Comparison of traditional and DNA metabarcoding
samples for monitoring tropical soil arthropods
(Formicidae, Collembola and Isoptera)

Yves Basset, Mehrdad Hajibabaei, Michael Wright, Anakena Castillo, David Donoso, Simon Segar, Daniel Souto-Vilarós, Dina Soliman, Tomas Roslin, M. Alex Smith, et al.

► To cite this version:

Yves Basset, Mehrdad Hajibabaei, Michael Wright, Anakena Castillo, David Donoso, et al. Comparison of traditional and DNA metabarcoding samples for monitoring tropical soil arthropods (Formicidae, Collembola and Isoptera). Scientific Reports, 2022, 12 (1), pp.10762. 10.1038/s41598-022-14915-2. hal-04914980

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Distributed under a Creative Commons Attribution 4.0 International License
1 Comparison of traditional and DNA metabarcoding samples for
2 monitoring tropical soil arthropods (Formicidae, Collembola and
3 Isoptera)

4

5 Running title: Metabarcoding of soil arthropods

6

7 Yves Basset¹,²,³,⁴*, Mehrdad Hajibabaei⁵, Michael T. G. Wright⁵, Anakena M. Castillo⁶,⁷, David A.

8 Donoso⁸,⁹, Simon T. Segar¹⁰, Daniel Souto-Vilarós³,¹¹, Dina Y. Soliman⁵, Tomas Roslin¹², M. Alex

9 Smith¹³, Greg P.A. Lamarre¹,²,³, Luis F. De León⁶,¹⁴, Thibaud Decaëns¹⁵, José G. Palacios-Vargas¹⁶,

10 Gabriela Castaño-Meneses¹⁷, Rudolf H. Scheffrahn¹⁸, Marleny Rivera¹, Filonila Perez¹, Ricardo

11 Bobadilla¹, Yacksecari Lopez¹, José Alejandro Ramirez¹, Maira Montejo Cruz¹⁶, Angela Arango

12 Galván¹⁶ & Héctor Barrios⁴

13

14 ¹ForestGEO, Smithsonian Tropical Research Institute, Apartado 0843-03092, Balboa, Ancon,
15 Panamá, Email bassety@si.edu

16 ²Faculty of Science, University of South Bohemia, 370 05 Ceske Budejovice, Czech Republic.

17 ³Biology Centre of the Czech Academy of Sciences, Institute of Entomology, 370 05 Ceske
18 Budejovice, Czech Republic.

19 ⁴Maestría de Entomología, Universidad de Panamá, 080814 Panama City, Republic of Panama.

20 ⁵Centre for Biodiversity Genomics, Biodiversity Institute of Ontario and Department of Integrative
21 Biology, University of Guelph, 50 Stone Road East, Guelph, ON, N1G2W1, Canada.

22 ⁶Centro de Biodiversidad y Descubrimiento de Drogas, Instituto de Investigaciones Científicas y
23 Servicios de Alta Tecnología (INDICASAT-AIP), P.O. Box 0843-01103 Panamá 5, Panama.

24 ⁷Department of Biotechnology, Acharya Nagarjuna University, Guntur 522 510, Andhra Pradesh,
25 India.
26 ⁸ Departamento de Biología, Escuela Politécnica Nacional, Quito, Ecuador.

27 ⁹ Centro de Investigación de la Biodiversidad y Cambio Climático, Universidad Tecnológica

28 Indoamérica, EC170103 Quito, Ecuador

29 ¹⁰ Agriculture & Environment Department, Harper Adams University, Newport, Shropshire, TF10

30 8NB, UK.

31 ¹¹Department of Ecology, Faculty of Science, Charles University, Vinicna 7, 128 44, Prague, Czech

32 Republic

33 ¹² Department of Ecology, P.O. Box 7044, Swedish University of Agricultural Sciences, SE-750 07

34 Uppsala, Sweden.

35 ¹³ Department of Integrative Biology, University of Guelph, Guelph, Ontario, Canada, N1G2W1

36 ¹⁴ Department of Biology, University of Massachusetts Boston, 100 Morrissey Blvd., Boston, MA

37 02125, USA.

38 ¹⁵ CEFE, University of Montpellier, CNRS, EPHE, IRD, Montpellier, France.

39 ¹⁶ Laboratorio de Ecología y Sistemática de Microartrópodos, Departamento de Ecología y Recursos

40 Naturales. Facultad de Ciencias, Universidad Nacional Autónoma de México, 04510, Mexico

41 city, Mexico.

42 ¹⁷ Unidad Multidisciplinaria de Docencia e Investigación, Facultad de Ciencias, Universidad

43 Nacional Autónoma de México, Campus Juriquilla. Juriquilla 76230, Querétaro, Mexico.

44 ¹⁸Fort Lauderdale Research & Education Center, 3205 College Avenue, Davie, Florida 33314,

45 U.S.A.

46

47 * Corresponding author: Yves Basset, bassety@si.edu

48

49
50 ORCID numbers:

51 Yves Basset: 0000-0002-1942-5717

52 Mehrdad Hajibabaei: 0000-0002-8859-7977

53 Michael T. G. Wright: 0000-0002-9594-7226

54 Anakena M. Castillo: 0000-0002-8510-9528

55 David A. Donoso: 0000-0002-3408-1457

56 Simon T. Segar: 0000-0001-6621-9409

57 Daniel Souto-Vilarós: 0000-0002-8803-5173

58 Dina Y. Soliman: 0000-0001-5070-4530

59 Tomas Roslin: 0000-0002-2957-4791

60 M. Alex Smith: 0000-0002-8650-2575

61 Greg P.A. Lamarre: 0000-0002-7645-985X

62 Luis F. De León: 0000-0001-9317-420X

63 Thibaud Decaëns: 0000-0003-0915-3882

64 José G. Palacios-Vargas: 0000-0001-9097-6813

65 Gabriela Castaño-Meneses: 0000-0002-5405-5221

66 Rudolf H. Scheffrahn: 0000-0002-6191-5963

67 Marleny Rivera: 0000-0003-1231-4579

68 Filonila Perez: N/A

69 Ricardo Bobadilla: N/A

70 Yacksecari Lopez: N/A

71 José Alejandro Ramirez: 0000-0001-6799-5207

72 Maira Montejo Cruz: N/A

73 Angela Arango Galván: 0000-0003-2873-8309

74 Héctor Barrios: 0000-0002-0542-1731

75

a mis en forme : Anglais (E.U.)
76 **Abstract**

The soil fauna of the tropics remains one of the least known components of the biosphere. Long-term monitoring of this fauna is hampered by the lack of taxonomic expertise and funding. These obstacles may potentially be lifted with DNA metabarcoding. To validate this approach, we studied the ants, springtails and termites of 100 paired soil samples from Barro Colorado Island, Panama.

The fauna was extracted with Berlese-Tullgren funnels and then either sorted with traditional taxonomy and known, individual DNA barcodes ("traditional samples") or processed with metabarcoding ("metabarcoding samples"). We detected 49 ant, 37 springtail and 34 termite species with 3.46 million reads of the COI gene, at a mean sequence length of 233 bp. Traditional identification yielded 80, 111 and 15 species of ants, springtails and termites, respectively; 98%, 37% and 100% of these species had a Barcode Index Number (BIN) allowing for direct comparison with metabarcoding. Ants were best surveyed through traditional methods, termites were better detected by metabarcoding, and springtails were equally well detected by both techniques. Species richness was underestimated, and faunal composition was different in metabarcoding samples, mostly because 37% of ant species were not detected. The prevalence of species in metabarcoding samples increased with their abundance in traditional samples, and seasonal shifts in species prevalence and faunal composition were similar between traditional and metabarcoding samples.

Probable false positive and negative species records were reasonably low (13-18% of common species). We conclude that metabarcoding of samples extracted with Berlese-Tullgren funnels appear suitable for the long-term monitoring of termites and springtails in tropical rainforests. For ants, metabarcoding schemes should be complemented by additional samples of alates from Malaise or light traps.

**Key words:** Barro Colorado Island, Collembola, false positive record, false negative record, Formicidae, Isoptera.
101 **Introduction**

102 Soil invertebrates *provide* many ecosystem services, such as carbon transformation and
103 sequestration, soil formation and recycling of nutrients [1]. Yet, the fauna of the soil remains one of
104 the least known components of the biosphere, and in the tropics, it is often considered a biotic
105 frontier [2,3]. Despite an increase in research on the soil fauna during recent years, one key obstacle
106 *that* remains *is* the taxonomic impediment.

107 What renders the monitoring of the tropical soil fauna more topical than ever is climate change.
108 Current projections estimate a global increase in average temperatures of 0.2°C per decade within
109 the coming century [4]. Such change is impacting forests worldwide and threatening biodiversity
110 and concomitant ecosystem services [5]. In tropical rainforests the effects of climate change on the
111 soil fauna are basically unknown but are most likely significant [6]. For example, increasing tree
112 mortality may greatly raise temperatures on the forest floor [7]. We may foresee substantial changes
113 in the species composition and functioning of tropical soils [6] but lack both the baselines and the
114 tools for detecting impending changes.

115 In the context of global change, molecular methods offer promising tools for lifting some of the
116 taxonomic impediments and thus allowing for sound and low-cost biological monitoring [8-11].
117 Molecular approaches present several advantages: first, individuals and species can often be
118 identified by sequencing standard gene regions (DNA barcodes) – for animals most often their
119 cytochrome c oxidase subunit I (COI) gene [12,13]. COI-based clusters allow for interim taxonomic
120 nomenclature, by e.g., using the Barcode Index Number (BIN; [14]). Second, samples including
121 many individuals and species can be treated by bulk methods, and COI amplicons *can be* analyzed
122 using high-throughput sequencing, “DNA metabarcoding” (hereafter “metabarcoding”; [15-16]).

123 This represents a powerful approach for screening numerous species-rich environmental samples
124 and offers new solutions for spatial and temporal monitoring [9,16,17], particularly for community
125 studies of soil assemblages [10,18-22]. Yet, there is a major challenge: to date, only presence-

*a supprimé:* are responsible for
127 absence data (prevalence in samples, as opposed to abundance) can be reliably retrieved with
128 metabarcoding [17, 23-27].

129

130 Among arthropods, three taxa represent major components of the soil fauna in tropical rainforests:
131 Formicidae (ants), Collembola (springtails) and Isoptera (termites). Ants represent an important
132 proportion of animal biomass in tropical rainforests where, in addition to exerting a formidable
133 predation pressure, they play critical roles in the maintenance and regeneration of the forest, such as
134 soil turnover, nutrient cycling, plant protection and seed dispersal [28]. Springtails are detritivorous
135 or fungivorous and play important roles in litter decomposition, the formation of soil
136 microstructure, and the release of humic acids, which are crucial for plant development. With their
137 considerable biomass, they also represent a crucial part in food webs [29]. Termites are another
138 group of dominant and abundant detritivorous arthropods in the tropics, which play a capital role as
139 an ecosystem engineer by participating in the formation of the soil structure and the creation and
140 maintenance of habitats for other soil organisms [1,30].

