Biodiversity Data Journal :
Research Article
|
Corresponding author: Irene Santos-Perdomo (isanper@ipna.csic.es)
Academic editor: Maciej Skoracki
Received: 26 Sep 2023 | Accepted: 23 Dec 2023 | Published: 10 Jan 2024
© 2024 Irene Santos-Perdomo, Daniel Suárez, María L. Moraza, Paula Arribas, Carmelo Andújar
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.
Citation:
Santos-Perdomo I, Suárez D, Moraza ML, Arribas P, Andújar C (2024) Towards a Canary Islands barcode database for soil biodiversity: revealing cryptic and unrecorded mite species diversity within insular soils. Biodiversity Data Journal 12: e113301. https://doi.org/10.3897/BDJ.12.e113301
|
Soil arthropod diversity contributes to a high proportion of the total biodiversity on Earth. However, most soil arthropods are still undescribed, hindering our understanding of soil functioning and global biodiversity estimations. Inventorying soil arthropods using conventional taxonomical approaches is particularly difficult and costly due to the great species richness, abundance and local-scale heterogeneity of mesofauna communities and the poor taxonomic background knowledge of most lineages. To alleviate this situation, we have designed and implemented a molecular barcoding framework adapted to soil fauna. This pipeline includes different steps, starting with a morphology-based selection of specimens which are imaged. Then, DNA is extracted non-destructively. Both images and voucher specimens are used to assign a taxonomic identification, based on morphology that is further checked for consistency with molecular information. Using this procedure, we studied 239 specimens of mites from the Canary Islands including representatives of Mesostigmata, Sarcoptiformes and Trombidiformes, of which we recovered barcode sequences for 168 specimens that were morphologically identified to 49 species, with nine specimens that could only be identified at the genus or family levels. Multiple species delimitation analyses were run to compare molecular delimitations with morphological identifications, including ASAP, mlPTP, BINs and 3% and 8% genetic distance thresholds. Additionally, a species-level search was carried out at the Biodiversity Databank of the Canary Islands (BIOTA) to evaluate the number of species in our dataset that were not previously recorded in the archipelago. In parallel, a sequence-level search of our sequences was performed against BOLD Systems. Our results reveal that multiple morphologically identified species correspond to different molecular lineages, which points to significant levels of unknown cryptic diversity within the archipelago. In addition, we evidenced that multiple species in our dataset constituted new records for the Canary Islands fauna and that the information for these lineages within online genetic repositories is very incomplete. Our study represents the first systematic effort to catalogue the soil arthropod mesofauna of the Canary Islands and establishes the basis for the Canary Islands Soil Biodiversity barcode database. This resource will constitute a step forward in the knowledge of these arthropods in a region of special interest.
Acari, COI, barcoding, soil mesofauna, biotic frontier, species inventory, oceanic islands, species delimitation methods
Soils harbour a vast proportion of total biodiversity on Earth (
The methodological and logistical issues that have hindered our understanding of soil biodiversity are particularly exacerbated for some edaphic groups and geographical areas (
