# Multifaceted biodiversity changes of lake microeukaryote communities in response to nutrient enrichment and climate change

Cécilia Barouillet, Eric Capo, Didier Debroas, Jean-philippe Jenny, Pierre Sabatier, Isabelle Domaizon

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Cécilia Barouillet, Eric Capo, Didier Debroas, Jean-philippe Jenny, Pierre Sabatier, et al. Multi-faceted biodiversity changes of lake microeukaryote communities in response to nutrient enrichment and climate change. Oikos, 2025, 10.1002/oik.11616. hal-05508636

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OIKOS

Research Article

# Multifaceted biodiversity changes of lake microeukaryote communities in response to nutrient enrichment and climate change

Cécilia Barouillet<sup>1,2</sup>, Eric Capo<sup>3</sup>, Didier Debros<sup>4</sup>, Jean-Philippe Jenny<sup>1</sup>, Pierre Sabatier<sup>5</sup> and Isabelle Domaizon<sup>1,2</sup>

**Correspondence:** Isabelle Domaizon (isabelle.domaizon@inrae.fr)

Oikos

2025: e11616

doi: 10.1002/oik.11616

Subject Editor: Silke Langenheder

Editor-in-Chief: Pedro Peres-Neto

Accepted 17 November 2025

Microeukaryote communities ensure a large panel of ecological functions as auto-
trophs, predators, parasites and saprotrophs. Through the analysis of the sedimentary
DNA from four peri-alpine lakes with contrasting histories of nutrient enrichment, we
reconstructed centennial-scale ecological dynamics and assessed amplitude of change,
resistance and resilience of microeukaryote communities in response to eutrophication
and warming. Various biological metrics, including alpha- and beta-diversity indices,
and a trait-based functional community approach were mobilized in order to depict
the multi-dimensional aspect of biodiversity changes. As the studied lakes switch from
oligotrophic to meso-oligotrophic condition, a uniform threshold response of the
microeukaryote communities was observed across all lakes indicating that, in deep
temperate lakes, increases in phosphorus concentration above a threshold of 10 µg l⁻<fnref n="1" />,
combined with declining bottom-water oxygen conditions, can trigger early changes
in the microeukaryote community, well before a lake become eutrophic. During the
nutrient enrichment period, our diversity metrics indicate a decline in alpha diversity,
and an increase in the turnover rate and richness difference, that might have arise from
species loss. The amplitude of change in the microeukaryote community composition
was lake-dependant and not proportional to the intensity of eutrophication. Following
the re-oligotrophication of the lakes, the microeukaryote communities did not show
signs of resilience. Indeed, although the communities recovered in terms of alpha
diversity, the community composition did not return to pre-disturbance composition.
Instead, we observe a similar functional and structural reorganisation of the recent
microeukaryote communities across all four lakes, likely driven by climate-related pro-
cesses. This multi-metric analysis allowed to demonstrate the complex and multifac-
eted dimension of the biological response to environmental changes.

Keywords: climate change, eutrophication, microeukaryote, resilience, resistance, sedimentary DNA

<figure><img src="image_3.png" /></figure>

<footnote n="1"><sup>1</sup>INRAE, Université Savoie Mont Blanc, CARRTEL, Thonon-les-Bains, France</footnote>

<footnote n="2"><sup>2</sup>Pole R&D ECLA, CARRTEL, Thonon-les-Bains, France</footnote>

<footnote n="3"><sup>3</sup>Department of Ecology and Environmental Science, Umeå University, Umeå, Sweden</footnote>

<footnote n="4"><sup>4</sup>Université Clermont Auvergne, CNRS, Laboratoire Microorganismes: Genome et Environnement, Clermont-Ferrand, France</footnote>

<footnote n="5"><sup>5</sup>Université Savoie Mont Blanc, CNRS, EDYTEM, Le Bourget du lac, France</footnote>
**Introduction**

Lakes are increasingly exposed to environmental changes, affecting the ecological functions and the associated ecosystem services they provide (Gozlan et al. 2019, Mammides 2020). As one of the most prevalent stressors, eutrophication have been a major driver of change in lakes inducing ecosystem shifts (Schindler 2009, Ho et al. 2019). Rehabilitation programs based on nutrient abatement have effectively reduce anthropogenic nutrient inputs, leading to the re-oligotrophication (phosphorus concentrations < 10 µg l⁻¹) of lakes (Van Donk et al. 2008). Nonetheless, substantial delays in recovery has often been observed (Jeppesen et al. 2017), whereby lakes remained at a meso-oligotrophic state (phosphorus concentrations between 10 and 30 µg l⁻¹). In some cases, global warming has limited the success of restoration efforts by mimicking eutrophication-like effects (Paerl and Huisman 2009). Understanding the response and recovery of lakes biological communities to eutrophication within the context of climate change is challenging as these drivers can interact in various ways. For instance, the long-standing impact of eutrophication on the freshwater communities can increase ecological vulnerability of lakes to climate change (despite a return to oligotrophic conditions), while in parallel, warming can influence and weaken the resilience of lakes to eutrophication (Meerhoff et al. 2022). As freshwater eutrophication is still prevailing and expected to increase worldwide (Ho et al. 2019), understanding how biological communities respond to this stressor over extended timescales, particularly in the global context of climate change, is becoming increasingly important.

Microeukaryote communities are widespread and diverse group of organisms (i.e. ciliates, amoeba, heterotrophic flagellates and phytoplankton) that ensure several ecological functions, as autotrophs, predators, parasites and saprotrophs (Caron et al. 2009, Burki et al. 2021, Singer et al. 2021). They play a key role in the transfers and recycling of energy and nutrients that governs lake biogeochemical cycles (Bjorbækmo et al. 2020). The biological response and recovery to eutrophication have often been evaluated through the study of photosynthetic microeukaryotes and blue green algae in the upper water layers (Smith 2003, Schindler et al. 2016), while little emphasise has been given on other biological groups of the microeukaryote community that develop within and below the photic zone. Yet, the impact of eutrophication and climate extend beyond the photic zone, with ecosystem-wide changes such as the deoxygenation of deep waters (Jane et al. 2021, Desgué-Itier et al. 2023). Assessing the biological response of the overall microeukaryote communities across the entire water column and at different trophic levels might provide a more comprehensive picture.

Uncovering the complex and multifaceted response of communities to environmental changes requires the use of multiple biological metrics and the long-term perspective. The combination of the paleolimnology and the sedimentary DNA (sedDNA) approaches allow to reconstruct the long-term ecological and functional dynamic of a wide variety of

biological communities in lakes, as well as provide invaluable insights into the natural variability of these communities prior to anthropogenic disturbances (Crump et al. 2021, Capo et al. 2023). SedDNA represents material produced throughout the entire water column that settles to the sediments, integrating signals from the different water compartments, including both photic and aphotic zones of deep lakes (Barouillet et al. 2022a, Vuillemin et al. 2023). Such integrative, long-term time series allow to uncover otherwise hidden baseline conditions, thereby providing new insights into natural variability in biodiversity, community stability, dynamic and long-term response to environmental perturbations (Trombetta et al. 2021, Codello et al. 2022, Ríos-Castro et al. 2022).

