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  <front>
    <journal-meta>
      <journal-id journal-id-type="publisher-id">2</journal-id>
      <journal-id journal-id-type="index">urn:lsid:arphahub.com:pub:45048D35-BB1D-5CE8-9668-537E44BD4C7E</journal-id>
      <journal-id journal-id-type="aggregator">urn:lsid:zoobank.org:pub:91BD42D4-90F1-4B45-9350-EEF175B1727A</journal-id>
      <journal-title-group>
        <journal-title xml:lang="en">ZooKeys</journal-title>
        <abbrev-journal-title xml:lang="en">ZK</abbrev-journal-title>
      </journal-title-group>
      <issn pub-type="ppub">1313-2989</issn>
      <issn pub-type="epub">1313-2970</issn>
      <publisher>
        <publisher-name>Pensoft Publishers</publisher-name>
      </publisher>
    </journal-meta>
    <article-meta>
      <article-id pub-id-type="doi">10.3897/zookeys.1263.147980</article-id>
      <article-id pub-id-type="publisher-id">147980</article-id>
      <article-categories>
        <subj-group subj-group-type="heading">
          <subject>Research Article</subject>
        </subj-group>
        <subj-group subj-group-type="biological_taxon">
          <subject>Trichoptera</subject>
        </subj-group>
        <subj-group subj-group-type="scientific_subject">
          <subject>Ecology &amp; Environmental sciences</subject>
        </subj-group>
        <subj-group subj-group-type="geological_era">
          <subject>Cenozoic</subject>
        </subj-group>
        <subj-group subj-group-type="geographical_area">
          <subject>North America</subject>
        </subj-group>
      </article-categories>
      <title-group>
        <article-title>﻿Gut microbiome composition of <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Trichoptera</tp:taxon-name-part></tp:taxon-name> larvae across functional feeding groups: a case study from the Provo River, Utah, USA</article-title>
      </title-group>
      <contrib-group content-type="authors">
        <contrib contrib-type="author" corresp="yes">
          <name name-style="western">
            <surname>Jijón</surname>
            <given-names>Gabriela</given-names>
          </name>
          <email xlink:type="simple">m.gabriela.jijon@gmail.com</email>
          <uri content-type="orcid">https://orcid.org/0000-0002-1893-3904</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - original draft</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
          <role content-type="http://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
          <role content-type="http://credit.niso.org/contributor-roles/investigation/">Investigation</role>
          <role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
          <role content-type="http://credit.niso.org/contributor-roles/software/">Software</role>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Hough</surname>
            <given-names>Connor</given-names>
          </name>
          <xref ref-type="aff" rid="A1">1</xref>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
          <role content-type="http://credit.niso.org/contributor-roles/visualization/">Visualization</role>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Gedris</surname>
            <given-names>Danyon</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0009-0009-1635-1044</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/investigation/">Investigation</role>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Frandsen</surname>
            <given-names>Paul B.</given-names>
          </name>
          <uri content-type="orcid">https://orcid.org/0000-0002-4801-7579</uri>
          <xref ref-type="aff" rid="A1">1</xref>
          <role content-type="http://credit.niso.org/contributor-roles/conceptualization/">Conceptualization</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - original draft</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/funding-acquisition/">Funding acquisition</role>
          <role content-type="http://credit.niso.org/contributor-roles/investigation/">Investigation</role>
          <role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
          <role content-type="http://credit.niso.org/contributor-roles/project-administration/">Project administration</role>
          <role content-type="http://credit.niso.org/contributor-roles/resources/">Resources</role>
          <role content-type="http://credit.niso.org/contributor-roles/supervision/">Supervision</role>
        </contrib>
        <contrib contrib-type="author" corresp="no">
          <name name-style="western">
            <surname>Chaston</surname>
            <given-names>John M.</given-names>
          </name>
          <xref ref-type="aff" rid="A1">1</xref>
          <role content-type="http://credit.niso.org/contributor-roles/writing-original-draft/">Writing - original draft</role>
          <role content-type="http://credit.niso.org/contributor-roles/writing-review-editing/">Writing - review and editing</role>
          <role content-type="http://credit.niso.org/contributor-roles/data-curation/">Data curation</role>
          <role content-type="http://credit.niso.org/contributor-roles/formal-analysis/">Formal analysis</role>
          <role content-type="http://credit.niso.org/contributor-roles/funding-acquisition/">Funding acquisition</role>
          <role content-type="http://credit.niso.org/contributor-roles/investigation/">Investigation</role>
          <role content-type="http://credit.niso.org/contributor-roles/methodology/">Methodology</role>
          <role content-type="http://credit.niso.org/contributor-roles/project-administration/">Project administration</role>
          <role content-type="http://credit.niso.org/contributor-roles/resources/">Resources</role>
          <role content-type="http://credit.niso.org/contributor-roles/software/">Software</role>
          <role content-type="http://credit.niso.org/contributor-roles/supervision/">Supervision</role>
        </contrib>
      </contrib-group>
      <aff id="A1">
        <label>1</label>
        <addr-line content-type="verbatim">Department of Plant and Wildlife Sciences, Brigham Young University, Provo, UT 84602, USA</addr-line>