141

142 The ForestGEO Arthropod Initiative represents a unique effort to monitor arthropods in tropical
143 rainforests. Arthropod surveys are performed within permanent forest plots monitored by the Forest
144 Global Earth Observatories (ForestGEO; http://www.forestgeo.si.edu/; [31]). In Panama, the
145 ForestGEO Arthropod Initiative has been monitoring several focal taxa on Barro Colorado Island
146 (BCI) since 2009 [32]. Decades of research on BCI and recent syntheses indicate that more than
147 200 ant, 100 springtail and 60 termite species may occur in the soil and litter on the island [33].
148 However, the long-term monitoring of at least the common species of these taxa remains
149 challenging.

150

150 For social insects, such as ants or termites, colony census may represent the best estimate of
151 population levels [34]. However, accurate censuses of often small and concealed colonies are

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problematic in tropical rainforests. In this case, surveying individual workers is more feasible.

Nonetheless, since ants and termites have strongly aggregated distributions, abundance data and
estimates of variability may be highly dependent on the distance to colony and related factors, such
as behavior and speed. Hence, species prevalence in samples (presence-absence data) is often used
as a surrogate of species abundance [34,35]. Here, we propose to use species prevalence to estimate
annual population indices based on metabarcoding data [36].

To provide an efficient, reliable, unbiased and sensitive monitoring protocol, the molecular methods
applied should essentially fulfill a series of criteria [9,10,17,36]. First, metabarcoding techniques
should be able to resolve the species composition of samples of known taxonomic content. Second,
DNA-based data on species prevalence (i.e., presence-absence data) should ideally reflect some
aspect of species abundance in the samples [37]. Third, metabarcoding techniques should resolve
ecological patterns in community composition, such as seasonal (or annual) shifts in species
prevalence. Fourth, any association between species biomass and DNA read frequencies [38] would
add to the information value of metabarcoding samples. Fifth, the prevalence data gained from
metabarcoding should neither be biased towards “false positive” records (i.e., species detected in
the sample although they are actually absent) or “false negative” records (i.e., species not detected
although they are present in the sample; [39-42]). Sixth, the data delivered by metabarcoding should
be equally reliable across taxa, i.e., free of taxonomic biases.

To validate the suitability of metabarcoding for next-generation monitoring of soil arthropods in the
tropics [43], we compared the fauna of paired soil samples either sorted morphologically with
traditional taxonomy and known barcodes (“traditional samples”) or processed with metabarcoding
(“metabarcoding samples”). We focused on ants, springtails and termites because (1) they are
dominant arthropod groups in tropical soils, and (2) we *previously generated* local barcode libraries
for these groups. Our emphasis was also on common as opposed to rare species, as only the former

**a supprimé:** managed to generate
177 can realistically be well monitored in tropical rainforests [44]. As a basis for the reliable

178 biomonitoring of the tropical soil fauna, we ask:

179 1. Are the species richness and faunal composition of focal groups (ants, springtails and termites) of traditional samples similar to that of metabarcoding samples, at least when considering common species?

182 2. In this local study, can species prevalence in metabarcoding samples (i.e., the number of samples in which a species is present) be used as a proxy for species abundance in traditional samples, at least for common species?

185 3. Do seasonal shifts in species prevalence and composition between the dry and wet season detected with traditional samples match similar seasonal shifts for metabarcoding samples?

187 4. Is there any association between species biomass and DNA read frequencies [38]?

188 5. Can we understand the prevalence of “false positive” or “false negative” records [39,40] based on species traits or characteristics?

190 6. Do patterns related to all the above questions differ between ants, springtails and termites?

191

192 **Material and methods**

193 Additional details for all method sections are provided in Appendix S1.

194 **Study site and field sampling.** Assemblages of ants, springtails and termites were surveyed on Barro Colorado Island (9.15°N, 79.85°W; 120–160m asl) in Panama. The 1,542ha Barro Colorado Island is covered with lowland tropical forest and was created around 1910, when the Chagres River was dammed to fill the Panama Canal. Samples were obtained from the 50ha ForestGEO vegetation dynamics plot, which is described in [31]. We considered ten locations (500m sections of trails inside or near the plot) that are used for long-term arthropod monitoring and described in [33,44] (Fig. 1 and Table 1).

201

a supprimé: for

a supprimé: a relation

a supprimé: records
Each location was divided into ten sub-locations (Fig. 1; see statistical methods below and Appendix S2 for explanation of this approach). For each of the ten locations, we randomly selected five sub-locations. At each of these selected sub-locations, we took two paired samples 10 cm distant from each other. We obtained 50 paired soil samples in March 2017, during the dry season. We repeated this sampling protocol in December 2017, during the wet season, with different random samples and obtained 100 paired soil samples for the two seasons. We used a 20 x 20cm frame (400cm²) to delineate one sample and scoop all litter and soil up to a depth of ca. 5cm or slightly deeper into a container, calibrating each sample to a final volume of 2 liters. We restricted our sampling to the first 5cm of the litter/soil as tropical soils tend to be shallow, with most of the faunal diversity concentrated in the top few centimeters [45]. To avoid contamination between samples in the field, we used laboratory gloves and disinfected all tools and receptacles with commercial bleach (Clorox de Centroamérica; hypochlorite of sodium 3.5%, hydroxide of sodium 0.3%), after which sampling gear was rinsed with distilled water, to clean bleach residues.

