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DNA barcoding of fogged caterpillars in Peru: A novel approach for unveiling host-plant relationships of tropical moths (Insecta, Lepidoptera)

  • Axel Hausmann ,

    Roles Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Methodology, Project administration, Resources, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    hausmann.a@snsb.de

    Affiliation SNSB-Zoologische Staatssammlung München, München, Germany

  • Juliane Diller,

    Roles Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

    Affiliation SNSB-Zoologische Staatssammlung München, München, Germany

  • Jerome Moriniere,

    Roles Data curation, Formal analysis, Methodology, Software, Validation, Writing – review & editing

    Affiliations SNSB-Zoologische Staatssammlung München, München, Germany, Advanced Identification Methods GmbH, München, Germany

  • Amelie Höcherl,

    Roles Conceptualization, Investigation, Methodology, Writing – review & editing

    Affiliation SNSB-Zoologische Staatssammlung München, München, Germany

  • Andreas Floren,

    Roles Investigation, Methodology, Writing – review & editing

    Affiliation Julius Maximilians Universität, Würzburg, Germany

  • Gerhard Haszprunar

    Roles Conceptualization, Funding acquisition, Project administration, Resources, Supervision, Writing – review & editing

    Affiliation SNSB-Zoologische Staatssammlung München, München, Germany

DNA barcoding of fogged caterpillars in Peru: A novel approach for unveiling host-plant relationships of tropical moths (Insecta, Lepidoptera)

  • Axel Hausmann, 
  • Juliane Diller, 
  • Jerome Moriniere, 
  • Amelie Höcherl, 
  • Andreas Floren, 
  • Gerhard Haszprunar
PLOS
x

Abstract

The present study aimed to perform molecular identification of lepidopteran larvae from canopy fogging including gut-content analyses. A total of 130 lepidopteran larvae were selected from 37 fogging samples at the Panguana station, district Yuyapichis, province Puerto Inca, department Huánuco, Peru. Target trees were pre-identified and subsequently submitted to molecular confirmation of identity with three markers (rbcL, psbA and trnL-F). The COI gene of 119 lepidopteran larvae was successfully sequenced and found to belong to 92 species: Comparison of DNA barcodes with the reference database of adult moths resulted in 65 (55%) matches at species level, 32 (27%) at genus level, 19 (16%) at subfamily or family level, three just to order level. Three larvae could not be assigned to a family. For these larvae the fogged target tree now suggests a potential host-plant relationship. Molecular gut content analysis, based on High-Throughput-Sequencing was successfully tested for ten larvae corroborating feeding on the target plant in some cases but elucidating several other cases of potential ‘alternative feeding’. We propose a larger-scale approach using this rapid and efficient method including molecular gut-content analyses for comprehensively testing the ratio of ‘alternative feeders’ and pitfalls caused by collateral fogging of larvae from neighboring trees.

Introduction

Despite much valuable work on host-relationships of Neotropical moths, e.g. from Ecuador [1,2], or Costa Rica [3,4,5], the relevant literature is still scarce and patchy compared with the huge species diversity of Lepidoptera in Central and South America. Apart from the aforementioned programs only few original data are published for host-plant relationships of Lepidoptera and much of the work focused on caterpillars found on plants of economic importance (pests and potential pests) (e.g. [6,7,8]).

Exemplified from one of the most diverse moth families, Geometridae, the largest project in Costa Rica so far revealed the huge amount of 22,957 geometrid moth records, the barcoded reared adults clustering to 566 BINs, of which 162 currently having Linnean species names (D.J. Janzen & W. Hallwachs pers. comm.). Brehm [1] presented 48 neotropical geometrid species with host-plant records, with 11 records added by Dyer et al. [9] and 59 records by Bodner et al. [2]. Thus, altogether for some 680 Neotropical geometrid species (about 270 of which with Linnean species names) host-plant relationships are known, covering approx. 8–10% (4% with Linnean names) of the described geometrid fauna of Central and South America (for estimations of total number of described geometrid species cf. Scoble et al. [10]: 6433 species; Heppner [11]: 7956 species).