        <institution>Brigham Young University</institution>
        <addr-line content-type="city">Provo</addr-line>
        <country>United States of America</country>
      </aff>
      <aff id="A2">
        <label>2</label>
        <addr-line content-type="verbatim">Department of Biology, Brigham Young University, Provo, UT 84602, USA</addr-line>
        <institution>Brigham Young University</institution>
        <addr-line content-type="city">Provo</addr-line>
        <country>United States of America</country>
      </aff>
      <aff id="A3">
        <label>3</label>
        <addr-line content-type="verbatim">Bean Life Science Museum, Brigham Young University, Provo, UT 84602, USA</addr-line>
        <institution>Brigham Young University</institution>
        <addr-line content-type="city">Provo</addr-line>
        <country>United States of America</country>
      </aff>
      <author-notes>
        <fn fn-type="corresp">
          <p>Corresponding author: Gabriela Jijon (<email xlink:type="simple">m.gabriela.jijon@gmail.com</email>)</p>
        </fn>
        <fn fn-type="edited-by">
          <p>Academic editor: Steffen U. Pauls</p>
        </fn>
      </author-notes>
      <pub-date pub-type="collection">
        <year>2025</year>
      </pub-date>
      <pub-date pub-type="epub">
        <day>10</day>
        <month>12</month>
        <year>2025</year>
      </pub-date>
      <volume>1263</volume>
      <fpage>165</fpage>
      <lpage>177</lpage>
      <uri content-type="arpha" xlink:href="http://openbiodiv.net/56A8D888-DFED-5B94-B2AE-AA284E97CD7B">56A8D888-DFED-5B94-B2AE-AA284E97CD7B</uri>
      <uri content-type="zenodo_dep_id" xlink:href="https://zenodo.org/record/17908358">17908358</uri>
      <uri content-type="zoobank" xlink:href="https://zoobank.org/9D4D05B6-6DD6-498B-817E-8A251E6C739D">9D4D05B6-6DD6-498B-817E-8A251E6C739D</uri>
      <history>
        <date date-type="received">
          <day>28</day>
          <month>01</month>
          <year>2025</year>
        </date>
        <date date-type="accepted">
          <day>02</day>
          <month>06</month>
          <year>2025</year>
        </date>
      </history>
      <permissions>
        <copyright-statement>Gabriela Jijón, Connor Hough, Danyon Gedris, Paul B. Frandsen, John M. Chaston</copyright-statement>
        <license license-type="creative-commons-attribution" xlink:href="http://creativecommons.org/licenses/by/4.0/" xlink:type="simple">
          <license-p>This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.</license-p>
        </license>
      </permissions>
      <self-uri content-type="zoobank" xlink:type="simple">https://zoobank.org/9D4D05B6-6DD6-498B-817E-8A251E6C739D</self-uri>
      <abstract>
        <label>﻿Abstract</label>
        <p>Diet is known to be a major factor in the gut microbiome of many groups of insects. Larvae from the insect order <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Trichoptera</tp:taxon-name-part></tp:taxon-name> have varied feeding behaviors, encompassing all functional feeding groups, making them an excellent group for studying the links between diet and gut microbe community variation. However, no previous study has examined these links in caddisflies. Here, we characterize the gut microbiome composition of four caddisfly genera belonging to four different functional feeding groups over two sampling periods using 16S metabarcoding. We found that the sampling date had the strongest influence on gut microbiome variation. Host functional feeding groups and phylogeny also influenced gut community composition; however, improved sampling is necessary to confirm this relationship. Our preliminary results point to interesting differences among larvae from different feeding groups and suggest future areas for research, including performing species-level identification of the caddisfly larvae, using more taxa within and between functional feeding groups, using temporal and larval stage-matched replicates, assessing the degree of microbiome residency in caddisfly guts, and performing deeper sequencing.</p>
      </abstract>
      <kwd-group>
        <label>Key words:</label>
        <kwd>16S metabarcoding</kwd>
        <kwd>aquatic insect larvae</kwd>
        <kwd>gut microbial community characterization</kwd>
        <kwd>host feeding ecology</kwd>
        <kwd>preliminary study</kwd>
        <kwd>temporal differences</kwd>
      </kwd-group>
    </article-meta>
    <notes>
      <sec sec-type="Citation" id="SECID0EDF">
        <title>Citation</title>
        <p>Jijón G, Hough C, Gedris D, Frandsen PB, Chaston JM (2025) Gut microbiome composition of Trichoptera larvae across functional feeding groups: a case study from the Provo River, Utah, USA. In: Ríos-Touma B, Frandsen PB, Holzenthal RW, Houghton DC, Rázuri-Gonzales E, Pauls SU (Eds) Proceedings of the 18th International Symposium on Trichoptera. ZooKeys 1263: 165–177. <ext-link xlink:href="10.3897/zookeys.1263.147980" ext-link-type="doi" xlink:type="simple">https://doi.org/10.3897/zookeys.1263.147980</ext-link></p>
      </sec>
    </notes>
  </front>
  <body>
    <sec sec-type="﻿Introduction" id="SECID0EYF">
      <title>﻿Introduction</title>
      <p>Some insects depend on symbiotic relationships with bacteria to survive and function (<xref ref-type="bibr" rid="B13">Gupta and Nair 2020</xref>), while others exhibit a lower reliance on microbes (<xref ref-type="bibr" rid="B14">Hammer et al. 2017</xref>, <xref ref-type="bibr" rid="B15">2019</xref>). Microbes located in the gut play an important role in physiology and digestion for some insects (<xref ref-type="bibr" rid="B27">Moran et al. 2013</xref>; <xref ref-type="bibr" rid="B20">Krishnan et al. 2014</xref>; <xref ref-type="bibr" rid="B29">Munoz-Benavent et al. 2021</xref>). For others, microbes may be transient in the gut and have a minimal functional role (<xref ref-type="bibr" rid="B14">Hammer et al. 2017</xref>, <xref ref-type="bibr" rid="B15">2019</xref>). Studies on insect-associated microbiomes have primarily focused on terrestrial insects, and relatively little is known about aquatic insect gut microbiota (<xref ref-type="bibr" rid="B22">Men et al. 2022</xref>; <xref ref-type="bibr" rid="B26">Millar et al. 2022</xref>). While some previous studies have documented insect host associations, developmental stage influence, and environmental impacts on microbiome composition in aquatic insects (<xref ref-type="bibr" rid="B7">Castillo et al. 2020</xref>; <xref ref-type="bibr" rid="B30">Nobles and Jackson 2020</xref>; <xref ref-type="bibr" rid="B26">Millar et al. 2022</xref>), only a few studies have focused on the connection between aquatic insect diet and gut microbiota.</p>
