SCIENTIFIC REPORTS

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OPEN

# Linking morphological and molecular taxonomy for the identification of poultry house, soil, and nest dwelling mites in the Western Palearctic

Received: 27 October 2018

Accepted: 20 March 2019

Published online: 08 April 2019

Monica R. Young<sup>1</sup>, María L. Moraza<sup>2</sup>, Eddie Ueckermann<sup>3</sup>, Dieter Heylen<sup>4,5</sup>, Lisa F. Baardsen<sup>6</sup>, Jose Francisco Lima-Barbero<sup>7,8</sup>, Shira Gal<sup>9</sup>, Efrat Gavish-Regev<sup>10</sup>, Yuval Gottlieb<sup>11</sup>, Lise Roy<sup>12</sup>, Eitan Recht<sup>13</sup>, Marine El Adouzi<sup>12</sup> & Eric Palevsky<sup>9</sup>

Because of its ability to expedite specimen identification and species delineation, the barcode index number (BIN) system presents a powerful tool to characterize hyperdiverse invertebrate groups such as the Acari (mites). However, the congruence between BINs and morphologically recognized species has seen limited testing in this taxon. We therefore apply this method towards the development of a barcode reference library for soil, poultry litter, and nest dwelling mites in the Western Palearctic. Through analysis of over 600 specimens, we provide DNA barcode coverage for 35 described species and 70 molecular taxonomic units (BINs). Nearly 80% of the species were accurately identified through this method, but just 60% perfectly matched (1:1) with BINs. High intraspecific divergences were found in 34% of the species examined and likely reflect cryptic diversity, highlighting the need for revision in these taxa. These findings provide a valuable resource for integrative pest management, but also highlight the importance of integrating morphological and molecular methods for fine-scale taxonomic resolution in poorly-known invertebrate lineages.

DNA barcoding<fnref n="1" /> alleviates many of the challenges associated with morphological specimen identification by comparing short, standardized fragments of DNA – typically 648 bp of the cytochrome c oxidase I (COI) gene for animals – to a well-curated reference library. The success of this method relies on the presence of a clearly defined ‘barcode gap’, where intraspecific divergences are much more constrained than interspecific divergences. Its presence not only enables rapid specimen identification, but also facilitates species delineation through molecularly defined taxonomic units, a process automated through the barcode index number (BIN) system². BINs correspond well with morphologically recognized species in lineages with well-curated taxonomy²⁻⁴ and can improve taxonomic resolution by elucidating hidden diversity⁵,⁶. Consequently, BINs are a powerful tool for characterizing diversity in poorly-known, hyperdiverse, invertebrates⁷⁻⁹, but have seen limited validation in these taxa.

<footnote n="1">¹Centre for Biodiversity Genomics, University of Guelph, Guelph, Canada. ²Departamento de Biología Ambiental, Facultad de Ciencias, Universidad de Navarra, Pamplona, Spain. ³Unit for Environmental Sciences and Management, Potchefstroom Campus, North-West University, Private Bag X6001, Potchefstroom, 2520, South Africa. ⁴Interuniversity Institute for Biostatistics and Statistical Bioinformatics, Hasselt University, Diepenbeek, Belgium. ⁵Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, United States of America. ⁶Evolutionary Ecology Group, University of Antwerp, Wilrijk, Belgium. ⁷Health and Biotecnology Group (SABIO), Institute for Game and Wildlife Research (CSIC-UCLM), Ciudad Real, Spain. ⁸Sabotec, Ciudad Real, Spain. ⁹Neweya’ar Research Center, Agricultural Research Organization, Ramat Yishay, Israel. ¹⁰The National Natural History Collections, The Hebrew University of Jerusalem, Jerusalem, Israel. ¹¹Koret School of Veterinary Medicine, The Hebrew University of Jerusalem, Rehovot, Israel. ¹²UMR 5175 CEFE, CNRS – Université de Montpellier – Université Paul-Valéry Montpellier -EPHE, Route de Mende, 34199, Montpellier, Cedex 5, France. ¹³Plant Protection and Inspection Services, Bet Dagan, Israel. Correspondence and requests for materials should be addressed to M.R.Y. (email: myoung02@uoguelph.ca)</footnote>
<figure><img src="image_2.png" /><figcaption>**Figure 1.** Map of the 53 sampling sites in seven countries across the Western Palearctic. The location markers correspond with site numbers specified in Table 1; sample type (bird nest, poultry house, soil) is indicated by the colour of the marker.</figcaption></figure>

