DOI: 10.5061/dryad.7wm37pw2s
title: Data from: Flower-derived environmental DNA reveals community diversity, species abundances, and ecological interactions in bee pollinators

description: Flower-derived eDNA holds great promise as a rapid and non-invasive tool
 for monitoring pollinators and their plant-associations. However,
 pollinators often only briefly interact with a plant and leave little
 eDNA, making them particularly challenging to detect. In addition,
 taxonomic biases in eDNA deposition and PCR amplification prevent
 quantitative analysis of pollinator diversity. These limitations have so
 far precluded the widespread use of eDNA in pollinator monitoring.
 Comparing flower-derived eDNA with conventional monitoring in flower
 strips, we here explore the utility of eDNA to detect community diversity,
 species abundances, and ecological specificity of plant-associated
 arthropods. We show that read abundances are a bad predictor of true
 abundances at the community level. Instead, the occupancy of individual
 species in replicated flower eDNA samples provides reliable quantitative
 estimates of pollinator biodiversity and detects their ecological
 specificity very well. Also, we find that pollinator eDNA can be collected
 non-invasively, by washing off from flowers in the field. Our work
 highlights eDNA analysis as a powerful tool for the rapid future
 monitoring of plant-arthropod interactions and plant-pollinator networks.
description: Comparing conventional monitoring and eDNA sampling in flower
 strips The study was carried out on three flower strips
 in Rheinmünster (Baden-Württemberg, Germany, N 48° 44' 56.9076 E 8°
 1' 38.7912, 125 m above sea level). They are all located relatively
 close together in an intensively farmed landscape where maize is the
 dominant crop. A small lake in the north-east, surrounded by small trees,
 and embankments along the roads are the only structural elements in the
 area. The three flower strips are 0.44 ha (7), 0.31 ha (15) and 0.27 ha
 (19) in size. The area was chosen because there is an ongoing conventional
 pollinator monitoring since 2010 by Bayer in cooperation with the
 Institute of Agroecology and Biodiversity (ifab) and the NABU forest
 institute (NABU-Waldinstitut - formerly Institut für Landschaftsökologie
 und Naturschutz (ILN) Bühl) (Buhk et al. 2018). It is important to note
 that the bee expert was only looking for wild bees and not for the Western
 Honeybee (Apis mellifera). Honeybees, however, were also not very
 prevalent on the studied sites. To obtain comparable
 results, sampling dates were co-timed with the visual bee monitoring in
 the flower strips. Conventional sampling took place four times during our
 sampling period (06/02, 06/20, 07/06, 07/20/2023). We collected eDNA
 samples on four sampling dates (06/02, 06/27, 07/10, 07/21/2023). We
 sampled only after at least 24 hours of dry weather to assure eDNA was not
 washed away by rain. Hence, the sampling dates between eDNA sampling and
 morphological identification were not always identical. On all sampling
 dates two different methods for obtaining arthropod eDNA from flowers were
 tested: 1) The first method was to pick flower heads.
 Here, samples of the 12 most abundant plant species were collected per
 flowering strip on every date. If there were not enough abundant species
 in a flowering strip, replicates of very abundant species were made (for
 the species list see suppl. Table 1). Flowers were collected using
 single-use gloves, which were changed for each plant sample to prevent
 cross-contamination. Care was taken to collect only the flower head and
 not the stem. Furthermore, the flowers were gently shaken to dislodge any
 small arthropods present. Flowers of the different species were collected
 in roughly equal quantities in zip-lock bags. 2) The
 second method is a non-invasive approach. The flower heads were sprayed
 with deionized water using a pressure sprayer (Prima 5, Witten, Germany)
 and the water was collected in zip-lock bags, held under the flower while
 spraying. The same 12 plant species were used as for the collection of
 flowers, to make the results comparable. The same number of flowers were
 rinsed as were collected. Again, single-use gloves were worn, and the
 flowers were gently shaken to remove arthropods before rinsing.
 All eDNA samples were stored in the field in an insulated box
 filled with ice and immediately frozen at -20°C on arrival at the
 laboratory until further processing. For the
 conventional method, direct observation was chosen to monitor the bee
 species (see Buhk et al. 2018). The areas were walked two times on every
 visit for 30 min each and all bees identified on sight if possible. If
 this was not possible, they were caught and identified afterwards
 morphologically.  DNA isolation, PCR amplification and
 sequencing Collected flower heads were weighed. 10
 times the amount of deionized water (in ml) was then added per plant
 weight (in gram) to the zip lock bags. The samples were then shaken for
 one minute and afterwards filtered through a 0.45 µm cellulose nitrate
 filter (Thermo Fisher Scientific Inc., Waltham, USA) using a vacuum pump.
 The same was done with the previously thawed water samples from the
 rinsing of the plants. Also, blank controls for the deionized water used
 in the field and laboratory were filtered. Filters were then stored in 1.5
 ml tubes in a freezer at -20°C. DNA isolation was done using the Qiagen
 Puregene Blood & Tissue Kit (Qiagen, Hilden, Germany). In the
 first step 450 µl ATL buffer was added, and the samples were then
 bead-beaten at 1000 rm for 45s. This bead-beating was repeated one time.
