Assessing flower-visiting arthropod diversity in apple orchards through environmental DNA flower metabarcoding and visual census

Arthropods are essential to maintaining healthy and productive agricultural systems. Apples are cultivated worldwide and rely on pollination. Honey bees are used for pollination but wild bees and other arthropods also contribute to pollination. Flower visitors can also be natural enemies or herbivores. In some cases, such as Syrphids, a group can have more than one role, adults being pollinators and the larvae being natural enemies of pests. In the present study, we assessed the biodiversity of arthropod flower visitors in four Danish apple orchards and compared the use of molecular and non-molecular techniques to study arthropod communities in agricultural ecosystems. Arthropod DNA collected from apple flowers was analysed by metabarcoding and pollinators were recorded through visual assessment in the orchards. These techniques resulted in two complementary lists of arthropods detected. Non-bee arthropods constituted a big part of the community of apple flower visitors by both methods. Metabarcoding detected 14 taxa and had 72% species resolution while visual census identified 7 different taxa with 14% species resolution. This study showed the importance of using different sampling methodologies to obtain a more accurate picture of fauna present. It also revealed the high presence of non-bee arthropods visiting flowers in apple orchards. The outcome of our study provides information regarding the effects of management practices on arthropod biodiversity, which can contribute to informing on suitable management practices to increase crop yield and maintain healthy agricultural systems.


Introduction
Arthropod biodiversity in agricultural systems is crucial to maintaining the agroecosystems' health resilience (Rader et  For this reason, a combination of methods will provide a more complete overview of arthropods present in apple orchards. Traditional techniques such as visual assessment rely on the observant´s knowledge and ability to capture and identify arthropod species, and the methods can relatively be labour-intensive (Russo et al., 2015;Evans and Kitson, 2020). Visual assessment is attractive in providing direct observation data of flower visiting arthropods, but observations require weather conditions that allows pollinator activity, which can be limited in Denmark in spring.
High-throughput sequencing (HTS) technologies have recently been applied in the study of arthropod communities in managed ecosystems. One such approach, metabarcoding, consists of the amplification and detection of a specific sequence of interest from a mixture of environmental DNA (Ruppert et al., 2019;Evans and Kitson, 2020). This technique has many applications, including pollen identification from different sources such as plant tissues or animal traces (Lucas et  Thomsen and Sigsgaard, 2019), we predict that i) pollinator diversity will be mainly represented by Hymenoptera and Diptera, ii) there will be significant differences in arthropod communities across the orchards, and iii) arthropods detected will be similar between the methodologies used.

Methods
Arthropods were sampled in four apple orchards on Sealand, Denmark; one 20 km north of Copenhagen (Frydenlund), two 25 km and 37 km south-west of Copenhagen respectively (Kildebrønde, and Ventegodtgaard), and finally the Pometum, 16 km west of Copenhagen, belonging to University of Copenhagen ( Fig. 1A and see Supplemental information 1 Table S1).
Sites were separated by at least 9 km and located in an agricultural matrix. While Frydenlund and Kildebrønde were relatively large orchards with more than 20 rows of apples (more than 100m wide), apple plots at the Pometum and Ventegodtgaard were only 7 and 10 rows wide respectively (less than 40m wide). Ventegodtgaard was managed organically while the other three followed integrated pest management (IPM). All the orchards had honey bee hives within the field (Frydenlund and Kildebrønde) or in the surrounding crops (the Pometum and Ventegodtgaard). Arthropods were sampled in four different distances from the margin (side of orchard where flowers strip was sown) of the orchard: row 1 (0 m from the margin, edge of the orchard (first row of apple trees)), row 3, row 5 and row 10, approximately 5, 10 and 25m from the margin, respectively (Fig. 1B). In the smallest orchard, at the Pometum, the last row was row 7, approximately 15m inside and facing a strip of grass followed by a pear orchard (Fig. 1B).
The visual assessment protocol was adapted from Westphal et al. (2008). The vegetation surrounding the orchards was different across them (See Supplemental information 1 Table S2).
Sampling was conducted when the percentage of open apple flower buds was between 50-90%.
This occurred in late May 2020 for two weeks till the end of the apple flowering period.

