Biodiversity Data Journal 11: e91577 OO)
doi: 10.3897/BDJ.11.e91577 open access
Data Paper

Ants of French Guiana: 16S rRNA sequence
dataset

Gaétan Rongiert, Audrey Sagne*, Sandrine Etiennet, Frederic Petitclerc?, Gaelle Jaouent,
Jerome Murienne§, Jerome Orivel*

+ UMR Ecologie des Foréts de Guyane (AgroParisTech, CIRAD, CNRS, INRAE, Université de Guyane,
Université des Antilles), Kourou, French Guiana

§ Laboratoire Evolution et Diversité Biologique (EDB UMR5174) CNRS, Université Paul Sabatier Toulouse 3,
IRD, Toulouse, France

Corresponding author: Gaétan Rongier (rongiergaetan1 5@gqmail.com), Jerome Orivel (jerome.orivel@cnrs.fr)
Academic editor: Brian Lee Fisher
Received: 12 Aug 2022 | Accepted: 30 Nov 2022 | Published: 07 Feb 2023

Citation: Rongier G, Sagne A, Etienne S, Petitclerc F, Jaouen G, Murienne J, Orivel J (2023) Ants of French
Guiana: 16S rRNA sequence dataset. Biodiversity Data Journal 11: €91577.
https://doi.org/10.3897/BDJ.11.e€91577

Abstract

This dataset represents a reference library of DNA sequences for ants from French
Guiana. A total of 3931 new sequences from the 16S rRNA gene has been generated. The
reference library covers 344 species distributed in 57 genera. Overall, 3920 sequences
have been assigned at the species level and 11 at the genus level. All these sequences
were submitted to DDBJ/EMBL/GenBank databases in the Bioproject: PRUNA779056: 16S
French Guiana Ants (Hymenoptera: Formicidae), sequence identifier KFFSOOQO00000.

Keywords

DNA sequencing, 16S rRNA, molecular identification, Formicidae, NGS, Neotropics

Introduction

The current biodiversity crisis calls for efforts to reach more rapid biodiversity
characterisation. Indeed, our global knowledge of biodiversity is still largely unknown, with

© Rongier G et al. 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.

2 Rongier G et al

ca. 80% of species to be described and more than 20 years, on average, for the
description of a new species following its discovery (Fontaine et al. 2012). Moreover, the
classical taxonomical identification of specimens relies most often on subtle morphological
criteria and expert knowledge, which is confronted by the shortage of taxonomists (Engel
et al. 2021). Such issues are not only valid for the description of extant species diversity,
but also to answer ecological questions, such as how communities are assembled and how
they respond to global change. In this context, DNA barcoding has proved its effectiveness
and has been successfully applied in taxonomical and ecological studies (Kress et al. 2015
). DNA barcoding allows identifying specimens at species level using a short sequence of
DNA as species tag (Hubert and Hanner 2015). If DNA barcoding is a robust and rapid
technology with applications in many scientific areas from taxonomy to ecology, its
accuracy and reliability relies on the completeness of a reference library.

With more than 16,000 described species to date (California Academy of Science 2022
(AntWeb)), ants constitute a moderately diversified group amongst insects. They are,
however, a major component of terrestrial ecosystems, being ecologically dominant in all
strata and involved in key ecological functions (Lori et al. 2010). Within tropical forests,
ants can make up to 25% of the total animal biomass (Hdlldobler and Wilson 1990). Their
study provided so far important insights into community ecology, global biodiversity
patterns or impacts of global change, which make them of the keystone taxa for studying
ecological patterns and processes (Lori et al. 2010).

French Guiana, the largest French overseas territory, is located in the Guiana shield on the
north-eastern coast of South America. Covered with primary forest on more than 90% of its
surface, it is part of the largest block of tropical forest worldwide, hosting a large diversity of
species. AS an example, the recent checklist of ants from French Guiana highlighted the
presence of 659 valid species and subspecies from 84 genera and 12 subfamilies,
representing ca. 10% of the ant diversity known in the Neotropical realm (Franco et al.
2019). Here, we provide a large dataset of ribosomal DNA sequences for ants of the region
using a short DNA marker (16S rRNA gene, 250-300 bp in length) that can be used to
describe and monitor ant biodiversity in the Neotropical area using Next Generation
Sequencing methods.

Methods
Sampling

Geographic range

Ants were sampled from 2013 onwards in a diversity of sites covering most of the major
forest habitats represented in French Guina (Guitet et al. 2015b) and topography: terra-
firme (29 plots distributed in 9 sites), swamp (16 plots / 6 sites), white-sand forests (11
plots / 5 sites), transitional forests (1 plot) on slope of inselberg, costal savannah (10 plots
/ 5 sites), cloud forest (9 plots / 2 sites) and pastures (4 plots / 2 sites) (Fig. 1).

