DOI: 10.5061/dryad.q2bvq83kx
title: Short-term temporal variation of coastal marine eDNA

description: Temporal variation in eDNA signals is increasingly explored for
 understanding community ecology in aquatic habitats. Seasonal changes have
 been addressed using eDNA sampling, but very little is known regarding
 short-term temporal variation that spans hours to days. To address this,
 we filtered marine water samples from a single coastal site in Denmark
 every hour for 32 h. We used metabarcoding to target both fish and broader
 eukaryote diversity and evaluated temporal changes in this marine
 community. Results revealed variation in fish species richness (15–27) and
 eukaryote class richness (35–64) across the 32 h of sampling, and we
 further evaluated sampling efforts needed to reach different levels of
 diversity saturation. Relative read frequency data for both fish and
 eukaryotes indicated a clear diel change in community composition, with
 different communities detected during daylight versus dark hours. The
 abundance signals in our data reflected biological variation rather than
 stochastic variation, since replicates taken at the same hour were more
 similar to each other than those taken at different hours. Our
 compositional results indicated a dynamic community, rather than a static
 pool of eDNA—even across a few hours. The fish data showed a daily pattern
 of relative species abundances, and the uncoupling of fish and broader
 eukaryote data suggest that variation in eDNA profiles across a single day
 can provide valuable information reflecting diel changes, at least for
 highly mobile organism groups. However, our results also point to several
 pitfalls in current eDNA experimental design, in which samples are taken
 over large areas without relative time-consistency or short-term
 replication. Our findings shed new light on short-term variation in
 coastal eDNA and have wide implications for experimental study design and
 for incorporating temporality into project conceptualization for future
 aquatic biodiversity monitoring.
description: This dataset represents
 environmental DNA sequencing data from Skovshoved Harbour (Denmark)
 collected on 11th-12th of September, 2017. Samples were collected every
 hour for 32 hours straight, starting from 10 AM on the 11th and until 5 PM
 on the 12th (see connected publication for additional details).
 DNA has been amplified with
 two different primer sets, namely the MiFish (for 12S fish data) and the
 18S_allshorts (for 18S eukaryote data) primers. MiFish primers consists of
 the forward primer MiFish-U-F (5′-GTCGGTAAAACTCGTGCCAGC-3′) and the
 reverse primer MiFish-U-R (3′-GTTTGACCCTAATCTATGGGGTGATAC-5′), targeting a
 163-185 bp fragment of 12S. Eukaryote amplification was done with forward
 primer 18S_allshorts forward (5’-TTTGTCTGSTTAATTSCG-3’) and reverse primer
 18S_allshorts reverse (5’-CACAGACCTGTTATTGC-3’), targeting ca. 110 bp of
 18S. The libraries have been sequenced using paired end NovaSeq 6000
 sequencing (150 BP PE). Libraries are named M1-M4 for the
 fish data and M1_KBJ-M4_KBJ for the eukaryote data.
description: We suggest you put the files in 8
 separate folders in order to demultiplex. The files should be placed
 accordingly: <strong>Folder 1: MiFish PCR
 replicate one</strong> M1_MD5.txt (to check sums) M1_UKKD19030017_HFCCTDSXX_L4_1.fq.gz M1_UKKD19030017_HFCCTDSXX_L4_2.fq.gz M1_12S_tags_1.list <strong>Folder 2: MiFish PCR replicate two</strong> M2_MD5.txt (to check sums) M2_UKKD19030018_HF7V2DSXX_L3_1.fq.gz M2_UKKD19030018_HF7V2DSXX_L3_2.fq.gz M2_12S_tags_2.list <strong>Folder 3: MiFish PCR replicate three</strong> M3_MD5.txt (to check sums) M3_UKKD19030019_HFCG7DSXX_L1_1.fq.gz M3_UKKD19030019_HFCG7DSXX_L1_2.fq.gz M3_12S_tags_3.list <strong>Folder 4: MiFish PCR replicate four</strong> M4_MD5.txt (to check sums) M4_UKKD19030020_HF7V2DSXX_L1_1.fq.gz M4_UKKD19030020_HF7V2DSXX_L1_2.fq.gz M4_12S_tags_4.list <strong>Folder 5: 18S_allshorts (eukaryote) PCR replicate one</strong> M1_KBJ_MD5.txt (to check sums) M1_KBJ_FKDL190745191-1a_H2WGFCCX2_L4_1.fq.gz M1_KBJ_FKDL190745191-1a_H2WGFCCX2_L4_2.fq.gz M1_KBJ_18S_tags_1.list <strong>Folder 6: 18S_allshorts (eukaryote) PCR replicate two</strong> M2_KBJ_MD5.txt (to check sums) M2_KBJ_FKDL190745192-1a_H2WGFCCX2_L3_1.fq.gz M2_KBJ_FKDL190745192-1a_H2WGFCCX2_L3_2.fq.gz M2_KBJ_18S_tags_2.list <strong>Folder 7: 18S_allshorts (eukaryote) PCR replicate three</strong> M3_KBJ_MD5.txt (to check sums) M3_KBJ_FKDL190745193-1a_H2WCNCCX2_L7_1.fq.gz M3_KBJ_FKDL190745193-1a_H2WCNCCX2_L7_2.fq.gz M3_KBJ_18S_tags_3.list <strong>Folder 8: 18S_allshorts (eukaryote) PCR replicate four</strong> M4_KBJ_MD5.txt (to check sums) M4_KBJ_FKDL190745194-1a_H2WGFCCX2_L2_1.fq.gz M4_KBJ_FKDL190745194-1a_H2WGFCCX2_L2_2.fq.gz M4_KBJ_18S_tags_4.list   Following this folder structure, each folder will now contain two sequence data files (paired end sequencing), a barcode/tag file and an MD5 file for checking sums. If you would like to demultiplex this data, all tag information needed is available in the ”list” files. Each tag file contains 52 samples, which are explained below: 10P-2.17P: Pooled samples (see associated article), with numbers denoting time of day the samples were taken. The prefix ”2.” denotes day two of sampling. CON1 and CON2: Field blanks taken at 11.00h and 17.00h on day one, respectively. 12.1, 12.2, 12.3, 20.1, 20.2, 20.3, 04.1, 04.2 and 04.3: Individual samples (see associated article) sequenced to inspect what happens during pooling and to inspect biological variation vs. stochastic variation. CNE_P5, CNE_P6, CNE_P7, CNE_P8, CNE_P9, CNE_P10, CNE_P11, and CNE_P12: Extraction blanks (one was included for each round of extraction). NTC_X4: PCR blanks. Four PCR blanks were run in each PCR setup using the same tag, so these blanks will all appear in the same sample.   The ”list” files include the sample name followed by the PCR replicate number. The two following columns represent the tags used for each sample (both forward and reverse primer were tagged). Tags are consistent across PCR replicates. After demultiplexing, you should be able to do as you please with the data. If you want to follow the exact filtering and data analysis done in our study, we refer to the manuscript for further details after the demultiplex step. If you have any questions, feel free to send an email to Mads Reinholdt Jensen with any questions you may have.