Estimation for Neotropical species diversity is based on approx. 37,000 described Neotropical moth species (Heppner [11]: 44,800 described Lepidoptera, including approx. 7800 Rhopalocera species [12]) and considering that (a) ‘Microlepidoptera’ are severely understudied and (b) the vast majority (>70%) of the Neotropical moth fauna is still undescribed as suggested by the ratio of undescribed species in some 380,000 Neotropical lepidopteran DNA barcodes on Barcode of Life Data Systems (‘BOLD’ [13]; accessed September 2019). Extrapolating the aforementioned data on species numbers and feeding records we estimate that for >98% of the putatively >100,000 Neotropical moth species authentic feeding records from nature are lacking.

Traditionally, most insect larvae are identified by rearing them to the adult stage and by analysing the morphology of the adult. Methodological constraints in this classic approach are (1) visual search and collecting on plant depending on the skills of the biologist, (2) the canopy region of trees hardly accessible, (3) nocturnal activity of many larvae requiring difficult search by night, (4) collecting without feeding observation may lead to misinterpretations ([14,15]: 20–50% „alternative feeders”on lichens, dead leaves, algae, etc), (5) beating, shaking, net-sweeping may obscure the real where-about of the larva, (6) feeding records in rearing may not reflect the natural host-plant association, (7) rearing to adult is time consuming, (8) rearing may fail (deseases, parasitoids), (9) identification and availability of host-plant (for rearing) often difficult.

Molecular identification of lepidopteran larvae and other insects through DNA barcoding (COI 5’) was repeatedly carried out successfully, e.g. [16,17,18,19,20], permitting an easy, cheap and rapid identification of larvae collected from their host-plants. Identification through DNA barcoding is possible even from dry skins after moulting and from empty pupal exuviae after hatching of the moths (own, unpublished data; [21]). Currently, there are large-scale projects devoted to the identification of larvae along with their host-plants in Papua New Guinea ([22]) and Costa Rica ([4]). Both are based on an integrative approach combining morphology, rearing and molecular techniques for the identification of the reared adults and/or their parasitoids.

Miller et al. [16] and Matheson et al. [17] investigated and ascertained relationships between plants and caterpillars through a method based on the DNA identification of the larval gut content, an effective but (in earlier times) expensive and time-consuming approach, especially as a routine application in larger surveys. Later on, molecular gut content analysis was proposed for unveiling insect-host plant associations e.g. for beetles [23,24,25,26], and for soil insects [27].

The aim of this pilot paper was to establish methodology to infer host-plant relationships of caterpillars based on the identification of larvae collected by insecticidal knock-down (canopy-fogging) on their food-plants through DNA barcoding and to use gut HTS-based content analysis to estimate potential pitfalls due to ‘alternative feeding’ or due to collateral fogging from neighbouring plants, lianas etc. (cf. Discussion).

Material and methods

Collecting and canopy fogging

Canopy fogging was performed by AmH und AF from the ground with a Swingfog SN 50 fogger, using natural Pyrethrum, diluted in a highly raffinated white oil, as knock-down agent to prevent the introduction of persistent chemicals into the environment. For details of the fogging procedure see [28]. In most cases, trees with dense foliage cover and little canopy overlap with neighboring trees were chosen. We made sure the fog reached the canopy and stood there for at least five minutes to affect the arthropods. In order to install the collecting sheets, larger saplings and other interferring vegetation elements were cleared below the tree projection area. All organisms dropping down from the trees were collected at least one hour after the fogging from expanded plastic sheets of 20 m2 size, covering an estimated minimum of 80% of the target tree canopy. All arthropods of each fogging event were pooled and then transferred into accurately labelled jars with 100% ethanol without pre-sorting. The following day the ethanol was renewed and excessive plant material with its high water content was removed. Samples were stored at room temperature for up to two weeks while in the research station Panguana. The ethanol was renewed again when the samples were added to the Zoologische Staatssammlung München in Bavaria, Germany (SNSB–ZSM).

The study site is located in the westernmost Amazonian Basin, eastern central Peru, department Huánuco, at the ACP Panguana station (-9.613393°N -74.935911°E; 222 m; see also [29]), the fogged target trees were all situated in a radius of less than 2000 meters around the station. Collecting was performed in the late afternoon, betwen 17 and 19 o’clock, from 24th of November to 8th of December 2017. For identification of the target trees see results.