In the present study, we assessed the response of freshwater microeukaryote communities to eutrophication and climate, using the sedDNA and the paleolimnological approaches. More specifically, we reconstructed the microeukaryote community composition of four deep, temperate, calcareous lakes over the last 120 years, covering a reference period (before nutrient enrichment), a nutrient enrichment period and a re-oligotrophication and warming period. We selected lakes along a gradient of nutrient enrichment in which two lakes had undergone strong eutrophication (i.e. Lake Geneva and Bourget), while the two other lakes had only undergone mild increases in phosphorus concentration and never reached eutrophication status (i.e. Annecy and Aiguebelette). All lakes are located within the same region and exposed to similar air temperature change. We hypothesize that 1) the microeukaryote communities of lakes that have been exposed to strong nutrient inputs display a marked rearrangement, proportional to the nutrient enrichment; 2) following re-oligotrophication and due to the effect of climate change on water temperatures, the microeukaryote communities fail to show resilience, as the community do not return to pre-disturbance, in terms of diversity, community composition and functional ecology. The microeukaryote community stability was assessed using a combination of statistical methods and several metrics of diversity to detect timing of significant change in the community, distinct community structure through time, assess change in the functional trophic ecology. The resistance and resilience to nutrient enrichment on the microeukaryote community was also assessed using a simplified version of the methods proposed by Gao et al. (2022) by calculating pairwise community dissimilarities and using the pre-disturbance community as a control for comparison.

**Material and methods**

**Study lakes**

Lakes Geneva, Bourget, Annecy and Aiguebelette are large, deep, hardwater, peri-alpine lakes located in the northwest of the French Alps (Fig. 1). All four lakes are warm monomictic, and exposed to a similar climatic context, but display contrasting nutrient enrichment histories (Fig. 2). All
<figure><img src="image_4.png" /><figcaption>Figure 1. Geographical localisation of the four study lakes within the geopolitical border of France (lake names are indicated in blue). The embedded table displays selected lake physical characteristics and level of anthropogenic disturbances (*information from Perga et al. 2015, †unpubl. data). For reference, cities with a population larger than 100 000 are also indicated on the map. The grey color gradient illustrates the elevation terrain (DEM; source: https://srtm.csi.cgiar.org/).</figcaption></figure>

lakes were historically oligotrophic. Starting in the 1920s, owing to the urban pollution (e.g. phosphate compounds in laundry detergent) of water ways, the total phosphorus (TP) concentrations of lakes Geneva and Bourget strongly increased to reach concentrations as high as 100 and 120 µg l⁻¹, respectively, at the peak of eutrophication in the 1970s (Berthon et al. 2013). After the 1970s, nutrient abatement strategies successfully reduced phosphorus inputs and led to the re-oligotrophication of these ecosystems (Berthon et al. 2013). In contrast, lakes Aiguebelette and Annecy underwent relatively small increases in TP, as TP concentrations remained under 40 µg l⁻¹ in Lake Aiguebelette, while, in Lake Annecy, TP barely reached meso-oligotrophic condition, fluctuating between 10 and 20 µg l⁻¹ during the nutrient enrichment period (Rimet et al. 2019, Frossard et al. 2024). Located within a radius of 100 km from each other, the four lakes are exposed to the same rate of change in air temperature (Auer et al. 2007). However, due to their specific hydrogeological settings, the four lakes are not equally vulnerable to climate change, and models of the past and future thermal regime of the four lakes demonstrated that the two largest lakes Geneva and Bourget tend to be more vulnerable (i.e. in terms of physico-chemical characteristics) to climate than Aiguebelette and Annecy (Desgué-Itier et al. 2023). More specifically, the rate of increase of the hypolimnetic temperature is higher in Geneva and Bourget. Nonetheless, this study also indicates a similar rate of increase of the epilimnion temperature in the four lakes by +0.46°C on average over the last 30 years (Desgué-Itier et al. 2023).

## Core samples and chronology

For lakes Bourget and Annecy, details on cores collection, and dating can be found in Capo et al. (2017a, 2017b). The core from Lake Geneva was retrieved in 2020, and the core from Lake Aiguebelette was retrieved in 2019. All cores were retrieved from the deepest part of the lake, using a UWITEC gravity corer with a diameter of 90 mm (UWITEC, Mondee, Austria). For the extrusion, the cores were split lengthwise by cutting the core tube walls and using two sterile, 2-m long, thin metal plates to section the core. One half of each core were photographed (for varve counting), described, and sectioned from the top to the bottom using thin metal plates that were sterilized between each sample. All sediment samples were manipulated with protective gears (lab coat, disposable gloves, and headcap) and all material and lab bench were sterilized. In a sterile laboratory dedicated for the manipulation of rare DNA samples, subsamples for DNA extractions were transferred into several sterile 3 ml EppendorfTM tubes and kept in the dark at -40°C. Resolution of the core sectioning varied for each lake (cf. data in zenodo repository for more details). Core dating for Lake Geneva was performed by varve counting. Varves were counted directly on the core if each doublet was relatively thick (0.2 mm). In order to check the dates estimation made by varve counting, dates were correlated with previously retrieved cores from 2009 using well-documented lithological markers (Alric et al. 2013, Berthon et al. 2013). Given the strong correlation in the sedimentation rate of the two cores in the varved portion of the core (Supporting information), rates of sedimentation
<figure><img src="image_5.png" /><figcaption>Figure 2. (a) Historical trends of environmental variables: Mean temperature of the entire water column (Water temp, °C) of the study region; source: Desgué-Itier et al. (2023) – the color-scale corresponds to cold (blue) and warm (pink) temperatures; *Daphnia*-inferred reconstruction (green triangles) and limnological records (purple circles) of total-phosphorus (TP) concentrations. Periods of significant increase are indicated in red and green for the Water temp. and TP, respectively, and periods of significant decline are indicated in blue. (b) Temporal detection of change in the microeukaryote community using the stratigraphically constrained clustering analysis (CONISS), the</figcaption></figure>
Figure 2. Continued.

zone identified through the broken-stick model are indicated with the dotted lines, and a rate-of-change (ROC) analysis output when applying a 5 (purple line), 7 (blue line), 10 (green line), and 12 (yellow line) bins, the timing of the tipping point detected by the ROC analysis is indicated by green dots. (c) Diversity indices: Temporal change in the Shannon diversity, periods of significant decline and increase are indicated in red and green, respectively. The subset of Bray–Curtis dissimilarities and nestedness component are used as a proxy of ecological resilience and resistance to eutrophication, boxes represent the 25th–75th percentiles (with the median as a horizontal line) and the whiskers show the 10th–90th percentiles. oClors indicate different subset of dissimilarities according to the three different periods (i.e. grey = β<sub>BE</sub>, green = β<sub>BE/NE</sub>, purple = β<sub>BE/AWE</sub>). On the Bray–Curtis dissimilarity graph the dotted and dashed lines indicate the average Bray–Curtis similarity for the BE and NE periods respectively. The asterisks above the NE and AEW periods indicate whether the subset of dissimilarities of these two periods are significantly different from the NE period according to the pairwise-PERMANOVA (p-value < 0.01).

estimated from a core previously dated by radioisotopic dating
were used to extrapolate the dating model beyond the varved
portion of the core. For Lake Aiguebelette, only the very top
of the cores were varved, as such the dating model was devel-
oped based on varve counting and short-lived radionuclide
measurements using 210Pb, 226Ra, 137Cs and 241Am. Briefly, 29
samples were taken every 1-cm and the activities of the radio-
isotopes were measured using a non-destructive gamma-spec-
trometry with high-efficiency, very low-background, Small
Anode Germanium (SAGe) high-purity well-type detector
at the Modane Underground Laboratory (LSM, Modane,
France). A constant flux and constant sedimentation rate
(CFCS, Krishnaswamy et al. (1971)) age-depth model was
developed with the R package ‘SERAC’ (Bruel and Sabatier
2020) using 210Pb unsupported excess activity-derived sedi-
mentation rates. More detailed information about the dating
model of the core can be found in Jenny et al. (2025).

