003e(a) The town of Aydat is in the Puy-de-Dôme department in the Auvergne-Rhône-Alpes region of central France, and (b) the map of Lake Aydat illustrates 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.\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/e909a66b03009b1a052d1610.png"},{"id":64747716,"identity":"e5dbe0e7-555a-4ceb-98f9-289462136438","added_by":"auto","created_at":"2024-09-18 10:01:39","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":674932,"visible":true,"origin":"","legend":"\u003cp\u003eMean meteorological variables (a), including rainfall intensities recorded from the Parsivel\u003csup\u003e2\u003c/sup\u003e 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 representing by continuous line and dotter lines for the lakes sampling carry out at surface and at 1.5 m deeper. Concentrations are expressed in mg. L\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage2.png","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/6a6851b61de68dd5e0e596c2.png"},{"id":64747145,"identity":"bc958160-efed-434d-892a-de62a1e576ef","added_by":"auto","created_at":"2024-09-18 09:53:39","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":548142,"visible":true,"origin":"","legend":"\u003cp\u003eBiovolumes of the dominant species monitored during the lake campaign were 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. Rectangles in blue represent the rain periods, while those in yellow correspond to the drought period.\u003c/p\u003e","description":"","filename":"floatimage3.png","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/39108e9e7539246b7ec6dac2.png"},{"id":64748526,"identity":"892ec132-d6eb-450b-aa34-f29449b66923","added_by":"auto","created_at":"2024-09-18 10:09:39","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":151258,"visible":true,"origin":"","legend":"\u003cp\u003eRedundancy analysis (RDA) carry out with metabolomics data (260 annotated metabolites), used as explain variables. Predictors, environmental variables are used as explanatory variables (showing in the plot with arrows colored in black). The metabolite samples are color-coded based on the date and representing 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. The wetland point (WP) site is representing by plus and cross symbols indicating water samples taken at surface and 1.5 meters, respectively. The biovolumes of phytoplankton are expressed in mm\u003csup\u003e3\u003c/sup\u003e.L\u003csup\u003e-1\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"floatimage4.png","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/d42d2a01c88fa8fa1d9df550.png"},{"id":64747148,"identity":"e428d1a5-6850-4e5e-978b-8a8737f561c7","added_by":"auto","created_at":"2024-09-18 09:53:40","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":498472,"visible":true,"origin":"","legend":"\u003cp\u003eUnsupervised multiblock sPLS model using lake samples collected (a, c \u0026amp; e) from August 02\u003csup\u003end\u003c/sup\u003e until 23\u003csup\u003erd\u003c/sup\u003e, 2021 (diatoms \u0026nbsp;co-occurrence period) and (b, d \u0026amp; f) from August 30\u003csup\u003eth\u003c/sup\u003e until September 27\u003csup\u003eth\u003c/sup\u003e 2021 (cyanobacterial co-occurrence period), representing the correlation circle plots (a-b) between component 1 and 2, integrating the environmental variables, colored in blue, the concentrations of ions, colored in red, the biovolume of phytoplankton, colored in green, and the untargeted annotated metabolites, colored in yellow; (c-d) the sample plots performed with metabolites, biovolume of phytoplankton, ions, and environmental variables, color-coded according to the dates; and the heatmap resulting from both models according the two temporal periods (c \u0026amp; e). The concentrations of ions are expressed in mg.L\u003csup\u003e-1\u003c/sup\u003e, the biovolumes of phytoplankton are expressed in mm\u003csup\u003e3\u003c/sup\u003e.L\u003csup\u003e-1\u003c/sup\u003e, the metabolites are expressed in relative unit abundance. The correlation cutoff threshold set at 0.4 for correlation circle plots and at 0.7 for the heatmaps.\u003c/p\u003e","description":"","filename":"floatimage5.png","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/acba6154831a418253e57f80.png"},{"id":64746455,"identity":"d0ed952b-3f1e-44f4-abb8-330fd35b6ae2","added_by":"auto","created_at":"2024-09-18 09:45:39","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":729560,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap from supervised mutiblock sPLS-DA models (DIABLO), using a correlation cutoff threshold set at 0.6, applying a scaling of multi-data sets and adjusting the trimming values to [-2, 2] range for cim visualization.\u003c/p\u003e","description":"","filename":"floatimage6.png","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/8da9e53e34ca2e688df742da.png"},{"id":66816744,"identity":"527674e8-935b-4ffe-956c-fd604834fe6a","added_by":"auto","created_at":"2024-10-16 19:16:43","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3400141,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/c1c24c0d-2a47-4901-bed7-e7918925d5ac.pdf"},{"id":64746456,"identity":"0f8ca133-88ee-41d2-bd0e-3a0e27ab7df0","added_by":"auto","created_at":"2024-09-18 09:45:40","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":1496451,"visible":true,"origin":"","legend":"","description":"","filename":"Supplementarymaterial08082024.docx","url":"https://assets-eu.researchsquare.com/files/rs-4880559/v1/30e8312b8e16cc8c2bc07104.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Spatio-temporal disparities in phytoplankton dynamics and metabolite production depending on weather conditions","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eConcern is growing about the dynamics of phytoplankton biomass and toxic cyanobacteria bloom in face of global changes. It is dreaded that phytoplankton biomass and cyanobacteria blooms should increase in response to current climate changes (Plaas and Paerl, \u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; J\u0026ouml;hnk et al., \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2008\u003c/span\u003e; Richardson et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Bartosiewicz et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Paerl and Otten, \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Except 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 drought period coupled with high intensity rain events (P\u0026ouml;rtner et al., \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2022\u003c/span\u003e). The influence of altered rainfall patterns on phytoplankton response is not extensively studied so far, leading to a still limited understanding of the phytoplankton adaptive mechanisms (Reichwaldt and Ghadouani, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Additionally, many studies have been conducted in subtropical climates, which are characterized by more frequent rains together with intense events such as tropical cyclones. However, these studies may not accurately reflect the discontinuity and the intensity of rainfall observed in temperate climates (Stockwell et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Znachor et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). 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 (Reichwaldt and Ghadouani, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Richardson et al., \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Znachor et al., \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2008\u003c/span\u003e). 