 The rest of the DNA isolation followed the manufacturer's
 protocol. We used the primer combination fNoPlantF_270
 (forward primer, RGCHTTYCCHCGWATAAAYAAYATAAG) and mlCOIintR_W (reverse
 primer, GRGGRTAWACWGTTCAWCCWGTNCC) (Krehenwinkel et al. 2022) to amplify
 our samples. This combination targets a 116 bp fragment of the
 mitochondrial CO1 gene for a broad range of arthropod taxa. Samples were
 amplified using the Qiagen Multiplex PCR kit (Qiagen, Hilden, Germany). 1
 µl each of the 5 µM primers, 5 µl multiplex master mix, 2 µl water and 1
 µl DNA were used for the PCR. PCRs were run with 35 cycles and an
 annealing temperature of 46 °C. Three PCR replicates for each sample were
 run and treated as different samples afterwards. After checking the
 success of the PCR using a gel image, five cycles and an annealing
 temperature of 55 °C were used for the following dual index PCR (see Lange
 et al. 2014). During this, Illumina TruSeq Adapters (Illumina, California,
 USA) were attached to the 5' end. The indexed products were again
 checked on an agarose gel. The intensity of the band on the gel image was
 used to pool the samples in their approximate proportions. Afterwards
 magnetic beads (1:1 sample to beads ratio, AMPure XP, Beckman Coulter,
 California, USA) were used to clean the pools from leftover primers. For
 each isolation, PCR and index PCR, negative controls were run along with
 the samples and sequenced to check for contamination. Using an Illumina
 Miseq the samples were then sequenced with a Miseq Reagent Kit v3
 (Illumina Inc, San Diego, California, USA). Data
 processing and Statistical analysis Samples were
 demultiplexes using CASAVA (Illumina Inc, San Diego, California, USA),
 with no mismatches allowed. PEAR (Zhang et al. 2014) was used to merge the
 resulting fastq files with a minimum quality score of 20% and a minimum
 overlap of 50 bp. Fasta files were created using the fastq filter (minimum
 90% of bases &gt;Q30) command of USEARCH (Edgar 2010). Primers were
 then trimmed of with the sed command in UNIX. Using USEARCH reads were
 dereplicated and then clustered into zero-radius operational taxonomic
 units (zOTUs). These were then compared against the full NCBI database
 with BLASTn (Altschul et al. 1990) and a minimum of 10 target sequences.
 Respective taxonomy was added using blast2taxonomy (Schöneberg 2024). Only
 zOTUs where all 10 hits were arthropods were retained. An OTU table was
 constructed using USEARCH. We annotated all sequences with a minimum of 90
 % similarity to order and 100 % as species. For the following analysis,
 only zOTUs were kept, which occurred in all the three PCR replicates.
 Also, only zOTUs with a minimum of 11 reads in a sample were retained to
 remove index carry over between samples. In addition, one species of
 bumblebee (<em>Bombus mucidus</em>) was removed from the
 dataset as it is unlikely to occur in the sampling area (Westrich
 2019). References Altschul, S. F.;
 Gish, W.; Miller, W.; Myers, E. W.; Lipman, D. J. (1990): Basic local
 alignment search tool. In: <em>Journal of Molecular
 Biology</em> 215 (3), S. 403–410. DOI:
 10.1016/S0022-2836(05)80360-2. Buhk, Constanze;
 Oppermann, Rainer; Schanowski, Arno; Bleil, Richard; Lüdemann, Julian;
 Maus, Christian (2018): Flower strip networks offer promising long term
 effects on pollinator species richness in intensively cultivated
 agricultural areas. In: <em>BMC Ecol</em> 18 (1), S. 55. DOI:
 10.1186/s12898-018-0210-z. Edgar, R. (2010): Usearch.
 Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United
 States). Krehenwinkel, Henrik; Weber, Sven; Künzel,
 Sven; Kennedy, Susan R. (2022): The bug in a teacup-monitoring
 arthropod-plant associations with environmental DNA from dried plant
 material. In: <em>Biology letters</em> 18 (6), S. 20220091.
 DOI: 10.1098/rsbl.2022.0091. Lange, Vinzenz; Böhme,
 Irina; Hofmann, Jan; Lang, Kathrin; Sauter, Jürgen; Schöne, Bianca et al.
