DOI: 10.5061/dryad.xgxd254p1
title: Data from: Environmental DNA metabarcoding reliably recovers arthropod interactions which are frequently observed by video recordings of flowers

description: Environmental DNA (eDNA) metabarcoding promises to be a cost- and
 time-efficient monitoring tool to detect interactions of arthropods with
 plants. However, observation-based verification of the eDNA derived data
 is still required to confirm whether the arthropods indeed previously
 interacted with the plant. Here we conducted a comparative analysis of the
 performance of eDNA metabarcoding and video camera observations to detect
 arthropod communities associated with sunflowers (Helianthus annuus, L.).
 We compared the taxonomic composition, interaction type, and diversity by
 testing for an effect of arthropod interaction time and occupancy on
 successful taxon recovery by eDNA. We also tested if pre-washing of the
 flowers successfully removed eDNA deposition from before the video camera
 recording, thus enabling a reset of the community for standardized
 monitoring. We find that eDNA and video camera observations recovered
 distinct communities, with about a quarter of the arthropod families
 overlapping. However, the overlapping taxa comprised ~90% of the
 interactions observed by the video camera. Interestingly, eDNA
 metabarcoding recovered more unique families than the video cameras, but
 approx. two-third of those unique observations were rare species. The
 eDNA-derived families were biased towards plant sap-suckers, showing that
 such species may deposit more eDNA than e.g. transient pollinators. We
 also find that pre-washing of the flower heads did not suffice to remove
 all eDNA traces, suggesting that eDNA on plants may be more temporally
 stable than previously thought. Our work highlights the great potential of
 eDNA as a tool to detect plant-arthropod interactions, particularly for
 specialized and frequently interacting taxa.
description: We compared flower-derived eDNA with video camera based
 observations of arthropods on sunflowers. The eDNA was recovered using a
 washing protocol. We also pre-washed the sunflowers in some samples and
 compared them to non-pre-washed samples to remove undisired eDNA
 contamination from before camera observation time. For further methods
 please see the paper: DOI: 10.1002/edn3.550
description: # Data from: Environmental DNA metabarcoding reliably recovers arthropod
 interactions which are frequently observed by video recordings of flowers
 We compared flower-derived eDNA with video camera-based observations of
 arthropods on sunflowers. The eDNA was recovered using a washing protocol.
 We also pre-washed the sunflowers in some samples and compared them to
 non-pre-washed samples to remove undesired eDNA contamination from before
 camera observation time. **Data collection** For more details please see
 DOI: 10.1002/edn3.550 The sampled sunflowers were situated in a commercial
 field facing southwest in front of steep vineyards in the outskirts of
 Trier (Rhineland-Palatinate, Germany), a typical agricultural habitat of
 the region. To capture plant–arthropod interactions, five video cameras
 were positioned in front of the flower heads and recorded between 9 a.m.
 and 5 p.m. for a maximum duration of 5 hours on August 25, September 1,
 and September 2, 2022. The duration of some recordings was shortened from
 the initially planned 5 h due to video camera batteries draining before
 the intended duration was completed. To test whether eDNA on the sunflower
 heads could be effectively removed with water before recording, 21 flower
 heads were prewashed using a hand-held pressure sprayer. We sprayed a jet
 of deionized water on the flower heads for 1 min by applying the stream
 evenly across the entire surface and letting the water drop of the flower
 head. As a control, ten samples were not treated with deionized water
 before the video camera recording. After recording, the flower heads were
 cut off with sterile stainless-steel scissors, leaving one centimeter of
 the peduncle intact. They were then placed in a plastic bag on dry ice in
 the field, transferred to a laboratory freezer, and stored at −28°C. To
 test for possible contamination of the sampling process and the materials,
 one plastic bag was filled with 100 mL of deionized water from the
 pressure sprayer in the field. We distinguished different qualitative
 interaction types of arthropods with plants by identifying the most likely
 interaction type for different arthropod families based on their taxonomy.
 The groups we assigned were honeydew feeders, predators (for example,
 spiders), plant gallers, plant sap-suckers, plant miners, and plant
 chewers. We further differentiated between the pairs of pollinating
 nectarivores and palynivores, parasitoids and parasites, as well as
 coprophagous and saprophagous arthropods. For the analysis of the
 ecological composition, the values of each of these pairs was combined
 into the following categories: “palynivore/nectarivore,” “parasitoid/
 parasite,” and “sopro-/ coprophag.” The combination of these groups was
 introduced to facilitate annotation at the family level (for example,
 nectarivores, and palynivores are not always distinguishable at the family
 level) and to tidy up the analysis by aggregating ecologically similar
 groups. The pairs were subsequently used as one interaction type. If more
 than one interaction type could be defined for a family, the values of the
 family would be treated as equal parts of a whole. For instance, when a
 single family was annotated to two different interaction types, the value
 of the family was multiplied by 0.5 and separately assigned to each
 interaction type. Hence, if one arthropod would appear in ten out of
 twenty samples, each of the two interaction types would be considered five
 out of twenty times. The interactions derived from the video camera
 recordings were quantified using three approaches, resulting in the values
 used for the analysis. First, we counted a single interaction whenever one
 arthropod entered and thereafter left the video camera frame (hereafter
 called interaction count). Second, we measured each arthropod's
 interaction time with the flower head as the number of seconds passed
 during one interaction count (hereafter called interaction time).
