Evaluating the Congruence Between DNA‐Based and Morphological

Limnol. Oceanogr. 9999, 2021, 1–20 © 2021 The Authors. Limnology and Oceanography published by Wiley Periodicals LLC on behalf of Association for the Sciences of Limnology and Oceanography. doi: 10.1002/lno.11856 Evaluating the congruence between DNA-based and morphological taxonomic approaches in water and sediment trap samples: Analyses of a 36-month time series from a temperate monomictic lake

Joanna Gauthier ,1,2* David Walsh ,2,3 Daniel T. Selbie,4 Alyssa Bourgeois,1 Katherine Griffiths,1,2 Isabelle Domaizon,4,5 Irene Gregory-Eaves1,2 1Department of Biology, McGill University, Montreal, Quebec, Canada 2Groupe de Recherche Interuniversitaire en Limnologie (GRIL), Montreal, Quebec, Canada 3Department of Biology, Concordia University, Montreal, Quebec, Canada 4Fisheries and Oceans Canada, Pacific Region, Science Branch, Ecosystem Sciences Division, Cultus Lake Salmon Research Laboratory, Cultus Lake, British Columbia, Canada 5CARRTEL, INRAe, Université de Savoie Mont Blanc, Thonon-les-Bains, France Abstract Paleolimnological studies are central for identifying long-term changes, yet many studies rely on bioindicators that deposit detectable subfossils in sediments, such as diatoms and cladocerans. Emerging DNA-based approaches are expanding the taxonomic diversity that can be investigated. However, as sedimentary DNA-based approaches are expanding rapidly, calibration work is required to determine the advantages and limitations of these tech- niques. In this study, we assessed the congruence between morphological and DNA-based approaches applied to sediment trap samples for diatoms and crustaceans using both intracellular and extracellular DNA. We also evaluated which taxa are deposited in sediment traps from the water column to identify potential paleolimnological bioindicators of environmental variations. Based on 18S rRNA gene amplicons, we developed and analyzed a micro-eukaryotic, monthly time series that spanned 3 years and was comprised of paired water column and sediment trap samples from Cultus Lake, British Columbia, Canada. Comparisons of assemblages derived from our genetic and morphological analyses using RV coefficients revealed significant correlations for diatoms, but weaker correlations for crustaceans. Intracellular DNA reads correlated more strongly with diatom morphology, while extracellular DNA reads correlated more strongly with crustacean morphology. Additional analyses of amplicon sequence variants shared between water and sediment trap samples revealed a wide diversity of taxa to study in paleolimnology, including Ciliophora, Dinoflagellata, Chytridiomycota, Chrysophyceae, and Cryptophyceae. Par- tial RDAs identified significant environmental predictors of these shared assemblages. Overall, our study demon- strates the effectiveness of DNA-based approaches to track community dynamics from sediment samples, an essential step for successful paleolimnological studies.

Over the past century, anthropogenic activities and climate change (Smol 2008; Bennion et al. 2011). Paleolimnological time change have induced significant alterations to freshwater ecosys- series have been useful in evaluating the adequacy of manage- tems, with an intensification of stressors since the 1970s (Reid ment practices (Perga et al. 2010) and may inform future scenario et al. 2018). Paleolimnological records have played a central role development (Smol 2008; Gillson and Marchant 2014; Saulnier- in quantifying the rate and magnitude of past ecological dynam- Talbot 2016). Biological community changes in the sediment ics and have served to identify the major drivers of ecosystem record have relied mostly on the study of a small subset of aquatic taxa that deposit detectable subfossils including diatom valves (Battarbee et al. 2001), chrysophyte cysts and scales (Zeeb and *Correspondence: [email protected] Smol 2001) and cladoceran (Alric and Perga 2011) and chirono- mid remains (Walker 2001). However, many pelagic organisms This is an open access article under the terms of the Creative Commons fi Attribution-NonCommercial-NoDerivs License, which permits use and dis- do not preserve as visually identi able subfossils in the sediments tribution in any medium, provided the original work is properly cited, the (e.g., fungi, soft algae, rotifers, and copepods), yet may be useful fi use is non-commercial and no modi cations or adaptations are made. as bioindicators of environmental change. Additional Supporting Information may be found in the online version of Applying DNA-based approaches to lake sediments has the this article. potential to expand the taxonomic diversity that can be

1 Gauthier et al. Evaluating DNA-based methods in lake sediments targeted in paleolimnological studies, and to provide an protists, fungi, and larger multicellular organisms such as crus- opportunity to study functional genes (Domaizon tacean species. We refer to the pool of taxa identified through et al. 2017). Molecular approaches in paleolimnology have sequencing herein as micro-eukaryotes. Our specific aims were proven to be effective in reconstructing the histories of some (1) to evaluate the congruence of widely used bioindicator taxonomic groups such as cyanobacteria (Domaizon groups (diatom and crustaceans) between morphological and et al. 2013; Monchamp et al. 2016), diatoms (Epp et al. 2011; DNA approaches as well as between water and sediment trap Stoof-Leichsenring et al. 2014, 2020) and micro-eukaryotic samples, (2) to compare the micro-eukaryotic communities communities (Capo et al. 2016, 2017, 2019). However, only a identified from the amplification of the 18S rRNA gene from few studies have evaluated the congruence between morpho- DNA in the water column and in the sediment traps to iden- logical and DNA-based taxonomic approaches in sediments tify potential taxonomic groups for future paleolimnological (Stoof-Leichsenring et al. 2012, 2014, 2020; Dulias studies, and (3) to evaluate the efficiency of extracellular DNA et al. 2017). Preliminary results showed that DNA-based vs. intracellular DNA to detect taxa in sediment traps when approaches may uncover greater richness (Stoof-Leichsenring looking at diatom, crustacean and micro-eukaryotic assem- et al. 2012), and generally, these two approaches are consid- blages. Given that several studies have reported a significant ered complementary (Jørgensen et al. 2012; Dulias congruence between the assemblages observed in the water et al. 2017). Additionally, there is a need to evaluate the column and those recorded in lake sediments (Capo extent to which the biological signal from the water column et al. 2015; Winegardner et al. 2015; Monchamp et al. 2016), is captured in the sedimentary record when applying DNA- we hypothesize that analyses of sediment traps would track based approaches. Some promising initial studies include: similar dynamics to those reflected in the water column using (1) Capo et al. (2015) who detected 71% of phylogenetic similar taxonomic approaches (morphological or DNA). In units from the water column in the sediments and (2) Mon- addition, the few studies that have compared assemblages champ et al. (2016) who found that pelagic cyanobacteria based on sedimentary DNA and visual count approaches have identified morphologically over 30 years from water column detected significant coherence (Jørgensen et al. 2012; Stoof- sampleswerehighlycorrelatedwhencomparedtopaleo- Leichsenring et al. 2012; Monchamp et al. 2016). Accordingly, genetic time series of cyanobacteria. we hypothesize that the genetically and morphologically Sedimentary DNA can be archived either as intracellular derived estimates of taxonomic composition in the sediments (i.e., intact cells) or extracellular DNA, where extracellular would be significantly correlated. DNA can be adsorbed to the sediment matrix, thereby reduc- ing its degradability (Dell’Anno et al. 2002; Corinaldesi Methods et al. 2005; Dell’Anno and Danovaro 2005). In marine envi- ronments, extracellular DNA can represent >90% of the sedi- Site description ’ ’ mentary DNA pool (Dell’Anno et al. 2002; Dell’Anno and Cultus Lake (49 03.3 N; 121 59.0 W) is a monomictic and Danovaro 2005) and may be a significant fraction of the DNA oligo-mesotrophic lake located in the Lower Mainland of Brit- archived for many organisms. To our knowledge, only two ish Columbia (BC), Canada, at ~ 50 km east of the outer limit paleolimnological study have evaluated whether DNA from of the Greater Vancouver Regional District (Fig. 1a). The sur- 2 pelagic organisms is preserved as extracellular DNA in the sedi- face area of Cultus Lake is 6.3 km with mean and maximum ments (Vuillemin et al. 2017: bacteria; Pansu et al. 2021: depths of 31 and 44 m, respectively (Shortreed 2007). Cultus fl eukaryotes). Lake is a relatively fast- ushing lake with a water residence 2 Since the use of DNA-based approaches in paleolimnology time of ~ 1.8 yr. The lake watershed area is ~ 75 km with a is expanding rapidly, careful examination of the strengths and small proportion (~ 19%) in the United States. limitations of the approach is required. In this study, we generated a sediment trap time series spanning 36 months in Sample collection order to advance our knowledge of how different components On a monthly basis from June 27th, 2014 to June 12th, of the pelagic communities may be detected from sedimentary 2017, water and sediment trap samples were collected and DNA by polymerase chain reaction (PCR)-based approaches. deployed at the offshore station where the Fisheries Although sediment traps are not perfect analogs for surface and Oceans Canada Lakes Research Program has been devel- sediments, analyses of sediment traps allow one to assess the oping a limnological time series since 2009 (Fig. 1a). For each potential coherence between water column and sediment sampling occasion, the limnology of the photic zone and the dynamics. In addition, sediment traps provide information on hypolimnion was monitored following the methods described the pelagic taxa that are deposited in the sediments, which is in Shortreed (2007). When the lake was thermally stratified, one of the key criteria for defining suitable bioindicators in water samples were collected in the epilimnion (from the sur- paleolimnology. We used general eukaryotic primers targeting face to the thermocline depth) and in the metalimnion (from the V7 region of the 18S rRNA gene as previous study by Capo the thermocline depth to the photic zone depth; Fig. 1b). et al. (2016) has demonstrated that these primers amplify Water samples in the photic zone were also collected for the

