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A combined DNA metabarcoding/in-situ macro photography approach to reliably analyse carnivorous plant prey spectraResults indicate that DNA metabarcoding allows for reliable analysis of prey spectra composition in carnivorous plants at a taxonomic resolution and level of completeness unachievable by traditional morphology-based approaches (as performed, for example, by4,5,6,7,9,10,11). Even in remote tropical northern Western Australia, where many (if not most) arthropod species have not yet been accessioned into the BOLD or GenBank barcode reference libraries, this method identified over 90% of obtained OTUs from our sample set; most of them at family-level, but 41% to genus-level, and 17% even down to species rank (Supplementary Data S1). Lekesyte et al.27 were able to identify 80% of the analysed prey items found on D. rotundifolia in England to species-level. However, their sampling was performed in western Europe, whose entomofauna is comparatively well studied taxonomically and has an excellent coverage in the BOLD reference library of DNA barcodes41. New insect barcodes are regularly added to the BOLD library through large-scale initiatives such as the international Barcode of Life Project (iBOL; https://ibol.org/) and its Australian node Australian Barcode of Life Network (ABOLN), hence accuracy of future metabarcoding research performed in Australia can be expected to increase to similar levels soon.In-situ macro photography was found to provide a valuable plausibility control tool for the prey taxa identified by metabarcoding. While many of the smaller prey taxa detected by metabarcoding were impossible to identify in the in-situ macro photographs due to their tendency to quickly degenerate after digestion into small, shapeless “crumbs”8, this control method considerably reduced the amount of prey taxa detected which were not actually present as prey in the Drosera samples. This flaw of metabarcoding is most commonly a consequence of procedural errors resulting in cross-contamination within the DNA extraction procedure27, usually resulting in low read numbers. However, in-situ macro photographs may also fail to detect species if prey captured by the sundew escaped from the trap33,42, or was stolen by larger animals. In both cases, a DNA imprint left on the Drosera leaves as excretions, detached scales, hairs or, frequently, as autotomised (shedded) body parts42 could have been detected by metabarcoding. Additionally, some barcoding-detected taxa may not constitute prey if they were associated with another captured prey taxon (either as part of its diet, or as a parasite). The latter may explain some barcode hits for taxa not immediately apparent from the in-situ macro photographs, as they are (endo)parasites of captured prey taxa. This was likely the case in the detected Strepsiptera (stylops) which are frequently contained as larvae and adult females in their hymenopteran and orthopteran hosts43. However, insect endoparasites and other non-obvious prey taxa were by default not excluded by the very conservative approach of pictorial plausibility control. Additionally, in the case of endoparasites, these organisms would also contribute to plant nutrition as “bycatch” after being digested together with their host, despite not having been actively attracted to the carnivorous traps. Finally, the control method tested in this study showed that even heavily digested prey items in the samples had sufficient amounts of intact (mitochondrial) DNA present to be detected by metabarcoding, as we found no instance of any prey item being clearly identifiable in the macro photographs but not present in the barcoding data.Prey spectra composition of the studied Drosera speciesThe analysed prey spectra of the three studied species from D. sect. Arachnopus most commonly contained flying insects (especially of the orders Diptera and Hemiptera, both present in 100% of the samples; Fig. 3), thus confirming earlier in-situ macro photography-based studies of closely-related D. sect. Arachnopus species by Krueger et al.8. All members of D. sect. Arachnopus are characterised by a large, erect growth habit and thread-like aerial leaves which usually do not contact the ground8,32, thereby excluding most ground-dwelling arthropods as prey. This result is also similar to other prey spectra studies of erect-leaved Drosera from different geographic areas, where flying insects (particularly Diptera) unanimously comprised almost the entire recorded prey5,11,44. Furthermore, this study confirmed the result of Krueger et al.8 that Hemiptera—and within this order especially the Cicadellidae—are exceptionally common in the prey spectra of