Metabarcoding
We followed the methods from Thomsen and Sigsgaard (2019) to analyse the environmental DNA (eDNA) present in the sampled apple flowers.
In the morning, five individual apple flowers were collected in rows 1, 3 and 7 of each orchard.
Thus, a total of 60 flowers were individually collected in sterile plastic tubes (50mL, Thermo Scientific). Collection was done using single-use sterile nitrile gloves, to avoid any pollution of samples. Plastic tubes were put on ice and stored at -20ºC prior to DNA extraction.

DNA extraction
DNA extraction was carried out at the Department of Plant and Environmental Science laboratories, University of Copenhagen. The experiment was performed in a PCR-free laboratory to prevent contamination. DNA was extracted using the Qiagen DNeasy Blood & Tissue Kit and protocol in a flow hood. First, the whole apple flower was transferred to a 2ml Eppendorf tube prior to DNA extraction. Lysis was performed by adding 900µl of a cell lysis solution (ATL buffer (QIAGEN)) and 100 µl of proteinase K (QIAGEN). Samples were disrupted in the TissueLyser II (QIAGEN) for 2 minutes at 30Hz and incubated at 56ºC with agitation in a rotor for 3 hours. Samples were vortexed for 10 seconds before transferring 800 µl of the lysis mixture to a new 2ml Eppendorf tube. 800 µl of lysis buffer (AL buffer (QIAGEN)) was added and the mixture was mixed thoroughly by vortexing before incubation at 56ºC for 10 minutes. 800 µl of absolute ethanol was added to the mixture, followed by vortexing before adding the mixture to the spin columns. The mixture was spun through the membrane filter over three rounds (700 µl per round) with 1.5 minutes of centrifugation at 8000rpm after each round. The flow-through was discarded every round. The Spin columns were washed by adding 600 µl of wash buffer (AW1 (QIAGEN)) and centrifuged for 1.5 minutes at 8000rpm, followed by adding 600 µl of AW2 (QIAGEN) and centrifuged for 3.5 minutes at 14000rpm. Each spin column was transferred to a new 2ml Safe-lock Eppendorf tube and DNA was eluted in 2x60 µl AE buffer (QIAGEN) with a 15 minute incubation step at 37ºC before centrifugation (1.5 minutes at 10000rpm). One extraction blank was included at the beginning of the process to test for possible contamination during the procedure.
DNA extracts from apple flowers collected from the same row, but different apple trees in the same orchard, were pooled according to the DNA concentration and 260/280 ratio, measured by Microvolume spectrophotometer (mySPEC, VWR) (See Supplemental information 1 Table S3).
This resulted in a total of 36 pooled DNA extracts and one extraction blank stored at -20ºC prior to further analysis.

PCR amplification
All DNA extracts including the extraction blank were sent to AllGenetics & Biology SL  Table S5). No sequences were found in the extraction blank and PCR blank when checked for possible contamination. The remaining sequences whose taxa identification was higher than species level were compared to BLAST, changing the final taxa name from only those with 100% of Query cover and percent identity (See Supplemental information 1 Table S6). Only taxa that belonged to the arthropod phylum were kept (See Supplemental information 1 Table S7). The resulting output was grouped according to four different orders: Blattodea (BT), Coleoptera (CP), Diptera (DI), and Lepidoptera (LP) (See Supplemental information 1 Table S8).