2 Gauthier et al. Evaluating DNA-based methods in lake sediments

Fig. 1. (a) Location and map of Cultus Lake (modified from Shortreed 2007). The star represents the approximate location of the limnological sampling site and where the sediment traps were deployed. (b) Sediment trap (ST) experimental design with sample types collected each sampling occasions and the number (n) of samples of each type (water epilimnion [epi]; water metalimnion [meta], sediment trap intracellular DNA [ST inDNA] and sediment trap extracellular DNA [ST exDNA]). enumeration and identification of nano- and microplankton. HCl on the day of the deployment, rinsed with lake water, Zooplankton was collected with a vertical haul from 30 m and then sealed and secured until deployment to avoid deep to the surface. The details on the collection, the enumer- contamination. ation, identification and measurements of plankton are reported in Shortreed (2007). To evaluate the congruence with DNA between the water Sediment trap processing samples and the sediment trap samples, 1 L of water was col- Once retrieved from the lake, the accumulation tubes of lected from the photic zone (epilimnion and metalimnion the sediment traps were oriented vertically and left at 4C when thermal stratification occurred; Fig. 1b) on the same day overnight to allow particles to settle. About 120 ml of water at as the limnological monitoring. The water samples were then the surface of the tubes were removed with a sterile syringe frozen at 20C until further laboratory analyses. Within the and the samples were frozen vertically and stored at 20C same week as the limnological sampling, sediment traps were until further processing. Accumulation tubes were thawed deployed, and the traps previously deployed were retrieved. overnight and the total amount of water with sediment parti- Sediment traps were built according to Bloesch and cles were transferred to 50 ml sterile tubes to be centrifuged at Burns (1980) specifications, with a length of 60.96 cm and a 3750 rpm for 10 min at 4C. After centrifugation, the water diameter of 10.16 cm (ratio of length to diameter of ~ 6). Sedi- supernatants were discarded, and the pellets of sediment parti- ment traps were deployed at ~ 3 m above the water–sediment cles were pooled. The sediment pellets were homogenized interface at a depth of ~ 37 m (Fig. 1b). Prior to each deploy- with a sterile spatula (previously soaked in 99% ethanol and ment, the traps and accumulation tubes were scrubbed and flamed) and ~ 0.3 g of wet sediments were subsampled for soaked in 10% bleach for ~ 2 h. The accumulation tubes were DNA extraction. The sediment pellets were weighed before also immersed for ~ 5 min in 10% HCl and rinsed with and after the subsampling for DNA extraction, and then fro- deionized water. The traps themselves were sprayed with 10% zen prior to freeze-drying for subsequent sample processing.

3 Gauthier et al. Evaluating DNA-based methods in lake sediments

Morphological analyses in sediment trap samples DNA extraction from sediment trap samples Microfossil diatom slides were prepared according to the To evaluate the differential preservation of taxa in the sedi- standard methods described in Battarbee et al. (2001). A ments as either extracellular or intracellular DNA, a phosphate known concentration of microspheres (Thermo Scien- buffer (NaP buffer, pH 8.0, 0.1 M) was first used to de-adsorb tific7000 Series Copolymer Microsphere Suspension, 6 μm) the extracellular DNA from the sediment particles (Taberlet were spiked in each sample and counted along the diatom et al. 2012; Alawi et al. 2014). Specifically, 500 ml of NaP valves for quantification. Diatom microfossils were identified buffer was added to ~ 0.3 g of wet sediments, resulting in a and enumerated using a Leica DM4500 B microscope at weight: volume ratio of ~ 1. Samples were mixed by slow rota- 1000 magnification and under differential interference con- tion for 15 min then centrifuged at 10,000 rpm for 10 min trast. The valves were counted in fields of views along parallel (both steps at room temperature). Only one addition of NaP transects until 400 valves were counted. From the raw buffer was used, as the quantity of extracellular DNA was on counts, the density (number of valves gDW 1) and the average ~ 25% of the total sedimentary DNA (Supplementary biovolume (μm3 gDW 1) were calculated. Multiple references Information S3). After centrifugation, the supernatants con- were used for the diatom identification and biovolume esti- taining the extracellular DNA were transferred to a sterile 2-ml mation (Supplementary Information S1). tube and the pellets were kept for the extraction of the intra- Cladocerans subfossils slides were prepared according to cellular DNA fraction. Sediment trap intracellular and extracel- standard procedures from Korhola and Rautio (2001). The lular DNA was extracted using the NucleoSpin Soil kit remains (headshields, carapaces, postabdomens, pos- according to the manufacturer instructions (Macherey-Nagel, tabdominal claws, and antennules) were identified and enu- Düren, Germany). For the extracellular DNA fraction, how- merated using a Leica DM2500 light microscope under ever, the lysis steps of the NucleoSpin Soil kit were skipped to 20–40 magnification. Only the most frequent remain for avoid further degradation of the DNA and to ensure that no each taxon was used as an index of the species abundance. lysis occurred for potentially resuspended cells. DNA concen- A minimum of 70 remains were identified and counted per trations for both water and sediment trap samples were mea- sample using the keys of Witty (2004), Szeroczyñska and sured using a Qubit2.0 Fluorometer (Invitrogen) for a broad Sarmaja-Korjonen (2007), and Korosi and Smol (2012). range of double-stranded DNA following the manufacturer From the raw counts, the densityandthebiomasswerecal- instructions (Qubit ds-DNA BR Assays, Invitrogen). DNA con- culated and expressed in relation to 1 g of dry sediment centrations in the blanks were below the detection limit weight (number of remains gDW 1 or μggDW 1). Multiple (0.1 ng μl 1). DNA samples were visualized on 1% agarose references were used to obtain an average length for cladoc- electrophoresis gel that contained ethidium bromide for DNA eran species identified in the sediment traps as well as the staining and visualization. equation to calculate the biomass (Supplementary Information S1). PCR amplification and sequencing of 18S rRNA gene A fragment of the V7 region of the 18S rRNA gene (~ 260 bp) was PCR amplified from water and sediment trap DNA samples DNA extraction from water samples using the general eukaryotic primers 960F (50-GGCTTAATT To minimize contamination of samples, the initial TGACTCAACRCG-30; Gast et al. 2004 from Capo et al. 2016) processing of sediment trap and water samples (collection of and NSR1438 (50GGGCATCACAGACCTGTTAT-30;VandePeer the sediments from accumulation tubes, filtration of water et al. 2000 from Capo et al. 2016) modified with overhang samples) and the extraction of DNA were performed in a sepa- adapters (~ 20 bases each) to attach the dual indices prior to rate facility from all downstream molecular analyses (PCR sequencing. These primers were identified as good candidates in amplification, library preparation, and DNA sequencing). To terms of coverage of eukaryotic diversity, yet length still suitable evaluate the potential introduction of contaminating DNA for paleogenetics (Capo et al. 2016). PCR was performed using during sample processing, blank water filtrations and blank Phire Hot Start II DNA Polymerase (Thermo Scientific). Each PCR DNA extractions using autoclaved deionized water were per- reaction (total volume of 25 μl) contained 5 μlof5Phirereac- formed along with the samples. Water samples were thawed tion buffer, 0.2 μMdNTPs,0.5μM of each forward and reverse overnight and filtered onto a 3-μm pore size filter. Multiple fil- primers, 1.25 μl of dimethyl sulfoxide (DMSO; 5% final concen- ters were used until ~ 1 L of water was filtered, and the total tration), and 0.5 μl of each DNA sample. The amplification con- water volume filtered was noted. Filters from each sample were ditions included an initial denaturation step at 98Cfor3min, pooled in a 2-ml tube and stored at 80C until DNA extrac- followed by 25 cycles of denaturation at 98Cfor5s,an tion. DNA was extracted using a method based on phenol- annealing step at 58C for 5 s, and an elongation step at 72C chloroform-isoamyl alchohol (PCI) DNA extraction method for 15 s, with a final elongation at 72C for 1 min. For several (details in Supplementary Information S2). Following the water (n = 17) and sediment trap (n = 11) samples, 35 cycles DNA extraction, DNA samples were stored at 20C until fur- were required. All PCR reactions were performed using C1000 ther analyses. Touch Thermal Cycler (Bio-Rad) and all PCR runs included