DNA barcoding, i.e. the use of short, standardised genomic regions to facilitate species identification and discovery, has revolutionised the study of biodiversity. Barcoding specimens using the standard barcode region (COI‐bcr) for metazoan DNA taxonomy (
The Canary Islands are an oceanic archipelago within the subtropical region of the North Atlantic Ocean with great conservational and patrimonial value in both a national and European context. The Canary Islands are recognised as a Special Territory of the European Union, where quantifying and controlling biodiversity loss is a priority. The diversity of soil mesofauna within oceanic islands needs to be better explored. Literature on the topic is limited (but see
Here, we initiate the Barcode Database of soil mesofauna from the Canary Islands (CISoilBiota) by: i) developing a standardised workflow that combines traditional morphological identification and COI barcoding of soil arthropod specimens within the framework of the BOLD System (
Mite specimens were retrieved from 24 soil samples collected between 2018 and 2020 in different localities of laurel forests, pine forests, heathlands and crops of the islands of Tenerife and Fuerteventura (Suppl. material
Before DNA extraction, several photos were taken of each intact voucher. A Canon EOS 750D camera attached to a microscope (Zeizz Axioskop 40) was used to take high-resolution pictures of the mite specimens submerged in ethanol and over a white background. For each voucher, a range from 30 to 90 photos was taken by using a 10x objective at different confocal distances depending on the size of each voucher, to subsequently compile with Zerene Stacker (Zerene Systems LLC) for a fully on-focus photographic record of each voucher. Photo alignment and stacking were done following PMax settings and the final image was saved in JPEG format with the highest quality (compression quality = 12). Images and mite vouchers, after non-destructive DNA extractions (see below), were subsequently studied by an expert taxonomist (co-author M.L. Moraza) for morphological identification. Each specimen was examined under a Nikon MSZ745 stereomicroscope and an Olympus Vanox with a phase contrast microscope. For further identification, specimens were cleared in Nesbitt’s liquid and mounted using Hoyer’s medium. Mite species were identified using taxonomic keys for the Palearctic Region (
Non-destructive DNA extractions were performed for each voucher. For that purpose, the exoskeleton of each specimen was punctured with an entomological pin and kept individually in 1.5 ml vials. Pins were sterilised under a Bunsen burner to avoid contaminations between samples. A volume of 110 µl of digestion buffer (pK ratio 1/10) was added to each voucher and digestion was done overnight at 60°C. The supernatant (DNA lysate) was transferred to the corresponding well within a deep-well plate and the (DNA-extracted) vouchers were maintained in the vials with ethanol for further morphological study (see above). Subsequent steps of the DNA extractions used the MAg-bind Blood & Tissue DNA extraction Kit (Omega Bio-tek) in the KingFisher robotic system (Thermo Fisher Scientific inc.). The default protocol was followed, but DNA lysate and reagent volumes were cut in half. The resulting 100 µl of genomic DNA extraction were split into a ‘stock plate’ directly frozen at -20ºC and a ‘working plate’ that was used to quantify DNA concentration using absorbance values within an Infinite M Nano (Tecan Trading AG).
PCR amplification was done for the 5’ end COI gene (standard barcode region for Metazoa;
Sequences were edited (trimming and primer removal) on Geneious Prime version 2020.0.3 (www.geneious.com). Edited sequences were deposited in BOLD Systems (
