1 DNA metabarcoding reveals modern and past eukaryotic communities in a 1 2 2 high-mountain peat bog system 3 3 Garcés-Pastor, Sandra a,b; Wangensteen, Owen S. c,d; Pérez-Haase, Aaron a,e; 4 5 4 Pèlachs, Albert f; Pérez-Obiol, Ramon g; Cañellas-Boltà, Núria h; Mariani, Stefano 6 7 5 c; Vegas-Vilarrúbia, Teresa a. 8 9 10 6 11 12 7 a Department of Evolutionary Biology, Ecology and Environmental Sciences, Universitat de 13 14 8 Barcelona, Barcelona, Spain 15 16 9 b Current address: Tromsø Museum, UiT The Arctic University of Norway, Tromsø, Norway. 17 18 19 10 c Ecosystems and Environment Research Centre, School of Environment and Life Sciences, 20 21 11 University of Salford, Greater Manchester, UK 22 23 12 d Current address: Norwegian College of Fishery Science, UiT The Arctic University of Norway, 24 25 13 Tromsø, Norway 26 27 14 Center for Advanced Studies of Blanes, Spanish Research Council (CEAB-CSIC), Blanes, Spain 28 29 15 f Department of Geography, Universitat Autònoma de Barcelona, Spain 30 31 32 16 g Botany Unit, Department of Animal Biology, Plant Biology and Ecology, Universitat Autònoma 33 34 17 de Barcelona, Spain 35 36 18 h Institute of Earth Sciences Jaume Almera (ICTJA-CSIC), Barcelona, Spain 37 38 19 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

20 Abstract 1 2 3 21 Peat bogs located in high mountains are suitable places to study local 4 5 22 environmental responses to climate variability. These ecosystems host a large 6 7 8 23 number of eukaryotes with diverse taxonomic and functional diversity. We carried 9 10 24 out a metabarcoding study using universal 18S and COI markers to explore the 11 12 13 25 composition of past and present eukaryotic communities of a Pyrenean peat bog 14 15 26 ecosystem. We assessed the molecular biodiversity of four different moss micro- 16 17 18 27 habitats along a flood gradient in the lentic Bassa Nera system (Central 19 20 28 Pyrenees). Five samples collected from different sediment depths at the same 21 22 29 study site were also analysed, to test the suitability of these universal markers for 23 24 25 30 studying paleoecological communities recovered from ancient DNA and to 26 27 31 compare the detected DNA sequences to those obtained from the modern 28 29 30 32 community. We also compared the information provided by the sedimentary DNA 31 32 33 to the reconstruction from environmental proxies such as pollen and macro- 33 34 35 34 remains from the same record. We successfully amplified ancient DNA with both 36 37 35 universal markers from all sediment samples, including the deepest one (~10,000 38 39 36 years old). Most of the metabarcoding reads obtained from sediment samples, 40 41 42 37 however, were assigned to living edaphic organisms and only a small fraction of 43 44 38 those reads was considered to be derived from paleoecological communities. 45 46 47 39 Inferences from ancient sedimentary DNA were complementary to the 48 49 40 reconstruction based on pollen and macro-remains, and the combined records 50 51 52 41 reveal more detailed information. This molecular study yielded promising findings 53 54 42 regarding the diversity of modern eukaryotic peat bog communities. Nevertheless, 55 56 57 43 even though information about past communities could be retrieved from 58 59 44 sediment samples, preferential amplification of DNA from living communities is a 60 61 62 63 64 65

45 caveat for the use of universal metabarcoding markers in paleoecology. 1 2 3 46 4 5 6 47 7 8 9 48 keywords: Sedimentary DNA, Community DNA, Peat bog paleoecology, 10 11 12 49 Eukaryotes, Pyrenees 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

50 Introduction 1 2 3 51 4 5 6 52 Depositional systems located in areas with low anthropogenic impact, such as 7 8 9 53 mountain peat bogs, are invaluable paleoenvironmental archives that enable 10 11 54 study of local environmental processes and responses to climate variability (Smol 12 13 55 et al. 2001). Communities living in these ecosystems can be considered sentinels 14 15 16 56 of past and current climate shifts. The study of the historical changes in their 17 18 57 biodiversity is crucial for understanding the dynamics of ongoing ecological 19 20 21 58 processes driven by climate forcings (Mann 2002). Previous paleoecological 22 23 59 studies on peatland communities traditionally used morphological remains of 24 25 26 60 living taxonomic groups and fossil material, such as vascular plants, mosses, 27 28 61 microalgae, chironomids and pollen (Charman 2002; Godwin; 1981; Smol et al. 29 30 62 2001). These studies, however, provide a limited sense of the total biodiversity, 31 32 33 63 depending on the availability of taxonomic expertise (Parducci et al. 2015). 34 35 36 64 Molecular methods that use high-throughput sequencing, such as 37 38 65 metabarcoding (Taberlet et al. 2012), are a comprehensive, objective and 39 40 41 66 efficient approach to molecular biodiversity assessment, which can often 42 43 67 outperform morphological surveys (Epp et al. 2012). The results of 44 45 46 68 metabarcoding analyses are critically dependent on the choice of metabarcoding 47 48 69 marker and, specifically, on the universality or specificity of the primer set 49 50 51 70 (Wangensteen and Turon 2017). Most applications of metabarcoding in 52 53 71 paleoecological studies have focused on past vegetation, using chloroplast 54 55 56 72 genetic markers such as trnL or rbcL in lake sediments (Domaizon et al. 2017; 57 58 73 Anderson-Carpenter 2011; Jørgensen et al. 2012; Pedersen et al. 2013). 59 60 74 Although universal (broad taxonomic range) eukaryotic markers have been 61 62 63 64 65

75 applied successfully to study the community DNA of modern environments such 1 2 76 as soils or marine benthos (Young et al. 2014; Guardiola et al. 2016; 3 4 5 77 Wangensteen et al. 2018a,b), they have been applied only rarely to study peat 6 7 78 bogs. Recently, Singer et al. (2016) studied the diversity of living Oomycetes in 8 9 10 79 peat bogs using the nuclear 18S rRNA marker. This marker has also been used 11 12 80 to study free-living soil Cercozoa (Harder et al. 2016) and microbial eukaryotic 13 14 81 communities in lakes (Capo et al. 2015, 2016, 2017). The present study focused 15 16 17 82 on a broad spectrum of eukaryotic diversity, and we used a multi-gene approach 18 19 83 that included the 18S marker and the mitochondrial cytochrome c oxidase subunit 20 21 22 84 I (COI), which to our knowledge had never been employed in peatland systems 23 24 85 before. 25 26 27 86 The DNA extracted from sediment samples is a combination of modern 28 29 30 87 DNA from living communities and ancient DNA from the remains of long-dead 31 32 88 organisms (Bellemain et al. 2013; Epp et al. 2012; Lejzerowicz et al. 2013b; 33 34 35 89 Pawlowski and Holzmann 2014). In general, sediment DNA studies rely on the 36 37 90 assumption that the age of the recovered DNA is the same as the age of the 38 39 91 sediments in which it is found, in the absence of contamination. This is generally 40 41 42 92 the case when using specific primers that selectively amplify the DNA from 43 44 93 remains of organisms such as vascular plants or photosynthetic microalgae that 45 46 47 94 do not currently live in the deep strata of the sediment. Universal primers, used 48 49 95 commonly to amplify the 18S rRNA gene and COI are able to detect a wide range 50 51 52 96 of microbial taxa, many of which may belong to living communities (Guardiola et 53 54 97 al. 2015, 2016). Moreover, considering the age of the sediment and DNA 55 56 57 98 degradation rates, the concentration of DNA from living organisms present in 58 59 99 sediment samples is expected to overwhelm that of ancient DNA by several 60 61 62 63 64 65

100 orders of magnitude and should be considered when interpreting results from 1 2 101 universal primers (Capo et al. 2015; Torti et al. 2015). 3 4 5 102 This study is a molecular exploration of the eukaryotic diversity present in 6 7 8 103 moss communities and sediment samples from mountain peat bogs. We selected 9 10 104 Bassa Nera, a wetland system in the Central Pyrenees (Pérez-Haase and Ninot 11 12 13 105 2006; 2017) as a case study. The locality was the subject of several previous 14 15 106 taxonomic studies of modern and past plant communities (Pérez-Haase and 16 17 18 107 Ninot 2006; Cañellas-Boltà et al. 2009; Cambra 2015; Garcés-Pastor et al. 2016, 19 20 108 2017), which enabled comparisons between molecular results and those of 21 22 109 morphological approaches. 23 24 25 26 110 27 28 29 111 Study site 30 31 32 112 33 34 35 113 Bassa Nera is a lentic system located in the peripheral zone of Aigüestortes i 36 37 114 Estany de Sant Maurici National Park at an altitude of 1891 m a.s.l (Fig. 1). 38 39 40 115 Previous paleoenvironmental studies reconstructed the development of the 41 42 116 modern peat bog from a previous lacustrine environment over the Holocene 43 44 45 117 (Garcés-Pastor et al. 2016, 2017). The vegetation of Bassa Nera catchment 46 47 118 forms a complex mosaic that ranges from a moderate-depth water body (zmax = 48 49 50 119 5 m) with flat shores surrounded by Sphagnum carpets, to Cyperaceae fens and 51 52 120 Sphagnum bogs, and subalpine forest of Pinus uncinata and Abies alba on 53 54 121 steeper slopes (Carrillo et al. 2008; Pérez-Haase and Ninot 2017). The main 55 56 57 122 habitats are geogenous fens (Scheuchzerio palustris-Caricetea fuscae) and 58 59 123 ombrogenous bogs (Oxycocco palustris-Sphagnetea magellanici) (Pérez-Haase 60 61 62 63 64 65

124 et al. 2010). Climate is subalpine with Atlantic influence and mean annual 1 2 125 precipitation (1152 mm) is well distributed across the seasons (Ninyerola et al. 3 4 5 126 2003). Mean annual temperature is 4.25 ºC, January being the coldest month (- 6 7 127 3 ºC on average) and July the warmest (14 ºC on average). 8 9 10 128 11 12 13 129 Materials and methods 14 15 16 17 130 18 19 20 131 We used an Illumina MiSeq high-throughput sequencer to analyse two 21 22 132 metabarcoding markers, 18S and COI, on two sets of samples: (1) modern 23 24 25 133 community samples from several peat bog microenvironments, used to establish 26 27 134 occurrence and abundance baselines for a wide array of eukaryotic taxa, needed 28 29 135 to characterize the extant diversity of high mountain peat bogs and to monitor 30 31 32 136 future changes in these communities, and (2) sediment samples, used to test the 33 34 137 suitability of 18S and COI universal markers to evaluate the past diversity of 35 36 37 138 several eukaryotic groups, accomplished by comparing the results from ancient 38 39 139 DNA to paleoenvironmental reconstructions based on morphological remains. 40 41 42 140 43 44 45 141 Field sampling and DNA extraction 46 47 48 49 142 50 51 52 143 Four different microhabitats were sampled along a water flooding gradient in 53 54 144 August 2016 to characterize the modern communities (Fig. 1). Three replicates 55 56 57 145 of 100 mL of the dominant mosses from each microhabitat were obtained and 58 59 146 stored in 96% ethanol. The sampled sites lie next to the mire monitoring plots 60 61 62 63 64 65