4 Gauthier et al. Evaluating DNA-based methods in lake sediments negative and positive controls. To assess the performance of the accurate comparisons between sample matrices when consid- PCR amplification, products were visualized on 2% agarose elec- ering the metalimnion samples, we removed any sediment trophoresis gel that contained ethidium bromide. The PCR trap samples that were deployed during the mixed period amplicons were sent to Genome Quebec for barcoding (dual (from Nov/Dec to April/May) from the statistical analyses. We attach indices and sequencing adapters), library preparation, and used the term epilimnion to refer to the mixed part of the paired-end (2 250 bp) sequencing on a MiSeq Illumina instru- water column for simplicity (regardless of whether the lake ment (San Diego, CA). was stratified; see Fig. 1b). To identify environmental gradients associated with different Bioinformatic processing and taxonomy assignment potential bioindicators that could be developed in future stud- The MiSeq reads were trimmed and filtered (no undefined ies, we applied multivariate partial redundancy analysis (partial bases, no sequencing error in primers, removing of primers), RDA). The potential bioindicators were identified in the partial the paired-end reads were merged, and the chimeras removed RDA triplots as taxa with the most distant coordinates from the using the package dada2 (Callahan et al. 2016) in R software centroid along the two main axes of variation, depending (R Core Team 2018, Vienna, Austria). In addition, the on their significance. The community data were Hellinger- sequences with a length >450 bp were removed because of transformed prior to partial RDA, and day of year and year were low sequencing quality. The taxonomy was assigned using the used as covariates to control for temporal trends in the datasets. version 4.10.0 of the Protist Ribosomal Reference database A suite of physico-chemical and biological variables from the (PR2)—SSU rRNA gene database (Guillou et al. 2013) at a mini- photic zone in the epilimnion and the metalimnion as well as mum bootstrap confidence level of 80%. We chose to conduct from the hypolimnion were used in partial RDAs, which our analyses on amplicon sequence variants (ASV) meaning includes different nutrient fractions (μgL 1 or mg L 1), chlo- that each ASV was represented by a unique DNA sequence. rophyll from different phytoplankton size fractions (μgL 1), photic zone depth (m), dissolved oxygen (mg L 1), and Statistical analyses water temperature (C). An extensive list of environmental To assess the extent to which the micro-eukaryotic compo- parameters considered is presented in Supplementary sition identified from sediments with DNA or morphological Information S4. Environmental variables were normalized approaches preserve the biological communities identified (Table S4.1) when possible and standardized prior to partial from the water column, comparisons of assemblages were RDAs. A stepwise selection procedure was applied to select made across sample matrices (epilimnion, metalimnion, sedi- the best predictors of the community composition. The sta- ment traps intracellular, and extracellular DNA). Comparisons tistical significance of the partial RDA models and their RDA were also applied for DNA-based approach between sample axes was tested with 999 permutations on the F-ratio. matrices for both the entire micro-eukaryotic communities The R statistical software v. 3.5.1 (R Foundation for Statisti- and the ASVs from the pool of micro-eukaryotes that were cal Computing, Vienna, Austria) was used to perform all statis- common between the water column and the sediment traps tical analyses. The libraries and functions used for the data (but excluding Crustacea and Bacillariophyta; i.e., the shared analyses is presented in Supplementary Information S5. ASVs). To evaluate the congruence between morphological and DNA identifications, the two approaches were compared Results across sample matrices as well. To make the comparisons, we first performed principal component analyses (PCA) on Contemporary limnology of Cultus Lake Hellinger-transformed assemblage data, which converts the Cultus Lake is an ice-free lake, characterized by a mixed data into relative abundances and then applies a square root water column period (from Nov/Dec to April/May) and a ther- transformation (Legendre and Gallagher 2001). For the data mally stratified period (April/May to Nov/Dec). From end of based on morphological identifications, we used the estimated June 2014 to mid-June 2017, the stratified period was charac- density and biomass of each group, whereas DNA-based iden- terized by deeper light penetration, greater phytoplankton tifications were represented by ASV compositional data. PCAs standing stock (especially in the metalimnion) and higher were applied to each sample matrix and taxonomic approach zooplankton biomass (Table 1). Overall, total phosphorus separately, and the three first axes of the site scores were (TP) and total nitrogen (TN) exhibited higher concentrations extracted. We then applied an RV coefficient to correlate two during the mixed period and lower concentrations in the epi- matrices with corresponding rows (sites). Between two vectors limnion during stratification (Table 1). Crustacean biomass of quantitative data, the RV coefficient corresponds to the calculated from morphological identification was high square of the Pearson correlation, and the RV coefficient is throughout the year, except for the mixed period when nota- thus homologous to an R2 when two matrices are compared ble decreases occurred (from December to March; Fig. 2a). Dia- (Legendre and Legendre 2012). As most of the variation in the tom biomass calculated from morphological identification in PCA was explained by the first three PC axes, we compared the photic zone increased substantially during two periods: only results from the first axis or the first three axes. To have in February and then over the stratified period (Fig. 2b).

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Summary statistics from 18S rRNA gene analyses phyla Opisthokonta (21%), Stramenopiles (14%), and Alveolata A total of 7,463,947 sequences were generated from the (11%). A relatively large fraction of the total ASVs (30%) were water and sediment trap samples and assigned to a total of not assigned to a phylum (unclassified Eukaryota), but the total 6812 ASVs (Table 2). The number of ASVs per sample varied pool of sequences associated with this group was relatively low between 4 to 598 ASVs (once rarefied; Table 2). Overall, the sed- with an average of ~ 6% and ~ 3% in water samples and sedi- iment trap extracellular DNA fraction exhibited slightly higher ment trap samples, respectively. average richness (based on rarefied richness; Table 2) compared The micro-eukaryotic communities differed substantially to other sample matrices. ASVs were mainly assigned to the between the water column (epilimnion and metalimnion) and

Table 1. Averages ( SE) of physical, chemical and biological variables for the mixed (Nov/Dec to April/May) and thermally stratiﬁed periods (April/May to Nov/Dec). (a) Physical and biological variables; (b) epilimnetic averages; (c) metalimnetic averages; (d) hypolimnetic averages. Averages were calculated with data spanning the sediment trap deployment period (from June 2014 to June 2017).

