DOI: 10.5061/dryad.zw3r228gj
title: Data from: Environmental DNA metabarcoding reveals temporal dynamics but functional stability of arthropod communities in cattle dung

description: Terrestrial invertebrates are highly important for the decomposition of
 dung from large mammals. Mammal dung has been present in most of Earth’s
 ecosystems for millions of years, enabling the evolution of a broad
 diversity of dung-associated invertebrates that process various components
 of the dung. Today, large herbivore mammals are increasingly introduced to
 ecosystems with the aim of restoring the ecological functions formerly
 provided by their extinct counterparts. However, we still know little
 about the ecosystem functions and nutrient flows in these rewilded
 ecosystems, including the dynamics of dung decomposition. In fact, the
 succession of insect communities in dung is an area of limited research
 attention also outside of a rewilding context. In this study, we use
 environmental DNA metabarcoding of dung from rewilded Galloway cattle in
 an experimental setup to investigate invertebrate communities and
 functional dynamics over a time span of 53 days, starting from the time of
 deposition. We find a strong signal of successional change in community
 composition, including for the species that are directly dependent on dung
 as a resource. While several of these species were detected consistently
 across the sampling period, others appeared confined to either early or
 late successional stages. We believe this is indicative of evolutionary
 adaptation to a highly dynamic resource, with species showing niche
 partitioning on a temporal scale. However, our results show consistently
 high species diversity within the functional groups that are directly
 dependent on dung. Our findings of such redundancy suggest functional
 stability of the dung-associated invertebrate community, with several
 species ready to fill vacant niches if other species disappear.
 Importantly, this might also be a potential mechanism buffering the
 ecosystem functions related to dung decomposition against environmental
 change. Interestingly, alpha diversity peaked after approximately 20-25
 days in both meadow and pasture habitats, and did not decrease
 substantially during the experimental period, probably due to preservation
 of eDNA in the dung after the disappearance of visiting invertebrates, and
 from detection of tissue remains and cryptic life stages.
description: The dataset consists of DNA reads from High Throughput sequencing
 of samples collected through an natural experiment investigating
 invertebrate succession in cattle dung. The experiment was conducted in
 the Mols Laboratory area in Denmark (56°13′36”N, 10°34′33″E), where 60 kg
 of fresh dung was collected, homogenized, and separated out in 18 pools,
 which were placed in six experimental plots. Three plots were located in
 meadows and three were located in pastures. Samples were collected from
 all 18 dung pools after 1, 4, 11, 25, 39, and 53 days, resulting in 108
 samples in total. In addition, three fresh dung samples were collected,
 and three samples were taken from the homogenized pool before if was
 divided in the 18 sub-pools, as starting-point controls. DNA was extracted
 from the samples with the Fast DNA Stool Mini Kit from Qiagen, and
 amplified by PCR reactions (see PCR reagents and thermal settings, etc. in
 the associated manuscript) with the BF-1/BR-1 primers (Elbrecht &
 Leese, 2017, https://doi.org/10.3389/fenvs.2017.00011), targeting a 217 bp fragment of COI optimized for invertebrates. During the laboratory pipeline, 13 extraction blanks, and nine PCR blanks were included, which were sequenced alongside the rest of the samples. Furthermore, 9 samples from another study was included in the sequencing libraries (sample names including "_tx.1" - "_tx.9"). Hence, in total, the uploaded sequencing output includes DNA reads obtained from 145 samples, which were separated in three batches, each in 4 PCR replicates. For 4 libraries (Pool7, 10, 11 & 12) additional sequencing were performed, and thus two separate raw data files exist from these libraries. See README.md for description of how to treat these in the bioinformatic pipeline. The raw sequencing data were run through the MetaBarFlow pipeline (https://github.com/evaegelyng/MetaBarFlow), with parameters following Thomassen et al. (2024) (https://doi.org/10.1111/mec.16847). The pipeline produces an ASV list (Succession_DADA2_nochim.otus), and a matrix with read counts of each ASV in each sample (Succession_DADA2_nochim.table), as well as a list with taxonomic assignment of all ASVs (Succession_classified.txt). The taxonomic identification of DNA sequencing reads were made by blasting (blastn) against a custom build COI database containing all COI sequences from BOLD (www.boldsystems.org) and NCBI Genbank (https://www.ncbi.nlm.nih.gov/) See Klepke et al. (2022) (https://doi.org/10.1002/edn3.340) for further description of how the database was built, and the associated publication for details of Blast parameters. For final taxonomic assignment (score_ID column in "Succession_classified.txt") was defined as the last common ancestor of all blast hits within the range of sequence similarity of hits to the best match, including hits within a 2% margin of the best ID, and species ID was only assigned