Visual census
The data from the visual assessment was generated as part of the Beespoke project, where the effect of arthropod flower visitor diversity and abundance was assessed as a function of distance from a flower strip. An observer noted all the arthropod flower visitors 2.5 meters on each side of the observer (covering two rows of apple trees). The type and number of arthropods were recorded for five minutes in each observational transect walk. Arthropods were identified to morpho-groups: Bumblebee (BB), Coleoptera (CP), Diptera (DI), honey bee (HB), Lepidoptera (LP), Syrphid (SY), and wild bee (WB). This classification was established following previous studies on typical pollinators found in apple orchards (Ramírez and Davenport, 2013). We did not sort Hymenoptera to family level but instead sorted them according to honey bees, wild bees and bumblebees as foraging habits and activity differ within the same family ( Transects were performed at each orchard during two different times of the day; mornings (between 9:00 and 12:00) and afternoons (between 13:00 and 16:00). We sampled twice in Kildebrønde, the Pometum and Ventegodtgaard, once in the morning and once in the afternoon.
We sampled thrice in Frydenlund, once in the morning and twice in the afternoon. Surveys were only carried out when wind speed was not exceeding 7m/sec and a threshold temperature of 10ºC on sunny days, and 15ºC on overcast days (Ramírez and Davenport, 2013). These factors decided when to select the days and times for sampling (using regional weather forecast from Danish Meteorological Institute).

Statistical analysis
For metabarcoding, statistical analysis was carried out using the R package vegan (Oksanen et al., 2020). Only taxonomic units assigned to Arthropoda and present in Denmark were considered (See Supplemental information 1 Table S8). Sequence counts were analysed in two different ways; presence/absence of each insect taxa measured using the frequency of occurrence For visual assessment, the sampling effort was different across the orchards. Due to meteorological conditions and orchard location, the four orchards were sampled on a different number of days. Honey bee (HB) data was not included in the arthropod richness analysis as it occurs in Denmark only as a managed species (Rasmussen et al., 2021a). We considered morphogroups as taxonomic units for arthropod composition analysis. The effect of honey bee hives on wild pollinators was studied. Orchards with honey bee hives within the field were considered orchards with close honey bee hives (Kildebrønde and Frydenlund) while those with hives in the surrounding crops were established as orchards with distant hives (Ventegodtgaard and the Pometum). Wild pollinators included all arthropods that were not honey bees. Chi-squared test was used to test for dependency between the variables while the type of relationship was measured by an ODDS ratio and Risk estimate from R package fmsb (Nakazawa, 2021).
To avoid biased results due to the small sample size and sampling effort, rarefaction tests were carried out for both the molecular and non-molecular analyses to assess sampling completeness and the relationship between arthropod richness and type of orchard (Gotelli and Chao, 2013;Russo et al., 2015). We used arthropod order and the orchard data to complement and compare both molecular and non-molecular methodologies. Arthropod communities were compared using 95% confidence intervals (Chao1 estimator) of the rarefaction curves and extrapolation of Hill

Metabarcoding
From our metabarcoding analyses, 4918 sequences were removed due to either unsuccessful PCR amplification or filtering parameters (See Supplemental information 1 Table S4). The final dataset consisted of 355 sequences with a minimum percentage identity of 98% to at least one taxon in the EMBL reference database after taxonomic assignment (See Supplemental information 1 Table S5). 129 of these sequences belonged to an oomycete (Peronosporaceae family). After merging the sample replicates, the final dataset consisted of 14 insect taxa (See Supplemental information 1 Table S8). The taxonomic resolution was 7% (1 taxa) at order level, 21% at genus level (3 taxa), and 72% at species level (10 taxa) ( Table 1). Two detected species (the Diptera Lonchoptera uniseta and the Blattodea Periplaneta americana) are not naturally occurring in Denmark (DanBIF Secretariat, 2021), except, in the case of P. americana as an occasional pest in bakeries (Jensen, 1993). Neither of them had 100% match to BLAST reference dataset. Therefore, Lonchoptera uniseta was kept at genus level (Lonchoptera) since other species of this genus, such as Lonchoptera lutea, are common in the area. It is possible that another species of cockroach may be found in the orchards, most likely Ectobius lapponicus, a forest cockroach that belongs to the same family and is common in Denmark (DanBIF Secretariat, 2021). Thus, we proposed this species to the list in Table 1.     8 Ramírez and Davenport (2013), 9 Holliday (1977), 10