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 contrasted (Winder and Sommer, \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Stockwell et al., \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Storms occurring after long dry period can induce an increase of the phytoplankton and cyanobacteria bloom due to high amounts of nutrients delivering to surface waters through run-off from the watershed. In contrast, depending on the intensity of the storms, de-stratification of water column 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 water column, rather favoring opportunistic fast-growing taxa (Reichwaldt and Ghadouani, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2012\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEnvironmental changes can directly impact the cellular physiology of organisms, as demonstrated by studies that integrate metabarcoding and metabolomics to elucidate the responses of plant, fish, and algae holobionts face to environmental stressors (Sotton et al., \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Hirai et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Paix et al., \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Marcellin-Gros et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). There is an increasing recognition that environmental factors exert a greater influence than phylogeny on the metabolomic profiles of green algae (Hughes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Indeed, metabolome composition can be altered under environmental conditions and biotic interactions, as previously suggested (Durham et al., \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Raina et al., \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Hughes et al., \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Marcellin-Gros et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Thus, Sadler and von Elert (\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) identified secondary untargeted metabolites from a natural phytoplankton community and proved that this approach is useful for revealing seasonal chemotypes succession in a cyanobacterial community. Untargeted metabolomics approach can also be used to investigate the influence of abiotic variations on phytoplankton metabolomes. McNabney et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) identified 33 primary untargeted intracellular metabolites from phytoplankton communities 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 phytoplankton 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 hypotheses and the detection of a higher number of metabolites is necessary to elucidate the metabolic pathways involved in the response of environmental changes (Sardans et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The number of eco-metabolomics studies in natural freshwater ecosystems remains very limited and critical information regarding the chemical profiles of phytoplankton in natural environments is still lacking (Sadler et al., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McNabney et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In the other hand, laboratory experiments often exhibit discrepancies in their results regarding variations in secondary metabolites from cyanobacteria, particularly microcystins, in response to abiotic factors such as light and nutrient availability (Long et al., \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2001\u003c/span\u003e; Wiedner et al., \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e2003\u003c/span\u003e; Briand et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). These discrepancies are frequently attributed to variations in experimental conditions and in the strains used in laboratory settings. Since laboratory experiments cannot fully replicate the complex interplay of multiple factors present in natural systems (Sardans et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), metabolomics approaches in natural system hold great promise in investigating the factors leading to variations in phytoplankton successions and aiding the identification of environmental stressors that trigger the subsequent production of algal/cyanobacteria toxins.\u003c/p\u003e \u003cp\u003eTo improve our understanding of the metabolome of the phytoplankton community and their adaptive response to meteorological events, 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 specifically carried out before and after rain events. Monitoring of atmospheric variables, such as wind and rain events, allowed us to explore the biologically response shifts under environmental changes. Concurrently, we continuously monitored abiotic lake variables to capture variations associated with meteorological events. To investigate changes in the metabolome of phytoplankton and decipher seasonal succession patterns driven by abiotic variations, we thus performed multivariate statistical analysis using the annotated untargeted metabolites (primary and secondary), phytoplankton biovolumes, abiotic lake variables and atmospheric variables.\u003c/p\u003e"},{"header":"2. Material and methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Lake site and instrumental setup\u003c/h2\u003e \u003cp\u003eLake Aydat (45.6\u0026deg;N; 2.9\u0026deg;E) is in the French Massif Central, around 15 km southwest of Clermont Ferrand, at 837 m above sea level (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003ea-b). It is a natural lake that was formed when the Veyre River was dammed by a basaltic lava flow 7,500 years ago. This small eutrophic dimictic lake has a total area of 0.6 km\u0026sup2;, a catchment area of 300 km\u0026sup2;, and a maximal depth of 15 m, and suffers recurrent cyanobacterial proliferations. 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., \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2013\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe instrumental setup includes a BBE FluoroProbe (FP; bbe Moldaenke GmbH, Kiel, Germany) instrument installed in the middle of the lake (referred to as \u0026ldquo;Middle Point\u0026rdquo;, MP) to ensure vertical profiles of water temperature, conductivity, and oxygen level. In addition, HOBO data loggers (Onset Computer Corp., Pocasset, MA) are positioned near the wetland area (referred to as \u0026ldquo;Wetland Point\u0026rdquo;, WP) to record temperature at intervals of 20 cm from the water surface to a depth of 2.8 m (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). 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 is assessed based on Secchi disc transparency measurements (SD) by multiplying SD by 2.5 to estimate the euphotic depth at both sites.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Meteorological data\u003c/h2\u003e \u003cp\u003eThe air temperature, wind speed, and relative humidity are continuously monitored by a weather station operated by the French meteorological network \u0026ldquo;M\u0026eacute;t\u0026eacute;o France\u0026rdquo; located at Saint-Gen\u0026egrave;s-Champanelle (45\u0026deg;43'22\u0026ldquo;N; 3\u0026deg;01'09\u0026ldquo;E), approximately 7 km northeast of Aydat, at 893 m above sea level. In addition, we record the rainfall rain using a disdrometer (Parsivel\u003csup\u003e2\u003c/sup\u003e), located 420 m from Lake Aydat at an elevation of approximately 10 m above the lake. The disdrometer is part of the instrumental suite deployed to characterize precipitation near the lake (Noirmain et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Lake sampling and analysis\u003c/h2\u003e \u003cp\u003eThe monitoring of Lake Aydat has been conducted from August 05th, 2021, to September 27th, 2021. During this period, water samples are collected from the surface and at a depth of 1.5 meters in the middle of the lake, refer as middle point (MP) and near the wetland area, refer as wetland point (WP) (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb). 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 are taken both prior to and following precipitation events. At each depth and location, approximately 10 liters of lake water are pre-filtered through a 250-micrometer Nylon filter to remove larger particles and kept phytoplankton, and then transferred into 15-liter containers. The use of a high volume of water helps minimize temperature variations during transport from Lake Aydat to the laboratory in Clermont Ferrand, which takes less than 30 minutes.\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section3\"\u003e \u003ch2\u003e2.3.1. Phytoplankton diversity\u003c/h2\u003e \u003cp\u003eThe lake water is immediately filtered in the laboratory using a 150-\u0026micro;m Nylon membrane to avoid the presence of zooplankton in the lake samples. The filtrate (under 150 \u0026micro;m in size) is 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 are keeping at 4\u0026deg;C in the dark until a counting process carried out under a microscope following the European Standard NF15204 (AFNOR, \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2006\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section3\"\u003e \u003ch2\u003e2.3.2. Chemical analysis\u003c/h2\u003e \u003cp\u003eFor analysis of the major inorganic ions, 1 L of the fresh lake sample are filtrated on a 0\u0026middot;2-\u0026micro;m Nylon membrane, pre-rinsed with 500 mL of ultra-pure Milli-Q water to avoid contamination, the pH is measured on fresh samples and the remaining is stored at -20\u0026deg;C until analysis by ion chromatography. The concentrations of dissolved nutrients, including \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{a}}^{2+}\\)\u003c/span\u003e\u003c/span\u003e,\u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\:\\text{C}\\text{l}}^{-}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{K}}^{+}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}\\text{g}}^{2+}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{a}}^{+}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{H}}_{4}^{+}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}\\text{O}}_{4}^{3-}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}\\text{O}}_{4}^{2-}\\)\u003c/span\u003e\u003c/span\u003e in the lake water are analyzed using ion chromatography (IC) on a Dionex ICS6000 system. An IonPac AG11-HC (guard-column 2 \u0026times; 50 mm) and an IonPac AS11-HC 260 (analytical column 2 \u0026times; 250 mm) are used for the analysis of anions, while an IonPac CG-16 (guard-column 2 \u0026times; 50 mm) and an IonPac CS16 (analytical column 2 \u0026times; 250 mm) are utilized for the analysis of cations. The elution is performed in gradient mode using KOH (1 mM to 60 mM in 35 minutes, flow rate of 0.36 mL.