The mites (Acari) may exceed one million species, but remain poorly known because of their small size and cryptic morphology¹⁰. While BIN-based surveys have expedited surveys of this hyperdiverse group⁷,¹¹,¹², the rapidly growing collection of mite barcodes generally lack lower-level taxonomy. For example, just 18% of the >12,400 mite BINs (from nearly 120,000 DNA barcode sequences) on the Barcode of Life Data System (BOLD, v4.boldsystems.org) are linked with a species name (accessed August 2018). Nonetheless, successful species delineation through DNA barcodes has been documented in several mite lineages, including the Ixodida¹³, Mesostigmata¹⁴, Sarcoptiformes¹⁵, and Trombidiformes¹⁶. DNA barcodes have also helped resolve issues like lumping due to cryptic morphology¹⁷, and splitting due to heteromorphy¹⁸. However, concordance between species and BINs has only been tested in a single mite lineage: medically important ticks from Canada¹⁹.

While many species of mites have detrimentally impacted human health and agriculture²⁰,²¹, others are recognized for their benefits as biological control agents²². The poultry red mite (PRM; *Dermanyssus gallinae* (De Geer, 1778), for example, is a widespread pest with significant economic costs²³. Since the PRM is now resistant to most acaricides, the need for novel biocontrol methods is greater than ever²⁴,²⁵. From this perspective, natural mite communities in soil and bird nests may provide novel predators for conservation biological control of the PRM, but have seen limited investigation²⁶⁻²⁸. In the present study we begin the development of a DNA barcode reference library for the identification of poultry litter, soil, and nest dwelling mites in the Western Palearctic. Specifically, we test the correspondence between BINs and traditionally recognized species, and analyze intraspecific divergences at COI to identify potentially cryptic taxa.

## Methods

**Specimen Collection and Preparation.** Samples of poultry litter and soil from the vicinity of poultry houses, as well as wild bird nests, were collected between 2015 and 2016 from 53 locations in Croatia, Belgium, France, Israel, Poland, and Spain (Fig. 1, Table 1). Mites were extracted from approximately 0.5 kg of substrate into 99% ethanol (EtOH) using modified Berlese-Tullgren funnels for five days. From each unique collection event (denoted by exact site and collection date), all mites, regardless of life stage or sex, were sorted to morphotype and identified to order using a standard stereomicroscope setup and keys in Krantz and Walter²⁹. Up to five specimens per morphotype were selected for molecular analysis. Each specimen was imaged using a Leica DVM6 microscope and arrayed into a 96-well microplate (Eppendorf) containing 30 µL of 99% EtOH, with one blank well serving as a negative control. The museum identification code (Sample ID), collection details, order level taxonomy, and specimen images were uploaded to BOLD, available in the dataset DS-SMRPM through at https://doi.org/10.5883/DS-SMRPM.