147 used by Pérez-Haase and Ninot (2006) to measure water table depth, so that 1 2 148 average moisture conditions, groundwater pH and electrical conductivity are 3 4 5 149 known for these sites (Pérez-Haase and Ninot 2017). The studied microhabitats 6 7 150 were: A) Hummock (Carici fuscae-Sphagnetum magellanici Bick 1985) B) Carpet 8 9 10 151 (Sphagno fallacis-Caricetum lasiocarpae Steffen ex Passarge 1964), C) Fen 11 12 152 (Tofieldio calyculatae-Scirpetum cespitosi Ballesteros, Baulies, Canalís et 13 14 153 Sebastià ex Rivas-Martínez et Costa 1998), and D) Floating mire of Sphagnum 15 16 17 154 and Drosera longifolia on the Equisetum fluviatile lake shore belt (Equisetetum 18 19 155 limosi Steffen 1931) (Pérez-Haase et al. 2010). 20 21 22 156 Sediment samples were obtained from core BSN-6 (270 cm long), 23 24 25 157 recovered from a hummock of Sphagnum magellanicum and S. capillifolium in 26 27 158 the littoral of Bassa Nera in 2011 (Pèlachs et al. 2016; Garcés-Pastor et al. 2017). 28 29 30 159 The core was processed and sampled following strict precautions to prevent 31 32 160 contamination in the Palaeoecology Laboratory of the Universitat Autònoma de 33 34 35 161 Barcelona. The external surface of the core was discarded following usual 36 37 162 paleoecological practices. Then the core was sliced and subsampled with 38 39 163 sterilized knives for different variables (Pèlachs et al. 2016). The samples were 40 41 42 164 stored individually in double plastic bags at -20 ºC to prevent external 43 44 165 contamination until DNA extraction. 45 46 47 166 The age-depth model was constructed with seven Accelerator Mass 48 49 50 167 Spectrometry radiocarbon dates, obtained from peat and macro-remains. The 51 52 168 270-cm core spans the last ~10,210 cal years, with an average confidence 53 54 -1 55 169 interval error of ca. 220 yr and a mean sedimentation rate of 0.07 ± 0.21 cm yr , 56 57 170 ranging from 0.016 to 0.86 cm yr-1. The age-depth model provide a robust 58 59 60 171 chronology for the interpretation of the molecular history (Garcés-Pastor et al. 61 62 63 64 65

172 2017). Five sediment samples were studied from the following depths: 31; 109; 1 2 173 160; 220 and 265 cm, which correspond to 140, 3795, 6165, 8339 and 10,094 3 4 5 174 cal yr BP, respectively (Electronic Supplementary Material [ESM] Table S1). 6 7 8 175 DNA extraction was performed at the Department of Evolutionary Biology, 9 10 176 Ecology and Environmental Sciences at the University of Barcelona. All 11 12 13 177 extraction procedures were carried out under a laminar-flow cabinet in a 14 15 178 dedicated pre-PCR laboratory, following strict precautions. All the equipment was 16 17 18 179 cleaned with 10% sodium hypochlorite solution and rinsed in deionised Milli-Q 19 20 180 water between samples. To avoid carryover contamination, the ancient sediment 21 22 181 samples were processed before the modern samples. Three different extraction 23 24 25 182 replicates were obtained from each sediment sample. Samples were 26 27 183 homogenized using a 600 W hand blender. A fraction of 0.3 g of each 28 29 30 184 homogenized sample was extracted using a Norgen Soil DNA Isolation Plus Kit 31 32 185 (www.norgenbiotek.com). An extraction blank consisting in 300 µL of molecular 33 34 35 186 biology-grade water was included in the batch, processed and sequenced along 36 37 187 with the rest of the samples. DNA concentrations of the purified DNA extracts 38 39 188 were estimated with 1 µL of the final elution, using a high-sensitivity dsDNA assay 40 41 42 189 in a Qubit fluorometer (www.thermofisher.com). 43 44 45 190 46 47 48 191 PCR, sequencing and bioinformatics pipelines 49 50 51 192 52 53 54 193 Two metabarcoding markers were used to identify a wide taxonomic range of 55 56 57 194 detected eukaryotic taxa. The V7 region of nuclear-encoded ribosomal 18S rRNA 58 59 195 gene was amplified using the 18S_allshorts primers (100–110 bp, 5’- 60 61 62 63 64 65

196 TTTGTCTGSTTAATTSCG-3’ and 5’-TCACAGACCTGTTATTGC-3’) (Guardiola 1 2 197 et al. 2015), which are expected to provide information for all eukaryotic groups. 3 4 5 198 The V7 fragment amplified by these primers is about 150 bp shorter than the one 6 7 199 targeted by Capo et al. (260 bp, 2016, 2017) and other 18S rRNA regions used 8 9 10 200 in other studies: V9 (180 bp, Singer et al. 2016) and V4 (350 bp, Capo et al. 2015). 11 12 201 This is convenient for paleoenvironmental studies in which DNA may be 13 14 202 fragmented. This primer set has been used successfully to assess eukaryotic 15 16 17 203 diversity of marine sediments (Guardiola et al. 2015, 2016) and shallow marine 18 19 204 hard-bottom communities (Wangensteen et al. 2018a,b). The Leray-XT primer 20 21 22 205 set, a novel degenerated primer set amplifying a 313 bp fragment of the 23 24 206 mitochondrial marker COI (miCOIintF-XT 5'- 25 26 27 207 GGWACWRGWTGRACWITITAYCCYCC-3'; Wangensteen et al. 2018b; and 28 29 208 jgHCO2198 5'-TAIACYTCIGGRTGICCRAARAAYCA-3'; Geller et al. 2013) was 30 31 209 also used. This marker features nearly full amplification coverage for almost all 32 33 34 210 main eukaryotic lineages with the remarkable exceptions of Viridiplantae and 35 36 211 Ciliophora (Wangensteen et al. 2018b). The conditions for PCR amplifications, 37 38 39 212 library preparation and sequencing are described in ESM File S1. 40 41 42 213 The bioinformatic analyses were based on the OBITools software suite 43 44 214 (Boyer et al. 2016) and followed similar pipelines used for the same markers in 45 46 47 215 previous works (Guardiola et al. 2016; Wangensteen et al. 2018a, 2018b, 48 49 216 Siegenthaler et al. 2019). Results of the Leray-XT primer set applied to unfiltered 50 51 52 217 environmental samples are known to include some bacterial sequences arising 53 54 218 from unspecific amplifications. Since our study is specifically focused on 55 56 57 219 eukaryotic diversity, the bioinformatics pipeline for COI included additional steps 58 59 220 for removing these bacterial sequences. All bioinformatics steps are described in 60 61 62 63 64 65

221 detail in ESM File S1. 1 2 3 222 4 5 6 223 Removal of edaphic organisms 7 8 9 224 10 11 12 225 Sediment DNA (i.e. DNA extracted from sediment samples) is a mixture of DNA 13 14 15 226 from long-dead organisms and from living organisms that are known to dwell in 16 17 227 soils/sediments (Fungi, Cercozoa, non-photosynthetic Chrysophyta, Oomycetes, 18 19 20 228 Ciliophora, Nematoda, Annelida, Platyhelminthes and Rotifera; Fierer et al. 2003; 21 22 229 Andersen et al. 2013; Asemaninejad et al. 2017). Representatives of these 23 24 25 230 groups may also live in surface bog habitats. Thus, detection of these phyla in 26 27 231 sediment samples should be interpreted with caution. High abundances of DNA 28 29 232 sequences from these taxa, compared to non-edaphic taxa, in the sediment 30 31 32 233 samples, could be interpreted as the result of amplification of DNA from living 33 34 234 organisms in deep soil communities, rather than the prevalence of these taxa in 35 36 37 235 ancient surface communities. To avoid this problem, when comparing ancient 38 39 236 and living communities, these groups were removed from our analyses so that 40 41 42 237 only those groups typical of surface peat bog environments, i.e. Bacillariophyta, 43 44 238 Arachnida, Insecta, Crustacea, Tracheophyta, Bryophyta, etc., were kept, 45 46 239 enabling more reliable reconstructions of past surface communities. 47 48 49 50 240 51 52 53 241 Statistical analyses 54 55 56 242 57 58 59 243 To compare modern and past communities, we applied the Jaccard dissimilarity 60 61 62 63 64 65

244 index of presence/absence. Nonlinear-MDS ordinations were performed with the 1 2 245 R package vegan (Oksanen et al. 2018). The significance of dissimilarities 3 4 5 246 between modern and ancient communities was assessed using the function 6 7 247 anosim in the same package. The function rarecurve in vegan was used to plot 8 9 10 248 rarefaction curves for every sample to check saturation in MOTU (Molecular 11 12 249 Operational Taxonomic Unit) richness. 13 14 15 250 Given that the decay rate of ancient DNA may differ among different 16 17 18 251 taxonomic groups (Zhu et al. 2005), the relative abundance of reads from ancient 19 20 252 taxa shows high levels of uncertainty, so that only presence/absence data were 21 22 253 used to compare ancient and living communities. For Viridiplantae, only results 23 24 25 254 from the 18S marker were used to compare modern and ancient plant 26 27 255 communities, whereas Arthropoda were compared using the COI marker. Given 28 29 30 256 the low amount of DNA reads from ancient communities, compared to the total 31 32 257 number of reads in sediment samples, a threshold of 1 in 10,000 total reads (after 33 34 35 258 the removal of singletons) was used as evidence of presence. 36 37 38 259 39 40 41 260 Paleoenvironmental data 42 43 44 261 45 46 47 262 To compare the information provided by sedimentary DNA with the 48 49 50 263 palaeoenvironmental reconstruction based on morphological methods, we used 51 52 264 the available palynological and macroremain data from the palaeoecological 53 54 265 study of Garcés-Pastor et al. (2017). Pollen and macroremain analyses were 55 56 57 266 performed according to standard procedures (Moore et al. 1991; Mauquoy et al. 58 59 267 2010). Details on pollen and macroremain methods can be found in Garcés- 60 61 62 63 64 65