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for anions and isocratic mode with MSA (methanesulfonic acid at 30 mM, flow rate of 0.25 mL.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) for cations. The chromatograms are recorded using a conductimetric cell detector and analyzed with Chromeleon 7.2 software. Three replicate measurements are conducted to determine the nutrient concentration. The limit of detection (LOD) is estimated with a signal-to-noise ratio of 3:1, while the limit of quantification (LOQ) is obtained using signal-to-noise ratio of 9:1 (Shrivastava and Gupta, \u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Calibration curves are also generated for each ion to validate the limits of detection (LOD) and limits of quantification (LOQ) obtained through the signal-to-noise method. For anions, the LOD ranged from 0.7 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}\\text{O}}_{4}^{3-}\\)\u003c/span\u003e\u003c/span\u003e to 2.3 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{l}}^{-}\\)\u003c/span\u003e\u003c/span\u003e and the corresponding LOQ ranged from 2.1 to 6.8 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, depending on the specific anions. For cations, the LOD ranges from 0.7 for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{K}}^{+}\\:\\)\u003c/span\u003e\u003c/span\u003e to 141 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e for \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{M}\\text{g}}^{2+}\\)\u003c/span\u003e\u003c/span\u003e, the corresponding LOQ ranged from 2.1 to 425 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, depending on the specific cations (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003e2.3.3. Metabolites extraction and analysis\u003c/h2\u003e \u003cp\u003eFor each depth and location, one liter of lake water is filtered on 10-\u0026micro;m nylon membranes using ultra-fast filtration and then are immediately transferred into 15 mL sterile falcon tubes and quickly flash-frozen in liquid nitrogen to preserve the samples for metabolomics analysis. This process, known as quenching, is performed as quickly as possible to ensure a reliable method and minimize any disturbance to the endo-metabolism of cells (Sardans et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Volmer et al., \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). The filtration time for each sample is less than 30 seconds, and the Falcon tubes are kept at -20\u0026deg;C until the day of extraction. This procedure is applied four times to ensure four replicates of each sample per site and depth. Blanks are performed by filtering 1 liter of sterile ultra-pure water.\u003c/p\u003e \u003cp\u003eOn the day of extraction, we rinse the filter with 1 mL of sterile MQ water mixed with 0.1% NaCl and then vortex it. The filter is then centrifuged at 4\u0026deg;C (5,000 g, 5 min) with the Falcon tube cap attached, and we transfer the pellet to an Eppendorf tube. The Eppendorf tube is centrifuged at 4\u0026deg;C (15000 g, 10 min) and the supernatant is discarded, allowing us to weigh the pellet. We adjust 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 \u0026micro;L of the ice-cold solvent mixture corresponds to 1 mg of biomass. For the blank, we systematically add 200 \u0026micro;L of the mixture.\u003c/p\u003e \u003cp\u003eNext, we subject the samples to mechanical disruption through three cycles of freezing and thawing using liquid nitrogen, alternating with three cycles of sonication. Each sonication cycle lasts 15 seconds and is performed at a power of 40% using a Fisherbrand\u0026trade; Model 120 Sonic Dismembrator. Throughout this process, we keep the samples in ice. After extraction, the samples are centrifuged at 4\u0026deg;C (15000 g, 10 min) before being transferred into 40 \u0026micro;L vials (AR0-9973-13, Verex\u0026trade; Vial Kit) and stored at -80\u0026deg;C until the detection of metabolites. In total, we extract 224 samples through four serial extractions, with a blank sample added systematically in each serial.\u003c/p\u003e \u003cp\u003eThe extracts are further analyzed by injecting 2 \u0026micro;L 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 is done at a flow rate of 300 \u0026micro;L.min\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e with a linear gradient of acetonitrile in 0.1% formic acid (5 to 90%) over a period of 21 minutes. Next, we analyze 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 is set at 2\u0026ndash;4 Hz in the mass range of 50-1500 m/z. We generate a feature peak list from recalibrated mass spectra, with a calibration 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 term of intensity, retention time and mass drift of ions was carefully inspected and recalibration was automatic performed individually by the software on raw data of all samples according to standard molecules from Na formate calibrant solution.\u003c/p\u003e \u003cp\u003eThe resulting data are processed using MetaboScape software (Bruker), enabling the detection of 4,446 untargeted metabolites. The data is filtered for a minimum intensity of 5,000 counts, a minimum occurrence of at least 10% in all samples, and the charge states and related isotopic forms are combined. Finally, we perform 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 (Suppl. Figure\u0026nbsp;1).\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Statistics\u003c/h2\u003e \u003cp\u003eTo investigate if the concentrations of ions and relative abundance of untargeted metabolites vary among the sites and depths, we have performed statistical analysis using non-parametric permutation based MANOVA (PERMANOVA) with the \u003cem\u003eadonis2()\u003c/em\u003e function (999 permutations), based on Euclidean distance metric. A Dunn test is performed to confirm the significant differences with adjusted p-value with holm method.\u003c/p\u003e \u003cp\u003eEnvironmental variables (abiotic and biotic), and metabolomics datasets are both integrated using multiblock model (unsupervised and supervised) to assess the links between the variables, using mixOmics R package (Rohart et al., \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2017\u003c/span\u003e). Additional pairwise models are realized to evaluate the correlation scores between each data sets.\u003c/p\u003e \u003cp\u003eThe 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 are log10-transformed and mean-centered prior to analysis. The performance of the models is tested, and the optimal number of variables per dataset is selected for DIABLO to ensure a sufficient number of variables for downstream validation and interpretation (Singh et al., \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For both the unsupervised sPLS and supervised sPLS-DA models, we define 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.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Results and discussion","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Different spatial responses of the chemical lake composition after rain events\u003c/h2\u003e \u003cp\u003eThe analysis of the recorded rain rate at Aydat during the lake sampling period allows us to identify two wet periods: from August 05th, 2021, until August 12th, 2021, and from September 3rd, 2021, until September 27th, 2021, with a drought period of 22 days between them (rain rate\u0026thinsp;\u0026lt;\u0026thinsp;0.1 mm.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea). The first wet period is characterized by a rainfall amount of 45 mm, while the second wet period is characterized by rain event with higher intensities, reaching 70 mm.