DOI: 10.5061/dryad.x69p8czkn
title: A national scale BioBlitz using citizen science and eDNA metabarcoding for monitoring coastal marine fish

description: Marine biodiversity is threatened by human activities. To understand the
 changes happening in aquatic ecosystems and to inform management,
 detailed, synoptic monitoring of biodiversity across large spatial extents
 is needed. Such monitoring is challenging due to the time, cost, and
 specialized skills that this typically requires.  In an
 unprecedented study, we here combined citizen science with eDNA
 metabarcoding to map coastal fish biodiversity at a national scale. We
 engaged 360 citizen scientists to collect filtered sea water samples from
 100 sites across Denmark over two seasons (1 pm on September 29th 2019 and
 May 10th 2020), and by sampling at nearly the exact same time across all
 100 sites, we obtained an overview of fish biodiversity largely unaffected
 by temporal variation. This would have been logistically impossible for
 the involved scientists without the help of volunteer citizens. We
 obtained a high return rate of 94% of the samples, and a total richness of
 52 fish species, representing approximately 80% of coastal Danish fish
 species and approximately 25% of all Danish marine fish species. We
 retrieved distribution patterns matching known occurrence for both
 invasive, endangered, and cryptic species, and detected seasonal variation
 in accordance with known phenology. Dissimilarity of eDNA community
 compositions increased with distance between sites. Importantly, comparing
 our eDNA data with National Fish Atlas data (the latter compiled from a
 century of observations) we found positive correlation between species
 richness values and a congruent patterns of community compositions. These
 findings support the use of eDNA-based citizen science to detect patterns
 in biodiversity, and our approach is readily scalable to other countries,
 or even regional and global scales. We argue that future large-scale
 biomonitoring will benefit from using citizen science combined with
 emerging eDNA technology, and that such an approach will be important for
 data-driven biodiversity management and conservation.
description: This dataset represents environmental DNA sequencing data from
 samples collected by citizen scientists in autumn 2019 and spring
 2020 from hundred coastal marine sites in Denmark (see connected
 publication for details). DNA has been amplified with
 Tele02 and Elas01 primers Taberlet et al. (2018), Elas01 was not used to
 analyze sequencing data in this study, thus only the
 Tele02-F (5′-AAACTCGTGCCAGCCACC -3′) and Tele02-R (3′-GGGTATCTAATCCCAGTTTG
 -5′), targeting a 167 bp fragment of the 12S rRNA gene. <br>The
 libraries have been sequenced using paired end NovaSeq sequencing (150 BP
 PE). The dataset consists of sixteen zipped libraries
 (two times four PCR replicates of samples for the two seasons) and
 sixteen txt files that can be used for demultiplexing purposes for each
 library. Libraries are named as described below, and
 each consist of two sequence data files (1 and 2) (paired end
 sequencing). SL1_1_L2_(1 and 2) --&gt; Autumn 2019
 samples PCR replicate one (total 44 samples, 1 CNE and 1
 NTC)<br>SL1_2_L2_(1 and 2) --&gt; Autumn 2019 samples PCR
 replicate two (total 44 samples, 1 CNE and 1 NTC)<br>SL1_3_L2_(1 and
 2) --&gt; Autumn 2019 samples PCR replicate three (total 44 samples, 1
 CNE and 1 NTC)<br>SL1_4_L2_(1 and 2) --&gt; Autumn 2019samples
 PCR replicate four (total 44 samples, 1 CNE and 1 NTC)
 B20_L4_(1 and 2) --&gt; Autumn 2019 samples PCR replicate one
 (total 52 samples, 7 CNE and 2 NTC)<br>B21_L4_(1 and 2) --&gt;
 Autumn 2019 samples PCR replicate two (total 52 samples, 7 CNE and 2
 NTC)<br>B22_L1_(1 and 2) --&gt; Autumn 2019 samples PCR
 replicate three (total 52 samples, 7 CNE and 2 NTC)<br>B23_L4_(1 and
 2) --&gt; Autumn 2019 samples PCR replicate four (total 52 samples, 7
 CNE and 2 NTC) pool1A1_L2_(1 and 2) --&gt;
 Spring 2020 samples PCR replicate one (total 49 samples, 6 CNE and
 1 NTC)<br>pool2A2_L2_(1 and 2) --&gt; Spring 2020 samples PCR
 replicate two (total 49 samples, 6 CNE and 1 NTC)<br>pool3A3_L3_(1
 and 2) --&gt; Spring 2020 samples PCR replicate three (total
 49 samples, 6 CNE and 1 NTC)<br>pool4A4_L1_(1 and 2)* --&gt;
 Spring 2020 samples PCR replicate four (total 49 samples, 6 CNE and
 1 NTC) pool5B1_L1_(1 and 2)* --&gt; Spring 2020
 samples PCR replicate one (total 49 samples, 6 CNE and
 1 NTC)<br>pool6B2_L3_(1 and 2) --&gt; Spring 2020 samples PCR
 replicate two (total 49 samples, 6 CNE and 1 NTC)<br>pool7B3_L1_(1
 and 2)* --&gt; Spring 2020 samples PCR replicate three (total
 49 samples, 6 CNE and 1 NTC)<br>pool8B4_L3_(1 and 2) --&gt;
 Spring 2020 samples PCR replicate four (total 49 samples, 6 CNE and
 1 NTC) Libaries with an asterix *, contains four files,
 these must be merge together according to their last digit "1 or
 2", before demultiplexing. (eg. for the two library files
 "pool4A4_L1_1" and "pool4A4_L1_2", there are also two
 other files "pool4A4_L3_1" and "pool4A4_L3_2",
 contains additonal sequence data and they have to be merged together with
 the two library files, so "pool4A4_L1_1" and
 "pool4A4_L3_1" are being merged and "pool4A4_L1_2" and
 "pool4A4_L3_2" are being merged, this must be done before
 demultiplexing)   If you would like to demultiplex this
 data, we refer to the sixteen txt files named according to the libraries.
 The txt files include the samples name followed by the PCR replicate
 number. The two following columns represent the tags used for individual
 samples. Tags are consistent across PCR replicates. The
 data were analyzed using the pipeline MetaBarFlow (Sigsgaard et al. 2022),
 which uses the python-based workflow tool gwf and parallel computing to
 process of metabarcoding data. The most up-to-date version of MetaBarFlow
 can be found at https://github.com/evaegelyng/MetaBarFlow, while the exact
 pipeline used for the current paper can be found at
 https://github.com/evaegelyng/Agersnap_et_al_2022. If
 you have any questions regarding the data, dont hesitate to contact Sune
 Agersnap. citations:<br>Sigsgaard, Eva Egelyng,
 Samuele Soraggi, Mads Reinholdt Jensen, Adrián Gómez Repollés, Emil
 Ellegaard Thomassen, and Philip Francis Thomsen. 2022. MetaBarFlow
 (version 0.1.0-alpha). Zenodo.
 https://doi.org/10.5281/zenodo.5898411. Taberlet,
 Pierre, Aurélie Bonin, Lucie Zinger, and Eric Coissac. 2018. Environmental
 DNA: For Biodiversity Research and Monitoring. Environmental DNA. Oxford
 University Press.
description: 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
 Sune Agersnap with any questions you may have.