Mixed period Thermally stratiﬁed period Variable Average SE Average SE (a) Physical and biological variables Depth of the photic zone (m) 13.5 (0.7) 17.2 (0.7) Zooplankton biomass (mg m 2) 1446.7 (391.7) 2727.8 (324.3) Epilimnetic temperature (C; 0–5 m average) 6.8 (0.5) 18.0 (1.0) Conductivity corrected at 25C 127.4 (1.2) 161.8 (3.3) (b) Epilimnion (photic zone) Total chlorophyll a (μgL 1) 1.9 (0.2) 2.1 (0.3) Total phosphorus (μgL 1) 8.4 (0.6) 5.4 (0.4) Total nitrogen (μgL 1) 290.3 (10.5) 216.1 (11.3) Dissolved oxygen (mg L 1) 10.9 (0.3) 10.2 (0.2) pH 7.2 (0.1) 8.1 (0.1) Nitrate (μgL 1) 133.0 (4.5) 26.1 (8.0) Dissolved organic nitrogen (μgL 1) 127.3 (10.4) 145.5 (10.2) Ammonia (μgL 1) 3.0 (0.5) 3.1 (0.3) Dissolved inorganic nitrogen (μgL 1) 136.04 (4.4) 29.2 (8.2) Particulate nitrogen (μgL 1) 27.0 (2.4) 41.5 (3.6) Particulate phosphorus (μgL 1) 4.1 (0.2) 2.6 (0.2) (c) Metalimnion (photic zone) Total chlorophyll a (μgL 1) –– 4.1 (0.5) Total phosphorus (μgL 1) –– 7.7 (0.3) Total nitrogen (μgL 1) –– 256.8 (9.7) Dissolved oxygen (mg L 1) –– 11.6 (0.4) pH –– 7.2 (0.1) Nitrate (μgL 1) –– 58.6 (10.1) Dissolved organic nitrogen (μgL 1) –– 127.2 (7.7) Ammonia (μgL 1) –– 3.3 (0.7) Dissolved inorganic nitrogen (μgL 1) –– 62.0 (10.6) Particulate nitrogen (μgL 1) –– 67.7 (7.3) Particulate phosphorus (μgL 1) –– 4.6 (0.2) (d) Hypolimnion (35 m deep) Total chlorophyll a (μgL 1) 1.5 (0.2) 0.7 (0.1) Dissolved oxygen (mg L 1) 10.1 (0.2) 7.3 (0.4) Total phosphorus (μgL 1) 7.5 (0.5) 5.3 (0.3) Temperature (C) 6.1 (0.2) 6.2 (0.2) Nitrate (μgL 1) 132.3 (4.3) 169.9 (2.6) Ammonia (μgL 1) 1.7 (0.4) 1.2 (0.3)

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Fig. 2. Time series from July 2014 to July 2017 demonstrating the biomass calculated from morphological identification of (a) crustaceans from water column (net hauls from 30 m deep; data from 2017 not available) and (b) diatoms from the photic zone. Proportion of sequences identified through 18S rRNA gene sequencing for different phyla of micro-eukaryotes represented as barplots in (c) the epilimnion; (d) the metalimnion; (e) the intracellular DNA fraction in the sediment trap samples; and (f) the extracellular DNA fraction in the sediment trap samples. The shaded zones represent the mixed period, when the thermal stratification was absent in the lake. Some genetic samples are not shown in the panels c to f as the samples were lost in the lake or there was no DNA amplification.

7 Gauthier et al. Evaluating DNA-based methods in lake sediments the sediment trap samples (Figs. 2c–f, 3). Specifically, ASVs first PC axis (Figs. 4a, S6.3a,b). Other dominant species also assigned to the Hacrobia phylum dominated the epilimnetic included Lindavia intermedia, L. ocellata,andL. michiganiana in and metalimnetic time series, except for some summer and fall the water column (Figs. 4a, S61a,b,d,e, S6.3a,b). However, in the months (Figs. 2c,d, 3a). Within the Hacrobia, the most abun- sediment traps, the assemblages were dominated by S. niagarae, dant ASVs in the water column communities belonged to the L. intermedia,andAulacoseira spp.(Figs.4b,S6.1c,f,b,S6.3c). Cryptophyta subphylum (Fig. 3a). In total, five ASVs were Although sequences of pennate diatoms were found in all assigned to the Cryptomonas genus while a sixth was within samples, they were in higher proportion in the sediment trap the Geminigera genus (Fig. 3a). Hacrobia ASVs were generally samples for both morphological and DNA-based approaches not detected in intracellular DNA (except for May 2017) and (Figs. S6.1–S6.4). The taxa found in water samples were mostly detected in low abundance in extracellular DNA in the sedi- araphid pennates whilst the taxafoundinsedimenttrapswere ment trap samples (Fig. 2e,f). botharaphidandraphidpennates(Figs.S6.1–S6.4). In general, most comparisons of diatom assemblages Diatom comparisons across approaches exhibited significant RV coefficients between PCA site scores, Diatom 18S rRNA gene sequences were less abundant in except for some comparisons made with the metalimnion water samples than in sediment trap samples (Table 2). Diatom matrix (Table 3). The strongest correlations were observed sequences represented 16% and 11% of the entire micro- when comparing only the first PCA axis, which is consistent eukaryotic community in sediment trap intracellular and extra- with the relatively large amount of variation explained on PC cellular DNA, respectively (Table 2). Proportion of araphid and axis 1 (Fig. 4). RV coefficients >0.6 were observed in cases raphid pennates sequences were higher in the intracellular where morphological count data were compared across sample DNA than in the extracellular DNA fraction of the sediment matrices, or when the sediment trap intracellular DNA was traps (Fig. S6.2). Sequences of Staurosira spp. were mostly pre- compared to the morphological analyses of the sediment trap sent in intracellular DNA fraction (Fig. S6.2). In general, a samples (Table 3). clearer dynamic signal was apparent in the sediment traps compared to the water column (Figs. 2, 3), based on both morpho- Crustacea comparisons across approaches logical and DNA-based approaches (Figs. 4, S6.1–S6.4). Crustacean 18S rRNA gene sequences were detected in all Across all samples, the diatom communities identified water and sediment trap samples. On average, crustacean rep- with DNA were dominated by two ASVs belonging to the Polar- resented 11% and 4% of all sequences in the epilimnion and centric-Mediophyceae (PCM) family and A. subarctica (belonging metalimnion, respectively (Table 2). Crustacean sequences to radial-centric-basal-Coscinodiscophyceae [RCBC] family; were more abundant in sediment trap intracellular and extra- Figs. 3a, 4c,d, S6.2, S6.4). Another RCBC was also dominant, cellular DNA compared to water samples, representing on mainly for the epilimnion and the sediment trap intracellular average 65% and 55% of the sequences, respectively (Table 2; DNA (Figs. 4c,d, S6.2c, S6.4c). Based on the morphological iden- Fig. 2c,d). The water and sediment trap micro-eukaryotic com- tifications, the biomass of two PCM diatoms, Stephanodiscus munities were mainly dominated by ASVs assigned to crusta- niagarae and Discostella stelligera indicated variations along the ceans, specifically by three Maxillopoda ASVs (Figs. 3a, 4c,d).

Table 2. Total number of sequences and amplicon sequence variants (ASV) for micro-eukaryotic taxa, diatoms, and crustaceans in the photic zone of the water column (epilimnion and metalimnion) and in the sediment traps (ST; intracellular (in) and extracellular (ex) DNA fractions). The percentage of sequences ampliﬁed, unique ASVs and the number of single and doubletons are presented for crustaceans and diatoms.