268 Pastor et al. (2017). 1 2 3 269 4 5 6 270 Results 7 8 9 271 10 11 12 272 DNA yield and sequencing depth 13 14 15 16 273 17 18 19 274 The DNA concentrations recovered from the sediment samples were in the range 20 21 275 from 0.077 to 14.9 ng/µl (ESM Table S1), lower than the DNA extracted from 22 23 24 276 modern samples (4.9 to 31.2 ng/µl). Replicates extracted from the sample at 220 25 26 277 cm depth (8339 cal yr BP) yielded only 0.077 ± 0.008 ng/µl (average ± SD) of 27 28 278 DNA. They were, however, included in our analyses because PCR amplifications 29 30 31 279 were successful. Results from this sample should nevertheless be interpreted 32 33 280 with caution. 34 35 36 281 After removal of bacterial sequences and singletons, a total of 3,566,813 37 38 39 282 DNA sequences (DNA reads) composed the final dataset for the 18S marker. Of 40 41 283 those, 2,165,734 reads (60.7%) belonged to modern communities (mean of 42 43 44 284 180,478 reads per sample) and 1,401,079 reads (39.3%) belonged to the 45 46 285 sediment samples (mean of 93,405 reads per sample). For COI, the final dataset 47 48 49 286 included 1,762,447 reads, with 1,140,928 reads from modern communities (mean 50 51 287 of 95,077 reads per sample) and 621,519 reads from sediment samples (mean 52 53 288 of 41,435 reads per sample). Rarefaction curves per sample (ESM File S2) 54 55 56 289 showed that this sequencing depth approached saturation in the number of 57 58 290 MOTUs detected for both markers in all samples, except for sediment samples 59 60 61 62 63 64 65

291 from 220 cm depth, probably because of the low number of reads obtained from 1 2 292 the low DNA recovered at this depth. 3 4 5 293 6 7 8 9 294 Modern community structure inferred from 18S and COI markers 10 11 12 295 13 14 15 296 The relative abundance of DNA reads from 18S and COI analyses showed 16 17 297 substantial differences among habitat types and between primers (Fig. 2). The 18 19 20 298 percentage of DNA reads that could not be assigned to a Phylum or lower rank 21 22 299 (unassigned Eukarya, Metazoa and Stramenopiles) was higher for COI (20.5%) 23 24 25 300 than for 18S (0.8%). As expected, 18S yielded high abundances of reads from 26 27 301 Bryophyta (39.0%), Tracheophyta (15.3%), and Arthropoda (23.0%). Conversely, 28 29 302 our COI primer set, which is unable to amplify most Viridiplantae, showed a 30 31 32 303 remarkable dominance of Arthropoda (57.9%). Occasionally, one replicate from 33 34 304 a community yielded more DNA reads of a specific Phylum than the other 35 36 37 305 replicates. This was the case for Platyhelminthes in Carpet-1 and Fen-2, and 38 39 306 Tracheophyta for Floating-3. These differences were mostly a consequence of 40 41 42 307 high abundance of reads from a particular MOTU in those samples, probably 43 44 308 related to the presence of a single large individual in that replicate (see tables in 45 46 309 Mendeley Data; DOI: 10.17632/j358x9sjjd for abundance of individual MOTUs). 47 48 49 50 310 The relative MOTU richness of the different groups is represented in Fig. 51 52 311 2c and 2d for 18S and COI markers, respectively. A higher dominance of MOTUs 53 54 312 with small body size is shown, compared to the barplots of DNA read abundance. 55 56 57 313 A relatively homogenous pattern of relative MOTU richness among the different 58 59 60 61 62 63 64 65

314 modern communities is shown. 1 2 3 315 4 5 6 316 Modern versus ancient samples 7 8 9 317 10 11 12 318 Removal of edaphic organisms highlights the similarities between modern and 13 14 15 319 sediment samples for both markers (Fig. 3). The 18S marker returned high values 16 17 320 for relative MOTU richness of Tracheophyta in the sediment samples. On the 18 19 20 321 other hand, COI detected high numbers of MOTUs from Arthropoda, Rhodophyta 21 22 322 and Bacillariophyta. 23 24 25 323 Non-metric multidimensional scaling ordination for the non-edaphic 26 27 28 324 communities of modern and sediment samples using Jaccard dissimilarities (Fig. 29 30 325 4) highlighted the significant differences between ancient and modern 31 32 -4 33 326 communities for 18S (ANOSIM R=0.98, p-value < 10 , N=27) and COI markers 34 35 327 (ANOSIM R=0.97, p-value <10-4, N=27). 36 37 38 328 39 40 41 329 Plant communities 42 43 44 330 45 46 47 48 331 The 18S relative read abundance and relative MOTU richness of plant 49 50 332 communities (Viridiplantae) are shown in Fig. 5. The amount of reads assigned 51 52 333 to plants was lower in sediment samples (4.5% of total reads, including edaphic 53 54 55 334 taxa) compared to modern samples (69.8%), and it decreased with depth. 56 57 58 335 Distinct patterns of community structure can be distinguished in modern 59 60 61 62 63 64 65

336 communities. Hummock and Carpet are dominated by Sphagnopsida, whereas 1 2 337 Fen and Floating have a higher proportion of Bryopsida. ESM Table S2 shows 3 4 5 338 the 20 most abundant MOTUs for each modern community. Sphagnum 6 7 339 dominated Hummock and Carpet, whereas the most abundant MOTU in Fen and 8 9 10 340 Floating communities was a sequence assigned to Bryopsida (ID = 0.99). The 11 12 341 hummock community also contained some Magnoliopsida (Sanguisorba and 13 14 342 Parnassia) and Liliopsida (Poaceae). The carpet community has a higher 15 16 17 343 proportion of Liliopsida (Cyperoideae and Poaceae), whereas Magnoliopsida are 18 19 344 represented by Asterales and Filipendula. The fen community is mainly 20 21 22 345 composed by Bryopsida, with some Magnoliopsida (Utricularia) and Liliopsida 23 24 346 (Poaceae). Finally, the floating community has higher amounts of Bryopsida and 25 26 27 347 Droseraceae, followed by Sphagnum, Cyperoideae and Utricularia. 28 29 30 348 The sediment samples showed higher abundances of Liliopsida and 31 32 349 Magnoliopsida, with some Pinopsida and Zygnematophyceae, whereas 33 34 35 350 Sphagnopsida were surprisingly almost absent (Fig. 5). ESM Table S3 shows the 36 37 351 rank of the 20 most abundant MOTUs for sediment samples after removing the 38 39 352 edaphic taxa. All samples reflect a relatively high number of Tracheophyta. A shift 40 41 42 353 from Pooideae to Cyperoideae can be observed over time, with Pooideae being 43 44 354 more abundant in the oldest samples (265-220 cm) and Cyperoideae dominating 45 46 47 355 samples from 160 to 31 cm. Sample 31 has 14.3% Viridiplantae DNA sequences, 48 49 356 dominated by Cyperoideae, some Mesangiospermae (Magnoliopsida) and 50 51 52 357 Bryopsida. Sample 109 (2.6% Viridiplantae) is also dominated by Cyperoideae, 53 54 358 Pooideae and Mesangiospermae. Sample 160 (1.8% Viridiplantae) has high 55 56 57 359 prevalence of Cyperoideae and Mesangiospermae, with some Pinidae and 58 59 360 Cupressaceae. Sample 220 (11.9% Viridiplantae) is dominated by Pooideae and 60 61 62 63 64 65

361 Magnoliopsida (rosids and asterids), with some Pinidae and Bryophyta. Sample 1 2 362 265 (0.07% Viridiplantae) is still dominated by Pooideae with some Pinidae and 3 4 5 363 a remarkable abundance of Desmidiales (Zygnematophyceae). ESM Fig. S1 6 7 364 shows the non-metric multidimensional scaling ordination for Viridiplantae (18S 8 9 10 365 marker) in modern and sediment samples using Jaccard dissimilarities. 11 12 366 Significant differences were found among modern and sediment communities 13 14 -4, N=27 15 367 (ANOSIM R=0.92, p-value < 10 ). 16 17 18 368 19 20 21 369 Arthropod communities 22 23 24 370 25 26 27 371 The Arthropoda communities in modern and sediment samples differed 28 29 30 372 appreciably (Fig. 6). The modern samples present high abundance of mites 31 32 373 (mainly Oribatida and Trombidiformes), whereas the sediment samples display 33 34 35 374 large inter-sample variability. For instance, sample 265 yielded a larger 36 37 375 proportion of Opiliones and Copepoda, whereas sample 220 showed proportions 38 39 376 more similar to modern samples. On the other hand, sample 160 featured high 40 41 42 377 abundances of an unassigned arthropod sequence. Finally, samples 31 and 109 43 44 378 have outstanding proportions of aquatic crustaceans (Copepoda and 45 46 47 379 Branchiopoda). Some orders, such as Ostracoda, only appeared in the modern 48 49 380 samples. ESM Table S4 shows the rank of the 20 most abundant MOTUs for COI 50 51 52 381 in modern samples. Many of the most abundant MOTUs are Arthropoda, 53 54 382 esspecially mites: Oribatida, Sarcoptiformes and Trombidiformes, basal 55 56 383 Hexapoda (Collembola), Insecta (Diptera, Coleoptera), Maxillopoda (Cyclopoida, 57 58 59 384 Harpacticoida) and Ostracoda. In some cases, the taxonomy could be assigned 60 61 62 63 64 65

385 to the species level. 1 2 3 386 ESM Table S5 shows the rank of the 20 most abundant MOTUs for COI in 4 5 387 ancient samples, without the edaphic taxa. Contrary to modern samples, most 6 7 8 388 MOTUs could be identified only to the levels of kingdom to order. From the 9 10 389 Arthropoda that could be identified, most were Branchiopoda, Maxillopoda and 11 12 13 390 Arachnida. There is a community shift from Arachnida, Insecta and Collembolla 14 15 391 (220, 265) in deeper samples, to Branchiopoda and Maxillopoda in the more 16 17 18 392 recent samples (31, 109, 160). ESM Figure S2 shows the non-metric 19 20 393 multidimensional scaling ordination for the Arthropoda (COI) in modern and 21 22 394 sediment samples using Jaccard dissimilarities. Significant differences were 23 24 25 395 found among modern and sediment communities for Arthropoda (ANOSIM 26 27 396 R=0.89, p-value <10-4, N=27). 28 29 30 397 31 32 33 34 398 Comparing sedimentary DNA to pollen and macroremains 35 36 37 399 38 39 40 400 The presence/absence patterns of 18S Viridiplantae sequences enabled us to 41 42 401 make comparisons with environmental reconstructions from pollen and 43 44 45 402 macroremains (Garcés-Pastor et al. 2017). Conifer and Bryophyta DNA 46 47 403 sequences were detected from all sediment samples (Fig. 7). Dicotyledon and 48 49 50 404 monocotyledon richness was higher in sample 220. On the other hand, aquatic 51 52 405 green algae were richer in sample 31. 53 54 55 406 Sample 31 presented high proportions of Cyperoideae and Bryopsida 56 57 58 407 DNA with a MOTU assigned to Mesangiospermae (Magnoliopsida) that also 59 60 408 appeared in samples 109 and 160 (BOG2_000000149). It also presented some 61 62 63 64 65