Ants of French Guiana: 16S rRNA sequence dataset 3

C=] Outside forests
Plains and Valleys
=] Lowland coastal forests
Sa Forests on sandy belts
EY] Coastal upland forests
[) Coastal torests on rock
[9] white sand forests
[inland peneplain forests
Forests on djougoung-pete
Plateaux
Ey Low valley forests
(irregular plateau forests
(Regular plateau forests
[Low plateau forests
Upland and highland forests
ese | Regular upland forests
a] Inselberg forests (2001 inventory)
[9 irregular upland forests
fa Highland forests
Es) Mid-montane torests
=| Submontane forests
Swamp Forest
HB Riparian, lowland, wet talwegs forests
=] Transitional forests (ecotones — wet facies)

HE Mangroves

@ Location of plots

Figure 1. EES]

Distribution of the sampling plots across French Guiana. Background colours represent the
main forest habitats in the region and a topographic layer from a 30 m resolution SRTM radar
image produced by NASA resolution sensu Guitet et al. (2015a).

Collecting method

Sampling was performed following the Ants of Leaf Litter Protocol (Agosti and Alonso 2000
). At each site, 0.12-ha plots (30 m x 40 m) were established in the different habitats locally
represented. Within each plot, 20 sampling points were established on a grid, with a 10 m
distance between each point. At each point, two sampling methods were used: pitfall traps
and mini-winkler (Bestelmeyer et al. 2000). Pitfall traps were 6 cm diameter containers
placed in the ground with an opening at surface level, partially filled with a soap and salt
water solution and left open for 72 h. At the same sampling point, 1 m? of leaf litter was
also sifted and then placed in mini-winkler extractors for a period of 48 h (Bestelmeyer et
al. 2000).

Sample processing

Specimens were preserved in 95% ethanol and then sorted to morphospecies in the lab.
One individual of each morphospecies was then mounted for morphological identification to
species using taxonomic resources available in the literature and the expertise of
taxonomy specialists. Voucher specimens were deposited in the Laboratorio de
Mirmecologia, Cocoa Research Centre CEPEC/CEPLAC (Itabuna, BA, Brazil) and at
EcoFoG in Kourou.

Although the mitochondrial gene encoding the cytochrome c oxidase subunit 1 (COI) has
been accepted has the consensus marker (Kress et al. 2015), its sequence length, i.e.
about 650 bp, turned out to be problematic when using High-Throughput sequencing

4 Rongier G et al

technology. Indeed, Illumina technology, the most used and accurate sequencing
technology, provides reads of 100 to 500 bp. As an alternative, an informative region of
16S rRNA gene of 135-276 bp has been shown as a Suitable alternative to COI for DNA
barcoding in insects (Elbrecht et al. 2016). Moreover, the variability in the COI primer
binding sites result in amplification biases that impair its use in metabarcoding studies
(Deagle et al. 2014). The 16S fragment provides promising results at least in insect
metabarcoding studies (Elbrecht et al. 2016, Marquina et al. 2018), but reference libraries
are still underdeveloped. Accordingly, this short 16S fragment has been sequenced here as
described below.

DNA extraction was performed from single leg or whole specimen for the smallest, with at
least three specimens per species. Each extract was amplified by PCR with the 16S rRNA
primer Ins16S_1 (Clarke et al. 2014) (TRRGACGAGAAGACCCTATA / TCTTAATCC
AACATCGAGGTC), using the "HotShot" protocol (Truett et al. 2000) with the following
cycles: 15 min at 95°C, 38 cycles of 95°C for 20 s (denaturation), 49°C for 30 s
(hybridation) and 72°C for 30 s, (elongation) and a final extension at 72°C for 5 min for the
six first runs and with the cycle: 15 min at 95°C, 40 cycles of 95°C for 30 s (denaturation),
50°C for 30 s (hybridisation) and 72°C for 30 s (elongation) and a final extension at 72°C
for 10 min for the two last runs. Samples were multiplexed with tagged primers to identify
sequences from each specimen. Products were verified and visualised by electrophoresis
on 0.8% agarose gels. Sequences shorter than 100 bp were removed by purification from
PCR reaction with the GeneClean Turbo Kit (MP Biomedicals, LLC, Sante Ana, CA., USA).
Finally, amplicon sequencing was performed using Illumina Miseg technology (2 x 250 bp)
by Fasteris (Plan-les-Ouates, Switzerland) or at the Genotoul platform (www.genotoul.fr).