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e during a 15-min interval on 25th September at 21:30 UTC and a rainfall amount of 560 mm. On the other hand, high mean wind speed (\u0026gt;\u0026thinsp;6 m s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) occurs only during the first period, especially on August 6th, 2021, reaching 6.4 m.s\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e in a 1-hour interval (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ea).\u003c/p\u003e \u003cp\u003eDuring the sampling season, both sites exhibit similar temporal variations in water temperature which vary following those of atmospheric temperature (Suppl. Figure\u0026nbsp;2). Thus, the water temperature increases from August 8th until the 16th, reaching 20.9\u0026deg;C and 21\u0026deg;C at MP and WP sites, respectively. It gradually decreases at the end of the drought period, on August 30th (17 \u0026amp; 18\u0026deg;C at MP and WP, respectively) and finally drops at 16.3\u0026deg;C and 17.1\u0026deg;C at MP and WP sites at the end of the lake sampling, on September 27th, 2021. We also observe a mean temperature difference of 0.8\u0026deg;C between the two sampling sites, with higher temperatures recorded at WP compared to MP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003eb). This temperature difference is associated with a stronger thermal stratification at WP, where the mean depth of the thermocline is 5.7 m, compared to a mean thermocline depth of 7.3 m at MP. The higher temperature at WP is likely attributed to its shallower water column, which has a depth of 9 m, compared to 15 m at MP.\u003c/p\u003e \u003cp\u003eBefore the drought period, similar variations of the concentrations of ions are observed at both sites, with low concentrations of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{H}}_{4}^{+}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}\\text{O}}_{4}^{3-}\\)\u003c/span\u003e\u003c/span\u003e consistently remain below the detection limit (LOD\u0026lt;16 \u0026micro;g.L\u003csup\u003e\u0026minus;1\u003c/sup\u003e and 0.7 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, respectively) at both sites, while very high concentration of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{a}}^{2+}\\)\u003c/span\u003e\u003c/span\u003e, reaching 9.1 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e and 9.2 \u0026micro;g.L\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e at MP and WP, respectively. During the drought, and especially after 18 days without rain, a general decrease in the concentrations of all ions is observed (on August 30th, 2021) (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). Interestingly, strong disparities in the chemical water composition are reported across the sites following the rain events occurring after the drought, from September 3rd, 2021, until the end of the lake sampling. Indeed, we observed significant higher concentrations of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\:{\\text{C}\\text{a}}^{2+}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{a}}^{+}\\)\u003c/span\u003e\u003c/span\u003e, \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{C}\\text{l}}^{-}\\)\u003c/span\u003e\u003c/span\u003e, and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{S}\\text{O}}_{4}^{2-}\\)\u003c/span\u003e\u003c/span\u003e (p-value=4,5.10\u003csup\u003e\u0026minus;2\u003c/sup\u003e; 3.10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e;1,2.10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e; 2,9.10\u003csup\u003e\u0026minus;\u0026thinsp;2\u003c/sup\u003e; and 3.10\u003csup\u003e\u0026minus;3\u003c/sup\u003e, respectively) at WP compared to MP from August 30th 2021, until September 27th 2021. At WP, most of the ions show a recovery to pre-drought levels, especially at 1.5 m deep. However, the concentrations of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e not recover to pre-drought level; instead, they reached 88% and 55% of the pre-drought levels at the surface and at a depth of 1.5 meters on September 6th, 2021, respectively, at WP (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003ec). In contrast, at MP, we do not observe a recovery of the ions after rain events following the drought.\u003c/p\u003e \u003cp\u003eContrasting results have been reported in the literature regarding variations in ion concentrations following rain events, depending on factors such as rain intensities, the trophic state of the lake, and the geomorphology (Chorus et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Huisman et al. (\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) suggested that intense rainfall enhances nutrient runoff, which can lead to profound nutrient enrichment of downstream waters, while Morabito et al. (\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) reported that pronounced rain events actually dilute nutrients rather than enrich them. Given the different mechanisms at play in various types of water bodies, the results are sensitive to the choice of water bodies and may not support generalizations (Chorus et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Aydat Lake, seems to experience a significant influx of ions primarily from the inflowing water of the Veyre River near the wetland area. This inflow contributes mainly to the increase in ions concentrations following rain events at WP. In contrast, we observed a lower increase in ions concentrations in the middle of the lake (MP), indicating that some of the ions from the Veyre River may have been consumed before reaching that area, as also suggested by Ishikawa et al. (\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2022\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Temporal disparities in phytoplankton\u0026rsquo; response following meteorological events\u003c/h2\u003e \u003cp\u003eThe presence of Euglenophyta remained consistently low during the lake sampling period, while the biovolume of Charophyta, Chlorophyta (green algae), Bacillariophyta (diatoms), Cyanobacteria, Cryptophyta, and Ochrophyta (brown algae) exhibited variations during the lake campaign (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Specifically, from August 5th until August 23rd, we observed higher biovolume levels of diatoms (\u003cem\u003eStephanodiscus, Cyclotella\u003c/em\u003e and \u003cem\u003eFragilaria\u003c/em\u003e), green algae (\u003cem\u003eSphaerocystis\u003c/em\u003e and \u003cem\u003eClosterium\u003c/em\u003e) and brown algae (\u003cem\u003eUroglena\u003c/em\u003e), while that of cyanobacteria was much lower (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Although this phytoplanktonic composition is not typically observed in August in this area, the presence of diatoms is not surprising as this group are known to thrive in habitats that are frequently stirred up (Padis\u0026aacute;k et al., \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Blotti\u0026egrave;re et al., \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2017\u003c/span\u003e; Pannard et al., \u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e2007\u003c/span\u003e). These atypical conditions are likely attributed to the low water temperatures (below 19\u0026deg;C) and the frequent occurrence of rain events accompanied by wind. These factors may explain the limited presence of cyanobacteria, as these species typically have a low tolerance to mixing and low water temperature (Reynolds, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e2006\u003c/span\u003e; Elliott, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Padis\u0026aacute;k et al., \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e2009\u003c/span\u003e). In contrast, chlorophyceae like \u003cem\u003eSphaerocystis\u003c/em\u003e is known for being less sensitive to temperature variations compared to cyanobacteria (Reynolds et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e), potentially explaning their prevalence in August (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ea-c). Significant shifts in phytoplanktonic composition were reported at the end of the drought period, especially on August 30th, marked by the increase of water temperature and the decrease in \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e concentration (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). Specifically, we noticed the increase of the biovolume of diazotroph cyanobacteria, such as \u003cem\u003eAphanizomenon\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). These observations aligns with previous studies indicating that diazotroph cyanobacteria thrive in eutrophic lakes with low nitrogen content, as they can fix atmospheric nitrogen (Wei et al., \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Reynolds et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Conversely, at the end of the drought period, the biovolume of diatoms, ochrophytes and cryptophytes were low at both sampling sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb, d and e).