      <p>Since diet influences microbiome composition, evaluating microbiome differences among feeding modes could reveal possible relationships between microbes and food intake in aquatic larvae. To understand the complex links between organisms in aquatic food webs, freshwater biologists characterize aquatic insects into functional feeding groups (<abbrev xlink:title="functional feeding groups" id="ABBRID0EUH">FFGs</abbrev>), which are defined by morphological and behavioral adaptations of aquatic macroinvertebrates to different food sources available in freshwater environments (<xref ref-type="bibr" rid="B24">Merritt et al. 2017</xref>). The feeding categories include filtering collectors, gathering collectors, shredders, scrapers, piercers-herbivores, and predators (<xref ref-type="bibr" rid="B11">Cummins and Klug 1979</xref>; <xref ref-type="bibr" rid="B24">Merritt et al. 2017</xref>). Larvae from the highly diverse insect order <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Trichoptera</tp:taxon-name-part></tp:taxon-name>, also known as caddisflies, are unusually ecologically diverse and encompass all functional feeding groups, consuming a large variety of in-stream food sources (<xref ref-type="bibr" rid="B11">Cummins and Klug 1979</xref>; <xref ref-type="bibr" rid="B38">Wiggins and Currie 2008</xref>; <xref ref-type="bibr" rid="B28">Morse et al. 2019</xref>).</p>
      <p>Despite their diversity in both species number and feeding types, there are no studies examining the microbiomes of larval caddisflies across varying feeding modes. Given the diversity of feeding types across <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Trichoptera</tp:taxon-name-part></tp:taxon-name>, caddisfly larvae can serve as models for investigating the relationships between diet and gut microbiota in aquatic insects. Here, we aim to provide a preliminary overview of the gut microbiome composition of caddisflies that possess diverse feeding behaviors by characterizing the gut microbiomes of 61 caddisfly larvae belonging to four genera and representing four functional feeding groups.</p>
    </sec>
    <sec sec-type="methods" id="SECID0E4AAC">
      <title>﻿Methods</title>
      <sec sec-type="﻿Sample collection" id="SECID0EBBAC">
        <title>﻿Sample collection</title>
        <p>We collected 61 caddisfly larval samples, 31 samples in the fall of 2022 (30 September) and 30 in the winter of 2023 (4 February) in the Provo River, Provo, Utah (<named-content content-type="dwc:verbatimCoordinates"><named-content content-type="geo-json" specific-use="{&quot;type&quot;:&quot;Point&quot;,&quot;coordinates&quot;:[-111.663500,40.265167]}" id="NCID0EKBAC">40°15.91'N, 111°39.81'W</named-content></named-content>), using D-net samplers. We field sorted the benthic samples and placed the live caddisfly specimens in containers with stream water. We transported the live organisms into the laboratory and placed them in dechlorinated tap water without food. We identified the larvae taxonomically at the genus level and classified them into a primary functional feeding group using <xref ref-type="bibr" rid="B23">Merritt and Cummins (1996)</xref>. We performed gut dissections the following day. Larval specimens of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Oecetis">Oecetis</tp:taxon-name-part></tp:taxon-name></italic> sp. and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp. were too small to dissect, so we sampled the whole body (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S1). From the fall collection, we sampled 12 <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hydropsyche">Hydropsyche</tp:taxon-name-part></tp:taxon-name></italic> sp. (filtering collector), 10 <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Oecetis">Oecetis</tp:taxon-name-part></tp:taxon-name></italic> sp. (predator), and 9 <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hesperophylax">Hesperophylax</tp:taxon-name-part></tp:taxon-name></italic> sp. (shredder) larvae. From the winter collection, we sampled 11 <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hydropsyche">Hydropsyche</tp:taxon-name-part></tp:taxon-name></italic> sp. (filtering collector), 13 <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp. (scraper), 4 <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hesperophylax">Hesperophylax</tp:taxon-name-part></tp:taxon-name></italic> sp. (shredder), and 2 <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Oecetis">Oecetis</tp:taxon-name-part></tp:taxon-name></italic> sp. (predator) larvae. All genera sampled represented a unique functional feeding group, and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp. larvae were collected only in the winter.</p>
      </sec>
      <sec sec-type="﻿DNA extraction and sequencing" id="SECID0E4DAC">
        <title>﻿DNA extraction and sequencing</title>
        <p>The V4 region of 16S rRNA was sequenced according to <xref ref-type="bibr" rid="B19">Kozich et al. (2013)</xref> for microbiota characterization. We extracted and sequenced the DNA for samples from each collection date separately. For DNA extraction, we used the Zymo Quick-DNA Fecal/Soil Microbe 96 Kit (Zymo Research, D6011). We placed the dissected gut or whole larval samples in tubes with 400 µL of BashingBead buffer and stored them at −20 °C until DNA extraction. The guts (and bodies when dissections were not possible) were homogenized using a 2010 Geno/Grinder (SPEX SamplePrep, Metuchen, NJ, USA) for 2 min at 1750 RPM. We followed the manufacturer’s instructions, including adding 0.5% (v/v) beta-mercaptoethanol to the genomic lysis buffer, and DNA was eluted in 50 µL Elution Buffer. We stored the extracted DNA at −20 °C until PCR amplification.</p>