Collection permits were released by Servicio Nacional Forestal y de Fauna Silvestre SERFOR: No. 007-2014-SERFOR-DGGSPFFS + No. 0406-2017-SERFOR-DGGSPFFS, export permits fauna: No. 003236-SERFOR; 003281-SERFOR; 003320-SERFOR; export permits flora: No. 003284-SERFOR + Resolution of General Direction No. 161-2018-MINAGRI-SERFOR-DGGSPFFS; No. 003333-SERFOR. Field site access was granted by SERFOR and Biological Research Station ACP Panguana. Further ethical approval was not required for the data analysis, since no in vivo experiments were performed.

Tissue sampling and identification of larvae (DNA barcoding, COI)

Out of 47 samples–each of them referring to a fogged tree (cf. Table 3 and S1 Table)–all lepidopteran larvae were separated, in total 130 specimens. The larvae were dried on paper, photographed and then separately stored in Eppendorf tubes. A list of all 130 larvae along with their fogging sample number is given in Table 1, examples are shown in Fig 1. Tissue sampling was carried out for all 130 larvae by using scissors and pincers, which were carefully cleaned after each tissue sampling in 100% alcohol followed by exposure to a burner to avoid contamination among samples. Tissues (one vertically cut segment, in very small larvae two segments) were transferred to a lysis plate, adding 0.5 ml of 100% alcohol to each well on the plate. On each plate one well was used for negative control.

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Fig 1. Lepidopteran larvae after selection from the alcohol-preserved fogging samples from Panguana, Peru, after drying and before tissue-sampling for the DNA analysis.

(Upper) row 1: larvae nr. 5, 13, 26; row 2: nr. 28, 46, 52; row 3: nr. 62, 65, 71; row 4: nr. 81, 83, 85; row 5: nr. 88, 93, 100; row 6: nr. 109, 113, 115; (bottom) row 7: nr. 119, 122, 124 (numbers and identification of larvae see Table 2).

https://doi.org/10.1371/journal.pone.0224188.g001

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Table 1. Overview on the 130 Lepidoptera larvae selected from 36 fogging samples from Panguana, Peru, sequencing success and number of target tree.

https://doi.org/10.1371/journal.pone.0224188.t001

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Table 2. Identification results from sequence blasting on BOLD for 119 successfully sequenced Lepidoptera larvae (see Table 1) and their distances from the nearest genetic neighbour.

https://doi.org/10.1371/journal.pone.0224188.t002

Tissue samples were submitted to the standard procedures of the Canadian Centre for DNA Barcoding (CCDB) for sequencing the mitochondrial 5’ cytochrome oxidase gene, subunit 1 (COI), the standard marker for the identification of most animals. LepF1 and LepR1 were the primers used for PCR and sequencing [30]. Sequences were blasted against the complete sequence database of the Barcode of Life Data systems (BOLD, [13]) in order to infere the closest matches using the BOLD Identification Engine (http://www.boldsystems.org/index.php/ IDS_OpenIdEngine). Also morphology of larvae and related (genetically near) adult moths were considered to test the reliability of the results. Nomenclature of scientific taxon names follows the catalogue used on BOLD database, which in many families is in accordance with the currently available catalogues (e.g. [31] for Geometridae). Vouchers of larvae are stored at the Zoologische Staatssammlung München, Germany. Sequences, images and related metadata are available open access on BOLD under the dataset DS-PANLARVA (dx.doi.org/10.5883/DS-PANLARVA).

Tissue sampling and morphology-based identification of target trees

The 47 target trees have been pre-identified in the field based on morphology (shape of tree growth and shape of leaves, rarely blossoms or fruits) by the native caretaker of the Panguana Station, “Moro” Carlos Vásquez Módena, to Peruvian vernacular names (see Table 3 and S1 Table), which usually cannot be unequivocally referred to scientific plant names, however. For a tentative assignment of vernacular names to botanical taxa see S1 Table. For nomenclature of plant names we follow the “Plant List” (available online at www.theplantlist.org/1/). For most target trees a small branch was collected, pressed and kept in a herbarium for identification. Identification of a selection of sampled leaves was performed by Hamilton Paredes, Museo de Historia Natural, Lima. A small leaf piece was cut as tissue sample for DNA-Barcoding. In addition to that, sapwood/cambium tissue samples were taken of each target tree by using a leather punch to extract a core from the stem. Then, a thin slice of sapwood/cambium was cut and immediately dried over silica gel.