 (2014): Cost-efficient high-throughput HLA typing by MiSeq amplicon
 sequencing. In: <em>BMC Genomics</em> 15 (1), S. 63. DOI:
 10.1186/1471-2164-15-63. Schöneberg, Yannis (2024):
 yschoeneberg/blast2taxonomy: Version 1.4.2: Zenodo. Available online at
 https://zenodo.org/records/10605493. Westrich, Paul (2019): Die Wildbienen Deutschlands. 2nd ed. Stuttgart: Verlag Eugen Ulmer. Available online at https://livivo.idm.oclc.org/login?url=https://ebookcentral.proquest.com/lib/zbmed-ebooks/detail.action?docID=6969869. Zhang, Jiajie; Kobert, Kassian; Flouri, Tomáš; Stamatakis, Alexandros (2014): PEAR: a fast and accurate Illumina Paired-End reAd mergeR. In: <em>Bioinformatics</em> 30 (5), S. 614–620. DOI: 10.1093/bioinformatics/btt593.
description: # Data from: Flower-derived environmental DNA reveals community diversity,
 species abundances, and ecological interactions in bee pollinators
 [https://doi.org/10.5061/dryad.7wm37pw2s](https://doi.org/10.5061/dryad.7wm37pw2s) ## Description of the data and file structure We have tested two eDNA methods to obtain eDNA from flowers. In addition, we tested how good the methods are at monitoring bees and compared the data with data from conventional monitoring. 1\) The first method was to pick flower heads. Here, samples of the 12 most abundant plant species were collected per flowering strip on every date. If there were not enough abundant species in a flowering strip, replicates of very abundant species were made (for the species list see suppl. Table 1). Flowers were collected using single-use gloves, which were changed for each plant sample to prevent cross-contamination. Care was taken to collect only the flower head and not the stem. Furthermore, the flowers were gently shaken to dislodge any small arthropods present. Flowers of the different species were collected in roughly equal quantities in zip-lock bags. 2\) The second method is a non-invasive approach. The flower heads were sprayed with deionized water using a pressure sprayer (Prima 5, Witten, Germany) and the water was collected in zip-lock bags, held under the flower while spraying. The same 12 plant species were used as for the collection of flowers, to make the results comparable. The same number of flowers were rinsed as were collected. Again, single-use gloves were worn, and the flowers were gently shaken to remove arthropods before rinsing. All eDNA samples were stored in the field in an insulated box filled with ice and immediately frozen at -20°C on arrival at the laboratory until further processing. For the conventional method, direct observation was chosen to monitor the bee species (see Buhk et al. 2018). The areas were walked two times on every visit for 30 min each and all bees identified on sight if possible. If this was not possible, they were caught and identified afterwards morphologically.  --- ### Files and variables #### **File: bee_species_list.csv** **Description:** List of bee species detected by the diffrent methods. **Variables** * conventional: List of the bee species found by the bee expert (blank cells - not found using this method). * plant: List of the bee species found by collecting flower heads and washing them off in the Lab (blank cells - not found using this method). * water: List of bee species found by washing of flower heads in the field (blank cells - not found using this method). **File: raw_tax.csv** **Description:** Original taxonomic annotation of the sequences; "Nan" are inserted when there are no information for this variable - for the variable "species" the cells are empty if there is no information **Variables** * otu_id: Name of the zOTU * perc_id: The percentage by which the sequence matches the comparison sequence from the database * sbjct_len: Length of the sequenz * kingdom: Annotated kingdom * phylum: Annotated phylum * superclass: Annotated superclass * class: Annotated class * subclass: Annotated subclass * order: Annotated order * infraorder: Annotated infraorder * superfamily: Annotated superfamily * family: Annotated family * genus: Annotated genus * species: Annotated species **File: raw_otu_table.csv** **Description:** Original otu table generated **Variables** * sample: sample names (numbers 1 to 3 at the end are PCR triplicates) * otuXXX: name of the zOTU and how often the sequenz was read during sequenzing **File: meta.csv** **Description:** Informations of the diffrent samples; "X" is used when there are no information for a sample for some variables e.g. the plant family for negative controls. **Variables** * sampling: Number of sampling * area: Area of the sampling * plots: Plot of the sampling * plant_family: Plant family of the plant species sampled * plant_gen: Plant genus of the plant genus sampled * plant_species: Plant species sampled * info: Method used for the sampling * date: Date of the sampling * sample: Name of the sample * extracted: Date of the extraction of the sample **File: tax_ecology.csv** **Description:** annotated taxonomic informations and ecological informations found for selected species; ; "Nan" is inserted by the programwhen there are no information for this variable - for the variables "species", "ecology" and "2 ecology" the cells are empty if there is no information **Variables** * otu_id: Name of the zOTU * perc_id: The percentage by which the sequence matches the comparison sequence from the database * sbjct_len: Length of the sequenz * kingdom: Annotated kingdom * phylum: Annotated phylum * superclass: Annotated superclass * class: Annotated class * subclass: Annotated subclass * order: Annotated order * infraorder: Annotated infraorder * superfamily: Annotated superfamily * family: Annotated family * genus: Annotated genus * species: Annotated species * spezialisation: Spezialisation of the bee species * ecology: Ecological information for selected species **File: otu_ento.csv** **Description:** species list of the bee expert transformed into an otu table **Variables** * sheet: otu_ento: * species: Name of the species * ento_XXX: How often the species was found **File: sequences.zip** **Description:** original sequenzes **File:** bee_species_list_bee_expert_e.csv **Description: Bee species found by the bee expert**  **Variables** * species: Name of the bee species * Flowerstrip: Number of the flower strip on which the bee was found * date: The date on which the bee was found * sum: The sum of how many individuals of a specific species were found on a specific date and flower strip ## Code/software All programs that can open csv files.