Each pair of samples consisted of two categories: “traditional samples”, from which the soil fauna was extracted and sorted manually according to morphology and molecular data (see below), and “metabarcoding samples” which were analyzed using DNA metabarcoding. Traditional samples were set 4-5 hours after collecting in Berlese-Tullgren funnels [46]. Arthropods were extracted during the first 24 hours at ambient external temperature (i.e., without heat), and subsequently during the next 48 hours with additional heat provided by 25W bulbs. Berlese-Tullgren extracts were washed, arthropods were separated manually from soil debris and stored in 95% ethanol in a freezer at -20°C until further analysis.

Metabarcoding samples were extracted similarly with Berlese-Tullgren with the difference that equipment was thoroughly disinfected with bleach and rinsed with distilled water before and after each sample extraction. Metabarcoding samples were stored at -20°C until being analyzed at the
Hajibabaei laboratory at the University of Guelph, Canada, which occurred approximately three months after collection. Geographic coordinates of the samples are indicated in [33] and Fig. 1 summarizes the workflow of samples.

234

**Processing of traditional samples.** Ants, springtails and termites were all identified via morphological and molecular data (individual-based DNA barcoding). For morphological identification, ants were pinned and assigned to species using the ant reference collection of the ForestGEO Arthropod Initiative at the Smithsonian Tropical Research Institute. Springtails were first cleared in 10% KOH, then in lactophenol, and mounted on microscopic slides in Hoyer's solution. The slides obtained were identified at the Laboratorio de Ecología y Sistemática de Microartrópodos at the Universidad Nacional Autónoma de México (UNAM) using UNAMs extensive reference collections of springtails and specialized literature. Termites were stored in ethanol and soldiers identified using the termite reference collection of the ForestGEO Arthropod Initiative.

245

The standard DNA barcode region of the gene cytochrome c oxidase subunit I (COI) was sequenced for a subset of the specimens collected. DNA barcoding using Sanger sequencing was conducted at the Centre for Biodiversity Genomics, University of Guelph, using methods described in Wilson [47]. When possible, we sequenced a maximum of five individuals per species. In total, we generated 324 new COI sequences, corresponding to 328 specimens of 171 ants, 114 springtails and 43 termites. These sequences were added to BCI projects BCIFO, BCICL and BCIIS of the Barcode of Life Data System (BOLD; http://www.barcodinglife.org/index.php), which summed 2,892 sequences (Appendix S1, Table S1). These molecular data were used to confirm identifications based on morphology. Each species was attributed a Barcode Index Number (BIN) according to BOLD, which can be used as a proxy taxonomic unit in absence of binomial identification [14] (e.g., "Ectatomma ACH3273 ruidum").

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257

258 DNA metabarcoding. Metabarcoding samples were transferred into separate 50mL tubes and centrifuged to collect soil and tissue at the bottom of the tube. Excess preservative ethanol was removed, and samples were left in an incubator at 65°C for 3-6 hours to evaporate any residual ethanol. Samples were briefly vortexed to dislodge and coarsely homogenize the dried pellet before transferring ~0.25g to a PowerSoil bead tube with sterile forceps. Whole DNA was extracted using DNeasy PowerSoil Kit (Qiagen: Toronto, ON, Canada) according to protocol, eluting with 30uL, buffer C5. Isolated DNA was amplified through a two-stage Polymerase Chain Reaction (PCR) for two overlapping amplicons from the DNA barcode region (Helix I to Helix V) of the COI gene.

266 These amplicons were: (1) BR5: size ca. 310bp; forward primer B:

267 5'CCIGAYATRGCITTYCCICG [23]; reverse primer ArR5: 5'GTRATIGCICCIGCIARIACIGG [24]. (2) F230R: size ca. 230bp; forward primer LCO1490: 5'-

269 GGTCAACAAATCATAAAGATATTGG [48]; reverse primer 230_R: 5'

270 CTTATRTTRTTTATICGIGGRAAIGC [49]. For the map of primers and amplicons see [50].

271 These were selected as proven to be effective to optimize recovery of species and genus richness in tropical arthropods [22,51]. Reactions had a standard mix as indicated in Appendix S1, for a total of 25µL per reaction. A negative control (i.e., reaction with 2µL water instead of DNA) was included to ensure PCR reagents were free of contamination. Reactions underwent 35 cycles of 94°C for 40s, 46°C for 60s, 72°C for 30s using an Eppendorf Mastercycler ep gradient S thermocycler. PCR amplification was visually confirmed through gel electrophoresis using a 1.5% agarose gel. PCR products were purified following the MinElute PCR Purification kit (Qiagen; Toronto, Ontario, Canada) standard protocol, eluting with 15µL molecular biology grade water. A second round of PCR with primers that included the Illumina adapter sequence was run under the same conditions, using the purified product from the first round of PCR as template. Two PCRs with 35 cycles are sufficient for providing consistent and robust results for many samples across different sample types. This protocol has been used routinely with consistent results for biodiversity, ecological and
283 biomonitoring studies (e.g., [22,49, 52]). Additionally, the high number of PCR cycles in the second
284 round was practical because amplification was inadequate at lower cycles, and some samples failed.
285 Purified second round PCR product was sequenced on an Illumina MiSeq using the v3 MiSeq
286 sequencing kit (300bp x 2).