409 Pooideae, Saxifragales, Petrosaviidae and Pinidae and traces of Vaccinium sp. 1 2 410 (ESM Table S3). The macroremains presented low proportions of Sphagnum, 3 4 5 411 with Ericaceae, Polytrichum and Equisetum. Pinus, Ericaceae and Poaceae 6 7 412 pollen frequencies were well represented, while Apiaceae had its highest values. 8 9 10 413 Sample 109 also presented high proportions of Cyperoideae, Pooideae 11 12 13 414 and Mesangiospermae DNA, along with Poaceae and asterids. The presence of 14 15 415 Betulaceae, Betula and Pinidae is also remarkable, with Equisetum, Bryophyta 16 17 18 416 and Desmidiales. Equisetum and Sphagnum macroremains were also found at 19 20 417 this depth. Pollen presented the highest amounts of Abies, Poaceae and 21 22 418 Cyperaceae. On the other hand, Pinus grains were well represented and Betula 23 24 25 419 had relatively low frequencies. Some ferns (Monolete-spore and Selaginella) 26 27 420 were also observed. 28 29 30 421 Sample 160 had high amounts of Cyperoideae and Mesangiospermae, 31 32 33 422 followed by Pinidae, Cupressaceae and some Betulaceae. It also contained 34 35 423 Desmidiales, Rhodophyta and Bryophyta. COI detected a MOTU assigned to 36 37 38 424 Porifera (ESM Table S5). Although freshwater Porifera are rare, their presence 39 40 425 was reported in Bassa Nera (Garcés-Pastor et al. 2017). In the morphological 41 42 43 426 paleoenvironmental reconstruction, no Sphagnum macroremains were found at 44 45 427 this depth, whereas pollen presented high amounts of Pinus, with Betula and 46 47 428 some Poaceae. 48 49 50 51 429 Sample 220 had high abundance of Pooideae, Pinidae and Bryopsida 52 53 430 DNA, along with some Betulaceae, Sapindaceae and Pinus. Traces of 54 55 56 431 Cyperoideae and Ericales were also detected. This depth did not present 57 58 432 Sphagnum or other macroremains. High proportions of Pinus, Betula and Corylus 59 60 433 were encountered. Poaceae and Cyperaceae had their lowest values. Some 61 62 63 64 65

434 Botryococcus were observed. 1 2 3 435 Sample 265 has the lowest DNA abundances, but high proportions of 4 5 436 Pooideae and Desmidiales, with some Pinidae and traces of Betula, 6 7 8 437 Brassicaceae, Prunus and Bryophyta. COI detected a MOTU each of 9 10 438 Rhodophyta, Porifera and Bacillariophyceae (ESM Table S5). No macroremains 11 12 13 439 were reported. Pollen of Betula and Artemisia reached highest values, with some 14 15 440 Pinus and Poaceae. Some ferns (Monolete-spore) and algae (Botryococcus and 16 17 18 441 Pediastrum) were also observed. 19 20 21 442 22 23 24 443 Discussion 25 26 27 28 444 Universal primers are suitable tools to assess modern peat bog communities 29 30 445 31 32 33 446 Our results suggest that the 18S marker is appropriate to detect and identify a 34 35 36 447 broad range of eukaryotes and assess relative abundances of Viridiplantae DNA 37 38 448 in peat bog environments. Because of its low natural variability, however, this 39 40 449 marker has lower taxonomic resolution than COI (Anslan and Tedersoo 2015; 41 42 43 450 Wangensteen et al. 2018b). On the other hand, the primer set used to amplify 44 45 451 COI proved to be useless to retrieve information about vegetal communities. This 46 47 48 452 primer set is most suitable to assess Metazoa, enabling characterization of many 49 50 453 Arthropoda, Annelida, and Rotifera to the species level, despite persistent known 51 52 53 454 gaps in reference databases (Murria et al. 2019). COI also yielded a higher 54 55 455 proportion of unassigned DNA sequences that could very well correspond to the 56 57 456 ones that 18S identified as Cercozoa or Ochrophyta, highlighting some important 58 59 60 457 gaps at the phylum level in current COI reference databases for eukaryotic 61 62 63 64 65

458 groups other than Metazoa (Wangensteen et al. 2018b). 1 2 3 459 The obtained MOTUs from the modern vegetation communities (ESM 4 5 460 Table S2) broadly correspond to the communities observed during the sampling. 6 7 8 461 For the case of the Floating-3 replicate, Drosera was recorded during sample 9 10 462 processing. Utricularia sp. was found in the floating and fen communities and was 11 12 13 463 also observed in the catchment (Pérez-Haase and Ninot 2006, 2017). The 14 15 464 MOTUs classified as Petrosaviidae may include DNA sequences of Poaceae, 16 17 18 465 Typhaceae and/or Cyperaceae, which are absent from the 18S reference 19 20 466 databases. All modern samples presented Petrosaviidae or Cyperaceae reads, 21 22 467 which is coherent with the catchment vegetation. There is a community shift from 23 24 25 468 Sphagnopsida to Bryopsida as samples get closer to the pond. The presence of 26 27 469 Tracheophyta such as Sanguisorba, Parnassia and Violaceae in the Hummock, 28 29 30 470 together with Filipendula in the carpet, is typical of less humid microenvironments. 31 32 471 On the other hand, the presence of carnivorous Utricularia and Droseraceae in 33 34 35 472 the fen and floating communities indicates wetter conditions and probably nutrient 36 37 473 deficit (Ellison 2006). 38 39 40 474 Our COI metabarcoding protocol was able to retrieve a high amount of 41 42 43 475 assigned DNA sequences of Arthropoda from modern communities (ESM Table 44 45 476 S4). Oribatida was the most abundant order, and different aquatic mite families, 46 47 477 such as Nothridae, Malaconothridae, Camisiidae, and Limnozetidae, dominate 48 49 50 478 each community (Thorp and Covich 2009). Hummock has Nothrus pratensis, 51 52 479 whereas carpet presents Tyrphonothrus maior. In the case of fen and floating 53 54 55 480 communities, there is no lower taxonomic identification for these mites below the 56 57 481 order level, probably because of gaps in the reference databases. Diptera and 58 59 60 482 Harpacticoida abundances increase with proximity to the floating area. The only 61 62 63 64 65

483 Diptera with high abundances in the hummock is the chironomid Limnophyes. 1 2 484 Carpet presents the tabanid Atylotus fulvus and the chironomid Paracricotopus. 3 4 5 485 The fen community has the ceratopogonids Stilobezzia ochracea and Culicoides 6 7 486 kibunensis and the chironomid Corynoneura. The Floating community has a 8 9 10 487 higher abundance of Diptera, the ceratopogonids Dasyhelea modesta and 11 12 488 Palpomyia lineata, and the chironomids Monopelopia tenuicalcar and 13 14 489 Polypedilum tritum. The harpacticoid copepod Bryocamptus pygmaeus is found 15 16 17 490 in the carpet, fen and floating communities. This species inhabits freshwater 18 19 491 environments in mountain regions and displays wide ecological plasticity 20 21 22 492 (Jersabek et al., 2001). With the 18S marker we also obtained good taxonomic 23 24 493 resolution for some Arthropoda (ESM Table S2). As occurs with COI, the order 25 26 27 494 Oribatida showed the highest dominance in all communities, although with lower 28 29 495 taxonomic resolution. There are some taxa that could be assigned to genus, such 30 31 496 as Hydrozetes, an aquatic mite in the fen and floating communities, or the 32 33 34 497 freshwater copepod Acanthocyclops in the fen. 35 36 37 498 The use of both markers enabled us to assess the extant community 38 39 499 structure of Bassa Nera. In order to use MOTUs as ecological indicators, high 40 41 42 500 taxonomic resolution, at the genus or species level, is desirable. Therefore, COI 43 44 501 would be more suitable than 18S for obtaining detailed ecologically relevant 45 46 47 502 information from arthropod taxa, whereas the better assignment rates of 18S 48 49 503 might make this marker more suitable for detecting changes in the relative 50 51 52 504 abundances of higher taxonomic assemblages. 53 54 55 505 56 57 58 506 Living edaphic taxa must be removed from metabarcoding results of sediment 59 60 61 62 63 64 65

507 samples in paleoecological DNA studies 1 2 3 508 One major caveat for metabarcoding analysis of sediment samples using 4 5 6 509 universal primers, is to distinguish DNA reads from living soil/sediment 7 8 510 communities (edaphic organisms) from those amplified from the remains of long- 9 10 11 511 dead organisms (Bellemain et al. 2013; Coolen and Shtereva 2009; Epp et al. 12 13 512 2012; Lejzerowicz et al. 2013a; Pawlowski et al. 2014). Many groups of 14 15 16 513 organisms are known to dwell in soils and sediments down to several meters 17 18 514 depth, such as Fungi, Cercozoa, non-photosynthetic Chrysophytes, Oomycetes, 19 20 21 515 Ciliophora, Nematoda and Annelida (Fierer et al. 2003; Andersen et al. 2013; 22 23 516 Asemaninejad et al. 2017) and they were detected in high abundances in this 24 25 517 study. After removing the possibly living edaphic taxa, the patterns of relative 26 27 28 518 MOTU richness for 18S and COI markers became more similar between 29 30 519 sediment and modern samples (Fig. 3). ANOSIM, however, showed that modern 31 32 33 520 and sediment communities are still significantly different. 34 35 36 521 Differences in the proportions of detected MOTUs between sediment and 37 38 39 522 modern samples might be a result of differential preservation rates of DNA among 40 41 523 different taxa. Our results for read abundance obtained from sediment samples 42 43 44 524 suggest that the 18S rRNA gene fragment is degraded faster for plants than for 45 46 525 animals. Moreover, the low detection rate of 18S from Sphagnopsida DNA in 47 48 49 526 sediment samples cannot be a consequence of primer bias or gaps in reference 50 51 527 databases, since this taxon was abundantly detected and identified from our 52 53 528 modern samples. Epp et al. (2012) also found lower amounts of bryophyte DNA 54 55 56 529 in sediment samples, whereas recent soil samples yielded high abundances. 57 58 530 They suggested that bryophytes may contain secondary metabolites that 59 60 61 62 63 64 65