Five different methods were applied for molecular species delimitation of the vouchers. First, the barcode index number (BIN) was implemented in the BOLD system. This approach provides an effective method for species delineation as each sequence cluster is assigned a unique alphanumeric (BIN URI, see Table
Species-level inventory in our dataset, including: morphological identification, number of barcoded sequences, entities delimited for each species delimitation methods (new generated BINs are highlighted by an ‘*’), representation in BIOTA database (and origin category), similarity percentage with best BOLD match and coherence between species-level identification and best BOLD match identification.
MORPHOLOGICAL IDENTIFICATION | Nº OF SEQUENCES | BIN | ASAP | mlPTP | BIOTA MATCH | NEAREST BOLD MATCH | BOLD COHERENCE |
Order Mesostigmata | |||||||
Fam. Laelapidae | |||||||
Pseudoparasitus dentatus (Halbert, 1920) | 2 | BOLD:AEI7729* | ASAP21 | mlPTP8 | yes/native | Ornithonyssus sylviarum (79.44%) | no: different species identification |
Fam. Macrochelidae | |||||||
Macrocheles (Macrholaspis) cf. recki Bregetova & Koroleva, 1960 | 1 | BOLD:AEI5961* | ASAP54 | mlPTP17 | yes/native | Mesostigmata (81.9%) | no: higher taxonomic resolution |
Fam. Ologamasidae | |||||||
Gamasiphis sextus Vitzthum, 1921 | 4 | BOLD:AEI1055* | ASAP13 | mlPTP18 | no | Gamasiphis sp. JCS03 (83.72%) | no: higher taxonomic resolution |
Fam. Parasitidae | |||||||
Holoparasitus sp. | 3 | BOLD:AEI8993* | ASAP6 | mlPTP23 | NA | Parasitidae (83.00%) | NA |
Parasitidae | 1 | BOLD:AEI6503* | ASAP39 | mlPTP22 | NA | Poecilochirus (84.57%) | NA |
Pergamasus crassipes (Linnaeus, 1758) | 1 | BOLD:ACQ8500 | ASAP2 | mlPTP21 | yes/introduced | Mesostigmata (98.34%) | no: higher taxonomic resolution |
Fam. Polyaspididae | |||||||
Uroseius cylindricus (Berlese, 1916) | 1 | BOLD:AEI1534* | ASAP44 | mlPTP45 | no | Polyaspinus higginsi (84.23%) | no: different species identification |
Fam. Trachyuropodidae | |||||||
cf. Trachyuropoda sp. | 1 | BOLD:ADH9050 | ASAP35 | mlPTP43 | NA | Uropodidae (99.5%) | NA |
Trachyuropoda sp. | 5 | BOLD:AAZ2213 | ASAP1 | mlPTP44 | NA | Mesostigmata (98.54%) | NA |
Order Trombidiformes | |||||||
Fam. Erythraeidae | |||||||
cf. Leptus sp. | 2 | BOLD:AEI0533* | ASAP47 | mlPTP66; mlPTP67 | NA | Arachnida (88.08%) | NA |
Erythraeidae | 1 | BOLD:AEI6506* | ASAP38 | mlPTP7 | NA | Trombiculidae (78.51%) | NA |
Order Sarcoptiformes | |||||||
Fam. Achipteriidae | |||||||
Campachipteria petiti (Travé, 1960) | 3 | BOLD:AEI0246*; BOLD:AEI6330* | ASAP20 | mlPTP54; mlPTP59; mlPTP60 | no | Achipteriidae (86.7%) | no: higher taxonomic resolution |
Fam. Ameridae | |||||||
Amerus cuspidatus (Berlese, 1883) | 2 | BOLD:AEI0247*; BOLD:AEI4260* | ASAP23 | mlPTP57; mlPTP58 | yes/native | Ceratozetes gracilis (83.02%) | no: different species identification |
Fam. Amerobelbidae | |||||||
Amerobelba decedens Berlese, 1908 | 3 | BOLD:AEI8991* | ASAP7 | mlPTP38 | yes/native | Eueremaeus (83.33%) | no: higher taxonomic resolution |
Fam. Carabodidae | |||||||
Carabodidae | 1 | BOLD:AEI9656* | ASAP40 | mlPTP29 | NA | Hermanniellidae (80.95%) | NA |
Cavernocarabodes trigonosternum (Pérez-Íñigo, 1976) | 1 | BOLD:AEI0245* | ASAP53 | mlPTP37 | yes/endemic | Oribatodes mirabilis (85.96%) | no: different species identification |
Odontocepheus elongatus (Michael, 1879) | 1 | BOLD:AEI0250* | ASAP60 | mlPTP2 | yes/native | Odontocepheus elongatus (74.48%) | yes |
Fam. Ceratoppiidae | |||||||
Ceratoppia bipilis (Hermann, 1804) | 1 | BOLD:AEH9721* | ASAP49 | mlPTP34 | yes/native | Ceratoppia (82.96%) | no: higher taxonomic resolution |