**Molecular Analysis.** The specimens were sequenced for the barcode region of COI using standard invertebrate DNA extraction³⁰,³¹, amplification³² and sequencing protocols³³ at the Canadian Centre for DNA barcoding (CCDB; http://ccdb.ca/). However, DNA extraction was modified following Porco et al.³⁴ to facilitate the recovery of voucher specimens. A cocktail (1:1 ratio) of LepF1/LepRI¹ and LCO1490/HCO2198³⁵ primers were chosen to
<table><caption>**Table 1.** Summary of the 53 collection locations including the type of sample collected at each locality.</caption><thead><tr><td>Site No.</td><td>Country</td><td>State/Province</td><td>Exact Site</td><td>Lat</td><td>Lon</td><td>Sample Type</td></tr></thead><tbody><tr><td>1</td><td>Belgium</td><td>Antwerp</td><td>Antwerpen</td><td>51.1911</td><td>4.4267</td><td>Bird nest</td></tr><tr><td>2</td><td>Belgium</td><td>Antwerp</td><td>Boechout (Boshoek)</td><td>51.1241</td><td>4.5228</td><td>Bird nest</td></tr><tr><td>3</td><td>Belgium</td><td>Antwerp</td><td>Bornem</td><td>51.1106</td><td>4.2284</td><td>Bird nest</td></tr><tr><td>4</td><td>Belgium</td><td>Antwerp</td><td>Brasschaat</td><td>51.2718</td><td>4.4852</td><td>Bird nest</td></tr><tr><td>5</td><td>Belgium</td><td>Antwerp</td><td>Hove (Boshoek)</td><td>51.1367</td><td>4.5096</td><td>Bird nest</td></tr><tr><td>6</td><td>Belgium</td><td>Antwerp</td><td>Lint</td><td>51.1266</td><td>4.4933</td><td>Bird nest</td></tr><tr><td>7</td><td>Belgium</td><td>Antwerp</td><td>Mechelen</td><td>51.0229</td><td>4.4848</td><td>Bird nest</td></tr><tr><td>8</td><td>Belgium</td><td>Antwerp</td><td>Niel</td><td>51.1010</td><td>4.3409</td><td>Bird nest</td></tr><tr><td>9</td><td>Belgium</td><td>Antwerp</td><td>Puurs</td><td>51.0847</td><td>4.3244</td><td>Bird nest</td></tr><tr><td>10</td><td>Belgium</td><td>East Flanders</td><td>Aalst</td><td>50.9397</td><td>4.0578</td><td>Bird nest</td></tr><tr><td>11</td><td>Belgium</td><td>East Flanders</td><td>Destelbergen</td><td>51.0539</td><td>3.8203</td><td>Bird nest</td></tr><tr><td>12</td><td>Belgium</td><td>East Flanders</td><td>Gerardsbergen</td><td>50.7741</td><td>3.9422</td><td>Bird nest</td></tr><tr><td>13</td><td>Belgium</td><td>East Flanders</td><td>Kalken</td><td>51.0358</td><td>3.9221</td><td>Bird nest</td></tr><tr><td>14</td><td>Belgium</td><td>East Flanders</td><td>Merelbeke</td><td>50.9446</td><td>3.7177</td><td>Bird nest</td></tr><tr><td>15</td><td>Belgium</td><td>East Flanders</td><td>Oudenaarde</td><td>50.8456</td><td>3.6093</td><td>Bird nest</td></tr><tr><td>16</td><td>Belgium</td><td>East Flanders</td><td>Zottegem</td><td>50.8951</td><td>3.8262</td><td>Bird nest</td></tr><tr><td>17</td><td>Belgium</td><td>Flemish Brabant</td><td>Boutersem</td><td>50.8561</td><td>4.8739</td><td>Bird nest</td></tr><tr><td>18</td><td>Belgium</td><td>Flemish Brabant</td><td>Kortenberg</td><td>50.8739</td><td>4.5413</td><td>Bird nest</td></tr><tr><td>19</td><td>Belgium</td><td>Flemish Brabant</td><td>Oud-Heverlee</td><td>50.8198</td><td>4.6675</td><td>Bird nest</td></tr><tr><td>20</td><td>Belgium</td><td>Flemish Brabant</td><td>Overijse</td><td>50.7701</td><td>4.5389</td><td>Bird nest</td></tr><tr><td>21</td><td>Belgium</td><td>Flemish Brabant</td><td>Rotselaar</td><td>50.9451</td><td>4.7487</td><td>Bird nest</td></tr><tr><td>22</td><td>Belgium</td><td>Flemish Brabant</td><td>Tielt-Winge</td><td>50.9243</td><td>4.8641</td><td>Bird nest</td></tr><tr><td>23</td><td>Belgium</td><td>Flemish Brabant</td><td>Tienen</td><td>50.8045</td><td>4.9321</td><td>Bird nest</td></tr><tr><td>24</td><td>Croatia</td><td></td><td>Zagreb</td><td>45.8248</td><td>15.969</td><td>Poultry litter</td></tr><tr><td>25</td><td>France</td><td>Auvergne Rhones Alpes</td><td>Issirac</td><td>44.7233</td><td>5.0411</td><td>Poultry litter</td></tr><tr><td>26</td><td>France</td><td>Auvergne Rhones Alpes</td><td>Lhuis</td><td>45.7482</td><td>5.5416</td><td>Poultry litter</td></tr><tr><td>27</td><td>France</td><td>Auvergne Rhones Alpes</td><td>Mionnay</td><td>45.8948</td><td>4.9199</td><td>Poultry litter</td></tr><tr><td>28</td><td>France</td><td>Auvergne Rhones Alpes</td><td>Relevant</td><td>46.0897</td><td>4.9450</td><td>Poultry litter</td></tr><tr><td>29</td><td>France</td><td>Auvergne Rhones Alpes</td><td>Relevant</td><td>45.8791</td><td>5.2552</td><td>Poultry litter</td></tr><tr><td>30</td><td>France</td><td>Auvergne Rhones Alpes</td><td>Rignieux</td><td>45.9491</td><td>5.1788</td><td>Poultry litter</td></tr><tr><td>31</td><td>France</td><td>Auvergne Rhones Alpes</td><td>Saint Etienne du Bois</td><td>46.2330</td><td>4.9319</td><td>Poultry litter</td></tr><tr><td>32</td><td>Israel</td><td>Central Coastal Plain</td><td>Moshav Satria</td><td>31.8915</td><td>34.8403</td><td>Poultry litter</td></tr><tr><td>33</td><td>Israel</td><td>Jerusalem</td><td>Jerusalem</td><td>31.7947</td><td>35.2410</td><td>Soil</td></tr><tr><td>34</td><td>Israel</td><td>Jerusalem</td><td>Nehusha</td><td>31.6284</td><td>34.9523</td><td>Soil</td></tr><tr><td>35</td><td>Israel</td><td>Northern</td><td>Bét Alfa</td><td>32.5176</td><td>35.4364</td><td>Soil</td></tr><tr><td>36</td><td>Israel</td><td>Northern</td><td>'En Ya'aqov</td><td>33.0093</td><td>35.2352</td><td>Poultry litter</td></tr><tr><td>37</td><td>Israel</td><td>Northern</td><td>Kammon</td><td>32.9154</td><td>35.3608</td><td>Soil</td></tr><tr><td>38</td><td>Israel</td><td>Northern</td><td>Kefar Yehoshua'</td><td>32.6747</td><td>35.1519</td><td>Poultry litter</td></tr><tr><td>39</td><td>Israel</td><td>Northern</td><td>Korazim</td><td>32.9070</td><td>35.5506</td><td>Poultry litter</td></tr><tr><td>40</td><td>Israel</td><td>Northern</td><td>New é Ya'ar</td><td>32.7056</td><td>35.1801</td><td>Soil</td></tr><tr><td>41</td><td>Israel</td><td>Northern</td><td>Ramat Zevi</td><td>32.1079</td><td>35.4158</td><td>Poultry litter</td></tr><tr><td>42</td><td>Israel</td><td>Northern</td><td>Sede Ya'aqov</td><td>32.6989</td><td>35.1439</td><td>Poultry litter</td></tr><tr><td>43</td><td>Israel</td><td>Northern</td><td>Zar'it</td><td>33.0985</td><td>35.2847</td><td>Soil</td></tr><tr><td>44</td><td>Poland</td><td>Masovian</td><td>Deba</td><td>51.4387</td><td>22.1781</td><td>Poultry litter</td></tr><tr><td>45</td><td>Poland</td><td>Masovian</td><td>Zygmunty</td><td>51.7810</td><td>21.6713</td><td>Poultry litter</td></tr><tr><td>46</td><td>Spain</td><td>Andalusia</td><td>Castilnovo</td><td>36.2530</td><td>-6.0803</td><td>Bird nest</td></tr><tr><td>47</td><td>Spain</td><td>Andalusia</td><td>La Barca de Vejer</td><td>36.2605</td><td>-5.9613</td><td>Bird nest</td></tr><tr><td>48</td><td>Spain</td><td>Castilla-La Mancha</td><td>Abenojar</td><td>38.8958</td><td>-4.4366</td><td>Bird nest</td></tr><tr><td>49</td><td>Spain</td><td>Castilla-La Mancha</td><td>Alcazar de San Juan</td><td>39.3899</td><td>-3.2109</td><td>Bird nest</td></tr><tr><td>50</td><td>Spain</td><td>Castilla-La Mancha</td><td>Almodovar del Campo</td><td>38.7312</td><td>-4.1880</td><td>Bird nest</td></tr><tr><td>51</td><td>Spain</td><td>Castilla-La Mancha</td><td>Cabaneros National Park</td><td>39.2852</td><td>-4.3392</td><td>Bird nest</td></tr><tr><td>52</td><td>Spain</td><td>Castilla-La Mancha</td><td>El Rostro</td><td>39.2971</td><td>-4.4165</td><td>Bird nest</td></tr><tr><td>53</td><td>Spain</td><td>Comunidad de Madrid</td><td>Rascafria</td><td>40.8717</td><td>-3.8982</td><td>Bird nest</td></tr></tbody></table>