**Molecular analysis**

Sample preparation, DNA extraction

Strict laboratory protocols and various methodological pre-
cautions were taken to achieve robust results and ensure the
validity of our molecular data. Details about the limits of the
sedDNA approach and how they have been considered in this
study are available in the supplementary material (Supporting
information). To prevent contamination with modern DNA,
sterile disposable materials (labware, gloves, etc.) were used
for all laboratory procedures, and the extraction of DNA
from the sediment matrix was carried out in specific rooms
dedicated for the analysis of ancient DNA. PCR reactions
were prepared under designated working stations. In order to
minimize biases associated with early diagenesis, the analysis
started a few centimeters down the surface of the core (i.e.
~1 cm down the core). Negative controls were included at
all steps of the procedure: during subsampling (open tubes
containing pure water), extraction (treatment of pure water)
and PCR preparation (blank PCR). The blanks were found
negative for the amplification of 18S rRNA markers and thus
were not included in the sequencing library. For each sedi-
ment layer, two DNA extractions were performed on 0.5 g of
wet sediment. Both intracellular DNA and extracellular DNA
were extracted from using the NucleoSpin soil kit, according
to the manufacturer instructions (Macherey-Nagel). PCR
targeting a 260-bp long fragment of the V7 region of the
18S rRNA gene were performed to do the inventory of the

microeukaryote communities. The general eukaryotic primers 960 F (5'-GGCTTAATTTGACTCAACRCG-3') and NSR1438 (5'-GGGCATCACAGACCTGTTAT-3') were used. Molecular tails were added to the forward primer (5'-C TTTCCCTACACGACGCTCTTCCGATCT-3') and to the reverse primer (5'-GGAGTTCAGACGTGTGCTCTT CCGATCT-3'). The PCR was performed in a total volume of 25 µl containing 1 µl of environmental DNA, 2.5 µl of 10X NH4 reaction buffer, 1 µl of 50mM of MgCl2, 0.2 µl of 100mM dNTP, 0.75 µl of each primer with molecular tails at 10 pmol µl-1, 0.3 µL of 10mg ml-1 BSA and 0.2 µl of 5Ci BioTaq (Bioline). The amplification cycle included an initial denaturation at 94°C for 10 min followed by 30 cycles of 60 s at 94°C, 60 s at 55°C and 90 s at 72°C. The PCR process was finalized by a 10 min extension of the amplicons at 72°C. PCR products from the two DNA extracts were kept separated and sent to Plateforme GeT-Biopuces INSA/TBI (Rangueil 31077 Toulouse Cedex) for amplicon purification, library preparation and paired-end (2 × 250 bp) sequencing on a MiSeq Illumina instrument. For Lake Bourget and Annecy, high-throughput sequencing data generated in Capo et al. (2017a, 2017b) were used.

Bioinformatic treatments

For the high-throughput sequencing dataset of the four lakes, the paired-end reads were merged together using UPARSE tools (option -fastq_mergepairs with a minimal overlap equal to 150 and no mismatch allowed) (Edgar 2013). Then four cleaning procedures were used to only keep high quality sequences: 1) no undefined bases (Ns), 2) a minimum sequence length of 200 bp, 3) no sequencing error in the forward and reverse primers and 4) no chimeras (Edgar et al. 2011). A total of 11 662 889 reads were obtained after cleaning. Following the recommendations from Mangot et al. (2013), the DNA sequences from the Bourget and Annecy dataset were clustered at a 95% similarity threshold using UPARSE 7.0 (Edgar 2013) to obtain the operational taxonomic unit (OTU) seeds (option -cluster_fast). Since the high-throughput sequencing dataset for Bourget and Annecy had been previously generated (cf. Capo et al. 2017a, 2017b for details), the sequences from the Geneva and Aiguebelette dataset were then mapped to these OTU seeds (i.e. previously created from the Bourget and Annecy dataset) using the command vsearch -usearch_global. About 98.94 and 99.51% of the sequences from Aiguebelette and Geneva, respectively, matched with the OTUs seed previously generated with the
Bourget and Annecy high-throughput sequencing dataset. The remaining 41 361 sequences were clustered at a 95% similarity threshold resulting in the creation of 4845 OTUs specific to Geneva and Aiguebelette. Singleton were removed from the dataset leading to a total of 96 537 OTUs. The taxonomic affiliation was performed using BLAST against the SSURef SILVA database (Pruesse et al. 2007) via PANAM2 after applying the following selection criteria: length > 1200 bp, quality score > 75% and a pintail value > 50. The SILVA database was enriched with lacustrine DNA sequences originating from various studies (Lefranc et al. 2005, Lepère et al. 2006, Mangot et al. 2013). The taxonomy of an OTU corresponded to the best hit in the similarity search. OTUs affiliated to metazoans and embryophytes and for which the taxonomical identification was limited at the superphylum levels taxa were removed to consider only microeukaryotes. This is with the exception of the rotifers that were well represented in the dataset and thus were kept, resulting in a total of 96 491 OTUs classified as microeukaryotes, and rotifers. Similarity between sample replicate were checked using cluster analysis and OTUs detected in only one of the two duplicates were removed from the analysis, in order to minimize the presence of falsely rare OTUs due to potential PCR biases (Egge et al. 2015, Lange et al. 2015). At the end a total of 46 546 OTUs were retained for the analysis. The number of DNA sequences were standardized by random subsampling using the SRS function of the SRS package (Heidrich et al. 2021).

## Statistical analysis

To test the first hypothesis (i.e. the microeukaryote communities of lakes that have been exposed to strong nutrient inputs display a marked rearrangement, proportional to the nutrient enrichment), the main periods of trophic change were identified using TP concentrations from monitoring and reconstruction data. The timing of change in the microeukaryote community composition through time was detected using the rate-of-change (ROC) analysis. Distinct periods of community structures were identified using the Stratigraphically constrained cluster analysis (CONISS). The amplitude of change, the resistance (i.e. capacity of a community to remain stable when exposed to disturbances), and resilience (i.e. capacity of a community to recover from disturbance and return to pre-disturbance state) of the microeukaryote community was evaluated and compared across the a priori identified periods of trophic changes using various diversity metrics (i.e. alpha and beta-diversity). The influence of the TP and water temperature on the microeukaryotes rearrangement was assessed using a distance-based redundancy analysis. Changes in the functional ecology of the lake were evaluated through the DESeq2 analysis framework. Together these analyses allowed to test the second hypothesis (i.e. due to cumulative effect of climate change, the microeukaryote communities fail to show resilience following re-oligotrophication, as the community did not return to pre-disturbance as the lakes return to oligotrophic conditions). All statistical analyses were done using R ver. 4.3.1 (www.r-project.org) in

the integrated development environment RStudio (www.r-project.org). All graphical figures were created using 'ggplot2' (Wickham 2016). The code used to conduct the analysis and specific parameters used for each step of the analysis can be found on the GitHub repository (https://github.com/Cecili aBarouillet/microeukaryote_perialpinelakes.git).

## Environmental parameters and identification of periods of change in trophic status

As an index of eutrophication, TP concentration measured bi-monthly (or monthly for the winter period) and taken from the deepest part of the pelagic zone of each lake, were used to calculate decadal total phosphorus (TP) concentrations (Observatory of Lakes, © OLA-IS, AnaEE-France, INRAE of Thonon-les-Bains, CIPEL or SILA or CISALB or CCLA; (Rimet et al. 2020)). To obtain a long-term TP trends this dataset was completed with the *Daphnia*-inferred total phosphorus (i.e. *Daphnia*-inferred TP) reconstruction from Berthon et al. (2014). This *Daphnia*-inferred TP is based on a transfer function inferring TP from the absolute abundance of *Daphnia* remains in sediment samples and was built from the strong and significant linear relationships linking the abundance of *Daphnia* subfossil remains retrieved from the sediment archives and TP concentrations monitored in the water column for the corresponding year (Alric et al. 2013, Berthon et al. 2014).