Fam. Ceratozetidae | |||||||
Trichoribates novus (Sellnick, 1928) | 2 | BOLD:AEI6507* | ASAP41 | mlPTP46 | no | Oribatella (84.86%) | no: higher taxonomic resolution |
Fam. Compactozetidae | |||||||
Cepheus latus Koch, 1835 | 1 | BOLD:AEI5729* | ASAP55 | mlPTP36 | yes/native | Neoliodidae (82.41%) | no: higher taxonomic resolution |
Conoppia cf. palmicincta (Michael, 1884) | 1 | BOLD:AEI8929* | ASAP46 | mlPTP48 | yes/native | Eremaeus (88.68%) | no: higher taxonomic resolution |
Fam. Damaeidae | |||||||
Damaeus recasensi Capilla, 1971 | 10 | BOLD:AEI4384*; BOLD:AEI4385*; BOLD:AEI4386*; BOLD:AEI7071* | ASAP14 | mlPTP70; mlPTP71 | yes/native | Epidamaeus (83.67%) | no: higher taxonomic resolution |
Metabelbella interlamellaris Pérez-Íñigo, 1987 | 2 | BOLD:AEI0249* | ASAP45 | mlPTP24 | yes/native | Damaeidae (80.81%) | no: higher taxonomic resolution |
Fam. Dampfiellidae | |||||||
Dampfiella ambigua Pérez-Íñigo, 1976 | 1 | BOLD:AEI5421* | ASAP50 | mlPTP1 | yes/endemic | Baryscapus servadeii (77.57%) | no: different species identification |
Fam. Euphthiracaridae | |||||||
Acrotritia ardua ardua (Koch, 1841) | 2 | BOLD:AAF9157 | ASAP57 | mlPTP33 | yes/native | Euphthiracaridae (95.34%) | no: higher taxonomic resolution |
Acrotritia penicillata (Pérez-Íñigo, 1969) | 1 | BOLD:ADX1060 | ASAP30 | mlPTP32 | no | Sarcoptiformes (99.5%) | no: higher taxonomic resolution |
cf. Euphthiracarus sp. | 1 | BOLD:AEH9008* | ASAP56 | mlPTP14 | NA | Euphthiracarus monodactylus (81.03%) | NA |
Mesotritia cf. grandjeani (Feider & Suciu, 1957) | 9 | BOLD:AEI8467*; BOLD:AEI6713* | ASAP22 | mlPTP3 | no | Arthropoda (78.86%) | no: higher taxonomic resolution |
Fam. Galumnidae | |||||||
Acrogalumna longipluma (Berlese, 1904) | 19 | BOLD:AEI1056*; BOLD:AEI5290* | ASAP9; ASAP10 | mlPTP55; mlPTP56 | yes/native | Acrogalumna longipluma (96.45%) | yes |
Galumna alata (Hermann, 1804) | 4 | BOLD:AEI4158*; BOLD:AEI8205* | ASAP17; ASAP18 | mlPTP49; mlPTP68; mlPTP69 | yes/native | Eupelops (82.09%) | no: higher taxonomic resolution |
Pilogalumna allifera (Oudemans, 1919) | 2 | BOLD:AEH9722* | ASAP31 | mlPTP19 | yes/endemic | Cepheus (82.72%) | no: higher taxonomic resolution |
Fam. Gustaviidae | |||||||
Gustavia longirostris Mihelcic, 1957 | 4 | BOLD:AEI3725* | ASAP33 | mlPTP26 | no | Chamobates cuspidatus (81.92%) | no: different species identification |
Fam. Humerobatidae | |||||||
Humerobates pomboi Pérez-Íñigo, 1992 | 1 | BOLD:AEI4261* | ASAP29 | mlPTP47 | yes/native | Humerobatidae (86.53%) | no: higher taxonomic resolution |
Fam. Hypochthoniidae | |||||||
Hypochthonius luteus Oudemans, 1917 | 18 | BOLD:AEI3587* | ASAP5 | mlPTP13 | yes/native | Hypochthonius luteus (96.34%) | yes |
Fam. Liacaridae | |||||||
Dorycranosus splendens (Coggi, 1898) | 1 | BOLD:AEI6712* | ASAP26 | mlPTP4 | yes/native | Oppiidae (76.08%) | no: higher taxonomic resolution |
Fam. Nothridae | |||||||
Nothrus reticulatus Sitnikova, 1975 | 1 | BOLD:AEI6500* | ASAP43 | mlPTP39 | no | Nothrus (95.27%) | no: higher taxonomic resolution |
Nothrus silvestris Nicolet, 1855 | 2 | BOLD:AEI0848* | ASAP28 | mlPTP40 | yes/native | Nothrus (82.57%) | no: higher taxonomic resolution |
Fam. Oppiidae | |||||||
Ramusella cf. clavipectinata (Michael, 1885) | 1 | BOLD:AEI7727* | ASAP32 | mlPTP28 | yes/native | Eremaeus (84.67%) | no: higher taxonomic resolution |
Fam. Oribatulidae | |||||||
Hemileius elongatus E.Pérez-Íñigo, 1978 | 4 | BOLD:AEI4159* | ASAP8 | mlPTP20 | yes/native | Hemileius initialis (85.82%) | no: different species identification |
Zygoribatula connexa (Berlese, 1904) | 1 | BOLD:AEI7730* | ASAP16 | mlPTP25 | yes/native | Oribatula tibialis (85.02%) | no: different species identification |