amplify and sequence a 652 bp fragment of DNA from the barcode region of COI because of their prior success in a broad array of mite taxa¹¹. The DNA extracts were archived in -80 °C freezers at the Centre for Biodiversity Genomics (CBG; biodiversitygenomics.net), and the specimen vouchers were stored in 95% EtOH and returned to the Newe-Ya'ar Research Center and the Centre d'Ecologic Functionnelle & Evolutine for morphological preparations.
<table><caption>**Table 2.** Summary of the number of specimens analysed, with the number of PCR products and barcode compliant sequences generated for each order. Success rates are provided in brackets.</caption><thead><tr><td>Taxon</td><td>Specimens</td><td>PCR Products</td><td>Sequences</td></tr></thead><tbody><tr><td>Mesostigmata</td><td>456</td><td>373 (81.8%)</td><td>254 (55.7%)</td></tr><tr><td>Opilioacarida</td><td>4</td><td>3 (75.0%)</td><td>0 (0.0%)</td></tr><tr><td>Sarcoptiformes: Astigmatina</td><td>106</td><td>48 (45.3%)</td><td>0 (0.0%)</td></tr><tr><td>Sarcoptiformes: Oribatida</td><td>10</td><td>10 (100%)</td><td>4 (4.0%)</td></tr><tr><td>Trombidiformes</td><td>76</td><td>65 (85.5%)</td><td>40 (52.6%)</td></tr><tr><td>Total</td><td>652</td><td>499 (76.5%)</td><td>298 (45.7%)</td></tr></tbody></table>