        <p>Libraries for 16S rRNA V4 sequencing were prepared as 20 µL PCR reactions using AccuPrime™ Pfx DNA Polymerase (ThermoFisher, 12344032) and dual-barcoded primers as in <xref ref-type="bibr" rid="B19">Kozich et al. (2013)</xref>. We added 2 µL template DNA, 1 µL each of 10 µM primer (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S1), and 0.25 µL polymerase per reaction (all else as in manufacturer's instructions). We included a reagent-only control for the DNA extractions, nuclease-free water and positive controls for the PCR reactions for the February samples. We then performed a 1% agarose gel electrophoresis on a random subset of samples to assess amplification success for the February samples. Amplification was visible for the subset of the samples (Suppl. material <xref ref-type="supplementary-material" rid="S1">1</xref>), and the negative PCR controls did not produce any visible bands in the gel electrophoresis.</p>
        <p>For PCR product normalization, we followed the manufacturer’s instructions when using the Just-a-Plate 96 PCR Normalization and Purification Kit (Charm Biotech, JN12010). All samples for each run were pooled together in equal volumes and concentrated to 12 µL using the gDNA Clean &amp; Concentrator -5 Kit (Zymo Research, 11-302C). We selected DNA fragments by size using a Select-a-Size DNA Clean &amp; Concentrator Kit (Zymo Research, D4080), following the manufacturer’s double-size selection protocol. Fragments of ≤ 200 bp and ≥ 700 bp were depleted. Fragment sizes were evaluated using an Agilent Bioanalyzer (Agilent Technologies, Santa Clara, CA, USA), followed by a 250–450 bp fragment size selection using a Sage Science Blue Pippin (Sage Science, Beverly, MA, USA), and the molarity of the pooled samples was assessed with qPCR at BYU DNA Sequencing Center in Provo, Utah, USA.</p>
        <p>Each pool was sequenced on a partial Illumina MiSeq run, pooled with other samples to target ~10,000 reads per sample. Before sequencing, we diluted each final library to 4.0 pM and pooled it with 15% PhiX. We loaded 600 uL to the MiSeq Reagent Kit v2 500 cycle cartridge (Illumina, MS-102-2003). Custom read1, read2, and index sequencing primers were loaded to the cartridge as described by <xref ref-type="bibr" rid="B19">Kozich et al. (2013)</xref>.</p>
      </sec>
      <sec sec-type="﻿Sequence analysis" id="SECID0E3EAC">
        <title>﻿Sequence analysis</title>
        <p>We used QIIME 2 v. 2023.2 (<xref ref-type="bibr" rid="B5">Bolyen et al. 2019</xref>) to analyze the 16S rRNA V4 region sequencing data on the raw sequencing reads that passed Illumina quality filters from each sequencing run. See Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S1 for the metadata table used for downstream processing.</p>
        <p>Sequence analysis, including denoising, sequence identification and amplicon sequence variant (<abbrev xlink:title="amplicon sequence variant" id="ABBRID0EMFAC">ASV</abbrev>) assignments, were performed using QIIME2 (<xref ref-type="bibr" rid="B5">Bolyen et al. 2019</xref>). Denoising was performed using Divisive Amplicon Denoising Algorithm (<abbrev xlink:title="Divisive Amplicon Denoising Algorithm" id="ABBRID0EUFAC">DADA2</abbrev>) v. 1.26.0 (<xref ref-type="bibr" rid="B6">Callahan et al. 2016</xref>). qiime dada2 denoise-paired (parameters = --p-trunc-len-f, --p-trunc-len-r) to filter phiX reads and trim Illumina amplicon sequences on the .qza files. Based on quality scores (≥30), the sequences were trimmed at 240 and 180 base-pair lengths for forward and reverse sequences, respectively, for both sampling dates. The forward and reverse-complement reads were merged using the default settings (at least identical 12 base-overlap) (<xref ref-type="bibr" rid="B6">Callahan et al. 2016</xref>). We used the GreenGenes classifier database 13_8_99 (<xref ref-type="bibr" rid="B21">McDonald et al. 2012</xref>) to assign microbial taxa to ASVs, and then chimeras were identified and removed (<xref ref-type="bibr" rid="B6">Callahan et al. 2016</xref>). We excluded any feature (i.e., <abbrev xlink:title="amplicon sequence variant" id="ABBRID0EIGAC">ASV</abbrev>) that was classified as <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="kingdom">Archaea</tp:taxon-name-part></tp:taxon-name>, Chloroplast or Mitochondria (parameter = --p-exclude <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="kingdom">Archaea</tp:taxon-name-part></tp:taxon-name>,Chloroplast,Mitochondria).</p>
        <p>Alpha and beta diversity metrics were generated for the combined dataset (including both dates) as well as separately for the September and February datasets. These metrics were generated with qiime diversity core-metrics-phylogenetic using a sampling depth of 412 (parameter = --p-sampling-depth) for all datasets. We carried out a non-metric dimensional scaling dissimilarity (<abbrev xlink:title="non-metric dimensional scaling dissimilarity" id="ABBRID0EYGAC">NMDS</abbrev>) analysis using metaMDS() (vegan v. 2.6-10; <xref ref-type="bibr" rid="B31">Oksanen et al. 2025</xref>) and PERMANOVA tests using qiime diversity beta-group-significance (parameter = --p-pairwise) on 999 permutations by collection date and FFG/taxonomic classification based on Bray-Curtis dissimilarity distances for the combined and separate September and February datasets. We ran qiime diversity alpha-rarefaction using 2,000 for the --p-max-depth option and resamplings of 10 (--p-iterations) to produce the alpha rarefaction plot for the combined dates and qiime taxa barplot to generate taxon bar plots of the combined and separate sampling dates.</p>
      </sec>
    </sec>
    <sec sec-type="﻿Results" id="SECID0EAHAC">
      <title>﻿Results</title>