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Table 3. Identification of target trees, results from blasting on NCBI (BLAST matches usually >99%).

https://doi.org/10.1371/journal.pone.0224188.t003

Identification of target trees through DNA barcoding (trnL-F, rbcL & psbA)

Because of the above mentioned uncertainties of target tree pre-identification, we have submitted plant tissue samples to DNA barcoding. For that purpose leaves were available for 37 out of the 47 target trees, pieces of cambium+sapwood for 46 trees. Plant tissues (leaves) were submitted to Sanger sequencing (AIM; Advanced Identification Methods GmbH– www.aimethods-lab.com) with two markers, rbcL and psbA using standardized protocols following [32,33]. An additional attempt was performed in CCDB (Guelph, Canada; primers: trnL-F, rbcL; standard Sanger sequencing procedure) using both leaves and sapwood samples, the latter supplementing those cases where no leaves were available for study. The third marker (trnL-F) was added for further resolving the identification to genus and species level in some cases. All resulting sequences were blasted against GenBank (NBCI) and BOLD data using standard blast functions. Sequences and related metadata are available open access on BOLD under the dataset DS-PANPLANT (dx.doi.org/10.5883/DS-PANPLANT).

Gut content analysis (rbcL, psbA)

For a subset of ten larvae, gut content analysis was tested for molecular identification of the larva’s ‘true’ diet. For that purpose, we performed a second vertical cut and submitted one segment of the larva to High-Throughput-Sequencing (HTS) with the two markers rbcL and psbA. Cut slices of the caterpillars were dried, homogenized and DNA extracted using the DNEasy Plant kit (Qiagen, Hilden, Germany). From each sample, 5 μL of extracted genomic DNA was used, along with plant TAQ (Bioline, Luckenwalde, Germany), and High Throughput Sequencing (HTS) adapted mini-barcode primers (trnH-psbA-f 5’-CGC GCA TGG TGG ATT CAC AAT CC-3’, trnH-psbA 5’-GTT ATG CAT GAA CGT AAT GCT-3’, [32,33], using the PCR conditions 95°C-4’– 35x 94°C-30”/55°C-30”/72°C-1’– 72°C-10’) were applied for PCR. Amplification success and fragment length were observed using gel electrophoresis. Amplified DNA was cleaned up and resuspended in 50 μL molecular water for each sample before proceeding. Successfully amplified products were used for a subsequent PCR reaction which adds Illumina Nextera XT indices to each PCR product, enabling a unique tagging of each sample. Illumina Nextera XT (Illumina Inc., San Diego, USA) indices were ligated to the samples in a second PCR reaction applying the same annealing temperature as for the first PCR reaction but with only seven cycles, and ligation success confirmed by gel electrophoresis (for detailed protocols see [34,35]). DNA concentrations were measured using a Qubit fluorometer (Life Technologies, Carlsbad, USA), and samples were combined into 40 μL pools containing equimolar concentrations of 100 ng each. Pools were loaded into a 1% agarose gel, run at 90 V for 45 minutes, bands of the target amplicon size were excised with sterilized razor blades, and purified with a GeneJet Gel Extraction kit (Life Technologies, Carlsbad, USA), following the manufacturer’s instructions. A final elution volume of 20 μL was used. High-Throughput Sequencing (HTS) was performed on an Illumina MiSeq using v2 (2*250 bp, 500 cycles, maximum of 20 mio. reads) chemistry. Negative controls for DNA metabarcoding analyses consisted of one negative-control-extraction (an empty DNEasy plant kit tube was extracted among the remaining ten caterpillar gut samples), one PCR negative control for each amplicon and one ligation negative control for each set of amplicons during library preparation with Nextera XT indices. Negative controls have been used to remove all OTUs with N(reads)< = 5 x sum of reads in negative controls (where sum of negative control reads is more than 20% of the number of reads in actual samples). All samples for each amplicon were separately pooled using equimolar amounts of 100 ng each. All samples were loaded on a single v2 2x250 bp MiSeq flow cell among other samples. Final DNA concentrations of amplicon pools were set to 380,000 total raw reads (190,000 paired end reads). The final concentration of the full library was 1.4 ng. Metabarcoding data are deposited and accessible on GenBank, BioProject ID PRJNA593715 (http://www.ncbi.nlm.nih.gov/bioproject/593715).

