DOI: 10.5061/dryad.jwstqjqn1
title: Data from: Comparative analysis of environmental DNA metabarcoding and spectro-fluorescence for phytoplankton community assessments

description: Quantifications of phytoplankton biomass and species composition are
 crucial for monitoring biodiversity and population dynamics in aquatic
 environments, and both direct microscopic counts and fluorescence-based
 methods have been widely used for monitoring. Recent advancements in DNA
 metabarcoding offer an alternative way of easily assessing diversity and
 species composition. However, a comprehensive comparison of the relative
 merits and limitations of DNA- and fluorescence-based methods is currently
 lacking. Here, we compare phytoplankton community composition measured via
 fluorescence and DNA metabarcoding in an outdoor, replicated mesocosm
 experiment. We show that there is a positive correlation between
 fluorescence-measured biomass and DNA read and amplicon sequence variants
 (ASV) numbers for cyanobacteria, but either weak or no correlation for the
 other phytoplankton groups assessed (cryptophytes, chromophytes, green
 algae). In addition, DNA metabarcoding was systematically better at
 detecting cryptophytes, which were rarely detected via fluorescence.
 Hence, while DNA metabarcoding may not provide reliable biomass estimates
 for the majority of phytoplankton groups, metabarcoding analysis offers
 higher taxonomic resolution and the capability to detect rare
 phytoplankton groups. Overall, our findings provide new insights into the
 strengths and limitations of each method and highlight the considerable
 potential and importance of including DNA barcoding in freshwater
 ecosystem assessment and biomonitoring programmes with a focus on
 biodiversity assessments.
description: <strong>Experimental setup</strong>
 To compare DNA metabarcoding and a traditional fluorescence
 method (ALA), we performed a biomonitoring study of the phytoplankton
 community composition and biomass in an outdoor, large-scale, replicated
 experiment. The experiment was conducted from April
 7<sup>th</sup> to October 20<sup>th,</sup> 2020,
 in a setup consisting of 24 cylindrical polyethylene mesocosms (diameter =
 0.7m, height = 1m) situated at Lund University (55.1° N, 13.2° E), in
 southern Sweden. The sides and base of each mesocosm were insulated, and
 each mesocosm was filled with 400L unfiltered lake water, i.e., contained
 organisms naturally occurring in the nearby eutrophic Lake Ringsjön (55.9°
 N, 13.5° E). Lake sediment was also added to the mesocosms to facilitate
 the natural succession, recruitment of resting stages and help stabilise
 water chemistry. The sediment, also collected from Lake Ringsjön, was
 drained and homogenised before 500g was placed into a plastic tray
 (14x14x10 cm) and placed at the bottom of each mesocosm.
 Filtered deionized water was added to compensate for evaporation
 losses, and the walls of the mesocosms were scrubbed to minimise growth of
 periphytic algae. In addition, 0.5 mL of commercially available plant
 nutrients (Blomstra växtnäring, Cederroth, Upplands Väsby, Sweden;
 Nitrogen: Phosphorus = 100:13) was added to each mesocosm every two weeks.
 The experiment consisted of four treatments with six replicate mesocosms
 per combination (randomly assigned): control ambient conditions mimicking
 the present climate state in a temperate shallow lake; temperature warming
 via heatwaves; multispecies invasion; and combined warming and invasion.
 For details about the treatment and experimental system, see Salis et al.
 (2023) and Hansson et al. (2013). However, we observed no significant
 effects of temperature warming or invasion treatments on the absolute and
 relative biomass concentrations (ALA) and normalised read counts (DNA
 metabarcoding) of the four phytoplankton groups (see Statistical Analysis
 section below and Supporting Information Table S1). Therefore, this paper
 focuses on the comparisons between the ALA and DNA methods.