      <p>The total demultiplexed sequence counts for the forward and reverse sequences were 468,085 (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S2). The mean count per sample for the forward and reverse sequences was 7,673.52. Post filtering, 420,682 sequences remained, with an average of 6,896.43 sequences per sample (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S3). A total of 404,087 non-chimeric, denoised, and merged sequences were obtained, with an average of 6,624.38 sequences per sample (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S3). After filtering for non-bacterial sequences, 1,360 unique ASVs and 381,990 sequences were recovered. At the set sampling depth (412), five samples from February 2023 and one from September 2022 were discarded, retaining 5.93% (22,660) ASVs and 90.16% (55) of the samples for the combined dataset. The September 2022 dataset retained 3.46% (12,360) ASVs and 96.77% (30) of the samples. The February 2023 dataset retained 42.08% (10,300) ASVs and 83.33% (25) of the samples.</p>
      <p>The rarefaction curves of alpha diversity for all remaining samples reached an asymptote before the set maximum depth of 2,000 (Fig. <xref ref-type="fig" rid="F1">1</xref>). The sample showing the greatest alpha diversity was a September <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hydropsyche">Hydropsyche</tp:taxon-name-part></tp:taxon-name></italic> sp. (ice 1412) sample. While the flatlining of the curve likely suggests we detected the most abundant organisms in the caddisflies, a deeper sequencing depth would likely have increased sensitivity and detected rarer taxa (<xref ref-type="bibr" rid="B39">Zaheer et al. 2018</xref>; <xref ref-type="bibr" rid="B32">Pereira-Marques et al. 2019</xref>).</p>
      <fig id="F1" position="float" orientation="portrait">
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        <label>Figure 1.</label>
        <caption>
          <p>Alpha rarefaction curves for the fall and winter combined dataset. Observed features (ASVs) were plotted against sequencing depth. Maximum depth was set at 2,000 and resampling at 10.</p>
        </caption>
        <graphic xlink:href="zookeys-1263-165_article-147980__-g001.jpg" position="float" orientation="portrait" xlink:type="simple" id="oo_1488932.jpg">
          <uri content-type="original_file">https://binary.pensoft.net/fig/1488932</uri>
        </graphic>
      </fig>
      <p><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Proteobacteria</tp:taxon-name-part></tp:taxon-name> was the most abundant phylum (52.96%) across all samples when combining both collection dates, followed by <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Actinobacteria</tp:taxon-name-part></tp:taxon-name> and <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Acidobacteria</tp:taxon-name-part></tp:taxon-name>, with a relative abundance of 11.85 and 6.94%, respectively. The most abundant classes were <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="class">Gammaproteobacteria</tp:taxon-name-part></tp:taxon-name> (31.96%) and <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="class">Alphaproteobacteria</tp:taxon-name-part></tp:taxon-name> (12.44%), and the most abundant orders were <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Enterobacteriales</tp:taxon-name-part></tp:taxon-name> (28.94%), followed by <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Actinomycetales</tp:taxon-name-part></tp:taxon-name> (6.73%).</p>
      <sec sec-type="﻿Microbiome differences across host functional feeding groups and phylogeny" id="SECID0EYJAC">
        <title>﻿Microbiome differences across host functional feeding groups and phylogeny</title>
        <p>Microbiome composition showed some divergence based on host FFG and phylogeny. <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Enterobacteriales</tp:taxon-name-part></tp:taxon-name> were the most abundant microbe for all taxa and <abbrev xlink:title="functional feeding groups" id="ABBRID0EDKAC">FFGs</abbrev> when combining both sampling dates (Fig. <xref ref-type="fig" rid="F2">2A</xref>). Reads assigned to the <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Enterobacteriales</tp:taxon-name-part></tp:taxon-name> comprised &gt; 28.9% of the gut microbiota, and <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Actinomycetales</tp:taxon-name-part></tp:taxon-name>, the second most abundant microbe, was 6.7% of the microbiota (Fig. <xref ref-type="fig" rid="F2">2A</xref>). These were the two most abundant taxa in every group except the <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp., where the second most abundant taxon was <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Thermotogales</tp:taxon-name-part></tp:taxon-name> (9.7% relative abundance). It is worth noting that the gut microbiome of a September <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hesperophylax">Hesperophylax</tp:taxon-name-part></tp:taxon-name></italic> sp. sample (ice1439) was composed almost entirely (&gt;99%) of <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Proteobacteria</tp:taxon-name-part></tp:taxon-name>.</p>
        <fig id="F2" position="float" orientation="portrait">
          <object-id content-type="doi">10.3897/zookeys.1263.147980.figure2</object-id>
          <object-id content-type="zenodo_dep_id">17908747</object-id>
          <object-id content-type="arpha">C3BB3C92-2652-526C-B3E1-941BBCFE6B09</object-id>
          <label>Figure 2.</label>
          <caption>
            <p>Microbiota fractional abundance taxon plots across: <bold>A.</bold> Functional feeding groups (<abbrev xlink:title="functional feeding groups" id="ABBRID0E2LAC">FFGs</abbrev>) of host and <bold>B.</bold> Sampling date and functional feeding groups of host on the two sampling dates: <bold>C.</bold> September 2022 and <bold>D.</bold> February 2023. The legend shows abundant microbial taxa with less abundant microbiota grouped as “other” (black).</p>
          </caption>
          <graphic xlink:href="zookeys-1263-165_article-147980__-g002.jpg" position="float" orientation="portrait" xlink:type="simple" id="oo_1488933.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1488933</uri>
          </graphic>
        </fig>