Results

Identification of larvae

A total of 130 caterpillar specimens were collected from 37 of the 47 target plants. No lepidopteran larvae were found in the samples of ten target trees. COI sequencing (DNA barcoding) was successful for 119 larvae (91.5%). The larvae belong to 92 different COI clusters (BINs), which are a good proxy for different species [36].

When blasting the DNA barcodes of the larvae on BOLD database, 65 larvae (55%) belonging to 48 species showed ‘close genetic similarity’–here defined as lower than 2.5%–with adult reference vouchers. Such genetic similarity is interpreted here as ‘species (or sister species) level matches’ (Table 2). 27 species have Linnean names on BOLD database, 20 are listed under ‘interim names’ (name codes) which either refer to described but not-yet-identified taxa or to undescribed species.

For 32 larvae (27%) belonging to 27 species the blasting on BOLD database revealed genus level matches, in five cases with disputable reliability. For 19 larvae (16%) assignment to subfamily or family level was possible, the reliability of 12 of these assignments needs to be tested by further extension of the reference database, since long branch attraction effects may have influenced the results in a few single cases. In just three cases belonging to one single species no family suggestion could be given based on the COI barcode.

Identification of target plants

Pre-identification of target trees, as performed by the local administrator of the Panguana station (see S1 Table), was supplemented by molecular identification (sequencing of leaves and sapwood with the markers trnL-F, rbcL and psbA) of all but one of the target trees. For all but one of the target trees (98%) molecular identification through blasting on BOLD and GenBank brought a reliable identification to at least family level (see Table 3 and S3 Table). In four cases, however, the analysis of leaf and sapwood pointed to two different families which apparently is due to sampling errors, taking leaf and sapwood from different, neighbouring plants. In 37 target trees (79%) identification was possible to genus or species level (see Table 3 and S3 Table).

Gut content analysis

Gut content analysis was performed for ten larvae based on Next-Generation-Sequencing with two markers rbcL and psbA. The two highest numbers of HTS-reads for rbcL and psbA genes and their genetically most similar species as resulting from BLAST-search in GenBank is shown for each larva in Table 4.

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Table 4. Gut contents of ten fogged larvae with identity of target tree and HTS results from molecular identification of gut content, only the BLAST identification of the fragments with the two most numerous reads shown.

https://doi.org/10.1371/journal.pone.0224188.t004

Discussion

When investigating host-plant relationships it is usually assumed that larvae feed on the plants from where they have been collected. This assumption is based on the behaviour of larvae usually resting on their feeding plant during their development. One needs to consider, however, that certain larvae abandon their host-plants searching for a hidden resting place during daytime and mature larvae often leave their food-plant in the last days before pupation looking for a suitable pupation site, sometimes far from their feeding plants. Moreover, in particular in rainwood forests “alternative feeders” may use epiphytes, lianas, lichens, algae, fungi or mosses [14,15], and in our fogging approach pitfalls are possible through collateral fogging of larvae from neighboring trees. Gut content analysis can shed light on true feeding biology.

Gut content matching identity of target tree

Only in one out of ten analysed larvae (see Table 4) the gut content revealed to match exactly the fogged target tree species: Physocleora AH02Pe (Geometridae; larva nr. 40) fogged from Mangifera indica. In a second case, Homoeopteryx near major (Saturniidae; larva nr. 98), the gut content revealed to be from the same plant family (Rubiaceae), genus Simira resulting from sequencing of plant sapwood and genus Faramea resulting from gut content analysis. The high percentage of eight out of ten larvae with a mismatch between target tree and gut content suggests that an a priori assignation of fogged larvae to the target trees usually is erroneous and that alternative feeding (epiphytes, algae, mosses etc.) or feeding on lianas and neighboring trees plays a major role. The rate of alternative feeding should be tested basing on a larger sample, ruling out a potentially biased ratio through external contamination of larvae by plant DNA (see below).

Gut content matching previously known host-plant but not the target tree

A larva of the genus Quentalia (Bombycidae, larva nr. 42, see Table 4) was fogged from a tree of the family Meliaceae, but the gut content pointed to feeding on Trophis racemosa (Moraceae). Since Quentalia larvae were previously recorded as feeding on Moraceae [4], Trophis racemosa is likely the true food-plant of the Quentinalia larva which may have been growing close to the target tree.