Modeled water temperature reconstruction from Desgué-Itier et al. (2022) were used as the climate signal. This dataset is well adapted for palaeoecological studies as they capture decennials to centennial water temperature trends. Given that the lakes show a similar rate of increase in water temperature in the epilimnion but contrasting trends in the hypolimnion (Desgué-Itier et al. 2023), mean annual water temperatures of the entire water column of the four lakes was calculated for each sediment strata based on the modeled temperatures.

Generalized additives models (GAMs) were used to model the long-term non-linear trends of the environmental variable time series (i.e. TP and mean water temperature) using the 'mcgv' package (Wood 2017). For the TP reconstruction, both *Daphnia*-inferred TP reconstructions and TP concentrations from monitoring data were used to generate the model. Following the methods from Simpson (2018), the first derivatives of the spline using the 'gratia' package (Simpson 2024) were used to identify timing of significant changes in TP and water temperature. The model used thinplate regression splines as the basis spline, and the restricted maximum likelihood (REML) was used to determine spline degrees of freedom. The periods of significative changes were then determined by computing the first derivatives of the fitted spline, periods for which the estimated first derivatives and associated 95% simultaneous confidence intervals were above or below corresponding to periods of significant increase or decrease, respectively.

Based on the identified periods of significant change in TP time series, three periods of trophic changes were identified: 1) reference period, or before nutrient enrichment (BE); 2) during the nutrient enrichment (NE) period; and 3) the
re-oligotrophication (after enrichment) and warming (AEW) period (Fig. 2, Supporting information).

## Compositional change and temporal breakpoint

Stratigraphy plots of the dominant taxonomic groups were built to visualize general temporal trends in the micro-eukaryote community composition of the four lakes. Stratigraphically constrained cluster analysis (CONISS) (Grimm 1987) was applied on the dominant taxonomic groups and the number of optimal zones to include was identified through the use of a broken-stick model using ‘the tidypaleo’ package (Dunnington et al. 2022). According to Bennett (1996), if the sum of square values for specific number of groups is higher than the broken stick value, the groups are termed significantly different. Unless otherwise specified, thereafter all community analyses were done on the normalized OTU counts.

In order to identify periods of significant compositional change, we applied a rate-of-change (ROC) analysis using the ‘RRatepol’ package (Mottl et al. 2021). The ROC is calculated between consecutive working units (WU). WUs are defined using the binning with a moving window method. More specifically, bins of selected size were selected and one stratigraphical level per time bin was selected as representative of each bin, thereby resulting in even number of WUs with similar count size in the assemblage. Dissimilarities between WUs were determined using Bray-Curtis dissimilarities. To detect timing of rapid and significant changes in the microeukaryote assemblages, a non-linear trend method was applied in which the SD is calculated from all the residuals, and a peak is considered significant if it is 1.5 SD higher than the model. This analysis can be particularly sensitive to the smoothing methods and the size selection of the time bin. As such, in order to choose the more suitable parameters, we tested the ROC analysis using several types of smoothing (i.e. none, moving average, Grimm’s smoothing and Shepard’s 5-term filter) and reiterated the analysis using different number of shifts (i.e. 5, 7, 10, 12).

## Microeukaryote alpha diversity

Alpha diversity was estimated through several indices of richness (Chao1), diversity (Shannon and Simpson), and evenness (Pielou), as well as using Hill numbers. Similar to the environmental variables, in order to assess significant periods of changes in alpha diversity, generalized additives models (GAMs) were used to model the long-term non-linear trends and significant periods of changes were identified using the first derivatives of the spline (Simpson 2018).

## Microeukaryote beta-diversity

For each lake, a principal coordinates analysis (PCoA) was performed to visualize temporal change in the beta-diversity using the *pcoa()* function from the *vegan* package. The analysis was performed using Bray-Curtis (relative proportions of OTUs reads) and Jaccard (presence/absence of OTUs) dissimilarities. The Bray-Curtis dissimilarity is based on occurrence data (abundance), while the Jaccard distance is based on

presence/absence data (does not include abundance information), as such the Jaccard distance is more sensitive to the rare signals. The comparison of both allows us to identify whether the trends observed using the Bray-Curtis dissimilarities are only driven by a few dominant OTUs and the community patterns are robust and do not depend on the choice of index (i.e. whether the differences between communities are not strongly driven by abundance patterns, since the change of structure is already captured by species presence/absence). A distance-based redundancy analysis (db-RDA) using Bray-Curtis distance was then performed to constrain the ordination to the environmental variables. The db-RDA allows to explore the relationship between the environmental variables with the principal coordinates and thus the main variation in the communities. Moreover, the species scores computed by the db-RDA analysis provided a mean to identify which biological groups from the microeukaryote communities influenced the community changes. The function envfit was used to detect correlations of the environmental variables with the db-RDA axes. The environmental variables that were significantly correlated with the axes where then projected onto the db-RDA. As the two environmental variables were continuous, the function ordisurf was then used to fit smooth surfaces of the environmental variables thus providing a better overview of the gradient distribution of these variables in the reduced, two-dimensional space of the db-RDA.

## Assessment of community resistance, resilience and nestedness component

Hereby, we define the resistance as the capacity of the structure of the microbial community to remain stable when exposed to nutrient enrichment. The resilience is the rate at which a community recover from disturbance and return to pre-disturbance state. Proxies of resistance and resilience are calculated based on the comparison of a control and a disturbed community over time to acknowledge for the natural variability of a control community. Here, we could not have access to a control community during the period of nutrient enrichment, as such the pre-disturbance community was considered the control. To evaluate the resistance and resilience of the microbial community, we followed similar methods as used by Gao et al. (2022). From the Bray-Curtis dissimilarity matrix generated for each lake, a subsets of similarity (i.e. Bray-Curtis dissimilarities) were extracted based on the three periods *a priori* identified by the GAMs (i.e. BE – reference period or Before nutrient enrichment; NE – during nutrient enrichment period; AEW – the re-oligotrophication (after enrichment) and warming period); $\beta_{BE}$, dissimilarities between samples within the BE period, provided insights into the ‘natural’ variability of the community; $\beta_{NE}$, dissimilarities between samples within the NE period; $\beta_{AEW}$, dissimilarities between samples within the AWE period; $\beta_{BE/NE}$ dissimilarities between samples from the BE and samples from the NE periods; $\beta_{BE/AEW}$ dissimilarity between the samples from the BE period and the samples from the AEW. The comparison of the $\beta_{BE/NE}$ and the $\beta_{BE}$ provided a proxy of the capacity of resistance of the community in response
to nutrient enrichment. Moreover, the length of the time
at which a community reaches its mean turnover (i.e. mean
β<sub>BE/NE</sub>) provided a proxy of type of resistance abrupt versus
gradual (i.e. the community reaching its mean turnover in
short versus long period, respectively, after the disturbance
started). The comparison of the β<sub>AEW/NE</sub> and β<sub>BE</sub> allowed to
assess the capacity of resilience of the communities following
the re-oligotrophication of the lakes.