Data processing

Sequence data (Suppl. material 1, Suppl. material 2, Suppl. material 3) were analysed
using Obitools, Obitools3 (Boyer et al. 2015) and dada2 (Callahan et al. 2016) packages in
R (R Core Team 2020). The two approaches provided complementary results despite their
different strategy of data processing and assignation. In Obitools and Obitools3 (Boyer et
al. 2015), paired-end read assembly, read demultiplexing and read dereplication were first
performed. Then, low-quality sequences (i.e. shorter than expected - under 80 bp),
singletons and sequences not assigned to samples were discarded. Chimera sequences
were also excluded using the uchime3_denovo algorithm from usearch tools (Edgar 2016).
Remaining sequences were assigned using the EMBL invertebrate database (Baker 2000)
with the Obitools assignation process. In dada2 (Callahan et al. 2016), sequences were
trimmed and demultiplexed using cutadapt (Martin 2011) and deML tools (Renaud and
Schmidt 2017), respectively. Then, low-quality sequences were discarded and remaining
sequences dereplicated. An error model was generated from data themselves and used for
creating amplicon single variants (ASVs). Finally, chimeras were deleted using the
“removeBimeraDenovo” function from dada2 and remaining sequences were identified
using the 16 rRNA sequences of the EMBL invertebrate database (Baker 2000) with the
RDP classifier algorithm implemented directly in dada2 (Wang et al. 2007). Finally, results
from the two workflows were assembled, the most abundant sequence was kept for each

Ants of French Guiana: 16S rRNA sequence dataset 5

sampled specimen and the molecular identification was compared with the morphological
one. Only groups of similar sequences corresponding to identical morphological taxonomic
assignation were conserved.

Quality checking

The quality of the sequences (Suppl. material 1) was checked using a taxonomic
congruence approach. For each species, multiple specimens were sequenced and the
corresponding sequences were expected to form a monophyletic group. Sequences were
aligned using Muscle (Edgar 2004) and a distance tree was performed using the BIoNJ
(Gascuel 1997) algorithm in phyml (Guindon et al. 2010). For species for which only a
single specimen was available, we considered the sequence to be correct if it was placed
in the correct genus and significantly different from the remaining species.

Taxonomy
Temporal coverage

Notes: 2013-present

Taxonomic coverage

This dataset (Suppl. material 1, Suppl. material 2) complements the GenBank library with
Ants from French Guiana sequences. Most of the sequences are from species that have
not been sequences so far using this maker or even sequenced at all. A total of 3931
sequences have been deposited, representing 344 species distributed in 57 genera. Most
of the sequences (n = 3920, 99.7%) have been assigned at the species level and the
remaining (n = 11) were at the genus level (i.e. close enough to sequence groups
belonging to the same genus, but not close enough to a sequence group forming a
species). Amongst the sequences assigned at the species level, 69% (i.e. 2698
sequences) have been attributed to fully described species, while the remaining (31%,
1222 sequences) represent morphospecies. On average, intraspecific species variation
was 4.5% (Suppl. material 4) when calculating with the identity matrix obtained through a
multiple alignment with clustalw (Sievers et al. 2011). New sequences will be added
periodically to the dataset when available.

Data Resources

This Targeted Locus Study project has been deposited at DDBJ/EMBL/GenBank under the
accession number KFFSO0000000. The version described in this paper is the first version,
KFFS01000000.