\u003c/p\u003e \u003cp\u003eInterestingly, the two rain events occurring following this drought period, from 03rd until 04th September and from 10th to 12th September, have shown contrasted impacts on cyanobacterial biovolume despite having similar characteristics in terms of rain intensity (less than 10 mm.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e): i) the first rain events following the drought, occurring from 3rd until 4th September, led to an important decrease of cyanobacterial biovolume on 6th September, especially of \u003cem\u003eAphanizomenon\u003c/em\u003e across all sampling points; ii) the second rain events after the drought, occurring from 10th to 12th September, had the opposite effect, resulting in an increase in the biovolume of the dominant cyanobacteria \u003cem\u003eAphanizomenon\u003c/em\u003e on the 13th September, reaching its maximum value in this study (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). We observed a contrasting pattern with the \u003cem\u003ecryptomonas\u003c/em\u003e genus (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ee), exhibiting an initial increase following the first rain events from 03rd until 04th September, followed by a decrease in its abondance on the 13th of September after the second rain events following the drought, from 10th to 12th September. So, rain event with low intensity (\u0026lt;\u0026thinsp;10 mm.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e) is probably not the main driver explaining the dynamics of phytoplankton species in our case. Surprisingly, the significant increase in nutrients brought by the first rain event did not lead to the expected enhancement in the total biovolume of cyanobacteria. However, there was an exception with the biovolume of \u003cem\u003eDolichospermum\u003c/em\u003e, which increased at the lake surface (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). In the same way, although the strength of thermal stratification is commonly recognized as a significant factor affecting cyanobacteria response to rain events (Chorus et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), our study found it to be less influential in our specific case as the first meters of the water column were already mixed before the rain events due to the unexpected decrease of water temperature from 21\u0026ndash;22\u0026deg;C to 17\u0026ndash;18\u0026deg;C at the end of August. This relatively low temperature for a month of August, could have induced a cold-stress response that appears to operate with a timescale of few days corresponding to the decrease of the cyanobacterial biovolume at the beginning of September, especially for \u003cem\u003eAphanizomenon\u003c/em\u003e which showed a higher decrease of its biovolume (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec). Such cold-stress have already been shown concerning microcystin biosynthesis when temperature reduce from 26\u0026deg;C to 19\u0026deg;C and where authors indicated that the processes involved operated on a different timescale (Martin et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). The timescale due to the cold stress might not exceed a few days as the cyanobacterial biovolume grown again on the 13th of September when water temperature rises again (19.5\u0026deg;C).\u003c/p\u003e \u003cp\u003eHence, it appears that the biovolume of \u003cem\u003eAphanizomenon\u003c/em\u003e is more influenced by water temperature rather than the volume of rainfall. Nevertheless, it's crucial to take into account the frequency and intensity of rain events: the rise in frequency towards the end of September led to a decrease in cyanobacteria at both sampling sites, even though the water temperature remained stable at the wetland point and slightly decreased at the middle point. These rain events were characterized by a high intensity up to 10 mm.h\u003csup\u003e\u0026minus;\u0026thinsp;1\u003c/sup\u003e, which could impact the global dynamics of phytoplankton by limiting the biovolume of cyanobacteria and increasing those of diatoms (\u003cem\u003eFragilaria\u003c/em\u003e and \u003cem\u003eNavicula\u003c/em\u003e) (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb-c), corresponding to the start of the increase in turbulence and autumnal conditions (Reynolds et al., \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). Finally, on September 23rd, when the water temperature dropped below 19\u0026deg;C, the biovolume of all phyla was lower compared to the rest of the lake sampling period.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Intracellular metabolite profiles\u003c/h2\u003e \u003cp\u003eIn our study, we detected 4,446 untargeted metabolites from phytoplankton biovolume and were able to annotate 260 of them thank to GNPS molecular network approach. The number of annotated metabolites remains very challenging as it depends on the design of the experiment, the biomass collected, the analytical techniques used, and the presence of reference molecules within public chemical databases. Therefore, high differences in the quantification and annotation of metabolites can be found across the different studies. For example, from previous studies using LC-MS-based untargeted metabolomics approaches carried out under lake systems, Sadler et al. (\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e) identified approximately 100 secondary metabolites, some of them being known as chemotrypsine inhibitor; while McNabney et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) focused on primary metabolites and were able to annotate 33 metabolites. The chemical libraries we used in the present case are public and comprise both generalist and cyanobacterial-specific databases, such as HMDB, GNPS or CyanoMetDB (Jones et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), respectively. Therefore, we attempt to find here a high number of primary metabolites together with secondary metabolites derived from cyanobacteria and other phytoplankton taxa. A serious challenge for eco-metabolomic studies is to determine and quantify the maximum number of metabolites as possible to satisfy the need to disentangle the biologically relevant components and response shifts under environmental changes (Sardans et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). However, generally only 2\u0026ndash;5% of the features detected in untargeted mass spectrometry analysis were matched with known metabolites in public libraries so far (da Silva et al., \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) and molecular network approach offer great opportunity to deeper explore the chemical diversity of natural microbial ecosystems. Indeed, the exact number of metabolites in natural samples remains challenging, even in the case of microorganisms with relatively simple and well-understood metabolism (Sardans et al., \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe carry out multivariate analyses with either 4,446 untargeted metabolites and 260 annotated metabolites using PCA (Suppl. Figure\u0026nbsp;4) and confirm the similar patterns according to the dates observed with both datasets. Since regression models become infeasible when the number of metabolites exceeds the sample size (Antonelli et al., \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2019\u003c/span\u003e), we carry out the subsequent statistical analyses using only the 260 annotated compounds to better explore the biological functions linked with environmental stressors. However, we keep in mind that our analysis focuses on annotated metabolites and thus does not reflect the entire metabolism pathways of the phytoplanktonic community. It is important to note that further work is required to annotate other unidentified metabolites, as various molecular clusters remain fully unannotated thank to automatic search with public database combining the already-described natural product part.\u003c/p\u003e \u003cp\u003eOur statistical analysis shows that our sampling and extraction methods allow to characterize the eco-metabolomes during the lake campaign. Indeed, the intracellular untargeted metabolomics data are strongly correlated with the biovolume of phytoplankton (R\u0026thinsp;=\u0026thinsp;0.82) and with abiotic variables such as ion concentrations (R\u0026thinsp;=\u0026thinsp;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 exhibits strong correlations with the intracellular metabolomic data (R\u0026thinsp;=\u0026thinsp;0.7) (Figs.