 <strong>Sample collection</strong>
 Water samples were collected from each mesocosm for spectral
 fluorometry analysis and environmental DNA metabarcoding analysis on 5
 sampling dates in 2020: July 28<sup>th</sup>, August
 11<sup>th</sup>, August 25<sup>th</sup>, September
 22<sup>nd,</sup> and October 20<sup>th</sup>. On
 each sampling date, 24 samples were taken to be analysed by each of the
 two sampling methods. For ALA analysis, a 100 mL water
 sample was taken from each mesocosm. The samples were immediately taken to
 the lab, where phytoplankton group-specific biomass was estimated using a
 spectral fluorometer (Algae Lab Analyser; bbe Moldaenke GmbH, Germany).
 The Algae Lab Analyser (ALA) measures <em>in vivo</em>
 chlorophyll <em>a</em> fluorescence to infer the relative and
 absolute biomass of major phytoplankton groups. The instrument detects
 fluorescence excited by five high-intensity light-emitting diodes (LEDs)
 with specific wavelengths (λ = 450, 525, 570, 590, and 610 nm), enabling
 the discrimination of four groups: green algae, chromophytes (including
 diatoms, chrysophytes, and dinoflagellates), cryptophytes, and
 cyanobacteria. Group-level discrimination is based on the linear
 independence of their norm spectra, as described in Beutler et al. (2002).
 The device provides quantitative estimates of total and group-specific
 chlorophyll <em>a</em> concentrations (µg/L) in the range of
 0–500 µg/L with a resolution of 0.01 µg/L and a lower detection limit of
 0.05 μg/L. For each of the 24 mesocosms, 25 mL water samples were measured
 in triplicate using a glass cuvette (25 mm × 25 mm × 70 mm). Measurements
 were performed at room temperature, and the mean of the three technical
 replicates was used for further analysis. For eDNA
 analysis, water was filtered through two consecutive filters: a 1.2 μm
 cellulose nitrate filter (Whatman), followed by a 0.22 μm Sterivex filter
 (Millipore) until the Sterivex filter was clogged (10-3000 mL). Both
 filters were then stored at -20°C. Three negative controls (filtered
 deionized water) were also taken on each sampling date. Genomic DNA was
 extracted using the Qiagen Blood and Tissue Kit (see other paper for more
 details) under sterile lab conditions (UV hood) and amplified using
 standard 18S (chromophytes, cryptophytes, and green algae) and 16S
 (cyanobacteria) markers. Amplification and sequencing were performed at
 The Integrated Microbiome Resource (IMR), Canada (Comeau et al. 2017). A
 single round of PCR was done using "fusion primers" (Illumina
 adaptors + indices + specific regions). For chromophytes, cryptophyte, and
 green algae, the E572F (5’-CYGCGGTAATTCCAGCTC-3’) and E1009R
 (5’-AYGGTATCTRATCRTCTTYG-3’) primer pair was used, while for
 cyanobacteria, the B989F (5’-ACGCGHNRAACCTTACC-3’) and BA1406R
 (5’-ACGGGCRGTGWGTRCAA-3’) primer pair was used (Comeau et al. 2011). DNA
 samples were amplified in duplicate using 1:1 and 1:10 template dilutions
 in 20 µL reactions containing: 2 or 0.2 μL of DNA, 4 μL of each 1 μM
 primer, 0.4 μL of 40 mM deoxyribonucleotide triphosphates (dNTPs), 4 µl of
 5X Phusion Plus Buffer, and 0.2 μL of Phusion Plus DNA polymerase (Thermo
 Scientific™). PCR negative controls (RNase/DNase-free water) were included
 to check for contamination. PCR conditions were initial denaturation at
 98°C for 30 s, followed by 25 cycles of denaturation at 98°C for 10 s,
 annealing at 55°C for 30 s, and 72°C for 30 s, with a final elongation
 step of 72°C for 4 min 30 s. Duplicate PCRs were pooled and checked
 visually on a high-throughput Hamilton Nimbus Select robot using Coastal
 Genomics Analytical Gels. PCR negative controls were verified to be free
 from any visible bands. The PCR reactions (including 4 PCR negative
 controls) were then cleaned up and normalized using the high-throughput
 Just-a-Plate 96-well Normalization Kit (Charm Biotech) and sequenced with
 10% PhiX on an Illumina MiSeq V3-600 2x300 bp (IMR, Canada; Comeau and
 Kwawukume 2023). <strong>Sequence
 Analysis</strong> Sequence processing was
 performed using R 4.0.3 (R Core Team 2023). The DADA2 pipeline (Callahan
 et al. 2016) was used to infer amplicon sequence variants (ASVs) based on
 error models to correct sequencing errors while accounting for abundance
 and sequence similarity (Callahan et al. 2017). The resulting ASVs are
 biologically meaningful sequence variants comparable to haplotypes
 (Callahan et al. 2017; Elbrecht et al. 2018; Porter and Hajibabaei 2020).