To assess whether amplitude of changes in beta-diversity
reflected species turnover (i.e. species replacement which may
reflect species sorting by the environment), nestedness (β<sub>ness</sub>)
or both, Bray-Curtis nestedness resultant dissimilarities were
computed and analyzed using the function *beta.pair()* originally
developed by Baselga (2010) and later implemented in
the 'betapart' package (Baselga and Orme 2012). The β<sub>ness</sub> is
obtained by calculating the difference between the Sorensen
and Simpson pairwise dissimilarities. The β<sub>ness</sub> evaluates
whether samples with a lower number of species are strict subsets
of the biotas from richer samples. Such measure implies
the disaggregation of the assemblages in some samples and
that the samples share at least one species in common. As discussed
by Legendre (2014), the nestedness component developed
by Baselga (2010) and used presently, reflects a richness
difference which may, in part, arise from species gain or loss,
but is not an absolute indicator of nestedness.

A pairwise multivariate analysis of variance (pairwise-
PERMANOVA) was conducted on Bray-Curtis dissimilari-
ties and nestedness resultant to assess significant change in
the composition of microeukaryotic communities between
the three periods of trophic change (i.e. BE, NE and AEW
periods), p-values were adjusted for multiple comparison
with Benjamini and Hochberg correction.

*Microeukaryote functional trophic ecology*

Based on the taxonomic assignment of the OTUs one of
the following five functional trophic groups was attributed:
photosynthetic, mixotroph, saprotroph, consumer, parasite
and endosymbiont. The information on trophic classification
of microeukaryotes was obtained from Keck et al. (2020),
which was based on the original classification from Adl et al.
(2019). The assignment of the functional ecology was only
possible for OTUs for which the taxonomic assignment was
fine enough (about 64% of the OTUs). The phylum level
was considered sufficient when the group exhibited a homo-
geneous trophic mode and function, whereas for phyla com-
prising taxa with highly diverse trophic modes and functions,
a finer taxonomic resolution was used.

The amplitude of change of the five functional groups was
assessed with the DESeq2 analysis framework (Love et al.
2014) applied on the count data. The logarithmic fold-
change value (Log2foldchange) was used to express how
much the number of reads changed between the periods and
the significance in the difference in abundance between peri-
ods was tested using a Wald test with p-values adjusted for
multiple hypothesis testing using the method of Benjamini
and Hochberg.

Results

Compositional change and temporal breakpoint

The microeukaryote community inventories of the four lakes
were dominated by the heterotrophic groups of fungi and
ciliates (Phylum: Ciliophora), the rotifers were also found in
high relative abundance (Supporting information). In terms
of community composition, in all four lakes, the NE period
was marked by an increase in the relative abundance of the
bacillariophyceae, chrysophyceae, ciliates and rotifers and
a decline of the sociozoans (Supporting information). An
increase in Pirsonia was also observed in lakes Geneva and
Bourget during the NE period. During the AEW period, all
lakes displayed an increase in chrysophyceae and dinophy-
ceae, while the ciliates and rotifers declined to reach back-
ground levels (i.e. similar relative abundance as during the
BE period).

The CONISS analysis and broken stick model identified four distinct assemblages in Lake Geneva and three distinct temporal assemblages within the microeukaryote community composition of Lake Bourget and two temporal assemblages in Lake Annecy (Fig. 2). The timing of change identified by the CONISS analysis were in the mid-1950s, the 1960s, and the 2000s in Lake Geneva, in the 1930s and 1980s in Lake Bourget, and the 1990s in Lake Annecy. In Lake Aiguebelette, the CONISS analysis identified two assemblages with a change occurring in the 1960s but these assemblages were not significantly different according to the broken stick analysis.

Outcomes of the ROC analysis was relatively stable regard-
less of the bin selected (Fig. 2). Tests for the selection of the
suitable type of smoothing for the ROC analysis showed that
across the four lakes the Shepard's 5-term filter, which is a
moving weighted average graduation based on the method of
ordinary least squares and central differences, was the most
robust and showed consistent results across bin sizes. A period
of significant compositional change in the microeukaryote
assemblages of the four lakes was identified by the ROC
analysis between the 1930s and 1940s for Lake Bourget, the
1940s and 1950s for Lake Geneva, the 1960s and 1970s for
Lake Annecy, and between the late-1960s and 1970s for Lake
Aiguebelette (peak point for which SD is 1.5 higher than the
model). As such, together the CONISS and ROC analysis
indicated at least one common timing of significant change
in the microeukaryote community composition at ca 1930
and ca 1950 in Bourget and Geneva, respectively. For all four
lakes, the compositional change identified by the ROC analy-
sis occurred at a time at which TP concentration reached the
threshold value of 15-20 µg l⁻¹ (Fig. 2b).

Microeukaryote alpha diversity

All alpha diversity indices showed similar trends (Supporting
information). Periods of significant changes were identified
with the GAMs in the temporal trend of the Shannon diver-
sity of lakes Geneva, Bourget and Annecy (Fig. 2). The alpha
diversity of lakes Geneva and Bourget displayed an inversed
bell-shaped curve with a period of significant decline of the Shannon index at ca 1950 and ca 1920, in Geneva and Bourget respectively, and a period of significant increase at ca 1990 in both lakes. Thereafter, the alpha diversity settled back to reference values (i.e. prior to the decline). In Lake Annecy, following a steep decline in the 1970s, the alpha diversity significantly increased at ca 1980s. In Lake Aiguebelette, no period of significant change was identified in the temporal trend of the alpha diversity, which slightly fluctuated around a mean of 4.5.

Microeukaryote beta-diversity

The PCoA analyses showed similar results when performed using the Bray–Curtis dissimilarities (Supporting information) and Jaccard index (Supporting information). Axis 1 and Axis 2 explained 33, 38, 35 and 48 percent of the total variance for lakes Geneva, Bourget, Annecy and Aiguebelette.

The PCoA of the microeukaryote community of lakes Geneva, Bourget and Aiguebelette showed a clear unidirectional trend, corresponding to the temporal changes across the three different periods (i.e. BE, NE, AEW; Supporting information). For lake Annecy, the PCoA also displayed a temporal trend; however, this trend did not correspond to a clear unidirectional trend related to the three distinct periods.

According to the db-RDA, the variance accounted for by the constraining environmental variables (i.e. TP and water temperature) is 35.8, 42.3, 28.2 and 32.7% for Geneva, Bourget, Annecy and Aiguebelette, respectively (Fig. 3). For the four lakes, the temporal changes in the microeukaryote community were significantly correlated with both TP (Geneva: r²=0.85, p-value < 0.01, Bourget: r²=0.67, p-value < 0.01, Annecy: r²=0.74, p-value < 0.01, Aiguebelette: r²=0.91, p-value < 0.01) and mean water temperature (Geneva: r²=0.84, p-value < 0.01, Bourget: r²=0.75, p-value < 0.01, Annecy:

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

Figure 3. Distance-based redundancy analysis (db-RDA) of the microeukaryote community of the four study lakes based on Bray–Curtis dissimilarity. The points are color-coded according to the trophic periods: grey = oligotrophic, green = nutrient enrichment, purple = reoligotrophic + warming. The percentage indicated for each axis corresponds to the variance accounted for by the constraining variables. The black arrows of environmental variables (TP = Total phosphorus, WT = mean water temperature) that were significantly correlated with the axis are plotted. The species scores for the species that were significantly correlated with the environmental variables are plotted on the middle panel. The bottom db-RDA display the fitted smooth surface of the environmental variables and statistical results from the *envi.fit()* are indicated above.
$r^2=0.90$, p-value < 0.01, Aiguebelette: $r^2=0.75$, p-value < 0.01). For Geneva Lake, Ciliates (Class: Litostomatea, Order: Haptoria), Pirsonia, and Rotifers were strongly correlated with TP. For lakes Bourget and Annecy Lake, Chrysophytes and Ciliates (Class: Litostomatea, Order: Haptoria) were strongly correlated with TP. In Lake Aiguebelette, the Fungi (Phylum: Cryptomycota) were strongly correlated with TP. Dinophyceae were strongly correlated with water temperature in lakes Geneva and Aiguebelette, while in Lake Bourget Chlorophytes and Ciliates (Class: Litostomatea, Order: Haptoria) were strongly correlated with water temperature and only Ciliates in Lake Annecy.