        <p>When comparing both sampling dates, the PERMANOVA test showed significant differences in gut microbiome composition among caddisfly genera (<italic>P</italic> &lt; 0.05; Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S5). The pairwise comparisons showed significant microbiome variation for <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp. compared to that of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hydropsyche">Hydropsyche</tp:taxon-name-part></tp:taxon-name></italic> sp. (collector filterer), <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hesperophylax">Hesperophylax</tp:taxon-name-part></tp:taxon-name></italic> sp. (shredder), and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Oecetis">Oecetis</tp:taxon-name-part></tp:taxon-name></italic> sp. (predator), and <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hydropsyche">Hydropsyche</tp:taxon-name-part></tp:taxon-name></italic> sp. to that of <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Oecetis">Oecetis</tp:taxon-name-part></tp:taxon-name></italic> sp. (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S5). When comparing the microbiome composition separately for each sampling date, there were no differences in microbiota composition among the different phylogenetic groups (Figs <xref ref-type="fig" rid="F2">2C, D</xref>, <xref ref-type="fig" rid="F3">3</xref>, Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S5).</p>
        <fig id="F3" position="float" orientation="portrait">
          <object-id content-type="doi">10.3897/zookeys.1263.147980.figure3</object-id>
          <object-id content-type="zenodo_dep_id">17908750</object-id>
          <object-id content-type="arpha">E6AC05A5-64F9-5858-B2BD-C15B6B84817B</object-id>
          <label>Figure 3.</label>
          <caption>
            <p><abbrev xlink:title="non-metric dimensional scaling dissimilarity" id="ABBRID0EUOAC">NMDS</abbrev> plot based on Bray–Curtis dissimilarity distances of gut microbiome composition between sampling dates. Point proximity indicates more similarity in community composition. <abbrev xlink:title="non-metric dimensional scaling dissimilarity" id="ABBRID0EYOAC">NMDS</abbrev> stress = 0.120582759937782.</p>
          </caption>
          <graphic xlink:href="zookeys-1263-165_article-147980__-g003.jpg" position="float" orientation="portrait" xlink:type="simple" id="oo_1488934.jpg">
            <uri content-type="original_file">https://binary.pensoft.net/fig/1488934</uri>
          </graphic>
        </fig>
      </sec>
      <sec sec-type="﻿Microbiome differences across sampling dates" id="SECID0EBPAC">
        <title>﻿Microbiome differences across sampling dates</title>
        <p>The microbiome composition of the larvae differed significantly between the two sampling dates (Figs <xref ref-type="fig" rid="F2">2B</xref>, <xref ref-type="fig" rid="F3">3</xref>). The PERMANOVA test (<italic>P</italic> = 0.001) revealed a statistically significant variation in gut community composition between the two sampling dates, producing the highest pseudo-F score compared to the other pairwise comparisons (i.e., FFG/taxonomy of host; Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S5). For instance, the September samples had a greater proportion of <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Enterobacteriales</tp:taxon-name-part></tp:taxon-name> (44.77%) than the February (9.94%) samples (Fig. <xref ref-type="fig" rid="F2">2B</xref>). Additionally, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Thermotogales</tp:taxon-name-part></tp:taxon-name> comprised 8.71% of the community’s relative abundance in the February samples. Other bacterial orders that comprised 6% of the gut microbiome were <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Actinomycetales</tp:taxon-name-part></tp:taxon-name> for both dates, and <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Rhizobiales</tp:taxon-name-part></tp:taxon-name>, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Rhodospirillales</tp:taxon-name-part></tp:taxon-name> and <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Acidobacteria</tp:taxon-name-part></tp:taxon-name>-6 iii1-15 for the February samples.</p>
      </sec>
    </sec>
    <sec sec-type="﻿Discussion" id="SECID0EYAAE">
      <title>﻿Discussion</title>
      <p>In this study, we evaluated the gut microbiome composition of caddisfly larvae, encompassing four genera and functional feeding groups. The microbiome composition of the caddisfly samples was primarily composed of <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Proteobacteria</tp:taxon-name-part></tp:taxon-name> and <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Actinobacteria</tp:taxon-name-part></tp:taxon-name>. <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Enterobacteriales</tp:taxon-name-part></tp:taxon-name> was the most abundant order of gut microbe, a finding mirrored in other insects, such as medflies (<xref ref-type="bibr" rid="B4">Behar et al. 2008</xref>; <xref ref-type="bibr" rid="B1">Aharon et al. 2013</xref>; <xref ref-type="bibr" rid="B2">Augustinos et al. 2015</xref>). While <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Enterobacteriales</tp:taxon-name-part></tp:taxon-name> was a predominant microbe for the fall samples, they were not as abundant in the winter samples. <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Actinomycetales</tp:taxon-name-part></tp:taxon-name> were also highly abundant across both sampling periods. <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="phylum">Actinobacteria</tp:taxon-name-part></tp:taxon-name> have been previously reported as a dominant gut microbiome in <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Spodoptera">Spodoptera</tp:taxon-name-part> <tp:taxon-name-part taxon-name-part-type="species" reg="frugiperda">frugiperda</tp:taxon-name-part></tp:taxon-name></italic>, a lepidopteran species (<xref ref-type="bibr" rid="B16">Han et al. 2023</xref>).</p>