287

288 **Bioinformatics.** The bioinformatics of metabarcoding remains in flux [27]. A wide array of
289 metabarcoding tools exists, and their choice (and relevant parameters) is essential for meaningful
290 results [53]. Available tools include, for example, Mothur [54], Qiime [55] or Obitools [56].
291 However, an important restriction that pertains to all these tools is the use of customized databases
292 for taxonomy classification. We relied on the mBRAVE cloud-based platform ("Multiplex Barcode
293 Research and Visualization Environment"; http://www.mbrave.net/; [57]) since it seamlessly
294 integrates BOLD data as reference datasets, which for BCI represent the best curated COI sequence
295 datasets available. mBRAVE employs a typical workflow based on mature bioinformatic methods
296 and is comparable to other available bioinformatics tools. The sequence reads resulting from
297 metabarcoding were uploaded into the project MBR-BCISOIL of mBRAVE. mBRAVE parameters
298 were optimized for classifying sequences with BOLD datasets which have already been curated and
299 the various sequence errors removed. Hence, the stringent read processing was a bit more relaxed.
300 All mBRAVE parameters are indicated in Supplementary Table S1, but the workflow can be
301 summarized as follows.

302

303 The paired ends of the two overlapping amplicons were pooled (i.e., treated as one fastq file without
304 any merging) as opposed to merging, since (a) we were expecting very high matches to mBRAVE
305 datasets (see below) with variation within a BIN being small; and (b) a sensitivity analysis, where
306 we varied most mBRAVE parameters one by one to optimize the greatest number of BINs
307 recovered in mBRAVE datasets, indicated an optimal recovery of BINs with pooled sequences
308 (Supplementary Appendix S1 and Table S2). Reads were trimmed at each end (25 bp) to remove

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primer and adapter sequence, then filtered for a minimum quality score of QV20 and for a minimum and maximum size of 100bp and 500bp, respectively (Appendix S1). Denoising, dereplication and removal of chimeric reads, were performed with VSEARCH [58], integrated into the mBRAVE pipeline (Appendix S1). Erroneous sequences were detected by a divergence range from reference sequences, with parameters as indicated in Supplementary Table S1. Sequences were identified to BIN level by matching them to reference BINs based on BOLD library datasets tailored for this study, using VSEARCH. These datasets included sequences obtained specifically for this study (2,571 BINs for BCI arthropods) and publicly available datasets within BOLD (Appendix S1, Table S1). BINs assignments were realized at 3%, a commonly employed threshold [59]. Reads without a match to reference BOLD datasets were clustered into operational taxonomic units (OTU) based on (1) the minimum OTU size (n=1 read); (2) OTU threshold (maximum distance inside a generated OTU, a conservative 2%); and (3) exclusion from the OTU threshold when sequencing error introduces spurious haplotypes (chimeras and/or sequence errors, 3%). The BOLD datasets that we used for analyses are currently the best ones available for ants, springtails and termites from BCI (Supplementary Appendix S1 and Table S1).

**Statistical analyses.** To ensure sound comparisons between traditional and metabarcoding samples, we removed five pairs of samples for which DNA metabarcoding samples failed (all from the dry season; PCR failed), leaving 95 pairs of samples for analysis. For monitoring, it is necessary to identify unequivocally species by their binomials or BINs, so that they can be cross-referenced among years. Hence, for species sorted manually, we only considered them if they had been sequenced and had a BIN, given that some species failed to be sequenced (see results). Common (as opposed to rare) species were defined as species belonging to the first quartile of the species-rank prevalence distribution [60], for species sorted in traditional samples. The 95 metabarcoding samples resulted in a total of 122 sequencing runs (i.e., single fastq files produced by the sequencer through demultiplexing or from a single sample) and hence sampling effort was not strictly identical
among locations sampled. However, unequal (but similar) sampling effort among locations did not affect any of our questions, and small differences in sample size among locations were ignored.

337

Despite harvesting traditional and metabarcoding samples only 10cm apart, we can still expect discrepancies in species abundance and faunal comparison between these samples. This results from soil organisms, such as ants, to be very patchily distributed when studied at the 1m² scale [61]. To reduce this effect, our analyses emphasized data in which samples were grouped per location, as to evaluate differences at a higher scale. This issue is further discussed in Appendix S2.

343

**Question1: similarity between traditional and metabarcoding samples.** To compare species richness between sample types (traditional and metabarcoding) and taxa (prevalence data), we computed rarefaction curves and estimates of total species richness with the R package iNEXT [62].

To visualize the differences in faunal composition between traditional and metabarcoding samples, we generated differential heat trees using the Metacoder R package [63]. These trees help identifying the taxonomic hierarchy with terminal nodes representing either BINs or binomial species. With a prevalence matrix, we used the function 'compare_groups' of Metacoder and the resulting mean difference between treatments to plot the differential heat tree. The differences, however, were formally tested using a Procrustes rotation, which rotates a configuration (for example ordination results) to maximum similarity with another configuration. First, for each dataset (traditional and metabarcoding), we pooled samples by the 10 locations at our study site (Table 1; Fig. 1) and considered matrices of species x locations, filled with the number of samples in which the species was recorded for each location (prevalence; maximum possible of 10 samples for each location). We then performed a non-metric multidimensional scaling (NMDS) for each of the traditional and metabarcoding matrices with the function 'metaMDS' of the R package vegan, followed by a Procrustes rotation of the two ordinations with the function 'procrustes' of the same package [64]. The function 'protest' of vegan was used to test for non-randomness ('significance')
361 between the traditional and metabarcoding NMDS (999 permutations). We performed Procrustes
362 rotations separately for ants, springtails and termite data, and either for all species surveyed or
363 restricted to common species.