531 enhance DNA degradation (Xie and Lou 2009) and this could potentially cause 1 2 532 proportionally higher DNA degradation rates compared to other groups. 3 4 5 533 Differential detectability can also depend on the initial DNA abundance. The 6 7 534 mitochondrial marker COI has in general high numbers of copies per cell (Pääbo 8 9 10 535 et al. 2004). On the other hand, the copy number of tandem rRNA sequences for 11 12 536 18S present in nuclear genomes may vary considerably between different 13 14 537 eukaryotic groups (Zhu et al. 2005). As a result, quantitative comparisons of 15 16 17 538 ancient community structure based on sequence abundances, is generally 18 19 539 impossible. 20 21 22 23 540 Our results suggest that even presence/absence molecular surveys of 24 25 541 sediment communities can differ considerably from their modern counterparts. 26 27 28 542 None of the reconstructed assemblages from ancient communities studied here 29 30 543 could be considered to reproduce any modern assemblage (Fig. 4). This 31 32 33 544 suggests that broader spatial and temporal sampling studies should be 34 35 545 performed to create modern community-DNA analogues for all Phyla. Moreover, 36 37 38 546 RNA metabarcoding (Guardiola et al. 2016; Lejzerowicz et al. 2013b) would be a 39 40 547 suitable technique to be used for assessing only living, or recently dead 41 42 548 organisms, whose results could then be compared to the results from total DNA 43 44 45 549 metabarcoding to distinguish living edaphic taxa from ancient DNA remains in 46 47 550 sediment samples. 48 49 50 551 51 52 53 552 DNA results from universal markers may still be useful for paleoenvironmental 54 55 56 57 58 59 60 61 62 63 64 65

553 reconstructions 1 2 3 554 4 5 6 555 Our results show that the reconstruction obtained from the 18S marker for ancient 7 8 9 556 DNA (Fig. 3) cannot attain fine taxonomic resolution. With some exceptions, most 10 11 557 of the recovered sequences could be assigned to the level of family or above. 12 13 558 This might constrain the interpretation and comparisons to pollen and 14 15 16 559 macroremain data. We found, however, that the studied paleoenvironmental 17 18 560 proxies offer complementary information that could be useful for paleoecological 19 20 21 561 reconstructions, even if our dataset included samples from only five sediment 22 23 562 depths. 24 25 26 563 Alhough the taxonomic resolution of the marker does not enable us to 27 28 29 564 identify to the species level, correspondence between assigned MOTUs and 30 31 565 pollen or macroremains were found for many taxa. For example, the Sphagnum 32 33 34 566 macroremain proportions could be related to the Bryophyta DNA sequences, and 35 36 567 Polytrichum macroremains might correspond to the MOTU assigned to Bryopsida. 37 38 568 Also, the Vaccinium sequence fits within the Ericaceae pollen. The high amounts 39 40 41 569 of Pinus and Abies pollen match with the DNA sequences of Pinidae, and so on. 42 43 44 570 Moreover, DNA analyses allow for the detection of many taxa, such as 45 46 571 Desmidiales, Streptophytina, Chlorophyceae and Scenedesmaceae, which 47 48 49 572 would be overlooked by pollen analysis. These taxa suggest moist environments, 50 51 573 also corroborated by the presence of COI DNA sequences of Bacillariophyceae 52 53 54 574 and Porifera (ESM Table S5). 55 56 57 575 Despite low DNA concentrations and the low sequencing depth recovered 58 59 576 from samples 220 and 265, the detected MOTUs match quite well with the 60 61 62 63 64 65

577 information recovered from pollen and macroremains for these samples. Our 1 2 578 results highlight that the interpretation of ancient sediment DNA does not overlap 3 4 5 579 perfectly with the reconstruction based on pollen and macroremains, but the 6 7 580 combination of both reconstructions reveals more detailed information about 8 9 10 581 plant paleocommunities than that achieved by either approach individually 11 12 582 (Jørgensen et al. 2012). Pollen analysis may provide information at a more 13 14 583 regional level, while macroremains and sediment DNA may provide more local 15 16 17 584 details (Alsos et al. 2018). A higher taxonomic resolution for plant species could 18 19 585 probably be obtained from using different metabarcoding markers, such as 20 21 22 586 chloroplast markers (Parducci et al. 2017). 23 24 25 587 26 27 28 588 Pros and cons and future improvements in peat bog metabarcoding 29 30 31 589 32 33 34 590 Our results suggest that 18S and COI markers are useful to assess the 35 36 37 591 biodiversity of modern peat bog communities, but there is a major caveat in the 38 39 592 application of universal eukaryotic metabarcoding markers to sediment samples, 40 41 42 593 related to the high proportion of DNA recovered from living edaphic communities. 43 44 594 A multi-marker approach is recommended to cover total community biodiversity 45 46 595 (Epp et al. 2012). Although some constraints could be related to limitations of 47 48 49 596 DNA extraction methods or primer specificities, we think that currently, the most 50 51 597 significant drawback in the analysis of community and sediment DNA is the lack 52 53 54 598 of complete reference databases. Such collections must contain a broad range 55 56 599 of barcode DNA sequences derived from accurately identified species, covering 57 58 59 600 all major lineages of Eukaryota. Nevertheless, DNA identifications can be more 60 61 62 63 64 65

601 easily standardized and are more traceable and objective in comparison with 1 2 602 morphology-based identification approaches (Jørgensen et al. 2012). 3 4 5 603 In this study, the 18S rRNA gene provided useful information about past 6 7 8 604 plant communities, whereas information from COI was mainly restricted to 9 10 605 Metazoa. The use of COI would enable high-resolution taxonomic assignment of 11 12 13 606 animal communities, if a complete reference database were available 14 15 607 (Wangensteen and Turon 2017). With the current reference database available 16 17 18 608 for Pyrenean peat bog communities, however, the taxonomic results from COI 19 20 609 are just slightly better than those from the 18S marker. This issue will undoubtedly 21 22 610 be solved in the future by improving barcoding efforts. To obtain a more detailed 23 24 25 611 description of the vegetation paleoenvironments, it would be desirable to use 26 27 612 chloroplast markers, which enable better taxonomic resolution than 18S for 28 29 30 613 higher plants. Markers used in this work provided insights on ancient communities 31 32 614 and results that agree broadly with those obtained from morphological analysis 33 34 35 615 of pollen and macroremains. The present work was an exploratory study with 18S 36 37 616 and COI markers on a small number of sediment samples. More extensive 38 39 617 studies with higher temporal resolution will enable more detailed understanding 40 41 42 618 of the information provided by DNA from past communities. 43 44 45 619 Current paleoecological studies that rely on morphological remains are 46 47 620 based mostly on the identification of vegetal material. Paleoecological studies 48 49 50 621 using other organisms such as Arthropoda or other Metazoa have been limited 51 52 622 to the scarce biological traces that remain in the sediment. With the proper 53 54 55 623 analysis of metabarcoding data based on modern analogues, this DNA technique 56 57 624 has the potential to offer a new paleoenvironmental multi-proxy approach 58 59 60 625 addressing diverse taxa from the same period. Such an approach would allow for 61 62 63 64 65

626 a better understanding of the relationships between animal and vegetation 1 2 627 communities and their response to past climate shifts. The advantage of 3 4 5 628 metabarcoding to study a large number of taxa simultaneously, in the absence of 6 7 629 morphological expertise, is obvious in the case of understudied or complex 8 9 10 630 groups. 11 12 13 631 Although the use of metabarcoding does not depend on taxonomic 14 15 632 expertise, it requires bioinformatics skills. The laboratory procedures and data 16 17 18 633 collecting may be considerably shorter than for morphological analyses, but the 19 20 634 use of appropriate bioinformatics pipelines and reliable reference databases is 21 22 635 crucial for obtaining accurate results. Further investigations are also needed to 23 24 25 636 study how DNA degradation affects the results with respect to markers from 26 27 637 different taxa. For example, in this study we found that the DNA from Sphagnum 28 29 30 638 and other mosses is probably not well preserved and might be undetectable in 31 32 639 ancient samples, with the markers used. 33 34 35 640 Another limiting factor is the scant knowledge of the autoecology of many 36 37 38 641 small metazoan groups, with some notable exceptions such as chironomids 39 40 642 (Tarrats et al. 2017). Once the reference databases are improved and the DNA 41 42 43 643 sequences are assignable to the genus or species level, the ecological 44 45 644 interpretation of this data will need current information on species distributions 46 47 645 and autoecological preferences. This knowledge would allow the acquisition of 48 49 50 646 reliable ecological information from a number of independent taxonomic sources 51 52 647 such as mites, collembola, and many other small arthropods and metazoans, 53 54 55 648 which would provide robust inferences of paleoecological reconstructions from 56 57 649 the detection of metazoan species (Pansu et al. 2015). 58 59 60 650 We have shown that metabarcoding of paleoecological communities using 61 62 63 64 65

651 universal markers is currently limited by the small number of DNA reads obtained 1 2 652 from past remains, compared to those derived from living edaphic taxa. This 3 4 5 653 limitation, however, can be easily circumvented by using new ultra-throughput 6 7 654 sequencing technologies, such as NovaSeq (Singer et al. 2019), which would 8 9 10 655 increase the sequencing depth per sample by two orders of magnitude, allowing 11 12 656 for higher number of reads from ancient remains and thus more robust 13 14 657 paleoecological inferences. 15 16 17 18 658 This study was the first attempt to sequence DNA in ancient samples from 19 20 659 Pyrenean peat bogs. We were able to amplify DNA and get useful sequencing 21 22 660 information from samples spanning a period of 10,000 years. Although the 23 24 25 661 number of sediment samples analysed in this preliminary work was small and did 26 27 662 not allow to obtain robust inferences, the ancient DNA interpretation was coherent 28 29 30 663 with the pollen and macroremain reconstruction, and the universal markers 31 32 664 enabled us to detect organisms that would be difficult to study using conventional 33 34 35 665 paleoecological techniques. These results open the way to more detailed 36 37 666 reconstructions of past communities using novel molecular proxies derived from 38 39 667 DNA metabarcoding. 40 41 42 43 668 44 45 46 669 Acknowledgements 47 48 49 670 50 51 52 671 We thank Professor Xavier Turon for providing us with the 18S primers. We are 53 54 672 indebted to Editor Mark Brenner and to three anonymous reviewers for their 55 56 57 673 suggestions, which contributed to improve upon earlier versions of this 58 59 60 61 62 63 64 65

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850 52:79-92 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

851 Figure Legends 1 2 3 852 4 5 6 853 Fig. 1 Sampling points of the modern and sediment samples of Bassa Nera. A) Location 7 8 854 of the study area. B) Topographic map of the region surrounding Bassa Nera C) Location 9 10 855 of the sampling points (black dots) and core extraction (star) 11 12 13 856 14 15 16 857 Fig. 2 Patterns of relative abundance of DNA reads (a, b) and relative MOTU richness 17 18 858 (c, d) per sample using 18S (a, c) and COI (b, d) markers in the four modern 19 20 21 859 communities 22 23 24 860 25 26 27 861 Fig. 3 Relative richness of non-edaphic taxa from sediment and modern samples, 28 29 862 according to the detected presence of MOTUs of 18S (a) and COI (b) markers, after 30 31 863 removal of edaphic taxa 32 33 34 864 35 36 37 865 Fig. 4 Non-metric multidimensional scaling ordination using Jaccard index analysis to 38 39 866 presence/absence dataset dissimilarities with non-edaphic MOTUs of samples for 18S 40 41 42 867 (a) and COI (b) markers 43 44 45 868 46 47 48 869 Fig. 5 Relative abundance or DNA reads (a) and relative richness of MOTUs (b) for the 49 50 870 divisions of Viridiplantae detected using 18S marker 51 52 53 871 54 55 56 872 Fig. 6 Relative abundance or DNA reads (a) and relative richness of MOTUs (b) for 57 58 59 60 61 62 63 64 65