Total Rarefied Crustaceans Diatoms Total amplified # Seq./ unique richness/sample % seq. Unique Single & % seq. Unique Single & † Site sequences* sample ASV (range) (range) ASV doubletons (range) ASV doubletons Epi. (n = 36) 2,182,177 60,616 2037 220 11% 37 0 8% 97 0 (116–302) (0–56.3) (0.11–53.1) Meta (n = 19) 1,144,096 60,216 1391 238 4% 10 0 8% 47 2 (151–349) (0.05–10.9) (0.15–22.5) STin (n = 34) 2,219,871 65,290 3393 231 65% 110 1 16% 131 2 (66–559) (0.8–98.1) (0.02–83) STex (n = 32) 1,917,803 59,931 3190 244 55% 91 2 11% 119 0 (4–598) (2.7–99.6) (0–60.5) *Number of sequences after filtering, trimming and removing chimeras. † The minimum sample size used to calculate the rarefied richness was 16,032 sequences.

8 Gauthier et al. Evaluating DNA-based methods in lake sediments

The differences in the PCA biplots of crustacean assem- column and sediment trap samples that were both identified blages mainly occurred between taxonomic approaches and using morphological characters (Table 3). The intracellular were related to the dominant species in the crustacean assem- and extracellular DNA fractions in the sediment trap were blages (Figs. 5, S6.5–S6.8). From morphology, most of the modestly correlated (Table 3). Another significant correlation dominant species belonged to the Branchiopoda class was found between the ASVs in the epilimnion and in the sed- (Figs. 5a,b, S6.5, S6.7) while the dominant ASVs from DNA iment trap extracellular DNA, but with a lower RV coefficient taxonomy belonged to the Maxillopoda class (Figs. 3a, 5c,d, (RV = 0.18 on PCA axis 1 site scores; Table 3). S6.6, S6.8). The dominant ASVs in the DNA taxonomy were mainly associated with the stratification period (Figs. 3, S6.6, Structure of shared assemblages based on 18S rRNA gene S6.8). Among the Branchiopoda class, Bosmina longiremis was analyses detected with DNA-based approaches, but Daphnia spp. were A total of 444 ASVs were shared among the epilimnion and not detected even though they were dominant in morphologi- both DNA fractions in the sediment trap samples, cal datasets (Figs. 5a,b, S6.5–S6.8). In the water column, a and 221 were shared among the metalimnion and sediment third dominant species, Diacyclops sp. (a copepod) was trap DNA fractions (Table S6.1). After removing crustacean detected with the morphological approach (Fig. 5a, S6.5a,c, and diatom sequences, the shared ASVs for the epilimnion S6.7a). The morphologically identified sediment trap assem- vs. sediment traps was reduced to 381, and to 206 for the blages were dominated by D. longirostris, D. pulex, and Bosmina metalimnion vs. sediment traps (Table S7.1). The most abun- spp. (Figs. 5b, S6.5b,d, S6.7b), as only cladoceran remains were dant shared ASVs included those assigned to Opisthokonta, well-preserved enough for morphological identification. Alveolata and Stramenopiles, which combined, accounted for Using RV coefficients, we found that the strongest correla- 62% and 58% of the total shared ASVs for the epilimnion tions between sample matrices and taxonomic approaches for and metalimnion, respectively (Tables S7.2, S7.3). The the crustaceans were between (1) ASVs in metalimnion and Opisthokonta phylum was mainly represented by Fungi and crustacean biomass from morphology in the water column Metazoa subphyla (Tables S7.2, S7.3). In the Alveolata phy- (RV = 0.44 on PCA axis 1 site scores), and (2) between water lum, the most abundant ASVs belonged to the Ciliophora

Fig. 3. PCA of the photic zone (epilimnion and metalimnion) and sediment trap (intracellular DNA [ST inDNA] and extracellular DNA [ST exDNA]) samples for the micro-eukaryotes identified through 18S rRNA gene sequencing. Number of sequences per ASVs were Hellinger-transformed prior to ordination. (a) The PCA shows the ASV species scores and identifies the dominant taxa (all scores are color coded by phylum). (b) PCA shows sites scores for epilimnion (light circles), metalimnion (light triangles) and sediment trap samples (dark squares as ST exDNA and dark circles as ST inDNA) and are color- coded to identify the period at the time of sampling: Mixed in blue or thermally stratified in orange. The number in the shapes indicates the sampling month. Taxa abbreviations in panel (a) are as follows: Aulacoseira subarctica (Aul.sub); Cryptomonas sp. (Crypto); Cryptomonas tetrapyrenoidosa (Cry. tetra); Eucyclops serrulatus (Euc.ser); Geminigera cyophyla (Gem.cyo); Maxillopoda (Maxillo); and Polar-centric-Mediophyceae (PCM).

9 Gauthier et al. Evaluating DNA-based methods in lake sediments sub-phylum, but the Dinoflagellata were also well represented The ordination biplots (Fig. S7.1) and triplots (Fig. 6) of the (Tables S7.2, S7.3). For the shared ASVs belonging to the Stra- shared ASVs for all matrices showed a clear separation of com- menopiles, they were mainly assigned into the Ochrophyta munity composition during the mixed and thermal stratifica- subphylum (mainly Chrysophyceae) and other Stramenopiles tion periods (Figs. 6a–c, S7.1a–c). The environmental drivers (Tables S7.2, S7.3). identified in the partial RDAs of shared ASVs communities

Fig. 4. Diatom PCA biplots from different sample matrices and taxonomic approaches: (a) biomass of morphologically identified specimens from water samples (depth weighted averages of epilimnion and metalimnion); (b) biomass of morphologically identified specimens from sediment traps samples; (c)ASVs from 18S rRNA gene analyses from epilimnion and metalimnion; and (d) ASVs from 18S rRNA gene analyses from sediment trap intracellular (ST inDNA) and extracellular DNA (ST exDNA). Biomass and number of sequences were Hellinger-transformed prior to ordination. The blue and orange circles in (a)and(c) represent the epilimnion and metalimnion, respectively; in (b)and(d), they represent the mixed and the stratified periods, respectively. The number in the circles indicates the sampling month. Taxa abbreviations are as follows: Amphora ovalis (Amp. ova); Asterionella formosa (Ast.for); Aulacoseira ambigua (Aul.amb); Aulacoseira subarctica (Aul.sub); Discostella stelligera (D.stelligera); Lindavia michiganiana (L.michiganiana); Lindavia ocellata (L.ocellata) Lindavia intermedia (L.intermedia); Polar-centric-Mediophyceae (PCM); Radial-centric-basal-Coscinodiscophyceae (RCBC); and Stephanodicus niagarae (S.niagarae).

10 Gauthier et al. Evaluating DNA-based methods in lake sediments

Table 3. RV coefﬁcients quantifying the congruence between PCA site scores of different taxonomic and sample matrices for crustacean and diatom ASVs. The signiﬁcant correlations are indicated in bold.