 For 18S, taxonomy was assigned using the Protist Ribosomal Reference
 database (PR<sup>2</sup>) version 4.13.0 (Guillou et al.
 2013). For 16S, taxonomy was assigned using the Silva database version
 138.1 (McLaren and Callahan 2021). After bioinformatic processing,
 1,387,818 18S and 1,625,749 16S reads were retained, assigned to 12,013
 and 22,143 ASVs, respectively. No reads were retained in the PCR negative
 control samples. The data were then combined into a phyloseq object using
 phyloseq version 1.44.0 (McMurdie and Holmes 2013) for subsequent
 processing. Sequence counts were normalised using the
 <em>varianceStabilizingTransformation</em> function in the
 DESeq2 R package version 1.30.1 (Love et al. 2014) to account for
 differences in sample sequencing depth (McMurdie and Holmes 2014). In
 order to compare the two methods, ASVs belonging to the four phytoplankton
 groups were then extracted. For cyanobacteria, this meant all ASVs from
 the phylum Cyanobacteria. For green algae, this included ASVs from the
 division Chlorophyta, and the classes Mesostigmatophyceae, Zygnemophyceae,
 and Coleochaetophyceae. For the chromophytes, this included ASVs of the
 classes Bacillariophyta, Dinoflagellata, and Chrysophyceae. Finally, for
 the cryptophytes, this included ASVs assigned to the division Cryptophyta
 as well as Rhodophyta. Diversity measures were calculated using the R
 package vegan version 2.6.4. <strong>Statistical
 Analysis</strong> All statistical analyses were
 performed using R 4.0.3 (R Core Team 202,3) and plots were created using
 the ggplot2 package (v3.4.2) and ggeffects (v1.3.1). Generalised linear
 mixed effect models (GLMMs) were performed using glmmTMB (v1.1.10). The
 performance package (v0.13.0) was used to calculate marginal and
 conditional R<sup>2</sup> values as well as test for
 singularity, while model diagnostics were performed using the package
 DHARMa version 0.4.7. Distributions were selected based on the nature of
 the response variable (Supporting Information Fig. S1). Negative binomial
 link = “log”) was used for overdispersed count data (normalised read
 counts and number of ASVs). Beta regression (link = “logit”) was used for
 proportion data bounded between 0 and 1 (relative number of reads,
 relative number of ASVs, relative ALA biomass). Lognormal was used for the
 absolute ALA biomass concentrations (continuous, positively skewed).
 First, GLMMs were used to test the effects of the temperature and invasion
 treatments on the absolute and relative ALA biomass concentrations, and
 eDNA normalised read counts and number of ASVs, of the four phytoplankton
 groups separately. Temperature treatment, invasion treatment, and their
 interaction were included as fixed factors, while mesocosm ID and sampling
 date were included as random factors in the model. Where the inclusion of
 random effects resulted in singular models (i.e, near-zero variance
 estimates), the structure was simplified accordingly. Model significance
 was assessed using Type III Wald chi-square tests via the Anova() function
 in the car package (v1.1-3). No significant effects of temperature or
 invasion treatments were observed on the absolute and relative ALA biomass
 concentrations, the relative number of reads and ASVs for the four
 phytoplankton groups, nor on the normalised read counts or number of ASVs
 of chromophytes, cyanobacteria, or green algae (Supporting Information
 Table S1). However, the normalised read counts and number of ASVs of
 cryptophytes showed a significant relationship with invasion treatment,
 consistent with previous findings in the same system (Salis et al. 2023).