Arthropod composition
The composition of arthropods found in the apple orchards differed based on the metabarcoding analysis used; frequency of occurrence (Fo) and relative read abundance (RRA) (See Supplemental information 1 Table S9). Based on Fo analysis, the occurrence of Diptera (DI) in apple orchards was the highest as compared to the other orders at 44% (Fig. 2A). Whereas, based on RRA analysis, Lepidoptera (LP) had the highest read counts at 49% (Fig. 2B). The least common arthropod groups were Blattodea (BT) and Coleoptera (CP) in both analyses ( Fig. 2A,   B).    S1). All arthropod orders were detected in Ventegodtgaard. Blattodea and Coleoptera were absent from Frydenlund, Blattodea was also absent from Kildebrønde, and Diptera was absent from the Pometum (See Supplemental information 1 Table S9).

Comparison between metabarcoding and visual census
Using metabarcoding, we did not detect any Hymenoptera or Syrphidae. However, we detected other orders such as Blattodea (BT) that were not found in the visual census. The arthropods detected from visual census were all adult pollinators that visited apple flowers (Fig. 3).
Coleoptera, Diptera, and Lepidoptera were detected using both metabarcoding and visual census (

Discussion
We demonstrated in our study the reproducibility of using environmental DNA (eDNA) from flowers to detect arthropod visitors in apple orchards (Thomsen and Sigsgaard, 2019). We further showed the complementarity of using visual census and metabarcoding methodologies to improve current knowledge of arthropod communities regardless of differences in emergence dates or foraging periods. Visual census found that pollinator communities were mainly represented by Hymenoptera and Diptera. Metabarcoding found DNA traces of several herbivores, some of them apples pests, and also revealed other flower-visitors which may be more night-active and hence missed by visual census. The fact that flowers were collected in the morning could explain that DNA traces from day active Hymenoptera were not captured by the molecular technique. Therefore, this study also highlights some challenges to consider when using either techniques for future studies.

Arthropods identified in apple orchards
From our metabarcoding results, we identified some common apple flower visitors such as Diptera and Coleoptera. Additionally, we detected Blattodea that has not commonly been

Comparison between metabarcoding and visual census
All arthropod orders found with both methodologies were previously recorded in apple orchards

Implications on orchard managements
Knowledge on beneficial and detrimental arthropods visitors to apple orchards is crucial to We demonstrated in our study using both visual census and metabarcoding the existence and importance of wild non-bee pollinators such as Diptera, whose presence in apple orchards has To conclude, our study provides the starting point for a more complete overview of biodiversity of arthropod flower-visitors found in apple orchards. The combination of molecular and traditional non-molecular techniques for arthropod assessment is complementary and can overcome some of the limitations inherent with using only one method. Going forward, we recommend the use of both molecular and non-molecular approaches in the assessment of arthropod diversity if time and budget permit. Otherwise, utilising a molecular approach such as metabarcoding with at least two primers can help to optimise arthropod detection. The outcomes of our study can support future management practices moving towards more resilient and environmentally friendly agricultural systems.

SUPPLEMENTAL INFORMATION
-README-This file contains a summary of all the supplementary material contained within this folder. -Supp1-Supplementary Tables S1 to S13 -Supp2-Supplementary 2-Figures S1 to S2 -rawdata.tgz-Raw sequence files from AllGenetics -cutadapt_OBItools.zip: Cutadapt trimmed files for OBITool -My_code_pollinators.bash: Bioinformatic script used to generate metabarcoding data contained in Supp1 -paper_Rscript-R scripts used to generate metabarcoding and visual census data (Rmd) contained in Supp1 and Supp 2 -paper_Rscript-R scripts used to generate metabarcoding and visual census data (html) contained in Supp1 and Supp 2 -paper_Rscript.zip-.csv files used for stats test on R-studio