Zygoribatula propinqua (Oudemans, 1902) | 2 | BOLD:AEI7725*; BOLD:AEI8260* | ASAP34; ASAP59 | mlPTP27 | yes/native | Eueremaeus silvestris (84.85%) | no: different species identification |
Zygoribatula undulata Berlese, 1916 | 2 | BOLD:AEI7728* | ASAP12 | mlPTP31 | yes/native | Achipteria coleoptrata (86.16%) | no: different species identification |
Fam. Phenopelopidae | |||||||
Eupelops acromios (Hermann, 1804) | 5 | BOLD:AEI2704*; BOLD:AEI3535* | ASAP36 | mlPTP64; mlPTP65 | yes/native | Eupelops (84.57%) | no: higher taxonomic resolution |
Fam. Phthiracaridae | |||||||
Archiphthiracarus sp. | 2 | BOLD:AEI0248*; BOLD:AEI7726* | ASAP42; ASAP58 | mlPTP10; mlPTP16 | NA | Phthiracarus globosus (95.78%) | NA |
Hoplophthiracarus cf. cazanicus Feider & Calugar, 1970 | 1 | BOLD:AEH8933* | ASAP24 | mlPTP15 | no | Austrophthiracarus costai (80.35%) | no: different species identification |
Phthiracarus cf. globosus (Koch, 1841) | 13 | BOLD:AEI2040* | ASAP11 | mlPTP11 | no | Phthiracarus (79.94%) | no: higher taxonomic resolution |
Phthiracarus cf. globus Parry, 1979 | 1 | BOLD:AEI5730 | ASAP25 | mlPTP9 | no | Phthiracarus (98.04%) | no: higher taxonomic resolution |
Phthiracarus cf. ligneus Willmann, 1931 | 5 | BOLD:AEH9005*; BOLD:AEI6501*; BOLD:AEI7167* | ASAP27 | mlPTP61; mlPTP62; mlPTP63 | no | Phthiracarus globosus (78.43%) | no: different species identification |
Fam. Steganacaridae | |||||||
Steganacarus tenerifensis Pérez-Íñigo, 1972 | 2 | BOLD:AEI8994* | ASAP19 | mlPTP12 | yes/endemic | Steganacarus magnus (84.21%) | no: different species identification |
Fam. Suctobelbidae | |||||||
Rhynchobelba machadoi Pérez-Íñigo, 1976 | 1 | BOLD:AEI6502* | ASAP48 | mlPTP6 | yes/endemic | Neogymnobates luteus (81.77%) | no: different species identification |
Fam. Tectocepheidae | |||||||
Tectocepheus alatus Berlese, 1913 | 1 | BOLD:AEI6504* | ASAP37 | mlPTP5 | no | Scutovertex sculptus (85.44%) | no: different species identification |
Fam. Trhypochthoniidae | |||||||
Trhypochthonius japonicus Aoki, 1970 | 1 | BOLD:AEI0244* | ASAP52 | mlPTP30 | no | Trhypochthonius tectorum (82.95%) | no: different species identification |
Fam. Xenillidae | |||||||
Xenillus discrepans canariensis Pérez-Íñigo, 1976 | 1 | BOLD:AEH9009* | ASAP51 | mlPTP42 | yes/endemic | Arachnida (80.16%) | no: higher taxonomic resolution |
Xenillus sp1 | 1 | BOLD:AEI9187* | ASAP15 | mlPTP50 | NA | Sarcoptiformes (81.48%) | NA |
Xenillus sp2 | 1 | BOLD:AEH9719* | ASAP3 | mlPTP41 | NA | Scheloribatidae (78.5%) | NA |
Xenillus sp3 | 2 | BOLD:AEI3741* | ASAP4 | mlPTP52 | NA | Parachipteria punctata (80.93%) | NA |
Xenillus sp4 | 3 | BOLD:AEI6505* | ASAP4 | mlPTP53 | NA | Sarcoptiformes (80.81%) | NA |
Xenillus tegeocranus (Hermann, 1804) | 2 | BOLD:AEI8992* | ASAP15 | mlPTP51 | yes/native | Liacaridae (82.13%) | no: higher taxonomic resolution |
To evaluate the previously unrecorded diversity in the Canary Islands that our dataset contains, we performed searches of the different species identified in our data within BIOTA. For each species within our dataset, we annotated the previously existing records and their status as endemic, native or potentially introduced taxa in the archipelago context.
To identify potential cryptic diversity within our dataset, we compare the morphological assignment provided to specimens with the results provided by the different species delimitation methods implemented. The number of morphological species that were split, merged or maintained was estimated for each delimitation method and the concordance amongst the specimens grouping resulting from each approach was evaluated.