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e \u0026amp; \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, two distinct metabolome fingerprints are identified through the RDA (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). The first metabolome fingerprint is explained by the biovolumes of species belonging to the phyla Bacillariophyta (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003eb) and Ochrophyta (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ed) during dates characterized by high wind speed and pH, from August 2nd to August 23rd, 2021. Instead, the second metabolome fingerprint is explained by the biovolume of Cyanobacteria, from August 30th to September 27th, 2021 (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec), characterized by high rainfall amount and deeper depth of the euphotic zone (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). These phyla are indeed associated with annotated metabolites, but it is important to note that other phyla may also produce additional unidentified metabolites during these dates. Hence, the correlations derived from subsequent statistical models, between phytoplankton biovolumes and the relative abundance of untargeted metabolites, may do not capture the entire chemical diversity related to phytoplanktonic composition. These correlations particularly highlight the dynamics of specific species that are positively or negatively correlated with the relative abundance of annotated metabolites. Consequently, we will concentrate on the period explained by these phyla and refer to the first period as the \"diatoms' co-occurrence period\" and the second period as the \"cyanobacteria co-occurrence period\". A previously study by McNabney et al, (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) has already identified a distinct metabolome fingerprint based on the phytoplankton community and sampling sites, which is characterized by specific abiotic variables such as nitrate and conductivity. In our current study, we provide clear evidence of differences in the metabolome based on changes in the phytoplankton community following summer meteorological events. These changes are influenced by abiotic variations, such as fluctuations in ions concentrations, which exhibit a decay over several days following drought. Notably, the concentrations of ions begin to decrease after 11 days without rain, probably indicating the time taken for consumption of nutrients by phytoplankton. Conversely, an increase in ions concentrations occurs immediately after rain events. It is worth noting that our monitoring was conducted 3 days after the rain events, so it is possible that this increase occurred even earlier.\u003c/p\u003e \u003cp\u003eTo enhance our understanding of metabolome changes and unravel the biological functions and response shifts under environmental changes with a high temporal resolution, we employed multiblock sPLS models during both diatoms and cyanobacterial dominance periods (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e). Interestingly, the identification of metabolites during these two periods exhibits striking differences, as evidenced by the heatmap resulting from the models (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee-f), with a high lipid content observed during the diatoms co-occurrence period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee) and a high cyanopeptides content observed during the cyanobacterial co-occurrence period (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). Our findings reveal strong positive correlations among the abundance of certain untargeted metabolites, abiotic variables, and phytoplankton biovolumes, as demonstrated by correlation circle plots (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea-b \u0026amp; e-f). These plots depict the rapid changes in the metabolome during the sampling periods characterized by the co-occurrence of diatoms (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ea) and cyanobacteria (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003eb). Consequently, we address these dynamics in two subsequent subsections, 3.3.1 and 3.3.2. in relation to meteorological events.\u003c/p\u003e \u003cp\u003e \u003cb\u003e3.3.1. Physiological and molecular strategies of phytoplankton following the drought: insight on the lipid accumulation to cope with the decreased concentrations of ions.\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe analysis from the multiblock sPLS-model carried out during the \u0026ldquo;diatoms\u0026rsquo; co-occurrence period\u0026rdquo; highlight different metabolomes according to the phytoplankton composition. Indeed, the biovolume of \u003cem\u003eChroococcus\u003c/em\u003e is strongly correlated at the onset of the lake campaign with amino acids, such as guanosine, tyrosine, and inosine (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). Instead, the biovolumes of \u003cem\u003eClosterium\u003c/em\u003e, \u003cem\u003eCyclotella\u003c/em\u003e and \u003cem\u003eScenedesmus\u003c/em\u003e are negatively correlated with amino acids but positively correlated during the drought with other kind of metabolites, such as sterols and glycerolipids, such as betaine lipids (lyso-diacylglyceryltrimethylhomoserine (LDGTS)) (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). The strong correlation between these lipids and phytoplankton may potentially suggest a physiological response due to the low levels of \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{P}\\text{O}}_{4}^{3-}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:{\\text{N}\\text{O}}_{3}^{-}\\)\u003c/span\u003e\u003c/span\u003e. Indeed, it has been suggested that under nutrient limitations, such as phosphorus and nitrogen limitation, lipid can be accumulated by phytoplankton cells, which disrupt anabolic processes (Morales et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Popko et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Murakami et al., \u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). Furthermore, betaine lipids and sterol intracellular content increase from August 5th until 23rd, while the abundance of some phospholipids, such as glycerol-phosphatidylethanolamine (PE), and phosphatidylcholine (PC), decrease (Suppl. Figure\u0026nbsp;6a-c). Similar patterns have been previously reported by Giroud et al., (\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1988\u003c/span\u003e) suggested that betaine lipids are produced to complement the reduction in phospholipids occurring during phosphorus limitation. These authors suggested that accumulation of betaine lipids may aim to re-allocating phosphate use from membrane lipid synthesis to other metabolic pathways (Giroud et al., \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e1988\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eInterestingly, we also report that abundance of amino acids is high during the onset the period, from August 5th until 9th, but then decrease during the drought. The negative correlation between the amino acid contents and the biovolume of \u003cem\u003eCyanothece\u003c/em\u003e, \u003cem\u003eClosterium\u003c/em\u003e, and \u003cem\u003eCyclotella\u003c/em\u003e, may suggested a downregulation of the production of amino acids, such as observed from culture of \u003cem\u003ePhaeodactylum tricornutum\u003c/em\u003e (diatom) during nitrogen-starvation (Popko et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). In addition, the study of Feng et al. (\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e) had also reported 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. The authors also reported a downregulation of energy metabolism, photosynthesis, amino acid, and nucleic acid metabolism (Feng et al., \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2015\u003c/span\u003e). In addition, the study of Gargallo-Garriga et al., (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) also observed an upregulation of the pathway for lipid metabolism in the dry season with regard with the wet season by comparing leaf metabolomic profiles of 54 species in two rainforests of French Guiana. Therefore, our results suggest that during the drought, when the concentrations of available ions decrease, \u003cem\u003eCyanothece\u003c/em\u003e, \u003cem\u003eClosterium\u003c/em\u003e, and \u003cem\u003eCyclotella\u003c/em\u003e may develop acclimatized strategies to cope with the decrease in ions, as shown by the increase of betaine lipids and decrease of glycerophospholipids.