## Assessment of community resistance, resilience and nestedness component

For all lakes, the pairwise-PERMANOVA indicated a significant difference in the Bray–Curtis dissimilarities between BE and NE periods (Geneva: F=14.7, p-value < 0.01, Bourget: F=7.5, p-value < 0.01, Annecy: F=3.2, p-value < 0.01, Aiguebelette: F=6.2, p-value < 0.01) (Fig. 2). The dissimilarities between BE and AWE periods were significantly different for lakes Geneva, Bourget and Annecy (Geneva: F=18, p-value < 0.01, Bourget: F=8.6, p-value < 0.01, Annecy: F=4.4, p-value < 0.01), but not for Aiguebelette (F=5.8, p-value > 0.01). The dissimilarities NE and AWE periods were significantly different for Geneva, Bourget and Annecy (Geneva: F=10.1, p-value < 0.01, Bourget: F=8.1, p-value < 0.01, Annecy: F=2.9, p-value < 0.01), but not for Aiguebelette (F=3.1, p-value > 0.01). The $\beta_{BE}$ were significantly different between lakes, with Lake Aiguebelette displaying the lowest mean dissimilarity within the BE period ($\beta_{BE}$) followed by Geneva, Bourget, and Annecy (mean=0.35, 0.4, 0.5 and 0.65, respectively). The temporal changes in the Bray–Curtis dissimilarities showed an abrupt, stepwise change of the beta-diversity in Lake Geneva around the 1950s (Fig. 2). In lakes Bourget and Aiguebelette, the change in beta-diversity compared to the BE period showed a gradual increase over time. For Lake Annecy, the temporal change was abrupt and dissimilarity patterns of $\beta_{NE/AEW}$ and $\beta_{AEW}$ showed a higher variability when compared to the reference period. Altogether, the proxy of resistance showed that at the onset of nutrient enrichment, the microeukaryote community of Lake Geneva was the least resistant (i.e. quick turnover rate) and underwent the largest amplitude of change across time followed by Aiguebelette, Bourget and Annecy (Fig. 2).

The nestedness was significantly different only between the BE and NE periods in lakes Geneva, Bourget and Aiguebelette (Geneva: F=8.2, p-value < 0.01, Bourget: F=11.1, p-value < 0.01, Aiguebelette: F=19.8, p-value < 0.01) (Fig. 2). In lakes Geneva, Bourget and Aiguebelette, nestedness (i.e. species loss) remained low (>0.15). In lakes Geneva and Bourget, nestedness increased during the NE period. In Lake Annecy, the nestedness was highly variable. Altogether, the beta-diversity indices indicated that turnover (i.e. species replacement) strongly contributed to beta-diversity in lakes Geneva ($\beta_{bray-mean}=0.67$, $\beta_{nes-mean}=0.07$),

Bourget ($\beta_{bray-mean}=0.56$, $\beta_{nes-mean}=0.06$) and Aiguebelette ($\beta_{bray-mean}=0.58$, $\beta_{nes-mean}=0.03$). In Lake Annecy turnover also contributed the most to beta-diversity ($\beta_{bray-mean}=0.68$) and the nestedness contribution to beta-diversity was slightly higher than that in the other lakes ($\beta_{nes-mean}=0.13$).

## Microeukaryote functional changes

According to the DeSeq2 analysis, the NE period of all four lakes was marked by a significant increase in photosynthetic microeukaryotes (the two-sided Wald test, p-value < 0.01, Fig. 4, Supporting information). In parallel, saprotrophs (i.e. fungi) and mixotrophs (i.e. mostly driven by mixotrophic Dinophyceae) significantly declined in all four lakes, except for the mixotrophs in Lake Bourget. Post-2000, mixotrophic groups mainly represented by Chrysophyceae and Dinophyceae significantly increased in all lakes (the two-sided Wald test, p-value < 0.01). The BE community composition and functional groups differed between the four lakes according to the DeSeq2 analysis (Supporting information). More specifically, phototrophic and mixotrophic groups were significantly higher in Lake Geneva than in the other lakes during the BE period (the two-sided Wald test, p-value < 0.01).

## Discussion

We reconstructed the long-term dynamic (i.e. over the last 120 years) of microeukaryote communities of four lakes that display contrasting nutrient enrichment histories. Our analysis demonstrated that, regardless of the gradient of eutrophication, the microeukaryote community composition of all lakes departed from their reference state (BE) and that significant changes were triggered as the lake reached meso-oligotrophic conditions (i.e. beginning of NE period). The microeukaryote communities of all studied lakes underwent changes in the structure, composition and functional ecology (Fig. 2). These changes were driven by both eutrophication and warming. In the most eutrophied lakes (i.e. Geneva and Bourget), diversity metrics indicated that eutrophication increased nestedness and changes in the community toward a compositionally and functionally less diverse community. Following the re-oligotrophication (AEW period), the lakes did not return to pre-disturbances conditions.

## Shift to meso-oligotrophic conditions triggered early response in the microeukaryote community

Our results indicate that regardless of the nutrient enrichment level, the microeukaryote community of all four lakes underwent significant compositional changes at the onset of meso-oligotrophication (TP > 10 µg l⁻¹ according to Wetzel (2011)). The period of change identified by the ROC analysis in all lakes coincided with a timing at which TP concentration reached a threshold value of 15–20 µg l⁻¹ (Fig. 2b), as lakes switched from an oligotrophic to a meso-oligotrophic status. The CONISS analysis also detected similar timing of changes in the most eutrophied lakes Geneva and Bourget.
<figure><img src="image_7.png" /><figcaption>Figure 4. Amplitude of change in the major functional groups based on the trophic ecology between the BE period (reference period, before nutrient enrichment) versus the NE period (nutrient enrichment period) and the NE period versus AEW period (after nutrient enrichment and warming). Magnitude of change is expressed in log2 fold change, as estimated by the *DESeq2* analysis. Colored bars represent groups for which the change was found significant (p-value < 0.05), while the light grey bars represent groups for which the change was not significant. Horizontal lines show the SE.</figcaption></figure>

Similar early response of the microeukaryote community at the onset of the meso-oligotrophication have been previously observed in other deep peri-alpine lakes, such as in Lake Constance (Ibrahim et al. 2021, Huo et al. 2022). While the exact mechanism remains elusive, reconstructions of the oxygen levels in lakes Geneva, Bourget and Annecy demonstrated that increases in TP above a threshold concentration of 8 to 10 µg l⁻¹ can trigger bottom waters hypoxia (Jenny et al.

2013, 2016, Soares et al. 2024). As the microeukaryote community recovered in our records includes organisms that live in bottom waters (i.e. including some ciliates, parasites and fungi), such shift in the physico-chemical conditions could explain the observed early response. This is further supported by the fact that, according to Jenny et al. (2014), hypoxia first appeared in 1952 ± 2 years in Lake Geneva, in 1933 ± 1 year in Lake Bourget, and in 1950 ± 2 years in Lake Annecy. These
dates correspond to the timing at which the lakes switched from oligotrophic to meso-oligotrophic conditions and the microeukaryote communities significantly changed, as identified by the ROC analysis (Fig. 2). The uniform threshold response of the overall microeukaryote community that we observed across the studied lakes thus suggests that, in deep temperate lakes, meso-oligotrophic conditions and associated hypoxic conditions, can trigger an early response in the microeukaryote community, well before a lake becomes eutrophic.