364

365 **Question 2: regressions between abundance and prevalence.** We considered the relationships
366 between the following variables, for each species: (a) total abundance vs. prevalence in traditional
367 samples; (b) total abundance in traditional samples vs. prevalence in metabarcoding samples; and
368 (c) prevalence in traditional samples vs. prevalence in metabarcoding samples. We tested these
369 three relationships for the three taxonomic groups together, and then for each group separately,
370 considering either all species or only common species. We first checked linear and non-linear
371 relationships with CurveExpert Professional [65], considering the highest coefficient of
372 determination (R²) and the lowest Akaike information criterion corrected (AICc) of all models.
373 Given that linear relationships provided the best fit for all models tested, we fitted ordinary least
374 squares regressions to all tested relationships.

375

376 **Question 3: seasonal shifts.** We first calculated, for each species collected both in the dry and wet
377 seasons (n=62), the difference in the prevalence in the wet season samples minus the prevalence in
378 the dry season samples, separately for traditional and metabarcoding samples. We then fitted
379 ordinary least squares regressions between the seasonal difference in traditional samples and that in
380 metabarcoding samples. We performed these regressions for all species, ants, springtails and
381 termites, but not separately for common species, because of low sample size. Eventually, to
382 evaluate the similarity between the traditional and metabarcoding samples regarding shifts in faunal
383 composition between the dry and wet season, we considered separately for traditional and
384 metabarcoding samples, a prevalence matrix of species (n=62) x season (dry or wet). We then
calculated the correlation between the two matrices using a Mantel test, computed with the function
'mantel' of the R package vegan (10,000 permutations, [64]).

387

**Question 4: regressions between biomass and read frequency.** We considered only ants, for which we had reliable measurements of different variables accounting for body size, as well as an allometric correlation of body length to body weight. Analyses were restricted to ant species present in metabarcoding samples for which we had good measurements (1-5 individuals measured per species, n = 28 species). We tested linear and non-linear relationships with CurveExpert Professional [65], including, for each species, as dependent variables the total number of reads, the relative read abundance [66], the total number of sequences and prevalence in metabarcoding samples, and as independent variables either body length (mm), pronotum width (mm), Weber's length (mm) or body weight (g), measured from ant workers in collections of the ForestGEO Arthropod Initiative.

398

**Question 5: false positive and negative records.** Assuming no taxonomic errors, we considered all species occurring in metabarcoding samples but not in traditional samples as "possible false positive", those occurring in traditional samples but not in metabarcoding samples as "possible false negative" and the rest of species as "well surveyed". Furthermore, we considered species as "probable false positive" and "probable false negative" when their prevalence in samples was ≥ 10, in metabarcoding samples and traditional samples, respectively (i.e., when at least one specimen was collected potentially at each of the locations). We performed a discriminant analysis to test whether morphological traits such as body length, pronotum width and Weber's length (Question 4) delineated some of the categories defined above. This analysis concerned only Formicidae and the species in categories "probable false negative" and "well surveyed", as sample size was too low for species in other categories (morphological measurements lacked for most of false positives).
410 Discriminant analysis was performed with the function 'discrim' of Systat version 13.1 (Systat
411 Software, Inc., Chicago, IL). Given that the barcodes for our false negative samples exist, we used
412 the ecoPCR [67] program to run an *in silico* PCR to test whether or not our primers could amplify
413 these regions. Barcodes were downloaded from the NCBI database to create a reference dataset to
414 match with our PCR primer pairs allowing up to 15 mismatches between primer and target
415 sequences. To test for differences between taxa (Question 6), whenever possible we kept analyses
416 separate among taxa when investigating Questions 1-5.

417

418 **Results**

419 Traditional samples yielded 1,970, 5,037 and 226 individuals classified into 80, 111 and 15
420 morphological species of ants, springtails and termites, respectively. Metabarcoding samples
421 produced 30.46 million reads, including 15.96 million post filter reads (mean sequence length = 233
422 bp). Most of the reads representing BINs included arthropods and more than half of these BINs
423 were assigned to the focal taxa studied (Supplementary Table S3). We validated 396 BINs of
424 arthropods (Supplementary Tables S4, S5) from our library datasets. Diptera, Hymenoptera,
425 Coleoptera, Collembola and Isoptera were best represented in terms of species richness (unique
426 BINs, Supplementary Table S5). Doubtlessly, total species richness in these samples may be higher
427 as most of arthropod species from BCI have not been barcoded yet and, hence, could not be
428 validated with our library databases. The validation of the remaining OTUs not assigned to BINs is
429 challenging, as many may be artefactual [68]. It is beyond the scope of the present contribution to
430 discuss this issue, although we identify complex of species in our focal taxa that may be worth
431 investigating in the future (Supplementary Appendix S2 and Table S6). Given the low resolution of
432 OTU data, we now focus on the detection of validated BINs detected in metabarcoding samples for
433 our focal taxa, which included 49, 37 and 34 BINs for ants, springtails and termites, respectively.
434 About 98%, 37% and 100% of species of ants, springtails and termites, respectively, observed in
435 traditional samples had BINs (DNA extraction failed for some species; full list in [33]), and could
hence be directly compared with metabarcoding samples. In sum, our study system consisted of

101, 52 and 39 species with BINs (hereafter “species”) of ants, springtails and termites,

respectively, that were amenable to comparisons between traditional and metabarcoding samples

(Supplementary Fig. S1). Table 1 summarizes the number of individuals, species and reads per

location.