To quantify the overall unrecorded mite diversity within our dataset, we used the Barcode of Life Data (BOLD) as the major source of barcode reference sequences available. BOLD contains 17,789,385 specimen records, of which 13,911,307 have barcode sequences (accessed at 02/08/2023). Of these, a total of 219,759 records are from mites, of which 181,682 have barcode sequences. Records with barcode sequence and identification to species level represent a total of 4,350 mite species, included in orders Holothyrida (1), Ixodida (334), Mesostigmata (992), Sarcoptiformes (769) and Trombidiformes (2,254). We have compared our sequences with those available on the platform to check the consistency of morphological identifications, detect potential cryptic diversity at the global scale and to investigate to what extent the diversity within our dataset is already represented in the BOLD repository.
First, we performed BOLD Identification System (IDS) (default setting parameters) for each sequence against the overall BOLD system (21/04/2023), including public and private barcode records. We extracted the taxonomic identification, based on morphology (species or genus level) and the similarity percentage to the best match using the BOLD Identification System (IDS) results for each sequence. Using this information: i) we estimated the overall similarity with BOLD sequences for each of our species and ii) we checked the coherence between the taxonomic identifications from BOLD and the morphological identifications of our specimens. In the cases where species-level identification agreed between both datasets, the overall similarity, the monophyly of the Canary Islands sequences and the geographical origin of BOLD sequences were evaluated to identify potential cryptic diversity within those mite species. Finally, as an additional indicator of how the diversity in our dataset is already reported within BOLD, the number of BINs that were only composed by our sequences (i.e. not including sequences already present on the repository) was recorded.
From the total 239 mite specimens selected, we recovered 168 barcode sequences, resulting in a success rate above 70%. Sequence lengths varied from 472 to 639 bp and included 153 haplotypes. The 168 barcoded specimens were morphologically identified as corresponding with 58 different morphological entities, of which 45 correspond to known species, four to unknown, likely new species of the genus Xenillus and nine entities (each represented by a single specimen) that could only be identified at the genus (six cases) or family (three cases) levels. Our barcoded dataset comprises entities from three orders (Mesostigmata, Sarcoptiformes and Trombidiformes) and 32 families, including Xenillidae (six entities), Phthiracaridae (five entities) and Euphthiracaridae and Oribatulidae (four entities each) as the four families with a higher number of species in our dataset (Table
The different molecular species delimitation methods implemented resulted in a range from 60 to 85 molecular species delimited. The barcode index number (BIN) implemented in the BOLD System showed that our 168 voucher sequences were grouped in 71 BINs. Sixty-five of those BINs were newly generated by BOLD and assigned to sequences contributed in this study, while 11 sequences were grouped into six pre-existing BINs (access numbers in Table
Distance-based UPGMA tree obtained using HKY corrected distances. Coloured horizontal blocks over the tree represent specimen clusters corresponding to morphological species. Vertical bars represent, from left to right: (i) morphological species, (ii) BINs classification in BOLD, (iii) species delimitation with ASAP, (iv) species delimitation with mlPTP, (v) 3% similarity clusters and (vi) 8% similarity clusters. At the bottom, each method's total number of species is presented. The X-axis represents genetic distance; with dotted lines corresponding 3% and 8% divergence thresholds (first half).
Distance-based UPGMA tree obtained using HKY corrected distances. Coloured horizontal blocks over the tree represent specimen clusters corresponding to morphological species. Vertical bars represent, from left to right: (i) morphological species, (ii) BINs classification in BOLD, (iii) species delimitation with ASAP, (iv) species delimitation with mlPTP, (v) 3% similarity clusters and (vi) 8% similarity clusters. At the bottom, each method's total number of species is presented. The X-axis represents genetic distance; with dotted lines corresponding 3% and 8% divergence thresholds (second half).
The concordance amongst the results of the different molecular delimitations implemented was relatively high. BINs, mPTP and 3% approaches resulted in a higher (but mostly concordant) number of delimited entities (Figs
Of the 45 morphological entities identified as known species in our dataset, 31 species (68%) were already registered as present in the archipelago, whereas the remaining 14 (31%) represented new species and genera records at the archipelago level (Table
Evaluating unrecorded diversity within the Canary Islands and global context. a Proportion of species in our dataset that are already registered in the BIOTA database; b Category of origin (i.e. native non-endemic, introduced or endemic) for species recorded in BIOTA as reported within the database; c Similarity values of best matches of obtained sequences representing each species against BOLD Systems; d Species-level identification coherence between the specimens in our dataset and BOLD best matches. When there is no coherence, we specify if the BOLD best match was identified at species or higher taxonomic level (genus or family level).