 Furthermore, the occupancy of arthropods was used to describe the relative
 number of samples where a positive detection of an arthropod could be
 found. For instance, if an arthropod is present in five out of ten
 samples, the occupancy would be 50%. We tested two sets of primers for
 arthropod DNA amplification (PCR amplification), both targeting the
 mitochondrial COI gene: The first primer combination was ZBJ-ArtF1c/
 ZBJ-ArtR2c and will hereafter be referred to as “ZBJ.” The second primer
 combination was NoPlantF_270/mlCOIintR\_ W and will hereafter be referred
 to as “NoPlant.” The water from the field, which was subsequently used as
 a control during filtering and extraction (hereafter called material
 control), and the PCR controls were processed and sequenced along with the
 experimental samples. Briefly, sequences were merged using PEAR (Zhang et
 al., 2014) and then quality filtered with the FastX Toolkit (Gordon &
 Hannon, 2010). The merged sequences were then dereplicated, cleaned from
 Chimera, and clustered into OTUs with USEARCH (Edgar, 2010). We generated
 zero-radius OTUs (e.g. haplotypes, hereafter called zOTU). The taxonomy
 was assigned using BLASTn, with a maximum of 10 target sequences, and
 against the NCBI GenBank reference database. The dataset was further
 filtered by using a minimum similarity of 98% for annotation to species
 level, 97% to genus level, and 95% for family level if the taxonomic
 annotation was used for the analysis. As the control samples mostly
 contained under three reads per zOTU, we set all reads in the dataset
 under three to zero. Two zOTUs of the ZBJ dataset that had three and six
 reads in the controls but were the most abundant zOTUs of all samples were
 not considered for the filtering cutoff. To retain only arthropods, all
 non-arthropod zOTUs were removed before the analysis. To quantify the
 interactions derived from eDNA metabarcoding, we used the relative read
 abundance within a sample by deviding the read number of each zOTU in the
 sample by the total read number of the sample. The occupancy of arthropods
 and their interaction types were assigned, as described before, for the
 video camera recordings. **Files** 1\. Arthropods_video_camera: This file
 contains the families and orders identifiied from the camera recordings.
 We counted each arthropod that entered and left the frame
 (interaction_count) and noted the passed time during that interaction
 count for each arthropod interaction (interaction_time). If the
 interaction time was very short, we noted 0 interaction time but an
 interaction count of 1. Since the original values were notated in minutes,
 we had to transform them to seconds, resulting in decimal numbers. \-
 sample_ID: ID of the sunflower  \- PCR_ID_NoPlant: sample_ID related ID of
 the NoPlant primer used for eDNA metabarcoding \- PCR_ID_ZBJ: sample_ID
 related ID of the ZBJ primer used for eDNA metabarcoding \- Order: Order
 of the arthropod that was identified via video cameras \- Family: Family
 of the arthropod that was identified via video cameras \-
 interaction_count: The interaction count is always one, since all
 occurences are listed in the file \- interaction_second: Time in seconds
 that passed during one interaction count 2\.
 Annotated_family_interactiontype: Interaction types annotated to the
 families observed with the "NoPlant" and "ZBJ" primer
 used for eDNA metabarcoding and the video camera recordings. \- Family:
 Arthropod family that was identified either by video camera or eDNA
 metabarcoding \- Family_interaction_type: Ecological guild that the
 observed family belongs to. At the family level, often more than one
 interaction type occure. 3\. Arthropod_list: List containing all species
 derived from NoPlant and ZBJ primer (cutoff of species with similarity
 under 98% to the reference from NCBI blast) and all families (cutoff at
 95%) found in the video camera recordings. \-
 Species_98_percent_similarity_to_zOTU: Species detected via eDNA
 metabarcoding \- Family_95_percent_similarity_to_zOTU: Family detected via
 eDNA metabarcoding \- Order: Order the species and family belong to. \-
 NoPlant_occurence: Does the family occure in the NoPlant dataset? \-
 ZBJ_occurence: Does the family occure in the ZBJ dataset? \-
 Camera_occurence: Does the family occure in the video camera dataset? 4\.
 Meta: Meta table \- sample_ID: The ID repeats three times, for each of the
 two PCR primers and the video camera samples \- PCR_ID: Unique ID that
 describes the samples. Note that the PCR_ID containing "_V"
 belongs to the camera samples and is naturally not processed using a PCR.
 \- primer: Primer used for PCR during eDNA metabarcoding processing. Video
 camera samples are represented with a NA. \- sampling_date: Field sampling
 date of both eDNA metabarcoding and video camera recordings. \-
 pre_wash_off: Yes = The samples were pre-washed before video camera
 observation, No = No wash off before video camera observations. We
 intended to wash off undisered eDNA contamination. All of those samples
 were then washed in the lab to recover eDNA from the surface of the
 pre-washed and non-pre-washed sunflowers that were exposed to arthropods
 interactions after pre-washing for up to 5 hours. \- obseravtion_time:
 Duration of video camera observation. After the observation time, the
 samples were cut off for eDNA recovery in the lab using a washing
 protocol. \- comment: Shows material control (field control of water, and
 all material used in field, filtering and in DNA extraction) and PCR
 controls that were not used as samples, but to control the eDNA
 metabarcoding results. 5\. OTU_tables: 2 sheets including all zOTUs
 (column OTU_ID) of all samples (header) from the NoPlant and ZBJ
 libraries. 6\. Taxonomic_annotation: 2 sheets including taxonomic
 annotation of the zOTUs shown in the OTU tables. \- hit_percentage:
 Similarity to the NCBI reference sequence \- base_length: length of the
 recovered fragment Missing data code : NA The zip file
 2023-01-26_HSP_fastq contains all fastq files used in the study.