In a second case, Eudocima procus (Erebidae; larva nr. 65, see Table 4) was fogged from a tree of the genus Hirtella (Chrysobalanaceae) but the gut content pointed to feeding on Tinospora smilacina (Menispermaceae). Since species of the genus Eudocima are known to feed on Menispermaceae [4,37,38], Tinospora smilacina is likely the true food-plant of the Eudocima larva. Tinospora is a liana and likely was associated with the target tree.

A similar case is also referring to liana-feeding: larva nr. 83 (see Table 4) was fogged from a tree of Annonaceae or Malvaceae, but in its gut content we found the DNA of the neotropical liana Echites yucatanensis (Apocynaceae).

Hence in three out of ten cases (30%) feeding on lianas or on a neighboring tree was recorded. Although the rate of feeding on such associated or neighbouring plants should be tested basing on a larger sample, the results of this pilot study clearly show that an ad hoc correlation of target tree and feeding biology is often premature and incorrect.

Gut content not matching target tree but potentially pointing to alternative feeding

In four cases (larvae nr. 82, 102, 107, 108, see Table 4) the larvae were fogged down from trees (genus Paullinia, Sapindaceae; genus Annona, Annonaceae; genus Apeiba, Malvaceae), but the cut content was pointing to alternative feeding on mosses (Bryophyta). In the case of the Lascoria species (Erebidae; larva nr. 107) such alternative feeding is not excluded as larvae of this species were already observed when grazing on algae in Costa Rica [4]. However, moss-feeding is very unusual in Lepidoptera, and this may also be caused by contamination since these fogging samples (under 80% alcohol) contained some leaves of Lejeunia mosses whose DNA may have invaded the larvae through their stigmata or contaminated them on their skin. Further research is needed to estimate the influence of contamination through the sample alcohol. For this purpose larvae should be de-contaminated by bleeching before sequencing. In addition, their gut content could be extracted carefully by cutting the larva longitudinally.

Inferring potential hostplant relationships (larvae without gut content analysis)

43 larvae with reliable identification to at least genus level, fogged from trees identified to at least genus level give first ‘hints’ on potential host-plants (Table 5). Almost all of them are new records, none of them was found in the ‘Hosts’ database [39] nor in Janzen & Hallwachs [4]. Alternative feeding, however, is not excluded (see notes to larvae nr. 18, 38, 43, 74 and 84 in Table 5), hence all suggested host-plant relationships require confirmation.

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Table 5. Potential host-plant relationships for 43 larvae identified to at least genus level and identity of the fogged target tree.

https://doi.org/10.1371/journal.pone.0224188.t005

Target tree confirming previously known host-plants (larvae without gut content analysis)

Among the 87 larvae successfully identified to genus or species and not subjected to gut content analysis, there are at least six cases where the fogged target trees match previously known host-plant relationships: larvae nr. 63 and 113 (Bombycidae, Anticla near antica) were knocked down from the trees nr. 14 and 34 (Moraceae); larvae nr. 117 (Geometridae; Semiothisa gambaria), 115 (Hesperiidae, Myscelus) and 144+126 (Erebidae, Sosxetra grata) from the trees nr. 35 and 41 (Meliaceae, Guarea), all confirming the relationships as previously recorded by Janzen & Hallwachs [4].

A powerful tool for future synecological research?

Our pilot study has revealed that (1) molecular identification of fogged, neotropical lepidopteran larvae works successfully in general and even down to species level (if already listed in BOLD), that (2) molecular identification of target trees usually works well at least to genus or family level and (3) molecular gut content analysis based on HTS techniques can be used for confirming or rejecting the feeding on the fogged target tree. With further completion of the DNA reference libraries in the future for (1) (Peruvian Lepidoptera; currently 12,746 sequences, 3532 BINs) and (2) (Peruvian plants) a better taxonomic resolution of identification will be achieved, whilst molecular gut content analysis (3) can be improved by de-contamination and/or isolated storage of the fogged larvae.