Crustaceans Diatoms RV coefficient RV coefficient RV coefficient RV coefficient of 1st PCA of 3 first PCA of 1st PCA of 3 first PCA axis of sites axes site axis of sites axes site Comparisons Matrix A Matrix B scores scores scores scores Morpho vs. Site scores from species Site scores from 0.07 (0.21) 0.1 (0.63) 0.03 (0.37) 0.36 (< 0.0001*) DNA—water density ASV—DNA epilimnion Site scores from species Site scores from 0.01 (0.64) 0.1 (0.67) 0.37 (0.0001*) 0.26 (0.0003*) (n = 36) biomass ASV—DNA Morpho vs. Site scores from species Site scores from 0.32 (0.07) 0.23 (0.45) 0.5 (0.001*) 0.53 (0.0003*) DNA—water density ASV—DNA metalimnion Site scores from species Site scores from 0.44 (0.03*) 0.29 (0.22) 0.31 (0.01*) 0.21 (0.24) (n = 19) biomass ASV—DNA Morpho vs. Site scores from species Site scores from 0.01 (0.59) 0.11 (0.68) 0.62 (< 0.0001*) 0.27 (0.0005*) inDNA—ST density ASV—inDNA (n = 33) Site scores from species Site scores from 0.01 (0.59) 0.12 (0.56) 0.53 (< 0.0001*) 0.27 (0.001*) biomass ASV—inDNA Morpho vs. Site scores from species Site scores from 0.11 (0.19) 0.26 (0.072) 0.3 (0.001*) 0.23 (0.008*) exDNA—ST density ASV—exDNA (n = 31) Site scores from species Site scores from 0.11 (0.18) 0.27 (0.058) 0.28 (0.002*) 0.2 (0.04*) biomass ASV—exDNA Water Site scores from ASV— Site scores from 0.002 (0.82) 0.05 (0.70) 0.08 (0.12) 0.34 (< 0.0001*) epilimnion water epilimnion ASV—ST DNA vs. ST— inDNA DNA (n = 33) Site scores from ASV— Site scores from 0.18 (0.02*) 0.16 (0.10) 0.13 (0.046*) 0.33 (0.002*) water epilimnion ASV—ST exDNA Water Site scores from ASV— Site scores from 0.002 (0.80) 0.14 (0.67) 0.16 (0.12) 0.31 (0.061) metalimnion water metalimnion ASV—ST DNA vs. ST— inDNA DNA (n = 16) Site scores from ASV— Site scores from 0.14 (0.15) 0.28 (0.16) 0.004 (1) 0.26 (0.18) water metalimnion ASV—ST exDNA Water Site scores from species Site scores from 0.34 (0.007*) 0.32 (0.01*) 0.21 (0.005*) 0.31 (< 0.0001*) epilimnion vs. density—water species ST—Morpho epilimnion density—ST (n = 34) Site scores from species Site scores from 0.43 (0.002*) 0.35 (0.005*) 0.66 (< 0.0001*) 0.26 (0.0006*) biomass—water species epilimnion biomass—ST Water Site scores from species Site scores from ––0.58 (0.0004*) 0.38 (0.002*) metalimnion density—water species vs. ST— metalimnion density—ST Morpho Site scores from species Site scores from ––0.11 (0.16) 0.25 (0.12) (n = 19) biomass—water species metalimnion biomass—ST ST inDNA vs. ST Site score from ASV— Sites score from 0.2 (0.004*) 0.3 (0.004*) 0.36 (0.0004*) 0.42 (< 0.0001*) exDNA ST inDNA ASV—ST (n = 32) exDNA *For the crustaceans when comparing morphology to DNA in the epilimnion or the metalimnion, note that the morphology was performed on the entire water column (30 m deep net haul).

11 Gauthier et al. Evaluating DNA-based methods in lake sediments

Fig. 5. Crustacean PCA biplots from different sample matrices and taxonomic approaches: (a) biomass of morphologically identified specimens from water samples (net hauls from 30 m deep); (b) biomass of morphologically identified specimens from sediment traps samples; (c) ASVs from 18S rRNA gene analyses from epilimnion and metalimnion; and (d) ASVs from 18S rRNA gene analyses from sediment trap intracellular (ST inDNA) and extracellular DNA (ST exDNA). Biomass and number of sequences were Hellinger-transformed prior to ordination. The blue and orange circles represent the mixed and the stratified periods, respectively; except in (c) where they represent the epilimnion and metalimnion, respectively. The number in the circles indicates the sampling month. Taxa abbreviations are as follows: Daphnia longispina (Dap. lon); Daphnia pulex (Dap.pul); Eucyclops serrulatus (Euc.ser); and Maxillopoda (Maxillo).

were water temperature, depth of the photic zone, nutrients selected as signiﬁcant predictors of the communities, but to a and algal production. In all partial RDA analyses of the epilim- lesser extent (Fig. 6a–c). EpiTemp was a signiﬁcant predictor of nion and sediment traps, water temperature (EpiTemp) as well the community composition for the metalimnion dataset as different fractions of nutrients, such as ammonia (NH3), dis- (Fig. 6d), but photic zone depth was more important for both solved inorganic nitrogen (DIN), soluble reactive silicon sediment trap datasets (Fig. 6e,f). Fractions of nutrients and (SRSi), soluble reactive phosphorus (SRP), dissolved organic algal production metrics were also selected in the partial RDAs nitrogen (DON), and particulate nitrogen (PN) were selected for the metalimnion as well as DO, but only for both DNA (Fig. 6a–c). DO and indicators of algal production were also fractions of the sediment trap datasets (Fig. 6d–f). The most

12 Gauthier et al. Evaluating DNA-based methods in lake sediments responsive ASVs belonged to similar groups for both dataset Cryptophyceae were found to be the most responsive across comparisons although the ASVs were not the same (Figs. 6, all ordinations (Figs. 6; S7.1). Dinophyceae were also a respon- S7.1a–c). ASVs assigned to the classes Chrysophyceae and sive component of the shared ASV assemblages (Fig. 6; S7.1).

13 Gauthier et al. Evaluating DNA-based methods in lake sediments

Table 4. RV coefﬁcients quantifying the congruence between PCA site scores of different combinations of DNA sample matrices for the entire micro-eukaryotic communities and for the shared ASVs excluding shared crustacean and diatom ASVs. The signiﬁcant correlations are indicated in bold.

Micro-eukaryotic ASVs Shared micro-eukaryotic ASVs RV coefficient RV coefficient RV coefficient of 3 first PCA RV coefficient of 3 first PCA of 1st axis of axes site of 1st axis of axes site Dataset Matrix A Matrix B sites scores scores sites scores scores Epilimnion— Site scores from ASV— Site scores from ASV— 0.48 (< 0.0001*) 0.48 (< 0.0001*) 0.67 (< 0.0001*) 0.56 (< 0.0001*) STinDNA—STexDNA Epilimnion STinDNA (381 shared ASVs) Site scores from ASV— Site scores from ASV— 0.19 (0.01*) 0.37 (< 0.0001*) 0.38 (0.0004*) 0.49 (< 0.0001*) Epilimnion STexDNA Site scores from ASV— Site scores from ASV— 0.72 (< 0.0001*) 0.54 (< 0.0001*) 0.04 (0.27) 0.55 (< 0.0001*) STinDNA STexDNA Metalimnion— Site scores from ASV— Site scores from ASV— 0.09 (0.3) 0.34 (0.04*) 0.85 (0.0001*) 0.5 (0.0003*) STinDNA—STexDNA Metalimnion STinDNA (206 shared ASVs) Site scores from ASV— Site scores from ASV— 0.03 (0.6) 0.41 (0.009*) 0.48 (0.004) 0.56 (< 0.0001*) Metalimnion STexDNA Site scores from ASV— Site scores from ASV— 0.48 (0.004*) 0.29 (0.08) 0.64 (0.0006*) 0.53 (0.0001*) STinDNA STexDNA

Although well represented in sediment trap intracellular DNA, significant for the shared ASVs when using the dataset Chytridiomycota ASVs appeared to be more responsive to including the epilimnion (Table 4). environmental conditions in the sediment trap extracellular DNA assemblages (Figs. 6c,f, S7.1c,f). Discussion In general, all comparisons between sample matrices for the three first PCA axes site scores were significant, with The development and application of DNA-based approaches higher RV coefficients when comparing only the shared ASVs in paleolimnology is expanding, with an acceleration in publi- rather than the entire micro-eukaryotic communities cation output over the last 5 years (Capo et al. 2021). However, (Table 4). When comparing the first PCA axis site scores, the only a handful of studies have evaluated the congruence correlation between metalimnion and sediment trap DNA between morphology and sedimentary DNA-based approaches fraction matrices were not significant for the entire micro- for taxonomic identification, including several diatom analyses eukaryotic communities but were highly correlated and sig- (Dulias et al. 2017; Huang et al. 2020; Stoof-Leichsenring nificant for the shared ASVs (Table 4). Although the RV coef- et al. 2020). Even fewer have assessed the degree to which the ficient was significant for the first PCA axis site scores assemblages preserved in sediments represent the assemblages between sediment trap intracellular and extracellular DNA identified in the water column using DNA-based approaches from the entire micro-eukaryotic communities, it was not (Capo et al. 2015; Monchamp et al. 2016). Our sediment trap