\u003c/p\u003e \u003cp\u003eAdditionally, we observed the synthesis of antioxidant compounds, like polyphenols, which are recognized for their ability to scavenge reactive oxygen species (ROS) as a protective mechanism (Morales et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Therefore, these molecules might be produced in reaction to oxidative stress induced by nitrogen limitation during drought, as observed in previous studies on microalgae cultures experiencing nitrogen starvation (Chokshi et al., \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e2017\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eSince our samples are obtained from a natural phytoplankton community, we cannot definitively conclude whether these lipids are specifically produced by all species or only by the dominant ones. Betaine lipids have been widely reported in organisms such as diatoms, microalgae, and cyanobacteria (Sato, \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e1992\u003c/span\u003e; Popko et al., \u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; K\u0026uuml;nzler and Eichenberger, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e1997\u003c/span\u003e). In addition, previous study from Heal et al., (\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) demonstrated that microalgae produce diverse sets of metabolites, with 17% of the untargeted metabolites commonly found in marine phytoplankton and identified 44 metabolites that were observed in over 80% of the phytoplankton species, including amino acids, primary metabolites and nucleic acids. Based on all these findings, we can suppose that the production of lipids might be a common response of the phytoplankton community to environmental changes, indicating strategies of acclimatation employed by phytoplankton to cope with decreasing ion concentrations and potentially counteract the increasing levels of reactive oxygen species due to nitrogen limitation.\u003c/p\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Temporal shifts in metabolites production according to the sites\u003c/h2\u003e \u003cp\u003eThe sPLS model carry out during the \u0026ldquo;cyanobacterial co-occurrence\u0026rdquo; period reveal two distinct metabolome fingerprints according to the sites (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ed). Most of the metabolites annotated correspond to cyanopeptides and are positively correlated with the biovolumes of \u003cem\u003eDolichospermum\u003c/em\u003e, thriving at both sites following rain events. In contrast, \u003cem\u003eAphanizomenon\u003c/em\u003e, the dominant genus among cyanobacteria, exhibits a low correlation between its biovolume and most of the metabolites present during the rainy period. One possible explanation is that metabolites associated with \u003cem\u003eAphanizomenon\u003c/em\u003e are not identified in the databases we used as only a small part of metabolites are currently known and annotated. Moreover, \u003cem\u003eAphanizomenon\u003c/em\u003e exhibited contrasting dynamics in response to rain events (section 3.2), unlike \u003cem\u003eDolichospermum\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec \u0026amp; Suppl. Figure\u0026nbsp;5). Therefore, correlations between the relative abundance of untargeted metabolites and the abundance of \u003cspan type=\"ItalicUnderline\" class=\"ItalicUnderline\" name=\"Emphasis\"\u003eDolichospermum\u003c/span\u003e are more robust compared to those observed with \u003cem\u003eAphanizomenon\u003c/em\u003e. These correlations need to surpass the correlation cutoff threshold (set at 0.4 for correlation circle plots and at 0.7 for the heatmaps) to be reported in the present statistical results (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e \u0026amp; Suppl. Figure\u0026nbsp;5). On the contrary, these annotated metabolites are negatively correlated with the biovolumes of \u003cem\u003eClosterium\u003c/em\u003e and \u003cem\u003eSphaerocystis\u003c/em\u003e (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ef). We do not find positive correlations between their biovolumes and any annotated metabolites, which is not surprising considering that we primarily utilized available generalist and specialist databases such as, respectively, HMDB and CyanoMetDB, which predominantly contain metabolites produced by cyanobacteria (Jones et al., \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). However, this does not imply the absence of metabolites produced from \u003cem\u003eClosterium\u003c/em\u003e and \u003cem\u003eSphaerocystis\u003c/em\u003e as, during the drought, the biovolume of \u003cem\u003eClosterium\u003c/em\u003e have been observed to be also positively correlated with certain lipids (Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003ee). In addition, previous studies have reported that cyanopeptides can be rather produced during the growth phase of cyanobacteria. Therefore, this could explain the high proportion of identified cyanopeptides during this period (Chorus et al., \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2021\u003c/span\u003e), especially on the 13th of September, when the cyanobacteria biovolume rich their maximum value (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003ec).\u003c/p\u003e \u003cp\u003eInterestingly, we observed significant variations in the intra-cellular content in metabolites between the sites during the wet period (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.014) while phytoplankton communities were similar (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; Suppl. Figure\u0026nbsp;5). To our knowledge, the only study that has demonstrated shifts in metabolome fingerprints in aquatic ecosystems is the recent work of McNabney et al. (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Their study showed contrasting metabolomes associated with the phytoplankton community, as well as the concentration of nitrate and conductivity across two sites separated by 200 km. In our study, the sites are only 200 m apart (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eb), highlighting a divergence in their respective metabolomic niches. This concept have been recently proposed by the study of Gargallo-Garriga et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) when demonstrating that trees at different location exhibit different functional niches.\u003c/p\u003e \u003cp\u003eTherefore, to better understand the differences across the sampling sites, we carry out a supervised multiblock sPLS-DA model (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; Suppl. Figure\u0026nbsp;4), since the metabolites appear significantly different across the sites (\u003cem\u003ep\u003c/em\u003e-value\u0026thinsp;=\u0026thinsp;0.04) (Suppl. Figure\u0026nbsp;4). This analysis shows, thank to hierarchical clustering, that endo-metabolome differs strongly across the sites, but only after August 30th (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Interestingly, most cyanopeptides have significant higher relative abundance at WP rather than at MP, such as anabaenopeptin, microginins, shinorine, and micropeptin B, as well as cyanotoxins, such as microcystin LR (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e). Most of these secondary metabolites are referred as bioactive compounds (Demay et al., \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2019\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn our study, since the relative abundances of cyanopeptides are strongly correlated with the concentrations of inorganic ions (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e), we suggest that the synthesis of these peptides is primarily favored by nutrients availability and may be related to biotic interaction function (\u003cem\u003ee.g.\u003c/em\u003e allelopathy), very likely providing competitive advantage to the producing cyanobacteria. In addition, these components may also act as potential as nitrogen reservoir, as previously suggested by several studies for microcystin-LR, aeruginosins, cyanopeptolins, and shinorine (Agrawal et al., \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Davis et al., \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2010\u003c/span\u003e; Sadler and von Elert, \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; Harke and Gobler, \u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Peinado et al., \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e2004\u003c/span\u003e; Horst et al., \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). Indeed, during nutrient limited conditions, it was hypothesized that the biosynthesis of arginine should be used by dinoflagellates and cyanobacteria as a nitrogen reservoir molecule.