D. sect. Arachnopus compared with all other, previously studied Drosera. A possible explanation for this may be the relatively high abundance of Cicadellidae in tropical habitats45 compared to subtropical or temperate habitats where the above-mentioned previous Drosera prey spectra studies were conducted.Of the five most commonly detected orders, Lepidoptera generally comprised the largest prey items in terms of body size or wingspan, respectively. This prey order was exceptionally common in D. finlaysoniana, being present in 100% of samples and also visually conspicuous in the in-situ photographs. Since this Drosera species had by far the largest trapping leaves among the three species studied with an average leaf length of 10.4 ± 0.6 cm (Suppl Appendix S7), and exhibits the largest leaves in D. section Arachnopus32, this may represent an example of large prey items being more easily captured by species with larger trapping leaves33. Additionally, the sampled population of D. finlaysoniana was huge and dense (see Supplementary Figure S1), probably attracting larger prey and enabling capture of larger prey items by “collective” trapping46. Alternatively, Fleischmann30 suggested that captured Lepidoptera themselves could attract further individuals of the same species by pheromone release, potentially explaining the very high numbers of this insect order observed in D. finlaysoniana.Differences among observed prey spectraComparison of prey spectra between the three studied Drosera species revealed significant differences at arthropod family-level but not at the higher level of arthropod orders, indicating that at a coarse taxonomic resolution, the same five arthropod orders (Diptera, Hemiptera, Hymenoptera, Lepidoptera and Thysanoptera) generally comprise most of the prey in D. sect. Arachnopus, regardless of given Drosera species or habitat. However, as strong differences were discovered in the ANOSIM comparison at family-level, it can be concluded that differences might likely increase with finer taxonomic resolution of prey taxa, a conclusion also reached by the carnivorous plant prey spectra meta-analysis of Ellison & Gotelli47. While these differences may be partially attributed to different morphological traits of the three species such as leaf scent8,30 or eglandular appendages31, the very high ANOSIM R-values returned and the large number of prey families contributing nearly equally to dissimilarity (Table 2) indicate that the most likely explanation is very different available prey spectra at the three study sites. Indeed, significant differences among different study sites, even within the same species, were previously reported for Drosera rotundifolia by Lekesyte et al.27 and for four species from D. sect. Arachnopus by Krueger et al.8. Notably, the three study sites feature different habitat types and climate regimes (Supplementary Fig. S1).Analyses indicate that there is likely little specialisation in prey capture by the three studied Drosera species. For example, the relatively high detection rate of Lepidoptera in the samples of D. finlaysoniana and D. hartmeyerorum compared to D. margaritacea may be explained by the lake margin habitats of the former two species, while the latter species was found in a completely dry drainage channel lacking any nearby waterbodies (Supplementary Fig. S1). Lepidoptera are likely to occur in much higher concentrations near water sources, especially during the dry season (May to November) when the surrounding areas are lacking other water sources (G. Bourke in Fleischmann30).Estimating prey quantityIn addition to providing a plausibility control for the compositional prey analysis by metabarcoding, the in-situ macro photography method facilitated an estimation of prey quantity per sample. Metabarcoding by itself is currently not a reliable tool for prey quantification due to the lack of a linear relationship between the number of sequence reads and organism biomass26,27.In contrast to Krueger et al.8, who generally found more prey items on larger trapping leaves in species of D. sect. Arachnopus (even when values were compared as per cm of trapping leaf length), the species with the largest leaves studied here (D. finlaysoniana) captured significantly less prey items than the smaller-leaved species D. margaritacea and D. hartmeyerorum (Fig. 4). However, while Krueger et al.8 was able to compare sympatric species (thus minimising any potential effects of the habitat or region on prey spectra), the three species in this study were studied at three different, geographically distant sites. While it is possible that overall prey abundance in the habitat was much lower at the D. finlaysoniana study site (Site 1), it can be hypothesised