Of the 31 species in our dataset that are present in the BIOTA, six species are endemic to the Islands (19%) and the rest are considered non-endemic native species (24 species, 77%) or introduced species (one species, 3%) (Table
BOLD Identification System (IDS) results for the 168 sequences in our dataset resulted in a range of similarity percentages, with the best matches ranging from 74.48% to 99.5%. Amongst the 45 morphological entities identified as known species, 38 (84%) reported a similarity below 92%, four (9%) from 92% to 97% and three (7%) above 97% (Table
The only cases where species-level identification agrees with the BOLD dataset are Acrogalumna longipluma, Hypochthonius luteus and Odontocepheus elongatus. In the case of Acrogalumna longipluma, the overall similarity of Canarian specimens with BOLD sequences was 96.45%. In the case of Hypochthonius luteus, the overall similarity of our Canarian specimens with the only one registered on BOLD was 96.34%. Finally, in the case of Odontocepheus elongatus, the overall similarity of Canarian specimens with BOLD sequences was 74.48%.
Our study initiates the Barcode Database of soil fauna from the Canary Islands (CISoilBiota) by developing a standardised workflow that combines specific soil sampling, Berlese extraction, sample sorting, COI barcoding and traditional taxonomic identification of barcoded specimens. The workflow has been applied to 239 mite specimens, of which we recovered 168 sequences. This represents a success of 70%, similar to success rates in other barcoding studies (e.g.
One of these difficulties is the incompatibility of the procedures used for the morphological study of these minute organisms (requiring microscopic preparations where specimens are cleared and fixed with different chemical products) and DNA preservation. Here, we solve that by implementing a protocol of specimen imaging and non-destructive DNA extractions for mites that allow the morphological study of the specimens after DNA extraction. Our results demonstrated that non-destructive DNA extraction of soil mites is feasible without compromising the morphological integrity of specimens.
Another difficulty in implementing barcoding to soil mesofauna is associated with the reduced body size of specimens and the low DNA concentration retrieved. The DNA extraction and PCR protocol performed here appears adequate under these low DNA conditions. DNA extraction was implemented using a magnetic-bead approach in a robotic platform; this semi-automated approach is optimal for implementing arthropod barcoding because it facilitates the standardised processing of high numbers of specimens while maximising the quality of DNA extracts for long-term storage (
We expect that barcoding effort over soil mites can be additionally improved by the application of High Throughput Sequencing (HTS) approaches, at the same time that costs are reduced (
The molecular species delimitations showed broad consistency amongst them and with morphological species identifications in our dataset (Figs
We have detected a series of cases where specimens, morphologically identified as a species, show intraspecific divergences higher than 3%. Part of these cases consists of monophyletic lineages where internal divergences are higher than 3%, but lower than 8%. Here we found the cases of: (i) Phthiracarus cf. ligneus with three lineages with divergences over 6%; (ii) Damaeus recasensi with two lineages (one with a single specimen) with divergences above 5%; (iii) Eupelops acromios with two lineages with divergences above 3%; and (iv) Gustavia longirostris with two lineages with divergences above 3%. In these four cases, the moderately high intraspecific divergences found are compatible with a single species, which is also suggested by molecular species delimitation methods, such as ASAP (Figs
We have also detected other cases where specimens classified as a single species are split into different lineages with divergences higher than 8% for the barcode fragment (Figs
Further implementation of the proposed barcoding workflow within the Canary Islands will contribute to elucidating the status of the reported cases and, presumably, to detect additional cases of cryptic diversity. An integrative approach, with parallel and interactive morphological and molecular work, will contribute to accelerating species inventory and discovery. For example, in our dataset and within the genus Xenillus, two already-described species are detected, with additional specimens showing morphological variation not matching any described species. Molecular analysis shows consistent results with morphology, suggesting the existence of four additional new species within the genus with divergences above 13.2% (Figs