      <p>We found that the microbiome was most strongly influenced by the collection date and, to a lesser extent, by the genus/functional feeding group of the host. The shift in microbiome composition observed between the two sampling dates may be attributed to a combination of biological and technical variability influencing the community composition. For instance, the similarities in gut bacteria composition of the different larvae at each sampling date could reflect strong environmental determinants of microbiota composition, which could homogenize their microbiome community across taxa at a given point in time (<xref ref-type="bibr" rid="B33">Ramírez and Gutiérrez-Fonseca 2014</xref>). Seasonal changes in the composition of food sources, such as periphyton and leaf litter, as well as microbial communities associated with these food sources and the aquatic environment, may be important drivers of gut microbiome change in caddisflies (<xref ref-type="bibr" rid="B40">Zeglin 2015</xref>; <xref ref-type="bibr" rid="B17">Hao et al. 2020</xref>; <xref ref-type="bibr" rid="B8">Cereghetti et al. 2025</xref>). In addition, although caddisflies possess various adaptations for acquiring food, they can demonstrate low selectivity for food resources, which may also impact microbiome variation across taxa (<xref ref-type="bibr" rid="B37">Wiggins and Currie 1996</xref>). There was no significant difference between dissected gut and whole-body samples, suggesting that the surface microbiota of the insects may be relatively sparse (Suppl. material <xref ref-type="supplementary-material" rid="S2">2</xref>: table S5).</p>
      <p>While sampling date had the strongest influence on microbiome composition, the gut microbiomes of scrapers, which are specialized feeders, differed from those of larvae belonging to other functional feeding groups (<xref ref-type="bibr" rid="B3">Barbour 1999</xref>). In particular, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Thermotogales</tp:taxon-name-part></tp:taxon-name> were higher in relative abundance in the scraper that we sampled, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp. In aquatic environments, <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Thermotogales</tp:taxon-name-part></tp:taxon-name> have been reported in biofilm streamer communities, particularly in thermal aquatic environments (<xref ref-type="bibr" rid="B25">Meyer‐Dombard et al. 2011</xref>). As a sedentary feeding specialist, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp. primarily consumes biofilm on streambed surfaces, which could possibly contribute to this higher proportion of <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Thermotogales</tp:taxon-name-part></tp:taxon-name> in its microbiome (<xref ref-type="bibr" rid="B34">Stief and Becker 2005</xref>; <xref ref-type="bibr" rid="B18">Ings et al. 2017</xref>; <xref ref-type="bibr" rid="B35">Terefe et al. 2018</xref>; <xref ref-type="bibr" rid="B9">Chen et al. 2022</xref>). However, <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> sp., was only sampled during the winter, possibly contributing to most of the observed compositional differences when the combined data were compared. More replicates for each taxon are likely necessary to more comprehensively characterize the influence that feeding behavior has on microbiome communities. Making microbiome comparisons using species-level determinations may also be critical, as different species within the same genus can have differing feeding behaviors, which may influence their microbiome. For example, a study on the feeding behavior of three <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Hydropsyche">Hydropsyche</tp:taxon-name-part></tp:taxon-name></italic> species found in the same river indicated that although all exhibited omnivorous feeding habits and seasonal changes in food intake, one species demonstrated higher levels of carnivory than the others, which could potentially contribute to differences in microbiome composition among species (<xref ref-type="bibr" rid="B12">Fuller and Mackay 1980</xref>).</p>
      <p>Previous studies have shown that diet is critical in shaping the microbiome across insects (<xref ref-type="bibr" rid="B36">Wang et al. 2011</xref>; <xref ref-type="bibr" rid="B10">Colman et al. 2012</xref>; <xref ref-type="bibr" rid="B29">Munoz-Benavent et al. 2021</xref>); however, the role that diet plays in shaping the microbiomes of aquatic insects is not well studied. Given their diversity in feeding modes, understanding whether the microbiota of caddisflies varies across feeding types is a crucial first step toward better understanding this potential relationship in aquatic insects (<xref ref-type="bibr" rid="B24">Merritt et al. 2017</xref>). While we have generated a first glimpse into the microbiomes of caddisflies in this experiment, future studies should consider sampling more widely and intensively across the order and include multiple caddisfly taxa within functional feeding groups to distinguish between the effect of host phylogeny and feeding type. A previous study on <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Lepidoptera</tp:taxon-name-part></tp:taxon-name> (butterflies and moths) larvae, the sister order to <tp:taxon-name><tp:taxon-name-part taxon-name-part-type="order">Trichoptera</tp:taxon-name-part></tp:taxon-name>, found a lack of a resident microbiome in their gut, indicating that the characterized microbiome was transient, likely influenced by the microbiome of their food and environment and played a reduced functional role (<xref ref-type="bibr" rid="B14">Hammer et al. 2017</xref>, <xref ref-type="bibr" rid="B15">2019</xref>). Assessing whether this is also the case for caddisflies is crucial to understanding the function of the gut microbiome in this diverse aquatic insect order. Additionally, further insights could be gained using a finer taxonomic resolution (i.e., species) to unravel potential specific associations among species, feeding behaviors, and gut microbiome composition. Lastly, other biological and environmental factors, such as habitat type and life stage, that may also shape the microbiome in aquatic insects should be considered (<xref ref-type="bibr" rid="B7">Castillo et al. 2020</xref>; <xref ref-type="bibr" rid="B30">Nobles and Jackson 2020</xref>).</p>