that the low total prey capture observed in this species may be due to the very large and extremely dense population resulting in strong intraspecific competition for prey (see Supplementary Fig. S1). This effect of population structure on prey capture has also been observed by Gibson48 and Tagawa and Watanabe46 who found a significant negative correlation between total prey capture and population density in different species of Drosera.Conclusions and outlookOur study is the first to employ a DNA metabarcoding approach supported by controls for species presence to analyse carnivorous plant prey spectra. When combined with in-situ macro photography, this method is clearly superior in terms of taxonomic resolution and completeness for analysis of environmental bulk samples (containing different organisms in highly variable states of preservation), as used here for the reconstruction of prey spectra of carnivorous plants. The capability of this method increases with new reference barcodes being regularly added to DNA barcode libraries (such as BOLD and NCBI GenBank) and it thus has the potential to become the standard methodology for future carnivorous plant prey spectra research.Additional studies are needed to test this method for other carnivorous plant species and genera, especially those possessing different trap types. Within Western Australia, three additional trap types occur: snap traps (Aldrovanda), suction traps (Utricularia) and pitfall traps (Cephalotus). In particular, it might be expected that in-situ macro photography will not work as well for the extremely small, typically submerged traps of Aldrovanda and Utricularia (which also completely enclose their captured, microscopic prey items49), potentially necessitating usage of alternative control methods for metabarcoding data. Furthermore, even within Drosera (adhesive traps) some species may require adjustments to the methodology presented here as they accumulate captured prey in a central point via tentacle movement (e.g., many climbing tuberous Drosera) or their leaves may be very difficult to place on paper sheets with the sticky side facing upwards (e.g., all pygmy Drosera). The latter problem may be solved by using reverse action forceps and photographing the leaves while held in place by the forceps.Extensive sampling of sites with co-occurring species from D. sect. Arachnopus is clearly required to better understand the ecological role of trap scent and eglandular appendages in this section. For example, manipulation experiments involving the removal of all yellow blackberry-shaped appendages of D. hartmeyerorum (which have been hypothesised to function as visual prey attractants31) and subsequent metabarcoding prey spectra comparisons of mutilated plants lacking emergences with control plants are proposed. Potential effects of population density on prey spectra (as hypothesised here for D. finlaysoniana) could be studied by comparing prey spectra of individual plants from within mass populations with more exposed-growing individuals of the same population. More

Of which at the third instar, the external morphology of larvae is quite similar; thus, the morphological identification used to differentiate between its genera or species, generally includes cephalophalyngeal skeleton, anterior spiracle, and posterior spiracles. The morphology of the posterior spiracle is one of the important characteristics for identification. A typical morphology of the posterior spiracle of third stage larvae was shown in Fig. 2. Based on studying under light microscopy, the posterior spiracle of M. domestica was clearly distinguished from the others. On the other hand, the morphology of the posterior spiracle of C. megacephala and A. rufifacies was quite similar. For C. megacephala and C. rufifacies, the peritreme, a structure encircling the three spiracular openings (slits), was incomplete and slits were straight as shown Fig. 2A,B, respectively. The complete peritreme encircling three slits was found in L. cuprina and M. domestica as shown in Fig. 2C,D, respectively. However, only the slits of M. domestica were sinuous like the M-letter (Fig. 2D). Their morphological characteristics found in this study were like the descriptions in the previous reports23,24,25.Figure 2Morphology of posterior spiracles of four different fly species after inverting the image colors; (A) Chrysomya (Achoetandrus) ruffifacies, (B) Chrysomya megacephala, (C) Lucilia cuprina, (D) Musca domestica.Full size imageFor model training, four of the CNN models used for species-level identification of fly maggots provided 100% accuracy rates and 0% loss. Number of parameter (#Params), model speed, model size, macro precision, macro