## Eutrophication affects the stability and triggered changes in the functional ecology of microeukaryote communities

The microeukaryote communities of the most eutrophied lakes (i.e. Geneva and Bourget) showed a decline in alpha diversity and an increase in turnover rate. Additionally, although nestedness represented only a small component of the beta-diversity in our dataset (Fig. 2), it was slightly higher during the NE period in lakes Geneva and Bourget. Our results support previous work that demonstrated that the trophic status can be an important predictor of nestedness in freshwater lakes (Soininen and König 2012), and indicate that changes in nutrient concentration can trigger an increase in the contribution of the nestedness component of beta-diversity. Nestedness can reflect non-random processes of extinctions or colonizations along gradients, or from spatial patterns of decreasing resource or habitat availability (Ulrich et al. 2009, Baselga 2010). Together the decline in alpha diversity and increase in nestedness indicates that changes in the community composition during the NE period of these two lakes were not only a result of community turnover but also reflects richness difference which may be, in part, due to species loss.

A meta-analysis of the nestedness and turnover components of beta-diversity across organisms and ecosystems demonstrated that the relative importance of the various beta-diversity components changes as a function of the trophic position (Soininen et al. 2018). More specifically, the primary producers tend to have a high turnover rate, whereas decomposers tend to display a relatively low nestedness. As such, the replacement of the BE community, which was dominated by heterotrophs, to a community dominated by autotrophs during the NE period could explain the observed increase in nestedness and turnover rates. While similar increases in nestedness has been previously observed as a response to eutrophication in zooplankton (Gianuca et al. 2017) and diatom communities (Zorzal-Almeida et al. 2021), our study is the first to demonstrate such effect on the overall microeukaryote community.

Our dataset suggests that changes in the microeukaryote community are observed across all trophic compartments, likely driven by bottom-up processes. Microeukaryotes encompass a large array of trophic strategies as phototrophs, heterotrophs and mixotrophs, and as such, include components from the herbivorous, parasitic and microbial loops (Burki et al. 2021, Singer et al. 2021). Located at the base of

the food web, autotrophs are directly influenced by changes in nutrient levels. The sensitivity of photosynthetic microeukaryotes response to eutrophication is well studied, and some key photosynthetic protists are already widely used indicators to track nutrient enrichment in freshwater ecosystems (Sandgren et al. 1995, Pawlowski et al. 2018, Vasselon et al. 2019). Supporting these previous observations, the BE communities dominated by saprotrophs and parasites were replaced by communities dominated by phototrophs following nutrient enrichment.

The decline in saprotrophs and parasites was in part driven by a decline in the relative abundance of parasitic Cryptomycota in lakes Geneva and Bourget, and saprotrophic fungi Ascomycota and Basidiomycota in all four lakes (Supporting information). Fungi can be sensitive to post-depositional degradation (Capo et al. 2015, 2017b), the higher abundance in the deeper part of the core is the opposite expected signal of early diagenesis. Moreover, the analysis of OTUs assigned to the fungi kingdom showed that the observed changes in the fungal communities are unlikely an artifact of DNA preservation (Supporting information: Material and methods), but a true signal of community change as a response to nutrient enrichment. This is further supported by the fact that the presence of Dikarya (Ascomycota and Basidiomycota) and Cryptomycota detected in the sedDNA signal, has been previously reported in the pelagic zone of lakes Geneva, Bourget and Aiguebelette (Lepère et al. 2019). Unfortunately, the targeted DNA sequence used in the present study is well adapted to recover the microeukaryote community signal but is not an accurate phylogenetic marker for fungi. As our analysis provides important primary insights into the response of fungal communities to environmental changes, investigations specifically targeting fungi communities would be necessary to gain further information.

Interestingly, in the strongly eutrophied lakes, the NE period of lakes Geneva and Bourget was characterized by an increase in the proportion of ciliates, rotifers, and parasitic nanoflagellate Pirsonia (Supporting information). Our data thus indicate that bottom-up process, initially triggered by the modification of the photosynthetic communities, increased the importance of the parasitic and microbial loop and suggests a change in the relative importance and mobilisation of different trophic chains under high nutrient conditions. These results are consistent with previously documented increase in food web complexity and predominance of the microbial loop in eutrophied lakes (Jeppesen et al. 2003, Havens 2014), and demonstrate that the sedDNA and paleolimnological approaches allow to reconstruct these food web dynamics through a long-term perspective.

## Lake-specific amplitude of change of the microeukaryote community in response to nutrient enrichment

The amplitude of change of the microeukaryote community composition, measured by the Bray-Curtis dissimilarity, was lake-dependant and not proportional to the intensity of nutrient enrichment. While Bourget was the most eutrophic
lake followed by Geneva, Aiguebelette and then Annecy, the microeukaryote community of Lake Geneva was the least resistance to nutrient enrichment as it displayed an abrupt shift in beta-diversity. In contrast Lake Bourget showed a gradual change and thus more resistance. Moreover, although the communities of all lake showed a departure from the reference conditions (BE period), the communities of lakes Aiguebelette and Geneva exhibited the largest turnover. Similar lake-dependant responses of the planktonic communities to environmental changes have been previously observed in large peri-alpine lakes (Perga et al. 2015, Merz et al. 2023). Historical diversity might pre-determined lake sensitivity to environmental changes. According to the biological insurance theory, higher diversity tends to provide more stability (Loreau et al. 2021). Because of higher functional redundancy, diverse communities are more likely to contain species that confer resistance thus acting as a buffer to the ecosystems against environmental fluctuations (Allison and Martiny 2008, Shade et al. 2012). Supporting this hypothesis, the historical alpha diversity of Lake Annecy was the lowest, likely explaining the relatively high turnover rate of the community of Lake Annecy even during the BE period (Fig. 2). Likewise, the slightly higher historical alpha diversity in Lake Bourget (i.e. during the BE period) could explain the more gradual changes in the microeukaryote community when compared to Lake Geneva. Overall, the pre-disturbance alpha diversity which varies between lakes might explain the lake-specific response to nutrient enrichment.

## Effect of climate and re-oligotrophication on the microeukaryote community structure and trophic ecology

Our data indicate that the microeukaryote communities of all studied lakes were not resilient, as the communities did not return to pre-disturbance conditions following nutrient abatement measures. As a response to the re-oligotrophication, the microeukaryote communities of the lakes recovered in terms of alpha diversity but the community composition including its functional composition did not return to reference conditions (BE period), β<sub>AEW</sub> and β<sub>AEW/BE</sub> were significantly different from β<sub>BE</sub> (Fig. 2). This lack of resilience but reversibility as the alpha diversity has been previously observed in the large peri-alpine Lake Constance (Ibrahim et al. 2021). Lake catchment connectivity influence recolonization ability, and therefore, can be a strong predictor of the resilience of an ecosystem (Poff et al. 2012, Markovic et al. 2017). As such, the relatively large surface area and long water residence times of the four studies lakes (Fig. 1) could explain the low resilience of the microeukaryote communities.

Our data suggest that climate change, and potentially other unknown regional environmental factors common to all four lakes, played a key role in shaping the recent microeukaryote communities, leading to similar functional and structural changes in the communities across lakes. The strong correlation of the community changes with the environmental variables suggests that, in freshwater lakes, the interactions and structures of microeukaryote communities are strongly

influenced by deterministic processes (i.e. environmental selection), and thus likely explain the unidirectional change of communities across lakes.