Fig. 6. RDA triplots with shared ASV dataset from 18S rRNA gene analyses for the combination of matrices of (a) epilimnion, (b) ST intracellular DNA (ST inDNA), (c) ST extracellular DNA (ST exDNA), and for the combination of (d) metalimnion, (e) ST inDNA, and (f) ST exDNA. Crustacean and diatom ASVs were excluded from the shared ASV dataset. Number of sequences per ASVs were Hellinger-transformed and environmental variables were normalized and standardized prior to ordination. The blue and orange circles represent the mixed and the stratiﬁed periods, respectively. The number in the circles indicates the sampling month. Environmental variables are as follows: dissolved inorganic nitrogen (DIN); dissolved oxygen (DO); dissolved organic nitrogen (DON); average water temperature from 0 to 5 m deep (EpiTemp); hypolimnetic total chlorophyll (HypoTotalChl); hypolimnetic total phosphorus (HypoTP); chlorophyll from phytoplankton > 20 μm (MicroChl); depth of the euphotic zone (PhoticZoneDepth); chlorophyll from phytoplankton > 2 μm (PhyChl); chlorophyll from phytoplankton ≤ 2 μm (PicoChl); particulate nitrogen (PN); ammonia (NH3); soluble reactive phosphorus (SRP); soluble reactive silicon (SRSi); total dissolved phosphorus (TDP); total chlorophyll (TotalChl). Taxa abbreviations are as follows: Aspidisca (Aspi); Botryococcus braunii (Botry.braunii); Centroheliozoa (Centro); Chaetonotus sp. (Chaeto); Chrysophyceae (Chryso); Chytridiomycota (Chyrtridio); Cryptophyceae (crypto); Cryptomonas tetrapyrenoidosa (Cry. tetra); Cyclotrichium sp. (Cyclo.); Desmodesmus communis (Desmo. comm.); Dinophyceae (Dino); Goniomonas sp. (Gonio); Gyrodinium sp. (Gyro.); Hypotrichia (Hypo); Eukaryota unclassiﬁed (Euk); Micronuclearia podoventralis (Micro. podo); Ochrophyta (Ochro); Ochromonas sphaerocystis (Ochro. sphae); Opisthokonta (Opistho); Psorospermium haeckeli (Psoro. hae); Pythiaceae (Pythia); Rhogostoma (Rhogo); Rhyzophidiales (Rhyzo.); Scuticociliatia (Scuti); Sphaeropleales (Sphae); Strombidiida (Strom); Tintinnopsis (Tintin); Telonemia (Telo); Tubulinea (Tubu); Vorticellidae (Vorti).

14 Gauthier et al. Evaluating DNA-based methods in lake sediments study spanning 36 months partly fills this gap and expands value and associated removal efficiency by herbivorous grazers our knowledge of the strengths and the weaknesses of using (Brett and Müller-Navarra 1997). Overall, higher richness values DNA-based approaches in paleolimnology. Overall, our study were detected with the genetic approach as well as in sediment indicates that sedimentary DNA-based approaches can be trap vs. water samples. insightful, but care must be taken in drawing conclusions about water column dynamics, and in considering which groups of Comparisons of diatom assemblages taxa are targeted. Many significant correlations were detected for diatoms across sample types and taxonomic approaches. From mor- General lessons learned in comparing taxonomic phological counts, winter samples had high biomass of approaches and sample types S. niagarae and Aulacoseira spp. (Fig. S6.1a–d), which are two Clearer dynamics were apparent in the sediment traps com- heavily silicified species. In the absence of mixing, both taxa pared to the water sample time series (Fig. 2c–f), which may be tend to sink more rapidly than other diatoms because of their explained by the spatio-temporal integration (~ 1 month) of large individual or colony size, and thus might preserve better sediments compared to the single point sampling of the water in sediments (Stockner and Lund 1970; Stoermer et al. 1985; column. Rarefied richness per sample was similar across all sam- Horn et al. 2011). In contrast, summer species might degrade ple matrices based on 18S rRNA gene sequences, but the faster in the water column once dead as higher temperatures observed range was higher in sediment trap than in water sam- and deeper light penetration increase bacterial activity during ples (Table 2). In the water column, the species present in the this period, and they also have to cross the physical barrier of samples collected were most likely alive at the moment of the the thermal stratification to settle in the sediments. collection. The filter pore size (3 μm) used for water filtration Overall, we found DNA-based and morphological and the DNA extraction method were selected to mainly collect approaches were generally significantly correlated for diatoms. living or intact cells. On the other hand, DNA from sediment Diatoms are clearly a suitable group for further comparison trap samples was extracted from bulk sediment samples, which between morphological and genetic approaches, and this can include degraded DNA, all taxa deposited in the sediments approach has been successfully applied across several lake sedi- and taxa living in the surface sediment layer. Consequently, ment records (Dulias et al. 2017; Huang et al. 2020; Stoof- the rarefied richness per sample and its range across samples Leichsenring et al. 2020). Furthermore, comparative taxonomic could be more stable for the water samples than for the sedi- approaches could be informative when there are gaps in the ment trap samples. One drawback of the genetic approach was curated database for freshwater diatoms (Rimet et al. 2018). the more limited taxonomic resolution, usually to family or Based on the distribution of taxa we detected through time, we genus, compared to morphology. We chose to use general infer that the unidentified PCM6 ASV (Fig. 5d; associated with primers to evaluate which micro-eukaryotic taxa can be depos- the mixed period) is likely S. niagarae that we identified under ited in the sediments, but more specific primers targeting key the microscope (Fig. 5b). Likewise, L. intermedia,showedsimilar groups, such as diatoms and crustaceans could be used in future dynamics to the ASV PCM3 (Fig. 5b,d). studies. Major differences in the taxonomic composition from DNA Comparisons of crustacean assemblages datasets were also found between water and sediment trap sam- The crustacean-specific results generally yielded modest to ples. The most notable difference was the dominance of weak correlations among sample matrices using 18S rRNA gene. Hacrobia sequences in the water samples compared to the domi- The strongest correlations associated with the 18S rRNA gene nance of Opisthokonta and Stramenopile (including diatoms) analyses were with sediment trap extracellular DNA, which sug- sequences in sediment traps. Although Hacrobia were detected gests that this fraction could be more effectively used to recon- in some sediment trap samples (mainly with extracellular DNA; struct past epilimnetic crustacean dynamics. Crustaceans go Fig. 2f), their proportion was low throughout the sampling time through multiple molts as they increase size (Sastri and series. The Hacrobia sequences in the sediment traps were Roff 2000), which could potentially lead to abundant amount mainly dominated by Cryptophyta taxa. Interestingly, Capo of extracellular DNA accumulating in the sediments. In total, et al. (2015) reported similar results, where Cryptophyta were we identified nine Branchiopoda and over 120 taxa within the underrepresented in recently deposited sediments compared to Maxillopoda families. Seventeen ASVs were assigned to specific the water column. This underrepresentation of Cryptophyta in copepod taxa (Cyclops spp., Macrocyclops spp.), and potentially sediments as well as the slightly higher detection in extracellular many more copepod taxa were present given numerous DNA compared to intracellular DNA (Fig. 2e,f) are likely because Maxillopoda detections. In contrast, cladocerans were poorly they are soft-bodied algae (Hoef-Emden and Archibald 2017); represented with DNA-based approaches and may be due to the this trait potentially leading to greater cell lysis and extracellular relatively long small-subunit rRNA gene in some arthropods. DNA degradation via DNAases. In addition, Cryptophyta cells For example, the 18S rRNA gene in Daphnia pulex has a total might not be as efficiently transported to sediments compared length of 2293 nucleotides, with particularly long hypervariable to other primary producers because of their high nutritional V4 and V7 regions (Crease and Colbourne 1998). Daphnids