recall, f1-score, and support value were also presented in Table 1. The result demonstrated that the AlexNet model provided the best performance in all indicators when compared among four models. The AlexNet model used the least number of parameters while the Resnet101 model used the most. For model speed, the AlexNet model provided the fastest speed, while the Densenet161 model provided the slowest speed. For the model size, the AlexNet model was the smallest, while the Resnet101 model was the largest which corresponded to the number of parameters used. Macro precision, macro recall, f1-score and support value of all models were the same.Table 1 Comparison of model size, speed, and performances of each studied model (The text in bold indicates the best value in each category).Full size tableAs the training results presented in the supplementary data (Fig. S1), all models provided 100% accuracy and 0% loss in the early stage of training ( More

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Chemicals and materialsAnalytical standard ( > 99% purity) of spiromesifen, BSN2060-enol, and chromafenozide were purchased from AB Solution Co., Ltd., Hwaseong-si, Gyeonggi-do, Republic of Korea. HPLC grade water and acetonitrile were supplied by Merck, Darmstadt, Germany. QuEChERS kit (4.0 g magnesium sulfate, 1.0 g sodium chloride, 1.0 g sodium citrate tribasic dihydrate, 0.5 g disodium citrate sesquihydrate) were obtained from Phenomenex, California, USA. Individual stock solutions of the target compounds were prepared in acetonitrile and stored at − 20 °C before use.Field experimentsThe trials were carried out in a greenhouse farm during the season 2018 at two different sites (with approximately 24 km distance between both sites) located in Chuncheon and Hongcheon-gun, Gangwon-do, Republic of Korea following the method described by the Organization for Economic Co-operation and Development (OECD)38. The field test of lettuce (Latuca sativa L.) crop was conducted in Chuncheon city, and perilla (Perilla frutescens (L.) Britton) crop in Hongcheon city. The area of each field was divided into two plots (treatment and control). The treatment plots were further divided into three replicates (subplots 33 m2). The control plot was separated by a buffer zone of 3 m2 from the treated site. To minimize spray overlap, buffer zones (1 m) were set up between subplots. The commercial products of spiromesifen 20% SC diluted 2000 times and chromafenozide 5% EC diluted 1000 times were sprayed twice at 7-days intervals using an automatic sprayer. After the second spray samples (lettuce and perilla leaves) were collected from each subplot at 0 (2 h after spraying), 1, 3, 5, and 7 days according to the Korean RDA23 method. Thirty samples 1.0 kg each from the collected crop were placed in polyethylene bag and labeled. After collection, the samples were transported to the laboratory, where they were chopped and homogenized. The homogenized samples were kept frozen at − 20 °C until analysis.We confirm all plant samples used in the current work comply with the IUCN Policy Statement on Research Involving Species at Risk of Extinction and the Convention on the Trade in Endangered Species of Wild Fauna and Flora.Samples pretreatmentA QuEChERS method was used for the extraction of the targeted compounds from lettuce and perilla leaves. A 10 g of previously homogenized samples were weighed into a 50 mL polypropylene centrifuge tube and mixed with 10 mL of water followed by 10 mL of acetonitrile. The samples were shaken at 1500 rpm in a shaker machine for 10 min. Then commercial QuEChERS kit was added, and the mixtures were shaken vigorously for 2 min in a shaker. Subsequently, the samples were centrifuged at 3584 g-force for 10 min. After centrifugation, the supernatant was filtered with a 0.22 μm membrane filter and transferred into the glass vial for instrumental analysis.LC-MS/MS analysisQuantitative determination of the tested compounds was carried out by using HPLC system Dionex Ultimate 3000 (Thermo Science, USA) coupled with tandem mass spectrometry (MS/MS) (TSQ Quantum Access Max (Thermo Science, USA). Water (solvent A) and acetonitrile (solvent B) containing 0.1% formic acid and 5 mM ammonium format were used as mobile phase at a flow rate of 0.4 mL/min and injection volume 1.0 µL. To obtain desirable chromatographic peaks, two different instrumental conditions were used. The chromatographic separation of spiromesifen was separated by Capcell core-C18 (2.1 mm I.D. × 150 mm × 2.7 μm, Shiseido Co., Ltd., Tokyo, Japan) and BSN2060-enol was performed by C18 column (Poroshell 120 SB-Ag, 2.1 mm I.D. × 100 mm × 2.7-μm, Agilent Technologies, Santa Clara, California, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5.0%; 