With that, the herewith presented approach has the potential for unveiling trophic interactions for primary consumers in tropical regions at a very large scale, which can be performed in a fast and cost-effective way considering the steadily dropping costs for DNA barcoding and HTS. The extremely high diversity of 92 species in 119 larvae in our study shows that canopy fogging and molecular analyses may improve synecological knowledge for a broad spectrum of arthropods. The availability of reliable data on trophic interactions is of great importance for forestry, agriculture, biodiversity and ecological research and–last but not least–for conservation purposes. Increasing such knowledge–particularly in megadiverse ecoregions–is an imperative in a world of unprecedented biodiversity losses. In this context, the proposed molecular approach of investigating host-plant relationships constitutes an important research tool, which fits well in the research plan of the recently launched BIOSCAN phase of the international Barcode of Life program ([40]; see also https://ibol.org).

Supporting information

S1 Table. Morphology-based identification of target trees.

Morphology-based identification of target trees to Peruvian vernacular names (mostly provided by the administrator of the Panguana station, Moro Carlos Vásquez Modena) and attempt to assign scientific family / genus / or species names (partly provided by Hamilton Paredes (“HP”), Museum of Natural History, Lima, based on leaf samples). # = fogging sample from target tree without lepidopteran larva; * = no plant tissue available, so far (hence no molecular confirmation possible): 1 = same vernacular name with two different molecular identifications.

https://doi.org/10.1371/journal.pone.0224188.s001

(PDF)

S2 Table. Target trees: Sequencing success and process identification numbers.

Data from BOLD, with fragment lengths in basepairs (bp). Sanger sequencing of rbcL, trnL-F and psbA genes, based on leaf (l) and cambium+sapwood (c) samples from the target trees.

https://doi.org/10.1371/journal.pone.0224188.s002

(PDF)

S3 Table. Molecular identification of target trees.

Molecular identification of the target trees after Sanger sequencing (rbcL, trnL-F and psbA genes) of leaf (l) and cambium + sapwood (c) samples. Results from blasting on NCBI, BLAST matches (highest percent identity (‘Max ident’) of all query-subject alignments) usually >99.5%, otherwise indicated. Plant species/genera with blast matches sometimes not mentioned when plants are exclusively distributed on other continents. # = fogging sample from target tree without lepidopteran larva in the sample. Anac. = Anacardiaceae; Anno. = Annonaceae; Cann. = Cannabaceae; Chry. = Chrysobalanaceae; Clus. = Clusiaceae; Euph. = Euphorbiaceae; Faba. = Fabaceae; Malv. = Malvaceae; Malp. = Malpighiaceae; Meli. = Meliaceae; Mora. = Moraceae; Myri. = Myristicaceae; Nyct. = Nyctaginaceae; Rubi. = Rubiaceae; Sapi. = Sapindaceae; Sapo. = Sapotaceae; Viol. = Violaceae. 1 = same vernacular name with two different molecular identifications; 2 = potential sampling error (tissue sampling from neighboring tree or tube flip in the lab process); 3 = exclusively Indo-Pacific; 4 = exclusively Old World.

https://doi.org/10.1371/journal.pone.0224188.s003

(PDF)

Acknowledgments

We thank “Moro” Carlos Vásquez Módena (Panguana) and Hamilton Paredes (Museum of Natural History, Lima) for identification of the target trees. We furthermore thank Julio Monzón (Lima / Freiburg) for providing the fogger and the chemicals. Víctor Meyer Ruiz Chota (Yuyapichis) was very helpful in taking the fogging samples, Johanna Brunner, Sarah Augustin and Stefanie Wothe (Munich) helped to take the cambium/sapwood samples. Dr. Andreas Fleischmann (SNSB, Botanische Staatssammlung, Munich) gave advice concerning the botanical aspects of the study. The genetic analyses have received considerable support from Paul D. N. Hebert and the Biodiversity Institute of Ontario (BIO) and the Canadian Centre for DNA Barcoding (CCDB University of Guelph). The data management and analysis system BOLD was provided by Sujeevan Ratnasingham. This study was part of the program ‘SNSB Innovativ’. We thank the Peruvian nature conservation authority and forestry office (Servicio Forestal y de Fauna Silvestre, SERFOR, Ministerio de Agricultura y Riego, MINAGRI) for collection permits (N° 007-2014-SERFOR-DGGSPFFS; N° 0406-2017-SERFOR-DGGSPFFS) and exportation permits (fauna: N° 003281-SERFOR, N° 003320-SERFOR; flora: N° 003284-SERFOR + N° 161-2018-MINAGRI-SERFOR-DGGSPFFS, N° 003333-SERFOR).

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