 Due to the general lack of treatment effects, temperature and invasion
 treatments were not included in the comparisons between the ALA and eDNA
 methods. GLMMs were then used to compare the absolute values (eDNA read
 counts vs. log-transformed ALA biomass, and number of ASVs vs.
 log-transformed ALA biomass) and relative abundances (relative read
 abundance vs. proportion of total chlorophyll <em>a</em> and
 relative number of ASVs vs. proportion of total chlorophyll
 <em>a</em>).  These were modelled both across all
 phytoplankton groups (including phytoplankton group as a random factor)
 and separately for cryptophytes, chromophytes, cyanobacteria, and green
 algae. For the absolute models, the predictor ALA biomass was
 log-transformed biomass (concentration) to reflect its multiplicative
 structure and to improve model fit. Mesocosm and sampling date were
 included as random factors in all models, unless singularity required
 their exclusion. Only samples where the algal groups were detected by both
 methods were included, and model significance was assessed using Type II
 Wald chi-square tests.
description: # Data from: Comparative analysis of environmental DNA metabarcoding and
 spectro-fluorescence for phytoplankton community assessments Dataset DOI:
 [10.5061/dryad.jwstqjqn1](10.5061/dryad.jwstqjqn1) ## Description of the
 data and file structure Datasets and R code for the manuscript titled
 "Comparative analysis of environmental DNA metabarcoding and
 spectro-fluorescence for phytoplankton community assessments" by
 Romana Salis and Lars-Anders Hansson. DOI: 10.1002/edn3.70097 We conducted
 a comparative analysis of phytoplankton community composition using
 fluorescence-based measurements and DNA metabarcoding in a controlled
 mesocosm experiment. ### Files and variables #### File:
 mapping_eDNA1_16S.csv **and** mapping_eDNA1_18S.csv
 **Description:** sample data for pre-processing of the 16S and 18S
 sequence data. ##### Variables * Sample date: Date the sample was taken *
 sampling week: Week of the experiment the sample was taken * treatment:
 Experimental treatment (heated and/or invasion) * Heated: Y/N if the
 mesocosm was exposed to the heat treatment * Invasion: Y/N if the mesocosm
 was exposed to the invasion treatment * Mesocosm: Identity of the mesocosm
 the sample was taken from * Sample: Sample identity (sampling week and
 mesocosm number) * rep: replicate (1-6) #### File:
 sample_data_eDNA1_16S_noNEG_vs.csv and
 sample_data_eDNA1_18S_pr2_mesocosms_vs.csv **Description:** sample data
 for the analyses of the 16S and 18S data sets, with samples as rows. #####
 Variables * X: Sample ID * Sample.date: Date the sample was taken *
 sampling.week: Week of the experiment the sample was taken *
 treatment: Experimental treatment (heated and/or invasion) * Heated: Y/N
 if the mesocosm was exposed to the heat treatment * Invasion: Y/N if the
 mesocosm was exposed to the invasion treatment * Mesocosm: Identity of the
 mesocosm the sample was taken from * Sample: Sample ID * rep: Replicate
 1-6 * sampling.occasion: Sampling occassion 1-5 #### File:
 tax_table_eDNA1_18S_pr2_mesocosms_vs.csv **Description:** Taxonomy table
 for the 18S dataset, with the Taxonomy (columns) for each ASV (rows) #####
 Variables * ASV: ASV identity / number * Columns: Kingdom, Supergroup,
 Division, Class, Order, Family, Genus, Species #### File:
 tax_table_eDNA1_16S_noNEG_vs.csv **Description:**  ##### Variables *
 ASV: ASV identity / number * Columns: Kingdom, Phylum, Class, Order,
 Family, Genus, Species #### File: asv_table_eDNA1_16S_noNEG_vs.csv
 **Description:**  ASV table for the 16S dataset, with normalised counts of
 ASVs (rows) in the samples (columns). ##### Variables * ASV: ASV identity
 / number * Columns: Samples wk0-m10 - wk22-m9 #### File:
 asv_table_eDNA1_18S_pr2_mesocosms_vs.csv **Description:** ASV table for
 the 18S dataset, with normalised counts of ASVs (rows) in the samples
 (columns). ##### Variables * ASV: ASV identity / number * Columns: Samples
 E-wk0-m10 - E-wk22-m9 #### File: temp_weeks_39.csv **Description:** Weekly
 average temperature measurement data from the mesocosms. ##### Variables *
 sampling.week: Week of the experiment the measurement was taken *
 AveTempC: Average temperature in the non-heated mesocosms *
 AveTempH: Average temperature in the heated mesocosms *
 Ave_tempdiff: Average temperature difference between heated and non-heated
 mesocosms * DegreeWeeks: Number of degree weeks * Ave_tempdiff_R: Average
 temperature difference between heated and non-heated mesocosms * M2 - M27:
 Temperatures in the mesocosms (degree C) #### File: ALA1_averages.csv
 **Description:** Algal Lab Analyser data (averages of
 technical triplicates). ##### Variables * Sample.date: Date the sample was
 taken * sampling.week: Week of the experiment the measurement was taken *
 treatment: Experimental treatment (heated and/or invasion) * Heated: Y/N
 if the mesocosm was exposed to the heat treatment * Invasion: Y/N if the
 mesocosm was exposed to the invasion treatment * Mesocosm: Identity of the
 mesocosm the sample was taken from * rep: Replicate (1-6) * A_TotalAlgae:
 Total algal chlorophyll a concentration (μg/L) * A_Green: Green algae
 concentration (μg/L) * A_Cyanobacteria: Cyanobacteria concentration (μg/L)
 * A_Chromophytes: Chromophyte concentration (μg/L) * A_Cryptophytes:
 Cryptophyte concentration (μg/L) ## Code/software #### R code for
 pre-processing of sequence data: sequence_processing_16S.R and
 sequence_processing_18S.R Sequence processing was performed using R 4.0.3.
 The DADA2 pipeline (v 1.22.0) was used to infer amplicon sequence variants
 (ASVs) based on error models to correct sequencing errors while accounting
 for abundance and sequence similarity. For 18S, taxonomy was assigned
 using the Protist Ribosomal Reference database (PR2 ) version 4.13.0. For
 16S, taxonomy was assigned using the Silva database version 138.1. The
 data was then combined into a phyloseq object using phyloseq version
 1.44.0 for subsequent processing. Sequence counts were normalized using
 the varianceStabilizingTransformation function in the DESeq2 R package
 version 1.30.1 to account for differences in sample sequencing depth. ####
 R code for data analyses: data_analysis_ALAmethods_eDNA.R All data and
 statistical analyses were performed using R 4.0.3. First, in order to
 compare the two methods, ASVs belonging to the four phytoplankton groups
 were extracted. For cyanobacteria, this meant all 16S ASVs from the phylum
 Cyanobacteria. For green algae, this included ASVs from the division
 Chlorophyta, and the classes Mesostigmatophyceae, Zygnemophyceae, and
 Coleochaetophyceae. For the chromophytes, this included ASVs of the
 classes Bacillariophyta, Dinoflagellata and Chrysophyceae. Finally, for
 the cryptophytes, this included ASVs assigned to the division Cryptophyta
 as well as Rhodophyta. Diversity measures were calculated using the R
 package vegan version 2.6.4. Plots were created using the ggplot2 package
 (v3.4.2) and ggeffects (v1.3.1). Generalized linear mixed effect models
 (GLMMs) were performed using glmmTMB (v1.1.10). The performance package
 (v0.13.0) was used to calculate marginal and conditional R 2 values as
 well as test for singularity, while model diagnostics were performed using
 the package DHARMa version 0.4.7. Model significance was assessed using
 the Anova() function in the car package (v1.1-3). ## Access information
 Other publicly accessible locations of the data: *
 [https://github.com/rksalis/metabarcoding-vs-spectrofluorescence](https://github.com/rksalis/metabarcoding-vs-spectrofluorescence/tree/main)