2.0 min 5%; 2.5 min, 95%; 6.0 min, 95%; 6.5 min, 5.0%; 10 min, 5.0%. Likewise, chromafenozide was separated by C18 column (Imtakt Unison UK-C18, 2.0 mm I.D. × 100 mm × 3.0-μm, Imtakt, Portland, USA) with a gradient elution as follows (mobile phase B%): 0.0 min, 5%; 1.0 min, 5.0%; 1.5 min, 90%; 5.0 min, 90%; 7.0 min, 5.0%; 10 min, 5.0%. An MS/MS system (TSQ quantum ultra, Thermo Science, USA) equipped with an electrospray ionization source operating in positive mode (ESI+) was used. The MS/MS parameters and selected product ions are shown in supplementary Tables S2 and S3.The calculation of spiromesifen total residuesThe total residues in lettuce and perilla samples were calculated using Eq. (1)23.Total residues of spiromesifen (mg/kg) = spiromesifen + (BSN2060 residue × 1.36). The conversion factor was calculated as follow;$${1}.{36},{text{(conversion}},{text{factor)}} = frac{{370.49left( {{text{spiromesifen}},{text{MW}}} right)}}{{272.34{ }left( {{text{BSN}}2060,{text{MW}}} right)}}$$
(1)
where MW molecular weight.Initial deposition calculationThe initial residues of spiromesifen and chromafenozide deposition in lettuce and perilla leaves were calculated from 0-day according to Eq. (2) described by Kang et al.12 as follow;$${text{A }},({text{mg}}/{text{kg}}) = {text{B(mg}}/{text{kg)}} times frac{100}{{{text{C}}({text{% }})}} times frac{1}{{text{E}}} times 1000$$
(2)
A: Initial residue (mg/kg), B: Residues (mg/kg) on 0 day, C: active ingredients, E: dilution factor.Method validationThe analytical method was validated in terms of different performance criteria such as linearity, accuracy, precision, and method limit of quantitation (MLOQ). Matrix-matched standards were used to construct the calibration curve by evaporating (0.01, 0.05, 0.1, 0.2, 0.5, 0.7 and 1.0 mg/kg) working solution (1 mL) and re-dissolved in the extract of control sample. The linearity of the matrix-matched calibration curve was evaluated by the values of the correlation coefficient (R2). The accuracy and precision were obtained in terms of recovery (70–120%) and repeatability (n = 3). The recoveries were determined by spiking the analytes at two concentrations levels (0.1 mg/L) and (0.5 mg/L) in 10 g control samples and were quantified by comparing the response of analytes in samples with response in calibration standard solutions prepared in matrix. The repeatability expressed as the relative standard deviation (RSD) of the analyzed samples was calculated from three repetitions. The MLOQ was calculated by Eq. (3) taking into consideration the following factors: the instrument limit of detection, volume of extraction solvent, injection volume, dilution factor, and sample amount39,40.$${text{MLOQ}},{text{(mg}}/{text{kg)}} = {text{A(ng)}} times frac{{text{B(mL)}}}{{{text{C(}}upmu {text{L)}}}} times frac{{text{D}}}{{text{E(g)}}}$$
(3)
where A: instrument limit of detection, B: volume of extraction solvent, C: injection volume, D: dilution factor, E: sample amount.Half-life calculationThe dissipation patterns of spiromesifen and chromafenozide in lettuce and perilla leaves over time were found following the first-order kinetics model28. The half-life was determined by the following equation:$${text{C}}_{{text{t}}} = {text{C}}_{0} times {text{e}}^{{ – {text{kt}}}} ,{text{DT}}_{{{5}0}} = {text{ln2}}/{text{k}}$$where Ct is the concentration of the insecticide, C0 represents the initial residue concentration of insecticide, t is the time (days) after insecticide application, and k is the constant rate.Safety assessmentIn this study, the safety assessments (percent acceptable daily intake; %ADI) of the target insecticides that are consumed with lettuce and perilla leaves were calculated by the ratio of estimated daily intake (EDI) to acceptable daily intake (ADI). The EDI was calculated using insecticide concentration and average consumption of food commodities per person per day. In addition, the theoretical maximum daily intakes (TMDIs) of both insecticides were calculated using the maximum residue limits (MRLs) and average body weight (60 kg) of adults in Republic of Korea. TMDIs were calculated following the equation described by Kim et al.41.$$begin{aligned} & {text{ADI (mg}}/{text{person}}/{text{day)}} = {text{ADI}},({text{mg}}/{text{kg}}/{text{body weight}}/{text{day}}),{text{of target insecticide}} times {text{6}}0,({text{average body weight}}) \ & {text{EDI (mg}}/{text{kg}}/{text{person)}} = {text{concentration of target insecticide (mg}}/{text{kg)}} times {text{ daily food intake (g)}} \ & % {text{ADI}} = {text{EDI}}/{text{ADI}} times {text{1}}00 \ & {text{TMDI}}% = sum % {text{ADI of all registered crops}} \ end{aligned}$$ More

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