873 orders of the phylum Arthropoda detected by COI marker 1 2 3 874 4 5 6 875 Fig. 7 Diagram with the presence/absence data of detected DNA sequences of 7 876 Viridiplantae and the abundances of pollen and macroremains from the morphological 8 9 877 study by Garcés-Pastor et al. (2017) 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65

Fig. 1. Sampling points of the modern and sedimentary samples of Click here to download Figure Fig 1 - study site.tif Bassa Nera. A) Location of the study area. B) Topographic map of Fig. 2. Patterns of relative abundance of DNA reads (a, b) and relative MOTU richness Click here to download Figure Figure 2 - modern communities.tif (c, d) per sample using 18S (a, c) and COI (b, d) markers in the four modern Fig. 3. Relative richness of non-edaphic taxa from sedimentary and modern samples, Click here to download Figure Figure 3 - modern and ancient communities.tif according to the detected presence of MOTUs of 18S (a) and COI (b) markers, after Fig. 4. Non-metric multidimensional scaling ordination using Jaccard index analysis to Click here to download Figure Figure 4 - barplot non-edaphic diversity of motus presence/absence dataset dissimilarities with non-edaphic MOTUs of samples for 18S MetaBog.tif Fig. 5. Relative abundance or DNA reads (a) and relative richness of MOTUs (b) for the Click here to download Figure Figure 5 - barplot Viridiplantae biomass and motus divisions of Viridiplantae detected using 18S marker MetaBog.tif Fig. 6. Relative abundance or DNA reads (a) and relative richness of MOTUs (b) for Click here to download Figure Figure 6 - barplot Arthropoda biomass and motus orders of the phylum Arthropoda detected by COI marker MetaBog.tif Fig. 7. Diagram with the presence/absence data of detected DNA sequences of Click here to download Figure Fig 7 - pollen, macroremains and DNA.tif Viridiplantae and the abundances of pollen and macroremains from the morphological Figure S1 (supplementary material) Click here to download attachment to manuscript Fig S1 - Viridiplantae.tif

Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Figure S2 (supplementary material) Click here to download attachment to manuscript Fig S2 - Arthropoda.tif

Click here to view linked References 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 Table S1 Click here to download attachment to manuscript Table S1.docx Click here to view linked References

Supplementary table S1. DNA concentration of sedimentary and modern samples from Bassa Nera peat bog extracted with Norgen Soil DNA Isolation Plus Kit. 1 2 3 4 Community Depth Inferred age DNA concentration 5 (cm) (cal yr BP) (ng/µl, mean ± SD) 6 7 Sedimentary 31 140 14.9 ± 1.8 8 9 Sedimentary 109 3795 2.83 ± 0.44 10 11 Sedimentary 160 6165 1.16 ± 0.29 12 Sedimentary 220 8339 0.077 ± 0.008 13 14 Sedimentary 265 10094 0.98 ± 0.14 15 16 Hummock 0 - 2 modern 31.2 ± 9.0 17 Carpet 0 - 2 modern 5.3 ± 4.7 18 19 Fen 0 - 2 modern 4.9 ± 0.7 20 21 Floating 0 - 2 modern 5.6 ± 2.8 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Table15 S2. 20 most abundant 18S MOTUs for modern samples Click here to download attachment to manuscript Table S2. 20 most abundant 18S 16 MOTUs for modern samples.docx Click17 here to view linked References 18 19 Table S2. 20 most abundant 18S MOTUs for modern samples. Best id = Best identity 20 21 22 23 Hummock Carpet Fen Floating 24 Best Total Best Total Best Total Best Total Scientific name Scientific name Scientific name Scientific name 25 id reads id reads id reads id reads 26 Sphagnum 1 211999 Sphagnum 1 144277 Bryopsida 0.99 212126 Bryopsida 0.99 249266 27 28 Desmonomata 1 82196 Bothrioplana semperi 1 62581 Bothrioplana semperi 0.99 77189 Droseraceae 1 126639 29 Tectocepheus sarekensis 1 40149 Rhynchoscolex simplex 1 25935 Utricularia 1 76531 Anystina 0.93 37122 30 31 Hygrocybe 0.99 20472 Cyperoideae 1 22051 Brachypylina 1 24896 Desmonomata 1 28406 32 Acrogalumna longipluma 1 20360 asterids 1 18330 Desmonomata 1 16016 Rhabdolaimus aquaticus 1 27565 33 34 Cernosvitoviella atrata 1 19699 Bryopsida 0.99 16462 Poaceae 0.99 14507 Hydrozetes lacustris 1 17888 35 Sanguisorba 1 17635 Aeolosoma sp. GG-2011 0.98 11302 Rhabdolaimus aquaticus 1 11974 Enochrus quadripunctatus 1 9841 36 37 Poaceae 0.99 17069 Poaceae 0.99 10116 Hydrozetes lacustris 1 11882 Podocopida 1 8806 38 Geocentrophora 39 Hydrophilinae 1 12190 Harpacticoida 1 10086 1 9342 Sphagnum 1 8590 40 sphyrocephala 41 Brachypylina 1 11678 Cernosvitoviella atrata 1 8355 Calyptostoma velutinus 0.94 8145 Calyptostoma velutinus 0.94 4164 42 43 Fungi 1 11436 Fungi 1 7457 Harpacticoida 0.96 7488 Tubificina 1 3032 44 Agaricomycetes 1 7709 Tubificina 1 7060 Podoplea 0,88 5362 Cyperoideae 1 2639 45 46 Violaceae 1 4612 Filipendula vulgaris 0.99 6371 Fungi 1 4934 Parasitengona 0,94 2503 47 Helicoon fuscosporum 1 4525 Brachypylina 1 6083 Harpacticoida 1 4382 Macrobiotidae 0.99 2456 48 49 Catenulida 0.95 4026 Chamaedrilus cognettii 1 5502 Lumbriculidae 1 4115 Zygoptera 1 2238 50 Steganacaridae 1 3886 Chaetonotidae 1 5376 Limnognathia maerski 1 2516 Utricularia 1 2126 51 52 Harpacticoida 0.98 3423 Entelegynae 0.98 4964 Acanthocyclops 1 1862 Harpacticoida 0.98 1949 53 Parnassia 1 2957 Tubificina 0.99 4753 Aeolosoma sp. GG-2011 0.98 1804 Chaetonotidae 1 1931 54 55 Prismatolaimus 1 2944 Lepidochaetus zelinkai 1 4494 Leotiomycetes 0.98 1737 Lumbriculus 1 1886 56 Fungi 1 2765 Naididae 0.97 4472 Peniophorella 0.99 1724 Brachypylina 1 1584 57 58 praetermissa 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Table15 S3. abundant 18S MOTUs for sedimentary samples Click here to download attachment to manuscript Table S3. 18S MOTUs for sedimentary 16 samples.docx Click17 here to view linked References 18 Table S3. 20 most abundant 18S MOTUs for sedimentary samples 19 20 31 cm 109 cm 160 cm 220 cm 265 cm 21 Best Total Best Total Best Total Best Total Best Total 22 Scientific name Scientific name Scientific name Scientific name Scientific name id reads id reads id reads id reads id reads 23 24 Cyperoideae 1 28063 Cyperoideae 1 2955 Cyperoideae 1 6802 Bicosoecida gen. 1 0.9 2335 Dinophyceae 0.87 3999 25 Mesangiospermae Bicosoecida gen. 1 sp. EK-2010a 0.97 9089 Pooideae 1 1795 0.92 5505 Pooideae 1 619 Heterophryidae 0.89 3965 26 BOG2_000000149 sp. EK-2010a 27 Mesangiospermae 28 Bryopsida 0.99 7058 0.97 1564 Bicosoecida 0.82 1645 Pinidae 1 377 Pooideae 1 80 29 BOG2_000000149 Mesangiospermae 30 Pooideae 1 776 Poaceae 0.99 178 0.97 748 Bryopsida 0.99 277 Desmidiales 0.94 50 31 BOG2_000000149 32 Paramonas Bicosoecida gen. 1 Bicosoecida gen. 2 0.87 585 0.92 151 Paramonas globosa 0.87 554 Navicula 1 216 0.85 46 33 globosa sp. EK-2010a sp. EK-2010a 34 Paramonas Bicosoecida gen. 1 35 0.87 547 asterids 1 143 0.9 412 Salamandroidea 1 127 Adeleidae 0.91 19 36 globosa sp. EK-2010a 37 Chaetonotus Bicosoecida gen. 1 38 1 281 0.88 97 Bicosoecida 0.82 369 rosids 0.99 114 Polypodiidae 1 17 39 acanthodes sp. EK-2010a 40 Saxifragales 0.98 267 Equisetum arvense 1 96 Salamandroidea 1 132 rosids 1 110 rosids 1 16 41 Bicosoecida gen. 1 42 0.92 218 Paramonas globosa 0.87 84 Pinidae 1 79 asterids 1 109 Petrosaviidae 1 13 43 sp. EK-2010a 44 Poaceae 0.99 202 rosids 1 80 Cupressaceae 0.99 39 Desmonomata 0.99 101 Dysderidae 1 13 45 Gregarinasina 0.89 171 Petrosaviidae 1 76 Paramonas globosa 0.91 27 Fragilariaceae 0 91 Salamandroidea 1 12 46 Bicosoecida gen. 1 Paramonas 47 Pyrenomonadales 0.77 140 Sapindales 1 43 0.88 22 Betulaceae 96 76 0.89 9 48 sp. EK-2010a globosa 49 Pinidae 1 121 Streptophytina 0.89 41 Desmidiales 0.91 21 Petrosaviidae 1 74 rosids 0.99 8 50 Mesangiospermae 51 Nuclearia 0.86 88 Desmidiales 0.94 29 0.97 19 Mesangiospermae 1 61 Pinidae 1 8 52 BOG2_000077141 53 Nuclearia 0.76 82 Navicula 1 27 Dysteridae 1 13 Sapindales 0.99 47 Bacillariophyta 0.88 7 54 Mesangiospermae Rhizidiomyces 55 Chaetonotidae 1 79 Betulaceae 1 24 0.96 12 1 40 Prunus 1 6 56 BOG2_000010361 apophysatus 57 Prunus 1 63 Brassicaceae 1 20 Navicula 1 9 Cymbellales 0.9 37 Petrosaviidae 0.98 5 58 Microdalyellia 1 62 rosids 0.99 18 Paramonas globosa 0.92 9 Chaetonotidae 0.93 37 Navicula 1 5 59 60 Chaetonotidae 1 59 Petrosaviidae 0.98 17 Lauraceae 1 7 Papilionoideae 1 34 Sinella curviseta 1 5 61 Paramonas 0.88 45 Pinidae 1 15 Sapinadaceae 1 7 Pinus 0.99 33 Atripliceae 1 4 62 63 globosa 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Table15 S4. 20 most abundant COI MOTUs for modern samples Click here to download attachment to manuscript Table S4. 20 most abundant COI 16 MOTUs for modern samples.docx Click17 here to view linked References 18 19 Table S4. 20 most abundant COI MOTUs for modern samples 20 21 22 23 Hummock Carpet Fen Floating 24 Best Total Best Total Best Total Best Total 25 Scientific name Scientific name Scientific name Scientific name 26 id reads id reads id reads id reads 27 Nothrus pratensis 0.99 47933 Malaconothridae 0.84 108107 Malaconothridae 0.84 10258 Malaconothridae 0.84 64168 28 29 Malaconothridae 0.84 32706 Tyrphonothrus maior 1 75834 Sarcoptiformes 0.85 4409 Trombidiformes 0.81 30644 30 Poduroidea 0.8 16875 Maxillopoda 0.82 38042 Stilobezzia ochracea 1 2286 Dasyhelea modesta 0.99 17267 31 32 Limnophyes sp.7SW 1 7517 Atylotus fulvus 0.98 15599 Maxillopoda 0.82 2210 Rotifera 0.78 10853 33 34 Microtrombidiidae 0.87 3937 Sarcoptiformes 0.85 14431 Corynoneura 0.99 1432 Enochrus ochropterus 0.99 10006 35 Sarcoptiformes 0.83 2588 Scheloribatidae 0.88 13342 Mycobatidae 0.91 1332 Sarcoptiformes 0.89 8325 36 37 Sordariomycetes 0.86 2489 Paracricotopus 1 10232 Eukaryota 0.79 1293 Eukaryota 0.76 5803 38 39 Tectocepheus 0.88 2324 Pristina 0.85 9395 Isotomidae 0.98 1220 Podocopida 0.88 5649 40 Neelipleona 0.89 2227 Didymium 0.85 7599 Platyhelminthes 0.84 968 Lumbriculus variegatus 0.99 5612 41 42 Anacaena lutescens 1 1973 Malaconothridae 0.99 7191 Bryocamptus pygmaeus 0.98 896 Eukaryota 0.73 5250 43 Eukaryota 0.76 1779 Sarcoptiformes 0.82 6714 Neocopepoda 0.82 725 Palpomyia lineata 1 5134 44 45 Planorbidae 0.8 1574 Eukaryota 0.79 6290 Culicoides kibunensis 0.97 636 Monopelopia tenuicalcar 1 4652 46 47 Eukaryota 0.78 1568 Bryocamptus pygmaeus 0.98 6275 Malaconothrus 0.84 565 Eukaryota 0.76 3972 48 Adineta 0.9 1437 Murrayon pullari 0.99 6041 Leohumicola 0.9 546 Eukaryota 0.78 3687 49 50 Eukaryota 0.71 1390 Cognettia glandulosa B SM2014 0.98 5978 Harpacticoida 0.84 518 Lecane cornuta 0.87 3630 51 52 Eukaryota 0.8 1342 Eukaryota 0.78 5767 Ploima 0.83 442 Polypedilum tritum 0.98 2703 53 Eukaryota 0.76 1311 Trombidiformes 0.81 5726 Sordariomycetes 0.88 440 Trebouxiophyceae 0.79 2605 54 55 Eukaryota 0.75 1148 Philodinidae 0.9 4961 Pristina 0.85 417 Ochrophyta 0.75 2287 56 Eukaryota 0.79 1114 Bdelloidea 0.9 4741 Cyclopoida 0.86 316 Bryocamptus pygmaeus 0.98 2225 57 58 Leohumicola 0.9 1085 Leotiomycetes 0.88 4325 Eukaryota 0.75 288 Maxillopoda 0.84 2216 59 60 61 62 63 64 65 1 2 3 4 5 6 7 8 9 10 11 12 13 14 Table15 S5. COI MOTUs for sedimentary samples Click here to download attachment to manuscript Table S5. COI MOTUs for sedimentary 16 samples.docx Click17 here to view linked References 18 Table S5. 20 most abundant COI MOTUs for sedimentary samples 19 20 21 22 31 cm 109 cm 160 cm 220 cm 265 cm 23 Best Total Best Total Best Total Best Total Best Total Scientific name Scientific name Scientific name Scientific name Scientific name 24 id reads id reads id reads id reads id reads 25 26 Bacillariophyceae 0.81 4774 Branchiopoda 0.73 251 Arthropoda 0.82 2867 Psychoda alternata 0.93 15 Rhodophyta 0.79 9248 27 Tyrphonothrus 28 Porifera 0.82 2032 Bacillariophyta 0.81 51 Porifera 0.75 333 1 9 Porifera 0.75 1841 29 maior 30 Planorbidae 0.8 1897 Navicula 0.84 24 Arthropoda 0.76 41 Malaconothridae 0.84 6 Opiliones 0.77 540 31 32 Maxillopoda 0.81 1028 Thalassionema 0.86 14 Branchiopoda 0.73 31 Rhodophyta 0.81 6 Bacillariophyceae 0.81 93 33 Ceratophysella 34 Branchiopoda 0.73 396 Bacillariophyta 0.83 14 Arthropoda 0.8 15 Nothrus pratensis 0.99 5 0.83 25 denticulata 35 36 Florideophyceae 0.84 362 Sellaphora 0.85 13 Bacillariophyta 0.84 13 Sarcoptiformes 0.85 3 Opiliones 0.77 24 37 Branchiopoda 0.73 166 Bacillariophyceae 0.82 13 Eimeria 0.72 12 Murrayon pullari 0.99 3 Mollusca 0.72 15 38 39 Branchiopoda 0.72 127 Bacillariophyceae 0.82 12 Naviculaceae 0.83 9 Rotifera 0.82 3 Branchiopoda 0.75 13 40 Porifera 0.82 110 Haslea 0.86 11 Bacillariophyceae 0.82 7 Coccomyxa 0.76 2 Tyrphonothrus maior 1 11 41 42 Rhodophyta 0.81 80 Bacillariophyceae 0.83 11 Tyrphonothrus maior 1 6 Bacillariophyceae 0.85 2 Araneae 0.92 8 43 Pyropia 0.82 78 Nitzschia 0.83 10 Harpacticoida 0.83 6 Crotoniidae 0.9 2 Diptera 0.9 8 44 45 Banchiopoda 0.75 74 Sellaphora 0.84 10 Murrayon pullari 0.99 6 Sarcoptiformes 0.85 2 Harpacticoida 0.79 8 46 Tyrphonothrus 47 1 73 Bacillariophyceae 0.83 10 Malaconothrus 0.87 4 Limoniidae 0.94 2 Haslea 0.84 7 48 maior 49 Pyropia 0.83 58 Bacillariophyta 0.83 10 Malaconothridae 0.84 4 Harpacticoida 0.84 2 Naviculaceae 0.83 7 50 51 Maxillopoda 0.82 57 Arthropoda 0.76 10 Othius angustus 0.99 4 Rhodophyta 0.8 1 Sellaphora 0.84 7 52 Branchiopoda 0.73 54 Sellaphora 0.87 9 Nothrus pratensis 0.99 3 Acutodesmus 0.75 1 Harpacticoida 0.82 7 53 54 Rhodophyta 0.83 44 Bacillariophyta 0.85 9 Asplanchna 0.81 3 Sellaphora 0.86 1 Bos 0.99 7 55 Tyrphonothrus 56 Rhodophyta 0.82 44 1 9 Rhodophyta 0.99 2 Banksinoma 0.95 1 Platyhelminthes 0.75 7 maior 57 58 Branchiopoda 0.74 41 Ovatella vulcani 0.92 9 Dysdera 0.86 2 Malaconothridae 0.99 1 Porifera 0.82 7 59 Malaconothrus 60 Maxillopoda 0.81 35 Stylochoidea 0.78 9 2 Sarcoptiformes 0.82 1 Porifera 0.82 7 61 mollisetosus 62 63 64 65 Supplementary file S1. Molecular and bioinformatics methods Click here to download attachment to manuscript Supplementary file S1. Molecular and bioinformatics Click here to view linked References