6 Rongier G et al

Resource 1

Download URL

https:/Wwww.ncbi.nim.nih.gov/Traces/wgs/?val=KFFSO1

Resource identifier

KFFS01000001-KFFS01003931

Data format

Usage Rights
Creative Commons Attribution (CC-BY) 4.0 License

Acknowledgements

Financial support for this study was provided by Investissement d'Avenir grants of the
Agence Nationale de la Recherche (CEBA: ANR- 10-LABX-25-01; DRIIHM: ANR-11-
LABX-0010; TULIP: ANR-10-LABX-41), by the Programme Convergence 2007-2013,
Region Guyane from the European community (BREGA, 757/2014/SGAR/DE/BSF) and by
the PO-FEDER 2014-2020, Region Guyane (BING, GY0007194 and BUG, GY0024253).
We would like to thank Sebastien Cally and Anna Grandchamp who participated with
specimen sequencing and data analysis. We thank the national park and natural reserve
managers for allowing our research programme in the protected areas. Specimens from
ltoupeé and Mitaraka were collected in the core area of the Parc Amazonien de Guyane.
The Itoupé expedition was organised and conducted in collaboration with the Parc
Amazonien de Guyane. The Mitaraka expedition was part of the “Our Planet Reviewed”
French Guiana-2015 initiative organised by the Muséum National d’Histoire Naturelle
(Paris) and the NGO Pro-Natura International and funded by the European Regional
Development Fund (ERDF), the Conseil Régional de Guyane, the Conseil General de
Guyane, the Direction de l'Environnement, de I'Aménagement et du Logement (DEAL) and
by the Ministére de I'Education nationale, de I'Enseignement Supérieur et de la Recherche.
Specimens from the Trinite area were collected in the Réserve Naturelle Nationale de La
Trinité managed by the Office National des Foréts. The expedition was funded by the
Réserve Naturelle Nationale de La Trinite and the DEAL Guyane. Data have been
collected from access to genetic resources in French Guiana, that has come through a
declarative process with non-commercial uses at the competent administrative authority, in
accordance with article L.421-7 of the environmental code (Authorization number
TREL1820249A/51; APA-973-1 and ABSCH-IRCC-FR-253854-1).

Ants of French Guiana: 16S rRNA sequence dataset 7

Conflicts of interest

The authors declare no conflicts of interests.

References

° Agosti D, Alonso LE (2000) The ALL protocol: A standard protocol for the collection of
ground-dwelling ants. In: Agosti D, Majer JD, Alonso LE, Schultz TR (Eds) Ants:
Standard methods for measuring and monitoring biodiversity. Smithsonian Institution
Press, Washington and London, 280 pp. URL: http://antbase.org/databases/
publications files/publications 20330.htm [ISBN 1560988851, 9781560988854].

° Baker W (2000) The EMBL nucleotide sequence database. Nucleic Acids Research 28
(1): 19-23. https://doi.org/10.1093/nar/28.1.19

° Bestelmeyer BT, Agosti D, Alonso LE, et al. (2000) Field techniques for the study of
ground-dwelling ants: An overview, description and evaluation. In: Agosti D, Majer JD,
Alonso LE, Schultz TR (Eds) Ants: Standard methods for measuring and monitoring
biodiversity. Smithsonian Institution Press, Washington and London, 280 pp. URL:
http://antbase.org/databases/publications _files/publications 20330.htm [ISBN
1560988851, 9781560988854].

° Boyer F, Mercier C, BoninA, Le Bras Y, Taberlet P, Coissac E (2015) Obitools Unix-
inspired software package for DNA metabarcoding. Molecular Ecology Resources 16
(1): 176-182. https://doi.org/10.1111/1 755-0998 .12428

° California Academy of Science (2022) AntWeb. htips://www.antweb.org. Accessed on:
2022-4-15.

° Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP (2016)
DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods
13 (7): 581-583. https://doi.org/10.1038/nmeth.3869

° Clarke L, Soubrier J, Weyrich L, Cooper A (2014) Environmental metabarcodes for
insects: In sillico PCR reveals potential for taxonomics bias. Molecular Ecology
Resources 14 (6): 1160-1170. https://doi.org/10.1111/1755-0998.12265

° Deagle B, Jarman S, Coissac E, Pompanon F, Taberlet P (2014) DNA metabarcoding
and the cytochrome<i>c</i>oxidase subunit | marker: not a perfect match. Biology
Letters 10 (9). https://doi.org/10.1098/rsbI.2014.0562

° Edgar R (2016) UCHIME2: Improved chimera prediction for amplicon sequencing.
bioRxiv https://doi.org/10.1101/074252

° Edgar RC (2004) MUSCLE: A multiple sequence alignment method with reduced time
and space complexity. BMC Bioinformatics 5 (1). https://doi.org/
10.1186/1471-2105-5-113

° Elbrecht V, Taberlet P, Dejean T, Valentini A, Usseglio-polatera P, Beisel J, Coissac E,
Boyer F, Leese F (2016) Testing the potential of a ribosomal 16S marker for DNA
metabarcoding of insects. PeerJ https://doi.org/10.7287/peerj.preprints.1855v1

° Engel MS, Ceriaco LMP, Daniel GM, Dellapé PM, Lébl I, Marinov M, Reis RE, et al.
(2021) The taxonomic impediment: A shortage of taxonomists, not the lack of technical
approaches. Zoological Journal of the Linnean Society 193 (2): 381-387. https://doi.org/
10.1093/zoolinnean/zlab072

Rongier G et al

Fontaine B, Perrard A, Bouchet P (2012) 21 years shelf life discovery and description of
new species. Current Biology 22 (22). https://doi.org/10.1016/j.cub.2012.10.029