if the best match was &gt;98% similar. The list of taxonomic assignement was manually checked for errors resulting from spurious reference database sequences or similar, and when such errors were spotted, the taxonomic assignement of the given ASV was corrected manually. Also, ASVs identified at higher levels than species were assigned to "putative species", which were units including the same possible IDs. This final manually edited IDs is found in the "final_ID" column in the file "Succession_classified_corrected.txt". See manuscript for further details about subsequent analysis. All raw sequencing output files are uploaded (end in .fq.gz), the files for demultiplexing (end in tags.txt) and an example file to use in MetaBarFlow (Succession_example_batchfileDADA2.list) and the following output files from MetaBarFlow: Succession_classified.txt (the original taxonomic classification file of ASVs from MetaBarFlow) Succession_classified_corrected.txt (the manually corrected taxonomy-file) Succession_DADA2_nochim.otus (List of all ASVs defines by MetaBarFlow) Succession_DADA2_nochim.table (ASV/sample read count matrix
description: # Data from: Environmental DNA metabarcoding reveals temporal dynamics but
 functional stability of arthropod communities in cattle dung #
 Bioinformatic pipeline: ## Setting up the directory structure on a HPC
 cluster mkdir root_dir mkdir root_dir/COI mkdir root_dir/COI/raw_data cd
 root_dir ## Place all downloaded files in the root directory (root\_dir)
 ## Set up directories for each library cd COI/raw_data mkdir Pool_1_1A_2
 Pool_2_1B_2 Pool_3_1C_2 Pool_4_1D_2 Pool_5_2A_2 Pool_6_2B_2 Pool_7_2C_2
 Pool_7_2C_2_additional Pool_8_2D_2 Pool_9_3A_2 Pool_10_3B_2
 Pool_10_3B_2_additional Pool_11_3C_2 Pool_11_3C_2_additional Pool_12_3D_2
 Pool_12_3D_2_additional cd .. ## Putting the files in the right places mv
 Pool_1_1A_2* COI/raw_data/Pool_1_1A_2/. mv Pool_2_1B_2*
 COI/raw_data/Pool_2_1B_2/. mv Pool_3_1C_2* COI/raw_data/Pool_3_1C_2/. mv
 Pool_4_1D_2* COI/raw_data/Pool_4_1D_2/. mv Pool_5_2A_2*
 COI/raw_data/Pool_5_2A_2/. mv Pool_6_2B_2* COI/raw_data/Pool_6_2B_2/. mv
 Pool_7_2C_2* COI/raw_data/Pool_7_2C_2/. mv Pool_8_2D_2*
 COI/raw_data/Pool_8_2D_2/. mv Pool_9_3A_2* COI/raw_data/Pool_9_3A_2/. mv
 Pool_10_3B_2* COI/raw_data/Pool_10_3B_2/. mv Pool_11_3C_2*
 COI/raw_data/Pool_11_3C_2/. mv Pool_12_3D_2* COI/raw_data/Pool_12_3D_2/.
 ## Move data for pools where additional sequencing were performed to a
 separate directory (Pool 7, 10, 11 & 12) cd COI/raw_data/Pool_7_2C_2
 mv *HGFCLDSX2* ../Pool_7_2C_2_additional/. cp Pool_7_2C_2_tags.txt
 ../Pool_7_2C_2_additional/. cd ../Pool_10_3B_2 mv *HGFCLDSX2*
 ../Pool_10_3B_2_additional/. cp Pool_10_3B_2_tags.txt
 ../Pool_10_3B_2_additional/. cd ../Pool_11_3C_2 mv *HGFCLDSX2*
 ../Pool_11_3C_2_additional/. cp Pool_11_3C_2_tags.txt
 ../Pool_11_3C_2_additional/. cd ../Pool_12_3D_2 mv *HGFCLDSX2*
 ../Pool_12_3D_2_additional/. cp Pool_12_3D_2_tags.txt
 ../Pool_12_3D_2_additional/. cd ../../.. ## Unzip the "fq.gz"
 files within each library (example run): cd COI/raw_data/Pool_1_1A_2/
 gunzip *.gz cd ../../.. ## Create file named
 "batchfileDADA2.list" in each sequencing directory ## Replace
 "File\_1" & "File\_2" in
 "Succession\_example\_batchfileDADA2.list" with the names of the
 raw data files (forward and reverse): ###### Example: ######
 Pool\_1\_1A\_2\_BDDP210001925-1A\_H5JT2DSX2\_L2\_1.fq
 Pool\_1\_1A\_2\_BDDP210001925-1A\_H5JT2DSX2\_L2\_2.fq ACWGGWTGRACWGTNTAYCC
 ARYATDGTRATDGCHCCDGC 100 ###### Make sure that the file is with UNIX
 linebreaks, and the line as examplified is followed by an empty line ##
 Rename all batchfiles and tag files in each library (example run): mv
 COI/raw_data/Pool_1_1A_2/Pool_1_1A_2_tags.txt
 COI/raw_data/Pool_1_1A_2/tags.txt mv
 COI/raw_data/Pool_1_1A_2/Succession_example_batchfileDADA2.list
 COI/raw_data/Pool_1_1A_2/batchfileDADA2.list **Continue for all libraries
 (Pool_1_1A_2, Pool_2_1B_2, a.s.o. by changing paths and filenames in the
 lines above accordingly - remember the directories for additional
 sequencing)** **You should now have one directories called COI, containing
 a directory called raw_data with 16 directories corresponding to the
 sequencing libraries - each directory should include two raw data files
 (forward/reverse reads), a file for demultiplexing (tags.txt) and a file
 which specifies input files, primer sequences and minimum length of reads
 to use** **Now the raw sequencing data, batchfiles and tag files are in
 the right places for running the MetaBarFlow pipeline found at
 [https://github.com/evaegelyng/MetaBarFlow](https://github.com/evaegelyng/MetaBarFlow)** ## Files generated by MetaBarFlow The remaining files in the root_dir folder are the files generated in the MetaBarFlow pipeline, which can be imported into R, where any additional analyses can be done Succession_classified.txt - The list of taxonomic assignments of each of the ASVs found in the dataset (note that this file has not undergone manual taxonomic edits and has not been through subsequent filtering steps listed in the manuscript) Succession_classified_corrected.txt - The manually corrected version of the classified list Succession_DADA2_nochim.otus - The list of ASVs found across all samples, which survived DADA2 and chimera filtering Succession_DADA2_nochim.table - The overview of which ASVs were found in which samples