The forward and reverse chromatograms were assembled into consensus sequences for each specimen and edited using CodonCode Aligner v. 4.2.7 and uploaded to BOLD. Each sequence meeting minimum quality criteria (≥500 base pairs, <1% ambiguous nucelotides, free of contamination and stop codons) was assigned a BIN by BOLD. The sequences were further validated by inspecting their placement in a Neighbor-Joining tree (K2P distance model, BOLD alignment) and corresponding specimen images using the 'Taxon ID Tree' function in BOLD (Supplementary Figs 1 and 2). Taxa with unexpected placement in the tree (i.e. conflicting identifications within a cluster, conspecifics forming outgroups, etc.) were blasted against all barcode records on BOLD using the 'Identification Engine' tool whereupon instances of contamination (i.e. bacteria, Insecta, etc.) were flagged and filtered from the reference library.

**Specimen Identification.** Following BIN assignment, up to five vouchers per BIN were prepared for light microscopy by either mounting the specimens directly into Hoyer's medium, or in the case of Oribatida, placing the specimen in lactic acid on a cavity slide. Since the specimens were sufficiently cleared during the tissue lysis stage of DNA extraction, the typical clearing procedures were not necessary. All remaining vouchers were prepared for SEM imaging on a Hitachi TM3000 TableTop Scanning Electron Microscope, with standard drying and coating procedures.

Each specimen was identified to the lowest possible level of taxonomy, and compared to identifications of other members of the same BIN. Some specimens were not slide mounted because of redundancy, or morphologically identified when precluded by their life stage, sex or voucher quality, and were thus assigned the lowest level of taxonomy in agreement with other members in the BIN. Specimens identified in this way were denoted by 'BIN Taxonomy Match' in the Identification Method field.