Lakes displayed a significant increase in mixotrophs and autotrophs in response to climate, thereby suggesting that climate is an important factor driving recent changes in the microeukaryote communities. The DeSeq2 analysis demonstrated that the pre-disturbance community composition of the four lakes differed greatly (Supporting information). In freshwater ecosystem, high heterogeneity between the microeukaryote communities of geographically nearby lakes has been previously observed, a pattern that have been attributed to the soil physico-chemical composition of the watershed (Garner et al. 2022) or to bedrock geology composition (Barouillet et al. 2022b). Our data thus demonstrate that previously heterogeneous communities of microeukaryotes are undergoing similar unidirectional changes under the pressure of common regional environmental perturbation.

Our analysis shows that as a response to warming water temperature, the metabolic balance of lakes has been shifting from a dominantly heterotrophic to phototrophic and mixotrophic processes in recent times (Fig. 3, Supporting information). Similar increased in the prevalence of both phototrophic and mixotrophic species in freshwater aquatic habitats have been previously observed (Capo et al. 2016, 2017a, Keck et al. 2020, Barouillet et al. 2022b). More specifically, our result shows an increase in the relative abundance of mixotrophic Dinophyceae and Chrysophyceae, and phototrophic Bacillariophyceae of all four lakes (Fig. 3, Supporting information). As mixotrophs tends to be more competitive than obligate phototrophs under low nutrient conditions (Mitra et al. 2014), their recent increase is, in part, a sign of recovery as a response to the re-establishment of oligotrophic conditions. Previous paleolimnological and experimental works recorded similar widespread increases in mixotrophic algae in freshwater lakes as a response to climate-driven enhanced inputs of allochthonous DOC (Arseneau 2016, Mushet et al. 2018, Dory et al. 2022). More recent work provided further evidence that recent increase in mixotrophic chrysophytes and dinoflagellates in the Experimental Lakes Area is a strong indicator of warming (Garner et al. 2025). In lakes Geneva and Aiguebelette, the increase in Dinophyceae was strongly correlated with warmer water temperature, thereby further supporting that their recent increase is driven by climate (Fig. 3). Importantly, given the key role that mixotrophs can play in the recycling and transfer of energy (Mitra et al. 2014, Ward and Follows 2016), such functional changes in the trophic ecology of these large peri-alpine lakes might strongly influence how the energy fluxes through the food web. Form a broader perspective, these homogeneous changes in the trophic ecology of several lakes could influence global biogeochemical cycles, as lakes act as important sinks or sources of carbon and other nutrients.

Although various precautions were taken to minimize potential bias associated with early diagenesis (Supporting information), particular caution must be taken when interpreted such upward trend in the sedDNA signal toward
the top of the core. This is particularly relevant for the continuous increase in the relative abundance of mixotrophic Dinophyceae and Chrysophyceae, and phototrophic Bacillariophyceae toward the top of the cores of all four lakes (Supporting information). SedDNA reconstruction of the diatoms (i.e. Bacillariophyceae) and dinoflagellates (i.e. Dinophyceae) communities of an Antarctic fjord demonstrated that dinoflagellates DNA can be particularly sensitive to post-depositional degradation at the Holocene scale (Boere et al. 2011). However, in shorter time scale (i.e. century scale) and temperate regions, comparison of long-term phytoplankton monitoring data with sedDNA reconstruction showed that sedDNA faithfully reconstructed the relative abundance and occurrence of chlorophytes, dinoflagellates, ochrophytes and bacillariophytes (Thorpe et al. 2024). The later study used the same set of primers as used in this study, thereby supporting that the upward trend observed in our sediment records is unlikely an artefact of early diagenesis. Moreover, available long-term phytoplankton monitoring of our four study lakes shows that since 1995, dinoflagellates and diatoms are the dominant phytoplankton classes in Annecy Lake, while for lakes Geneva, Bourget and Aiguebelette, the relative biomasses of diatoms and chrysophytes, and to a less extent dinoflagellates are continuously increasing (Rimet et al. 2019, Frossard et al. 2024, Jenny et al. 2024, Rimet 2024). As such, the increasing trend in mixotrophic Dinophyceae and Chrysophyceae, and phototrophic Bacillariophyceae toward the top of the core is unlikely an artifact of early diagenesis, but a true signal of community changes.

## Implication for management and conservation

The longer temporal perspective offered by the paleolimnological approach extends our insights into the resilience and resistance of freshwater microeukaryote communities. Lakes tend to be particularly sensitive to surrounding environmental factors, leading to relatively more heterogeneous and diverse community response to environmental changes when compared to marine and terrestrial biomes (Pearman et al. 2020, Garner et al. 2023). The results from the present study indicate a lake-dependent amplitude of change to environmental changes. Our data also demonstrate that, despite the observed lake-dependant response, the microeukaryote communities of all lakes displayed functional and compositional changes as the trophic status of the lake switches from oligotrophic to meso-oligotrophic conditions, and all lakes show no resilience after reoligotrophication. From a management and conservation perspective, such information has important implications as it suggests that even in the same region, management and conservation practices might require lake-specific adaptations and an a priori knowledge of the reference conditions, to better predict and mitigate lake responses to environmental changes. Despite the successful return to oligotrophic condition, the microeukaryote communities of the studied lakes did not return to reference conditions both in terms of beta-diversity and functional ecology, suggesting that the lakes might have reached an alternative stable

state. Such results support the needs for the 'restoration conundrum' as the recovery towards a pristine reference state might not be archivable (Reyes-Aldana 2024). Furthermore, through the lens of the microeukaryote community, our data demonstrate that the trophic ecology of lakes has fundamentally changed, as mixotrophs and phototrophs have increased in recent times. Given the crucial role that microeukaryote communities play at the base of the trophic food webs of freshwater lakes and in the recycling and the transfers of energy and nutrients, the newly established communities might imply important changes in the biogeochemical processes within lakes.

**Acknowledgements** – We acknowledge Jean-Christophe C. Hustache for the technical fieldwork support, Cécile Chardon for biomolecular laboratory support and training. EDYTEM for landing the coring material. The authors thank the Laboratoire Souterrain de Modane (LSM) facilities for the gamma spectrometry measurements.

**Funding** – CB received financial support from the Pole Research & Development on Lacustrine Ecosystems (ECLA) of the French Biodiversity Agency (OFB).

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

## Author contributions

**Cécilia Barouillet**: Conceptualization (equal); Data curation (equal); Formal analysis (lead); Investigation (equal); Methodology (equal); Validation (equal); Visualization (lead); Writing – original draft (lead); Writing – review and editing (lead). **Eric Capo**: Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Writing – original draft (supporting); Writing – review and editing (supporting). **Didier Debroas**: Data curation (supporting); Formal analysis (supporting); Investigation (supporting); Methodology (supporting); Writing – original draft (supporting); Writing – review and editing (supporting). **Jean-Philippe Jenny**: Conceptualization (supporting); Funding acquisition (supporting); Investigation (supporting); Writing – original draft (supporting); Writing – review and editing (supporting). **Pierre Sabatier**: Data curation (supporting); Investigation (supporting); Methodology (supporting); Writing – original draft (supporting); Writing – review and editing (supporting). **Isabelle Domaizon**: Conceptualization (equal); Formal analysis (equal); Funding acquisition (lead); Investigation (equal); Methodology (equal); Project administration (lead); Supervision (lead); Validation (equal); Visualization (supporting); Writing – original draft (supporting); Writing – review and editing (supporting).

## Data availability statement

Data are available from the Zenodo Repository: https://doi.org/10.5281/zenodo.15229137 (Barouillet et al. 2025).

## Supporting information

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