Supplementary file S1. Methods used for PCR amplification, library preparation, sequencing and bioinformatics pipelines 1 2 3 4 PCR amplifications 5 6 7 8 The V7 region of nuclear-encoded ribosomal 18S rRNA gene was amplified using the 9 10 18S_allshorts primers (5’-TTTGTCTGSTTAATTSCG-3’ and 5’- 11 TCACAGACCTGTTATTGC-3’) (Guardiola et al. 2015), which provide information for all 12 13 eukaryotic groups. The Leray-XT primer set, a novel degenerated set amplifying a 313 bp 14 15 fragment of the mitochondrial marker COI (miCOIintF-XT 5'- 16 17 GGWACWRGWTGRACWITITAYCCYCC-3'; Wangensteen et al. 2018b; and jgHCO2198 18 19 5'-TAIACYTCIGGRTGICCRAARAAYCA-3'; Geller et al. 2013) was also used. This marker 20 features nearly full amplification coverage for almost all main eukaryotic lineages with the 21 22 exception of Viridiplantae and Ciliophora (Wangensteen et al. 2018b). 23 24 The PCR amplifications were performed at the dedicated environmental DNA 25 26 laboratory at the University of Salford. 8-base sample-specific tags for identifying the 27 28 multiplexed samples and a variable number (2-4) of leading random bases, for increasing 29 30 DNA sequence diversity, were attached to the metabarcoding primers. The amplification 31 mix for the 18S_allshorts primers included 10 µl of AmpliTaq Gold DNA polymerase 32 33 master mix (Applied Biosystems), 1 μl of each 5 μM forward and reverse 8-base tagged 34 35 primers, 3 μg of bovine serum albumin and 5 ng of extracted DNA in a total volume of 20 36 37 μl per sample. The PCR conditions consisted in a first denaturation step of 10 min at 95 38 39 °C and then 45 cycles of denaturation at 95 °C for 30 s, annealing at 45 °C for 30 s and 40 41 elongation at 72 °C for 30 s. For the amplification of COI using the Leray-XT primers, the 42 mix included 10 µl of AmpliTaq Gold DNA polymerase master mix (Applied Biosystems), 1 43 44 μl of each 5 μM forward and reverse 8-base tagged primers, 3 μg of bovine serum albumin 45 46 and 5 ng of extracted DNA in a total volume of 20 μl per sample. The PCR profile for COI 47 48 included 10 min at 95 ºC, 35 cycles of 94 ºC 1 min, 45 ºC 1 min and 72 ºC 1 min, and 5 49 50 min at 72 ºC. The concentration of the DNA recovered from one of the sedimentary depths 51 52 was too low, thus 0.5 ng of template DNA was used instead of 5 ng for the PCR of this 53 sample replicates. 54 55 56 57 PCR products pooling and library preparation 58 59 60 61 After PCR, the PCR products were multiplexed into two libraries (one per marker) along 62 63 64 65 with other samples from an unrelated project and these pools were purified and concentrated using Minelute PCR purification columns (QIAGEN). The number of samples 1 2 sequenced for this study were 28 per marker: 12 amplifications from modern communities 3 4 (four communities with three ecological replicates per community), 15 amplifications from 5 6 sedimentary samples (five depths with three extraction replicates per depth) and the 7 8 extraction blank. A PCR blank using molecular biology grade water as template was run 9 10 along with the samples, but was not include in neither of the pools, since no amplification 11 bands were observed by electrophoresis in agarose gel. The total number of samples per 12 13 multiplexed library (including samples for this study and the unrelated project) was 83. Two 14 15 Illumina libraries were built from the amplicon pools using the NEXTflex PCR-free DNA 16 17 library prep kit (www.biooscientific.com). Both libraries were sequenced together in a 18 19 single run of Illumina MiSeq using v3 chemistry 2x250 bp paired-end. 20 21 22 Bioinformatics pipelines and statistical analyses 23 24 25 26 The bioinformatics analyses were based on the OBITools software suite (Boyer et al., 27 28 2016) and followed the same pipelines used for these markers in previous works 29 30 (Guardiola et al. 2016; Wangensteen et al. 2018a, 2018b), with the exception of the MOTU 31 clustering step. Briefly, the paired-reads were assembled using illuminapairedend. The 32 33 resulting aligned datasets with alignment quality score > 40 were demultiplexed using 34 35 ngsfilter, and the 28 samples belonging to this study were selected for further processing. 36 37 A length filter (obigrep) was applied to the assigned reads (75-180 bp for 18S and 300-320 38 39 bp for COI). The obtained reads were dereplicated using obiuniq and chimeric DNA 40 41 sequences were removed with vsearch (Rognes et al. 2016) using the uchime_denovo 42 algorithm. Individual sequences were clustered into molecular operational taxonomic units 43 44 (MOTUs) using the step-by-step aggregation clustering algorithm implemented in SWARM 45 46 v2 (Mahé et al. 2015) with a resolution of d=1 for 18S and d=13 for COI. These values for 47 48 d have been previously used for similar metabarcoding datasets (Wangensteen and Turon 49 50 2017; Macías-Hernández et al. 2018; Kemp et al. 2019; Siegenthaler et al. 2019). 51 52 Singleton sequences (MOTUs of abundance = 1 read) were removed after the clustering. 53 The taxonomic assignment of the representative sequences for each MOTU was 54 55 performed using ecotag (Boyer et al. 2016) on custom local reference databases, as 56 57 explained in Wangensteen et al. (2018b). Both databases are publicly available from 58 59 http://github.com/metabarpark/reference_databases. Ecotag is able to assign sequences 60 61 without a perfect match using a phylogenetic approach, which selects the best hit in the 62 63 64 65 reference database and builds a set of reference sequences which are at least as similar to the best hit as the query sequence is. Then, the sequence is assigned to the taxon of 1 2 the NCBI taxonomy tree including all the reference set sequences. With this procedure, 3 4 the assigned taxonomic rank varies depending on the similarity of the query sequences 5 6 and the density of the reference database, so that some sequences can be assigned at 7 8 the species level, whereas other sequences can be assigned, for example, at the family, 9 10 order or phylum levels, in case a closer reference sequence is not available for them. 11 After taxonomic assignment, a blank correction step was performed, following 12 13 Wangensteen and Turon (2017), where MOTUs with higher than 10% values for the 14 15 abundance in the blanks to total abundance ratio were removed. The final MOTU datasets 16 17 were manually checked. Those DNA sequences assigned by ecotag to bacteria or to the 18 19 root of the tree of life were removed. Other sequences considered as potential 20 contaminants related to human presence or activity (e.g. human DNA and cultivated 21 22 plants), and sequences from marine organisms (originated by tag switching from the 23 24 unrelated samples that were sequenced together in the same Illumina run) were also 25 26 removed. In order to further improve the detection of bacterial sequences produced by 27 28 unspecific amplifications, an additional refining step was used in the COI pipeline. 29 30 Sequences of COI MOTUs were queried against a bacterial nucleotide database from 31 Genbank using BLASTn (McGinnis and Madden 2004), and MOTUs which matched a 32 33 bacterial sequence with an E-value of 10-50 or lower were removed. This step allowed to 34 35 remove an additional 5.9 % of putative bacterial COI MOTUs that had not been assigned 36 37 as prokaryotic sequences by ecotag. A summary of all software used in the bioinformatics 38 39 pipelines is available in table S0 (below). 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 Table S0. Summarized pipelines for the complete metabarcoding procedure including two markers: COI and 18S. Names beginning in “owi_” are custom R scripts available at 1 2 http://github.com/metabarpark. 3 4 5 6 Sampling (preservation in ethanol) 7 8 Pre-processing: (homogenization of samples) 9 10 DNA extraction: (Norgen Soil DNA Isolation Plus Kit) 11 PIPELINE FOR COI PIPELINE FOR 18S 12 13 PCR Tagged Leray primers Tagged Allshort primers 14 15 Library preparation NEXTflex PCR-free (BIOO) NEXTflex PCR-free (BIOO) 16 17 HT Sequencing Illumina MiSeq V3 2x250 bp Illumina MiSeq V3 2x250 bp 18 19 Raw sequences QC fastqc fastqc 20 21 PE alignment illuminapairedend illuminapairedend 22 23 Demultiplexing obiannotate/obisplit obiannotate/obisplit 24 25 ngsfilter ngsfilter 26 27 Length filter obigrep 300-320 bp obigrep 75-180 bp 28 29 Dereplication obiuniq obiuniq 30 31 Rename identifiers obiannotate MBOG1 obiannotate MBOG2 32 33 Chimera removal vsearch uchime_denovo vsearch uchime_denovo 34 Clustering SWARM v2 d=13 SWARM v2 d=1 35 36 obitab obitab 37 38 owi_recount_swarm owi_recount_swarm 39 delete singletons delete singletons 40 41 42 43 Taxonomic ecotag using db_COI_BOLD ecotag using db_18S 44 assignment 45 46 Add higher taxa owi_add_taxonomy owi_add_taxonomy 47 48 Final refinement Blank correction Blank correction 49 Removal of contamination MOTUs Removal of contamination MOTUs 50 51 Removal of bacterial sequences Removal of bacterial sequences 52 53 assigned by ecotag assigned by ecotag 54 Removal of bacterial sequences 55 56 using BLASTn 57 58 59 Community analyses and integration of the results 60 61 62 63 64 65