Beyond the prevalence of cryptic diversity within the soil mites of the Canary Islands, our results point to the generality of this pattern globally. The analyses comparing our sequences with the BOLD database found three paradigmatic cases of potential cryptic diversity within worldwide distributed species. The first one is the case of Acrogalumna longipluma, with available barcode sequences from Canada, Germany, Finland, UK and the Canary Islands, forming five differentiated geographically coherent lineages with similarities below 97%. Of these, two lineages are exclusively found in the Canary Islands with divergences higher than 12% and not showing a sister taxa relationship (Suppl. materials
If a reference database is poorly populated for a specific group, the probability of inacurate taxonomic assignment is higher and placement to high taxonomic ranks is frequent (
Within the context of the Canary Islands, our results point to a massive under-representation of the diversity of soil mites in biodiversity databases. The BIOTA database contains records for 474 species and subspecies of mites, of which 425 are considered native species and 49 introduced species. Of those species classified as native, 110 species are considered endemic to the Canary Islands and 104 species endemic to the Macaronesian Region. Regarding our data, 14 of the 45 (31%) species, for which we obtained a species-level identification, represent the first record for the Canary Islands, all of them also providing the first record at the genus-level. All of these are species known from outside the Canaries and are now reported to the Canaries for the first time. These species may correspond to native non-endemic species or introduced species, according to their known distribution outside the Canaries, but given the absence of reference sequences for most of these species, we cannot discard that they represent additional cases of cryptic diversity. For example, in our dataset, we found four cases in which, although it is not the best match in BOLD, there has been a match with a sequence of the same species. Acrotritia ardua ardua and A. penicillata have a 78.86-79.57% similarity with several sequences named Rhysotritia ardua (junior synonym) from Canada, Poland and Norway (see Suppl. materials
This study provides and demonstrates the efficiency of a standardised workflow that combines traditional morphological identification and COI barcoding for the challenging soil fauna of mites within the framework of the BOLD System. Despite our reduced sampling, our results on interrogating the generated biodiversity data demonstrate the remarkable unrecorded mesofauna diversity present in the soils of the archipelago. This study represents the first attempt to document COI barcodes for soil mesofauna in the Canary Islands and provides the basis for the Canary Islands Soil Biodiversity barcode database (CISoilBiota). The wider implementation of this barcoding workflow within the Canaries holds the promise for a massive biodiversity discovery.
This work was supported by projects CGL2015-74178-JIN (AEI, Spain/FEDER, EU) awarded to CA, PID2021-126883NA-I00 (AEI, Spain/FEDER, EU) and Junior Leader Fellowship (LCF/BQ/ PR21/11840006) by ‘la Caixa’ Foundation (ID 100010434) and the European Union's Horizon 2020 Research and Innovation Programme under the Marie Skłodowska-Curie grant agreement No847648 awarded to PA. ISP was funded by the ‘Academia Canaria de Investigación Gobierno de Canarias’ through an FPI PhD fellowship (ID TESIS2022010039). DS was funded by the ‘Ministerio de Ciencia e Innovación’ through an FPI PhD fellowship (grant no. PRE2018-083230). PA and CA were funded through the Ramón y Cajal programme by the AEI (Spain/FEDER, EU, IDs RyC2020-029196-I and RyC2021-034291-I, respectively). We extend our gratitude to David Lugo Pérez and Antonio J. Pérez Delgado for their help during sample processing and laboratory assistance, respectively and to the regional government of Canarias and the local council (Cabildos) of Tenerife and Fuerteventura for facilitating the collecting of samples (AFF 144/18 and N-7473, respectively).
Sampling localities, with data on habitat type, coordinates, altitude and date of collection.
Maximum Likelihood (ML) phylogenetic tree obtained for the barcode dataset with IQ-TREE using the best fitting substitution model for each codon partition and nodal support obtained by 1,000 ultrafast boot-strap replicates.
Specimens morphological identification at order, family, genus and species levels.
Data of studied specimens within the BOLD project CISoilBiota, subproject CIACA (Acari of the Canary Islands).
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Acrogalumna longipluma (lineage A).
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Acrogalumna longipluma (lineage B).
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Hypochthonius luteus.
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Odontocepheus elongatus.
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Acrotritia ardua ardua.
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Acrotritia penicillata.
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Phthiracarus cf globosus.
Distance-based phylogenetic tree generated by BOLD using K2P corrected distances including best BOLD matches of Xenillus tegeocranus.