\u003c/p\u003e \u003cp\u003eAfter rain events, according to the geomorphology of the lake, spatial changes of abiotic conditions can be observed. Indeed, we recorded a higher water temperature and a stronger lake thermal stratification at the wetland area (WP) compared to the middle of the lake (MP). In this study, we report that during the wet period a higher abundance of polyphenol compounds and glycerophospholipids are detected at MP, while most cyanopeptides have been identified at WP (Fig.\u0026nbsp;\u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e \u0026amp; Suppl. Figure\u0026nbsp;6). This result seems also to indicate a distinctive molecular response of this cyanobacteria between the two sites, distant of only 200 meters. Therefore, the untargeted metabolomic approach is well-suited for highlighting ecological acclimatation by providing a measurable method to identify the corresponding responses of the phytoplankton community to abiotic variations triggered by meteorological events. Consequently, eco-metabolomic approaches can be utilized to study the metabolomic niches, as previously proposed by Gargallo-Garriga et al. (\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e2020\u003c/span\u003e) in the context of trees located at different locations. To the best of our knowledge, this is the first time that we demonstrate the acclimatized response of phytoplankton in lake ecosystems following meteorological events, revealing a strong heterogeneity based on sampling dates and sites.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Conclusion","content":"\u003cp\u003eThe study on changes in the phytoplankton metabolome following meteorological events in natural systems has been sparsely investigated and was still lacking of 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., \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2014\u003c/span\u003e; McNabney et al., \u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In our study, we identify a set of both primary and secondary untargeted metabolites, form a total of 4,446 detected metabolites whom 260 could have been thus annotated. The investigation of these metabolites allows us to confirm that eco-metabolome fingerprints reflect the phytoplankton community, driven by abiotic variations following meteorological events. We observe rapid changes in the metabolomes of phytoplankton in response to meteorological events, resulting in variations in abiotic conditions triggered by drought and rain events. For example, during the drought, we observe a strong lipid accumulation, such as betaine lipids, probably in response to nitrogen and phosphorus limitation.\u003c/p\u003e \u003cp\u003eIn 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 \u003cem\u003eDolichospermum\u003c/em\u003e. These cyanopeptides have a significantly higher relative abundance at WP than at MP, suggested their potential role in biotic interactions and/or in nitrogen reservoir since WP is characterized by significantly higher concentrations of ions following the rain events. 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 meaningful, considering the heterogeneity across the sampling locations at different spatio-temporal scales.\u003c/p\u003e"},{"header":"Declarations","content":"\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eFN was responsible for writing the original draft, methodology, conceptualization, investigation, and visualization.BM provided expertise in metabolomics, conceptualization, review \u0026amp; editing. BL provided technical expertise in phytoplankton enumeration.JVB provided expertise in atmospheric measurements, supervision.DL was responsible for conceptualization, supervision, review and editing.\u003c/p\u003e\u003ch2\u003eAcknowledgement\u003c/h2\u003e\u003cp\u003eWe thank the FRE (F\u0026eacute;d\u0026eacute;ration des Recherches en Environnement), 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 (Fr\u0026eacute;d\u0026eacute;ric Peyrin and Claude Hervier), and operation (Philippe Cacault). We thank Fr\u0026eacute;d\u0026eacute;ric 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 spectrom\u0026eacute;trie de masse bio-organique, Mus\u0026eacute;um National d\u0026rsquo;Histoire Naturelle, Paris, France. Finally, we thank \u0026ldquo;Volcans vacances\u0026rdquo; lodging, where the atmospheric instruments were hosted.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe data related to this article are available online at https://doi.org/10.6084/m9.figshare.c.7393468.v1\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAFNOR: NF EN 15204, in: Qualit\u0026eacute; de l\u0026rsquo;eau. Norme guide pour le d\u0026eacute;nombrement du phytoplancton par microscopie invers\u0026eacute;e (m\u0026eacute;thode Uterm\u0026ouml;hl), 39, 2006.\u003c/li\u003e\n\u003cli\u003eAgrawal, M. K., Zitt, A., Bagchi, D., Weckesser, J., Bagchi, S. 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Sci., 70, 77\u0026ndash;86, https://doi.org/10.1007/s00027-007-7033-x, 2008.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"phytoplankton, cyanobacteria, untargeted metabolites, meteorological events","lastPublishedDoi":"10.21203/rs.3.rs-4880559/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4880559/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eVarious studies suggest that global change is causing an increase in phytoplankton biomass, cyanobacteria prevalence and cyanotoxin production. However, there are conflicting reports regarding the response of cyanobacteria blooms to global warming and meteorological events, probably because of the lack of global approaches. Metabolomics approaches in natural system hold great promise in investigating the factors leading to variations in phytoplankton successions and subsequent cyanotoxin production. However, eco-metabolomics studies are still scares in literature and suffer to adequately unravel the biologically relevant variables under environmental changes. In this study, we investigate the temporal and spatial dynamics of phytoplankton community and the production of their primary and secondary untargeted metabolites in response to local meteorological events. Thus, we collected water samples in two points of the Aydat Lake (France): near the inflowing waters from Veyre River and at the middle of the lake during the 2021 summer. Untargeted intracellular metabolites were measured using ultra-high-performance liquid chromatography coupled with a high-resolution mass spectrometer, as well as phytoplankton biovolume and diversity and physicochemical lake\u0026rsquo;s parameters. Primarily, our results show the increase of the biovolume of diazotrophic cyanobacteria at the end of the drought and after rain events at both sites. During the drought, we observe a strong increase of intracellular lipid contents, probably in response to sudden nitrogen and phosphorus limitation. Differently, during the wet periods, we observe an increase of the phytoplankton glycerophospholipid content, especially at the middle of the lake, whereas significantly higher abundance of secondary metabolites was monitored at site near the wetland area. Since then, we report a strong correlation between the abundance of different cyanopeptides and the biovolume of \u003cem\u003eDolichospermum\u003c/em\u003e, which is present at both sites, we suggest acclimative responses to cope with the phytoplankton growing stimulation related with the increase of the nutritive ion influx following the rain events. The significant difference in the intra-cellular content in metabolites between the 2 sampling sites, separated by only 200m, while phytoplankton communities were similar suggests the existence of local metabolomic niches.\u003c/p\u003e","manuscriptTitle":"Spatio-temporal disparities in phytoplankton dynamics and metabolite production depending on weather conditions","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-09-18 09:45:35","doi":"10.21203/rs.3.rs-4880559/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5c22749e-8505-44f0-b828-f7ce32902a29","owner":[],"postedDate":"September 18th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-10-16T19:08:32+00:00","versionOfRecord":[],"versionCreatedAt":"2024-09-18 09:45:35","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-4880559","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4880559","identity":"rs-4880559","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}