**Data Analysis.** Sampling completeness was assessed by constructing a BIN accumulation curve and by estimating total BIN richness using the incidence coverage estimator (ICE) in EstimateS³⁶. Maximum intraspecific and minimum interspecific p-distances were calculated for all morphologically identified specimens using the 'Barcode Gap Analysis' tool on BOLD. Species correspondence with BINs were characterized by one of four categories: matches (perfect correspondence between one species and one BIN), splits (one species is represented by more than one BIN), merges (two or more species are assigned to a single BIN), and mixtures (a combination of splits and merges) as described in Ratnasingham and Hebert².

## Results

**Sequence Recovery.** Barcode compliant sequences were recovered from 298 of the 652 specimens analysed, with an overall PCR success rate of 76.5% and sequencing success rate of 45.7%. Success varied greatly among the major lineages. PCR success, for example, ranged from a high of 85% in the Trombidiformes, to a low of 45% in the Astigmatina (Sarcoptiformes). Sequencing success, on the other hand, ranged from a high of 56% in the Mesostigmata to a low of 0% in the Astigmatina (Sarcoptiformes) and Opilioacarida (Table 2). Non-target amplification was detected in 28 sequences, including cross-mite contamination, insects, and occasionally bacteria. These sequences were flagged on BOLD, removed from the BOLD identification engine, and excluded from subsequent analyses.

**DNA Barcode Reference Library and Sample Completeness.** Minimum quality requirements for BIN assignment were met by 298 sequences representing 70 BINs in total (x̄ = 4.2 specimens/BIN). Of these 70 BINs, 48 (68.6%) were morphologically identified to the species level, while genus was the lowest identification for six BINs (8.6%), family for 15 BINs (21.4%), and one BIN was identified only to the order level (1.4%). In total, 35 species, 27 genera, 24 families, and three orders were identified in our barcode reference library (Table 3). The slope of the BIN accumulation curve remains steep, indicating incomplete sampling of the fauna (Fig. 2), and the estimate of total BIN richness was more than double the current observations (ICE = 172 BINs).

**Barcode Gap and BIN Analysis.** Of the 35 morphologically identified species with BINs, 19 (61%) perfectly corresponded with BIN assignments, while eight (26%) resulted in BIN splits, and two cases of BIN mixtures affecting four species (13%) were detected (Fig. 3, Table 3). The barcode gap analysis revealed nine species in which maximum interspecific p-distance exceeded minimum intraspecific p-distance (Fig. 3), all of which were involved in BIN splits or mixtures. Maximum intraspecific p-distances averaged 7.7%, and dropped to 0.9% when BIN splits and mixtures were excluded from analyses.
<figure><img src="image_3.png" /><figcaption>**Figure 2.** The observed (solid line) and estimated (dashed line) accumulation of BINs with increasing sample size for the 298 specimens with BIN assignments.</figcaption></figure>

<figure><img src="image_4.png" /><figcaption>**Figure 3.** Comparison of maximum intraspecific and minim interspecific divergences (p-distances) of the 35 morphologically identified species. Data points are colourized based on species correspondence with BINs, and the diagonal red line indicates the 1:1 ratio of divergences. The barcode gap is present in species that fall above the line, and absent in those below.</figcaption></figure>

Discussion

Through the integration of morphological and molecular taxonomic methods, we provide DNA barcode coverage for 35 described species and 70 mite BINs from soil, bird nest, and poultry house-associated assemblages in the Western Palearctic. The integrity of most vouchers was sufficiently maintained for morphological identification, and SEM imaging of diagnostic characters (see the following BIN page for example: BOLD:ADA3054). While only 13 of these species have been previously associated with the poultry red mite<sup>27,37</sup>, additional species are undoubtedly present in our dataset but remain undetected because of low sequencing success combined with several BINs lacking identifications. Our failure to generate any sequences for Astigmatina (Sarcoptiformes) may be explained by low primer affinity, considering amplification rates were also lowest in this group. Primer affinity, however, does not justify the low successes in other lineages with higher amplification rates. Comparable methods, for example, have yielded much higher successes (77%) among soil and leaf litter mites (including Astigmatina) from subarctic Canada<sup>11</sup>, demonstrating the broad applicability of these primers among a diverse array of taxa. Since 40% of the amplification products generated uninterpretable chromatograms, poor quality DNA template may be responsible for low sequencing successes among taxa.