    </sec>
    <sec sec-type="﻿Conclusion" id="SECID0EGHAE">
      <title>﻿Conclusion</title>
      <p>In this study, we offered a preliminary characterization of the gut microbiome composition of caddisflies across functional feeding groups. While the greatest influence on microbiome composition was sampling date, the microbiome composition of scrapers in the genus <italic><tp:taxon-name><tp:taxon-name-part taxon-name-part-type="genus" reg="Tinodes">Tinodes</tp:taxon-name-part></tp:taxon-name></italic> varied from other genera that we sampled. Future studies should consider including species-level classification and feeding information of larvae, more sampling replicates across time and additional taxa within different functional feeding groups, assessment of the degree of functionality and residency of the microbiome to better understand the impact and role of diet and feeding behaviors on the gut microbiome composition of aquatic larvae.</p>
    </sec>
  </body>
  <back>
    <ack>
      <title>﻿Acknowledgements</title>
      <p>We thank BYU’s College of Life Sciences for funding this project. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health.</p>
    </ack>
    <sec sec-type="﻿Additional information" id="SECID0EXHAE">
      <title>﻿Additional information</title>
      <sec sec-type="Conflict of interest" id="SECID0E2HAE">
        <title>Conflict of interest</title>
        <p>The authors have declared that no competing interests exist.</p>
      </sec>
      <sec sec-type="Ethical statement" id="SECID0EAIAE">
        <title>Ethical statement</title>
        <p>No ethical statement was reported.</p>
      </sec>
      <sec sec-type="Use of AI" id="SECID0EFIAE">
        <title>Use of AI</title>
        <p>No use of AI was reported.</p>
      </sec>
      <sec sec-type="Funding" id="SECID0EKIAE">
        <title>Funding</title>
        <p>This work was supported by BYU’s College of Life Sciences for funding this project. Research reported in this publication was supported in part by the National Institute of General Medical Sciences of the National Institutes of Health under award number R15GM140388.</p>
      </sec>
      <sec sec-type="Author contributions" id="SECID0EPIAE">
        <title>Author contributions</title>
        <p>Conceptualization: PBF. Data curation: GJ, JMC. Formal analysis: CH, JMC, GJ. Funding acquisition: PBF, JMC. Investigation: DG, PBF, GJ, JMC. Methodology: JMC, GJ, PBF. Project administration: JMC, PBF. Resources: JMC, PBF. Software: GJ, JMC. Supervision: PBF, JMC. Visualization: CH. Writing - original draft: GJ, JMC, PBF. Writing - review and editing: GJ, CH, DG, JMC, PBF.</p>
      </sec>
      <sec sec-type="Author ORCIDs" id="SECID0EUIAE">
        <title>Author ORCIDs</title>
        <p>Gabriela Jijón <ext-link xlink:href="https://orcid.org/0000-0002-1893-3904" ext-link-type="uri" xlink:type="simple">https://orcid.org/0000-0002-1893-3904</ext-link></p>
        <p>Danyon Gedris <ext-link xlink:href="https://orcid.org/0009-0009-1635-1044" ext-link-type="uri" xlink:type="simple">https://orcid.org/0009-0009-1635-1044</ext-link></p>
        <p>Paul B. Frandsen <ext-link xlink:href="https://orcid.org/0000-0002-4801-7579" ext-link-type="uri" xlink:type="simple">https://orcid.org/0000-0002-4801-7579</ext-link></p>
      </sec>
      <sec sec-type="Data availability" id="SECID0EKJAE">
        <title>Data availability</title>
        <p>Sequencing reads are available under BioProject PRJNA1260018.</p>
      </sec>
    </sec>
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    <sec sec-type="supplementary-material">
      <title>Supplementary materials</title>
      <supplementary-material id="S1" position="float" orientation="portrait" xlink:type="simple">
        <object-id content-type="arpha">EB12EDCA-B50B-5536-A459-78D970E8FB0E</object-id>
        <label>Supplementary material 1</label>
        <caption>
          <p>An image depicting a 1% agarose gel electrophoresis used to verify amplification success</p>
        </caption>
        <statement content-type="dataType">
          <label>Data type</label>
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          <uri content-type="original_file">https://binary.pensoft.net/file/1488935</uri>
        </media>
        <permissions>
          <license xlink:type="simple">
            <license-p>This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.</license-p>
          </license>
        </permissions>
        <attrib specific-use="authors">Author: Gabriela Jijón</attrib>
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      <supplementary-material id="S2" position="float" orientation="portrait" xlink:type="simple">
        <object-id content-type="arpha">5D0C1087-AD8B-55D3-9510-70DC4D61C62C</object-id>
        <label>Supplementary material 2</label>
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          <p>Supplementary information</p>
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          <label>Data type</label>
          <p>xlsx</p>
        </statement>
        <media xlink:href="zookeys-1263-165_article-147980__-s002.xlsx" mimetype="application" mime-subtype="vnd.openxmlformats-officedocument.spreadsheetml.sheet" position="float" orientation="portrait" xlink:type="simple" id="oo_1488936.xlsx">
          <uri content-type="original_file">https://binary.pensoft.net/file/1488936</uri>
        </media>
        <permissions>
          <license xlink:type="simple">
            <license-p>This dataset is made available under the Open Database License (http://opendatacommons.org/licenses/odbl/1.0/). The Open Database License (ODbL) is a license agreement intended to allow users to freely share, modify, and use this Dataset while maintaining this same freedom for others, provided that the original source and author(s) are credited.</license-p>
          </license>
        </permissions>
        <attrib specific-use="authors">Author: Gabriela Jijón</attrib>
      </supplementary-material>
    </sec>
  </back>
</article>