Franco W, Ladino N, Delabie JHC, DejeanA, Orivel J, Fichaux M, Groc S, Leponce M,
Feitosa RM (2019) First checklist of the ants (Hymenoptera: Formicidae) of French
Guiana. Zootaxa 4674 (5): 509-543. https://doi.org/10.11646/zootaxa.4674.5.2
Gascuel O (1997) BIONJ: An improved version of the NJ algorithm based on a simple
model of sequence data. Molecular Biology and Evolution 14 (7): 685-695. hittps://
doi.org/10.1093/oxfordjournals.molbev.a025808

Guindon S, Dufayard J, Lefort V, Anisimova M, Hordijk W, Gascuel O (2010) New
algorithms and methods to estimate maximum-likelinood phylogenesis: Assessing the
performance of PhyML 3.0. Systematic Biology 59 (3): 307-321. https://doi.org/10.1093/
sysbio/syq010

Guitet S, Brunaux O, Granville JJd, Gonzalez S, C R (2015a) Catalogue des habitats
forestiers de Guyane. DEAL Guyane. URL: https://horizon.documentation.ird.fr/exl-doc/
pleins textes/divers15-09/010065207.pdf

Guitet S, Pélissier R, Brunaux O, Jaouen G, Sabatier D (2015b) Geomorphological
lanscape features explain floristic patterns in French Guiana rainforest. Biodiversity and
Conservation 24 (5): 1215-1237. https://doi.org/10.1007/s10531-014-0854-8
Hdlldobler B, Wilson E (1990) The Ants. Springer-Verlag, Berlin, 732 pp. https://doi.org/
10.1046/j.1420-9101.1992.5010169.x

Hubert N, Hanner R (2015) DNA Barcoding, species delineation and taxonomiy: A
historical perspective. DNA Barcodes 3 (1). https://doi.org/10.1515/dna-2015-0006
Kress WJ, Garcia-Robledo C, Uriarte M, Erickson D (2015) DNA barcodes for ecology,
evolution and conservation. Trends in Ecology & Evolution 30 (1): 25-35. https://doi.org/
10.1016/j.tree.2014.10.008

Lori L, Catherine LP, Kirsti LA (2010) Ant ecology. Oxford University Press, New York,
420 pp. [ISBN 9780199544639] https://doi.org/10.1093/acprof:oso/
9780199544639.001.0001

Marquina D, Andersson A, Ronquist F (2018) New mitochondrial primers for
metabarcoding of insects, designed and evaluated using in silico methods. Molecular
Ecology Resources 19 (1): 90-104. https://doi.org/10.1111/1 755-0998.12942

Martin M (2011) Cutadapt removes adapter sequences from high-throughout
sequencing reads. EMBnet.journal 17 (1). https://doi.org/10.14806/ej.17.1.200

R Core Team (2020) R: A language and environment for statistiacal computing. R
foundation for statistiacal computing, Vienna, Austria. URL: https://www.R-project.org/
Renaud G, Schmidt J (2017) deML: Maximum likelihood demultiplexing for NGS data.
1.13. URL: https://github.com/grenaud/deML

Sievers F, Wilm A, Dineen D, Gibson TJ, Karplus K, Li W, Lopez R, McWilliam H,
Remmert M, Sdéding J, Thompson JD, Higgins DG (2011) Fast, scalable generation of
high-quality protein multiple sequence alignments using Clustal Omega. Molecular
systems biology 7: 539. https://doi.org/10.1038/msb.2011.75

Truett GE, Heeger P, Mynatt RL, Truett AA, Walker JA, Warman ML (2000) Preparation
of PCR-quality mouse genomic DNA with hot sodium hydroxide ad tris (HOotSHOT).
BioTechniques 29 (1): 52-54. https://doi.org/10.2144/00291bm09

Wang Q, Garrity G, Tiedje J, Cole J (2007) Naive Bayesian classifier for rapid
assignment of rRNA sequences into the new bacterial taxonomy. Applied and
Environmental Microbiology 73 (16): 5261-5267. https://doi.org/10.1128/aem.00062-07

Ants of French Guiana: 16S rRNA sequence dataset

Supplementary materials

Suppl. material 1: Non-aligned sequences dataset EZ

Authors: Gaétan Rongier
Data type: FASTA
Download file (1.02 MB)

Suppl. material 2: Aligned sequences dataset [EI

Authors: G. Rongier, J. Orivel
Data type: FASTA
Download file (2.24 MB)

Suppl. material 3: Specimen-associated metadata EE)

Authors: G. Rongier
Data type: Collection data
Download file (166.74 kb)

Suppl. material 4: Intraspecific variations in sequences [El

Authors: G. Rongier
Data type: % of variation in sequences at the intraspecific level
Download file (17.15 kb)