Data from: Multi-taxon inventory reveals highly consistent biodiversity responses to ecospace variation
Data files
Jun 19, 2020 version files 2.07 GB
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ESW-M3NR-R1A-Art-BW1_S1_L001_R1_001.fastq.gz
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ESW-M3NR-R1A-Art-BW1_S1_L001_R2_001.fastq.gz
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ESW-M3NR-R1B-Art-BW1_S2_L001_R1_001.fastq.gz
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ESW-M3NR-R1B-Art-BW1_S2_L001_R2_001.fastq.gz
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ESW-M3NR-R1C-Art-BW1_S3_L001_R2_001.fastq.gz
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ESW-M3NR-R1D-Art-BW1_S4_L001_R1_001.fastq.gz
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ESW-M3NR-R2B-Art-BW1_S7_L001_R1_001.fastq.gz
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ESW-M3NR-R2E-Art-BW1_S10_L001_R2_001.fastq.gz
Abstract
Methods
The Biowide project including major aspects of collection of soil and Malaise trapping has been described elsewhere (Brunbjerg et al 2019), with specific publication and analysis of the fungal data in Frøslev et al (2019) and the eukaryote data in Fløjgaard et al (2019). For the arthropod data, a malaise trap ws placed in the centre of each of the 130 sampling sites and left open for 1 week. Two such trapping events were performed with some weeks apart in the same year. The arthropod DNA dataset was produced by extracting DNA from the ethanol from the bulk insect Malaise traps and metabarcoding with the arthropod specific COI primers ZBJ-ArtF1c and ZBJ-ArtR2c. 45 ml ethanol and 1.5 ml of 3M sodium acetate were added to a 50 ml centrifuge tube, and left in a freezer for DNA precipitation overnight, then centrifuged for 40 minutes. The dried pellet was extracted with the Qiagen DNeasy blood and tissue kit (Qiagen, Germany) with minor modifications. The extracted DNA was normalized, amplified, sequenced and analyzed according to the overall procedures described in Brunbjerg, et al (2019). As for the eukaryote and fungal datasets OTU tables were constructed following the overall pipeline suggested in Frøslev, et al. (2017), to derive OTUs that approximates species level delimitation.This consisted of an initial processing with DADA2 to identify exact amplicon sequence variants including removal of chimeras and post-clustering curation using LULU. Taxonomic assignment was done with a custom script (as in Fløjgaard, et al 2019). OTUs not assigned to Arthropoda were discarded before further analyses.
Brunbjerg, A. K., Bruun, H. H., Brøndum, L., Classen, A. T., Dalby, L., Fog, K., ... & Høye, T. T. (2019). A systematic survey of regional multi-taxon biodiversity: evaluating strategies and coverage. BMC ecology, 19(1), 43.
Fløjgaard C, Frøslev TG, Brunbjerg AK, Bruun HH, Moeslund J, Hansen AJ, & Ejrnæs R. 2019. Predicting provenance of forensic soil samples: Linking soil to ecological habitats by metabarcoding and supervised classification. PloS one, 14(7), e0202844.
Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Hansen AJ, Læssøe T, Heilmann-Clausen J. 2019. Man against machine: Do fungal fruitbodies and eDNA give similar biodiversity assessments across broad environmental gradients? Biological Conservation 233, 201-212.
Frøslev TG, Kjøller R, Bruun HH, Ejrnæs R, Brunbjerg AK, Pietroni C, Hansen AJ. 2017. Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity estimates. Nature Communications 8:1188. doi:10.1038/s41467-017-01312-x.
Usage notes
We studied species richness of different organism groups in 130 sites representing the major terrestrial habitat types in Denmark. Some of these groups were represented by DNA metabarcoding data. This dataset represents the DNA data for arthropods trapped in Malaise traps. Datasets for eukaryotes and fungi from soil eDNA were previously published and used in conjunction with the present data. Overall we found the abiotic environment (ecospace position) to be pivotal for the richness of primary producers (vascular plants, mosses, and lichens) and, more surprisingly, little support for ecospace continuity as a driver.
Here we provide the "raw" Illumina files. They need to be demultiplexed using the information of primers and tags. All this information including all further downstream analyses are given in the github repository connected with the manuscript (https://github.com/tobiasgf/biowide_synthesis).