15 Gauthier et al. Evaluating DNA-based methods in lake sediments

(and most likely other cladoceran taxa) are likely underrepre- many of the cysts are currently unknown (Smol 2008). As such, sented in our datasets as short amplicons will be preferentially DNA analysis of chrysophyte taxa could enrich our understand- sequenced over long amplicons. We recommend using primers ing of this group. Chrysophyceae DNA has already been that amplify a different region of the 18S rRNA gene or a differ- detected in high proportions in several sediment archives ent gene altogether if the goal of the study is to target crusta- (Capo et al. 2016, 2017, 2019). In our partial RDA triplots, two ceans (Andújar et al. 2018). Nonetheless, the detection of species of chrysophytes (Chryso26 and Chryso28) were usu- – copepods with 18S rRNA gene is very interesting as this is a ally associated with high concentrations of NH3 (Fig. 6a c). group that does not produce subfossils that preserve well in Optimal environmental conditions of Chrysophyceae species sediments (Korhola and Rautio 2001). Our study echoes the are usually well-defined(ZeebandSmol2001;Kristiansen finding by Coolen et al. (2013) who used DNA-based and Škaloud 2017), and thus, DNA-based approaches could approaches to detect different copepod taxa in a sediment core enhance the classical paleolimnological analyses of chryso- from the Black Sea. phytes (Zeeb and Smol 2001). With the growing interest in aquatic infectious diseases Potential bioindicator taxa based on shared ASV analyses since the 1970s (Dudgeon et al. 2006; Reid et al. 2018), OuranalysesofsharedASVsidentified new potential bio- chytrids species could be powerful bioindicators of specific indicators suitable for tracking pelagic ecological dynamics in lake aquatic host–parasite dynamics in paleolimnology. According sediments. Our shared ASVs analyses, which were deposited in to our partial RDA analyses, specific taxa of chytrids were asso- the sediment traps from the pelagic environment, and which ciated with particular months of the year, which could be have the potential to track environmental changes, fulfill two key related to the timing of host presence in the lake (Fig. 6c,f). criteria for defining useful bioindicators in paleolimnology. In par- Chytrids have also previously been detected in a sediment ticular, we identified Ciliophora, Dinoflagellata, Chytridiomycota, core archives from European lakes (Capo et al. 2016, 2017). Chrysophyceae and Cryptophyceae as groups that were both pre- Finally, the results from the shared ASVs analyses indicated sent in the water column and sediment trap DNA samples and that cryptophytes assigned to Cryptomonas spp. were domi- that showed associations with several physico-chemical and nant in all sample matrices, even though cryptophyte biological variables (Fig. 6). The potential to use each of these sequences were generally less abundant in sediment traps rela- groups as bioindicators in paleolimnology will be informed going tive to the water column. Therefore, the most abundant and forward by synthesizing knowledge of their ecological niches as dominant species of cryptophytes could be used as bio- well as ensuring that their DNA is preserved in sediment archives indicator species to track changes in lake ecological dynamics over longer timescale. through time. For instance, the most abundant cryptophyte According to our partial RDA analyses, several ciliates could taxa (Crypto4 and Crypto7) from the water column were be suitable bioindicators for dissolved inorganic nitrogen deposited in sediment traps and identified as potential bio- (DIN) level as we found a group of ciliate taxa associated with indicators according to our partial RDA analyses (Fig. 6a–e). high concentration of DIN (i.e., Strombidiida, Cyclotrichium Cryptomonads are known to be low-light specialists and can sp., Hypotrichia, Scuticociliatia, Tintinnopsis sp., Aspidisca sp., thus be important primary producers under periods of low and Vorticellidae; Fig. 6a–c,e). Ciliates have also previously light penetration (e.g., winter, which was characterized by been detected as a dominant group in sediment core archives high turbidity in Cultus Lake). More broadly, it is clear that from a few lakes (Capo et al. 2016, 2017, 2019). Likewise, DNA-based approaches are useful for identifying the presence ciliophora species abundance, diversity and composition have of different cryptophyte taxa as distinguishing species from previously been used to indicate and evaluate aquatic ecosys- the same genus based on morphology can be laborious (Hoef- tem quality (reviewed in Lynn 2017). Emden and Archibald 2017). However, the soft-bodied cells of Our partial RDA analyses also showed that the dinoflagel- cryptophytes make them fragile to cell disruption (Hoef- late communities were separated along gradients of nutrients, Emden and Archibald 2017), and thus, metabarcoding of sedi- fi such as DIN, NH3, and TDP (Fig. 6b,d,e). These ndings echo ments might only detect a limited diversity of what can be earlier reports that dinoflagellate community composition found in the water column. Using the same set of primers, ear- varies across nutrient, pH, grazing intensity, and vegetation lier study by Capo et al. (2015, 2016, 2019) have detected gradients (Saldarriaga and Taylor 2017). Interestingly, DNA- cryptophyte DNA sequences in lake sediment archives from based approaches have already been used to detect dinoflagel- France, Sweden and Greenland, which suggest that the poten- lates in paleoceanographic studies (Amacher et al. 2009; Boere tial to detect this group with molecular approaches is fairly et al. 2011; Coolen et al. 2013). widespread. Chrysophyceae taxa are established bioindicators in paleo- Although some information on the niches of potential bio- limnology as they can be microscopically identified by their indicator groups are mentioned above, substantial work is still scales or resting cysts (Smol 2008). However, only ~ 15% of the needed to identify more precisely their environmental range chrysophytes species (including Chrysophyceae and Syn- and optima. In addition, these potential bioindicator groups urophyceae) have siliceous scales and the species producing could be used in a multi-proxy paleolimnological study, which

16 Gauthier et al. Evaluating DNA-based methods in lake sediments is now common as the use of several indicators helps to recon- Nonetheless, several crustacean taxa were clearly abundant in struct more fully the past ecological and environmental condi- DNA sequences, suggesting that DNA-based approaches could tions of lakes (Saulnier-Talbot 2016). Using a multi-proxy be used to track copepod dynamics, and improved upon approach, several molecular studies have detected substantial further with more specific crustacean primers. Given that temporal changes in taxonomic groups related to climate or copepods also play essential ecological roles in lakes and are environmental responses (Ahmed et al. 2018; Monchamp currently not accounted for in traditional morphological- et al. 2018; Keck et al. 2020). With further studies, it will be based approaches, this is an area of substantial interest. Addi- necessary to clearly identify the degree to which potential bio- tionally, comparative analyses are needed to better understand indicator group are preserved in sediments. Lakes that have whether DNA-based approaches can track cladocerans in sedi- long-term water column records would serve as ideal sites to ments. Finally, the shared ASV analyses identified potential investigate further these new potential bioindicators. novel bioindicators that could be incorporated in future paleolimnological studies. The relationships we identified Efficiency of intracellular and extracellular DNA between environmental variables and potential DNA-based Generally, the ecological patterns inferred from intracellu- bioindicator taxa represent a critical first step. DNA burial and lar and extracellular DNA were similar for the entire preservation over time was not addressed in our study, micro-eukaryotic community as well as for the diatom and and thus subsequent work is needed to define which taxo- crustacean assemblages, specifically. The dominant ASVs that nomic groups are adequately archived and preserved as DNA exhibited the most change over time were the same for both in older sediments. Overall, our sediment trap study spanning intracellular and extracellular DNA, which led to high congru- 36-months enhances our knowledge of using DNA-based ence between the two different DNA fractions. For diatoms, approaches in paleolimnology and provides an essential foun- intracellular DNA seemed to be more suitable to identify taxa dation for future study. from sediment samples, as higher correlations with both morphological and DNA datasets from the water column were observed (compared to extracellular DNA). 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