The concordance between BINs and mite species was much lower than in some well-studied invertebrates (e.g. perfect concordance in 92% of beetles<sup>4</sup> and ticks<sup>19</sup>). However, similar concordance levels have been reported
for many taxa including geometrid moths³⁸ (67%), true bugs³⁹ (70%), and spiders⁵ (54%). Low concordance is mainly driven by species with large intraspecific divergences (>3% p-distance) resulting in the assignment of two or more BINs. While this does not preclude accurate barcode-based identification, it highlights potentially cryptic species because most BIN splits formed widely separated clades (e.g. >15% p-distance) lacking intermediate haplotypes. In fact, 16S and 18S rRNA gene topologies for *Androlaelaps casalis* (Berlese, 1887) and *Proctolaelaps scoelyti* Evans, 1958 were congruent with BIN splits, further supporting our cryptic species hypothesis in these taxa²⁷. *Rhodacarellus silesiacus* Willmann, 1936, on the other hand, also formed two distinct but narrowly separated clades (<3% divergence), with divergences similar to those in species with concordant BINs (e.g. *Dermanyssus gallinae* and *Gamasodes spiniger* (Oudemans, 1936)), such that additional sampling may reveal intermediate haplotypes causing the BINs to collapse into one²⁸.

More problematic for the barcode based identification of mites are the two cases of shared barcodes con-
founded by BIN splits (BIN mixtures) affecting four species: *Dendrolaelaps longiusculus* (Leitner, 1949)/D. *prese-
pum* (Berlese, 1918), and *Proctolaelaps parascolyti* Costa, 1963/P. *pygmaeus* (Müller, 1859). Since multiple species
are assigned to the same BIN, mixtures impede accurate identifications, but may also represent taxonomic errors².
Misidentification is unlikely, since procedures were in place to evaluate and correct such errors. However, both
cases of BIN mixtures involve closely allied congenerics which may be subjected to hybridization or incomplete
lineage sorting⁴⁰. Given the large intraspecific divergences observed, though, a more probable explanation is the
presence of cryptic diversity compounded by inadequate species descriptions. Future work should scrutinize the
morphology of genetic clusters from both mixtures and splits for more effective characters to discriminate these
potentially cryptic species.

This study represents the first step towards development of a DNA barcode reference library for the identifica-
tion of poultry litter, soil, and nest dwelling mites from the Western Palearctic, which may in turn reveal natural
enemies key to the control of PRM. Although sequencing success rates should be improved, we demonstrate
that nearly 80% of the species analysed can be accurately identified through DNA barcodes. Our BIN analysis,
however, indicates a high proportion of cryptic diversity and some potential taxonomic confusion. This method
consequently presents a powerful tool not only for the identification of unknown specimens, but as the founda-
tion for integrative taxonomy and diversity estimation in hyperdiverse invertebrates such as mites.

Data Availability

All specimen and sequence data is available in the BOLD dataset DS-SMRPM through the following, https://
doi.org/10.5883/DS-SMRPM. Valid sequences were also deposited in GenBank under the following accessions:
MH983560-MH983861.

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## Acknowledgements

We would like to express our gratitude to the Canadian Centre for DNA Barcoding (CCDB) for their technical assistance and support for molecular analysis, as well as the Barcode of Life Data System (BOLD) team at the Centre for Biodiversity Genomics for their support on data management and storage. We would also like to thank Jandir Cruz Santos for critically analysing nomenclature and providing helpful comments on the manuscript. The study was carried out within the framework of the European Cooperation in Science and Technology (COST) Action (FA1404-COREMI) 'Improving current understanding and research for sustainable control of the poultry red mite *Dermanyssus gallinae*.'

## Author Contributions

M.R.Y., L.R., Y.G. and E.P. devised the study; M.A., L.B., D.H., J.F.L.B., S.G. and E.G.R. collected the specimens and prepared the mites for molecular analysis and museum archiving; M.L.M. and E.U. identified the specimens; E.P. managed and coordinated the activities of the study; M.R.Y. wrote the manuscript and analysed the data; all authors contributed to discussion of ideas and manuscript revisions.

## Additional Information

Supplementary information accompanies this paper at https://doi.org/10.1038/s41598-019-41958-9.

**Competing Interests:** The authors declare no competing interests.

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