441

**Question1: similarity between traditional and metabarcoding samples.** Observed species

richness (95 samples), extrapolated species richness (200 samples) and total estimated species

richness was higher in traditional samples than in metabarcoding samples for ants (Fig. 2;

traditional samples: 78 species observed, 117.0 ± 18.6 (s.e.m.) spp. estimated, metabarcoding

samples: 49 spp., 73.7 ± 14.9 spp.). In contrast, total species richness in springtails was rather

similar between traditional and metabarcoding samples (with 41 vs. 37 species observed,

respectively; 51.0 ± 8.9 vs. 43.3 ± 5.9 species estimated), while for termites, it was lower in

traditional samples than in metabarcoding samples (15 vs. 34 species observed, respectively, 26.0 ±

8.9 vs. 45.0 ± 7.9; Fig. 2). These patterns were broadly similar when we restricted the data to

common species, suggesting also that the number of common species discovered by twice the

number of metabarcoding samples would not be much different than currently detected in ca. 100

samples (Supplementary Fig. S2; for the identity of common species, see Question 2).

454

The heat trees visualizing differences in the number of BINs detected in traditional and

metabarcoding samples indicated that termites were better surveyed with metabarcoding samples

(Fig. 3). When considering all species surveyed, the Procrustes rotations performed with the NMDS

including traditional and metabarcoding data for ants, springtails and termites were not significantly

correlated (Supplementary Fig. S3; all correlations with p > 0.05; statistics detailed in the figure).

These patterns were similar when we restricted the analyses to the 34 most common species, except

for common termite species, whose two ordinations were significantly correlated (Supplementary
Fig. S3). In this case the information provided by the Procrustes rotation was rather low, as this concerned only four species. Note that after removal of the outlier location "WHE2" in Collembola (Supplementary Fig. S3), the Procrustes rotations for both all and common species were still not significantly correlated (all species: n = 52 species considered, m12 = 0.785, r = 0.46, *p* = 0.34; common species: n = 10, m12 = 0.713, r = 0.54, *p* = 0.19).

**Question 2: correlation between abundance and prevalence.** There were strong positive relationships between total abundance and prevalence in traditional samples when all species were analyzed together (67% of variance explained), as well as for ant (69%) and springtail species (72%) analyzed on their own, but not for termite species (4%; Table 2). The relationship between abundance in traditional samples and prevalence in metabarcoding samples was weaker. The strongest positive relationship existed for all ant species (24% of variance explained), followed by all springtail species (16%). This relationship was not significant for termite species. Restricting the data sets to common species did not notably improve the fit of relationships (Table 2). There were also significant but relatively weak correlations between the prevalence of species in traditional and metabarcoding samples. The highest proportion of variance explained was observed for termites (35%), springtails (29%) and ants (19%). Data sets restricted to common species did not improve the relationships, with the notable exception of Formicidae (40% of variance explained; Table 2). Fig. 4 summarizes visually the prevalence of species in traditional and metabarcoding samples.

**Question 3: seasonal shifts.** The regression for all species between the seasonal difference in prevalence in traditional samples and that in metabarcoding samples was significant, and explained 26% of variance (F<sub>1,60</sub> = 20.5, *p* < 0.001; Fig. 5). This regression was strong for springtail species (F<sub>1,24</sub> = 26.2, *p* < 0.001, R<sup>2</sup> = 0.523), was significant for ant species (F<sub>1,24</sub> = 6.6, *p* = 0.017, R<sup>2</sup> = 0.215), but not for termite species (F<sub>1,8</sub> = 0.09, *p* = 0.76, R<sup>2</sup> = 0.012). The Mantel test indicated that
487 the matrix of species x seasons of traditional samples was significantly correlated with that of
488 metabarcoding samples (all species considered; r = 0.336, p = 0.003).

489

490 **Question 4: correlation between biomass and read frequency.** For the 28 ant species amenable
491 to analyses we observed in metabarcoding samples strong but expected linear relationships between
492 the number of reads for one species and the number of sequences representing its BIN (R² = 0.825,
493 F₁,₂₆ = 122.6, p < 0.001), as well as between the number of reads and the species prevalence(R² =
494 0.964, F₁,₂₆ = 703.1, p < 0.001). There was a weak positive linear relationship between estimated
495 body weight and read frequency (R² = 0.142, F₁,₂₆ = 4.3, p = 0.048). Similarly, there was a weak
496 logistic (sigmoid) relation between body weight and relative read abundance (y = a/(1 + b*e^(-cx));
497 R² = 0.141, F₁,₂₆ = 4.2, p = 0.048). However, there was no linear relationship between body weight
498 and prevalence in metabarcoding samples (R² = 0.054, F₁,₂₆ = 1.5, p = 0.235).

499

500 **Question 5: false positive and negative records.** Out of 192 species in total, 58 species were
501 classified as possible false positive (30% of the total) and 72 species as possible false negative
502 (37%; Fig. 4). This was expected *a priori* since the proportion of rare species is always high in
503 tropical insect surveys [69] (32.6% of species collected/detected as singleton in either traditional or
504 metabarcoding samples in this study). More revealing, “probable” false positive and negative
505 species represented 14.0 % and 20.0 % of the number of species that occurred in 10 or more
506 samples (n= 50), and 12.5 % and 18.8 % of the total number of common species, respectively.
507 Probable false positives included three ant, two termite and two springtail species, whereas probable
508 false negatives included six springtail and four ant species (Table 3). The COI barcodes of all of
509 these false negative species proved to be amplifiable using our primers in our *in silico* evaluation
510 (Supplementary Table S7).