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 References

1 2 Boyer F, Mercier C, Bonin A, Le Bras Y, Taberlet P, Coissac E (2016) Obitools: a unix-inspired 3 4 software package for DNA metabarcoding. Mol Ecol Res 16; 176-182 5 6 Guardiola M, Wangensteen O, Taberlet P, Coissac E (2016) Spatio-temporal monitoring of deep- 7 8 sea communities using metabarcoding of sediment DNA and RNA. PeerJ 4: e2807 9 10 Geller J, Meyer C, Parker M, Hawk H (2013) Redesign of PCR primers for mitochondrial 11 12 cytochrome c oxidase subunit I for marine invertebrates and application in all-taxa biotic 13 surveys. Mol Ecol Res 13: 851-861 14 15 16 Kemp J, López-Baucells A, Rocha R, Wangensteen OS, Andriatafika Z, Nair A, Cabeza M (2019) 17 Bats as potential suppressors of multiple agricultural pests: A case study from Madagascar. 18 19 Agric Ecosyst Environ 269: 88-96 20 21 Macías-Hernández N, Athey K, Tonzo V, Wangensteen OS, Arnedo M, Harwood JD (2018) 22 23 Molecular gut content analysis of different spider body parts. PLoS ONE 13: e0196589 24 25 McGinnis S, Madden TL (2004) BLAST: at the core of a powerful and diverse set of sequence 26 27 analysis tools. Nucleic Acids Res 32(S2): W20-W25 28 29 Rognes T, Flouri T, Nichols B, Quince C, Mahé F (2016) VSEARCH: a versatile open source tool 30 31 for metagenomics. PeerJ 4: e2584 32 33 Siegenthaler A, Wangensteen OS, Benvenuto C, Campos J, Mariani S (2919) DNA metabarcoding 34 35 unveils multiscale trophic variation in a widespread coastal opportunist. Mol Ecol 28: 232-249 36 37 Wangensteen OS, Turon X (2017) Metabarcoding techniques for assessing biodiversity of marine 38 39 animal forests In: Rossi S, Bramanti L, Gori A, Orejas C (eds) Marine Animal Forests. The 40 41 Ecology of Benthic Biodiversity Hotspots, Springer International Publishing, Switzerland, ISBN 42 978-3-319-21011-7 pp. 445-473 43 44 45 Wangensteen OS, Cebrian E, Palacín C, Turon X (2018a). Under the canopy: community-wide 46 effects of invasive algae in marine protected areas revealed by metabarcoding. Mar Poll 47 48 Bull 127: 54-66 49 50 Wangensteen O S, Palacín C, Guardiola M, Turon X (2018b) DNA metabarcoding of littoral hard- 51 bottom communities: high diversity and database gaps revealed by two molecular markers. 52 53 PeerJ 6: e4705. 54 55 56 57 58 59 60 61 62 63 64 65 Supplementary file S2. Rarefaction curves Click here to download attachment to manuscript Supplementary file S2. Rarefaction curves.pdf Click here to view linked References

Supplementary file S2. Rarefaction curves for individual samples using 18S rRNA V7 and COI Leray-XT markers in modern and sedimentary communities (excluding singletons). 1 2 3 4 5 6 Hummock 18S Carpet 18S 7 8 9 10 S S

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33 N N 34 500 500 35 36 37 0 0 38 39 0 50000 150000 250000 0 50000 150000 250000 40 41 Number of reads Number of reads 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 BSNA31 COI BSNA109 COI

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29 r r e 400 e 400 30 b b m m 31 u u N N 32 33 200 200 34 35 36 0 0 37 38 0 20000 60000 100000 140000 0 20000 60000 100000 140000 39 40 Number of reads Number of reads 41 42 43 BSNA260 COI 44 45 800 46 47

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Dear Prof. Mark Brenner, 1 2 3 4 Thank you very much for reading the manuscript and checking the language, we think that the 5 manuscript has improved a lot. 6 7 Inclosed is the final version of our manuscript "DNA metabarcoding reveals modern and past 8 eukaryotic communities in a high mountain peat bog system" with the final changes you proposed. 9 10 We thank you very much for your nice comments. 11 12 13 14 Best wishes, 15 16 Sandra Garcés-Pastor 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65