511

a supprimé: in
For ants, the discriminant analysis delineating the possible false negative (n = 28) and the well surveyed species (n = 25) was overall significant (Wilks's Lambda = 0.844, *p* = 0.039), with Weber's length and body length as loading most on the first discriminant axis (eigenvalue = 0.185; canonical correlation = 0.395). The trend was for possible false negative species to have lower values of Weber's and body length in comparison with well surveyed species.

518

## Discussion

The development of molecular methods allows for greater opportunities to monitor the lesser-known biodiversity, including arthropods from tropical soils. Previous metabarcoding studies have discussed topics such as profiling communities, beta diversity, dispersal, community assembly and body size [18-22]. However, few studies have been considering practical questions related to monitoring over time and the elaboration of time series. The current project represents one of the first attempts to develop protocols for monitoring in the long term the soil fauna in the tropics.

526

After considering 95 spatially paired samples processed either traditionally or with metabarcoding, representing a total of 101, 52 and 40 species of ants, springtails and termites, respectively, we provide a list of positive items for developing a DNA metabarcoding protocol for the monitoring of tropical soil arthropods. Termites were better surveyed by metabarcoding than by traditional samples and springtails equally well. Doubling the number of samples would have not resulted in the detection of many more species. There was a positive correlation between the abundance of ant and springtail species in traditional samples and their prevalence in metabarcoding, although it was relatively weak (12-27% of variance explained). Seasonal shifts in species prevalence between the dry and wet season were overall correlated between traditional and metabarcoding samples. Further, changes in faunal composition between dry and wet season were also correlated between traditional and metabarcoding samples. Probable false positive species represented 14.0% of the number of species that occurred in 10 or more samples. Close scrutiny of these results indicated that 3 species

a supprimé: can report on

a supprimé: Studies realized with m
542 could be classified as true errors (see below), lowering the proportion of probable false positive
543 species to 6.3%.

544

545 Conversely, the following items appear more challenging for developing a comprehensive
546 monitoring protocol. The overall species richness of metabarcoding samples was underestimated in
547 comparison to traditional samples. This effect was mostly due to ants, emphasizing the selectivity of
548 metabarcoding for particular taxa. This pattern also prevailed when we restricted the analyses to
549 common species. This may result from different factors, including selective PCR primer
550 amplification bias [51], better efficiency of nuclear DNA over mitochondrial DNA to delineate
551 species in ants [70], amplification bias in AT rich Hymenopteran genomes [71] or extensive
552 heteroplasmy in the mitochondrial DNA of certain species complex, such as *Ectactomma ruidum*
553 [72]. The faunal composition of metabarcoding samples was significantly different from that in
554 traditional samples, and this pattern was similar when we restricted the data to common species.

555 There was no correlation between the abundance of termite species in traditional samples and their
556 prevalence in metabarcoding samples. Although there was a weak correlation between body weight
557 and relative read abundance, no relation existed between body weight and prevalence in
558 metabarcoding samples. This suggests that species' body weight may not be useful for refining
559 estimates of species prevalence in metabarcoding samples. Probable false negative species
560 represented 20.0% of the number of species that occurred in 10 or more samples. They included
561 four ant species with high abundance in traditional samples, and that are well collected on BCI with
562 different protocols (see below). It is difficult to interpret these results, but for ants the discriminant
563 analysis suggests that false negative species tended to be smaller than well surveyed ant species.

564 Patterns were often different between the three focal taxa, which challenges the possibility to
565 develop a unique protocol that may be sound for a variety of taxa. Appendix S2 details some
566 methodological considerations emphasizing possible limitations in our study.

567
<figure><img src="image_2.png" /></figure>

Thus, the list of positive findings motivates us to continue tackling the remaining challenges to optimize a monitoring protocol. We should now return to our specific questions as targeting key criteria for an efficient, unbiased, and reliable monitoring protocol.

a supprimé: All in all

<figure><img src="image_3.png" /></figure>

<figure><img src="image_4.png" /></figure>

<figure><img src="image_5.png" /></figure>

<figure><img src="image_6.png" /></figure>

**Differences between traditional and metabarcoding samples.** Regarding our first question, metabarcoding appears to be rather selective at recording certain taxa and species. For example, the proportion of ant species which are overall common (first quartile of the distribution) in litter samples on BCI (data from the ForestGEO Arthropod Initiative, 2008-2019) and which were also recorded in our metabarcoding samples was 43%, whereas the corresponding figure for termites was 75%. From the viewpoint of the field researcher, metabarcoding represents yet another

<figure><img src="image_7.png" /></figure>

<figure><img src="image_8.png" /></figure>

<figure><img src="image_9.png" /></figure>

<figure><img src="image_10.png" /></figure>

<figure><img src="image_11.png" /></figure>

<figure><img src="image_12.png" /></figure>

“sampling method” with inherent biases, but with the obvious benefit that time and expertise needed for handling and sorting samples is greatly reduced. Apart from the sensitivity of PCR primers, the reasons for the selectivity observed at the species level are not well understood [73].

<figure><img src="image_13.png" /></figure>

<figure><img src="image_14.png" /></figure>

<figure><img src="image_15.png" /></figure>

Development of better, more general primers (or more specific primers to target focal taxa) could improve current assessments [51,74]. This is not necessarily detrimental to long-term monitoring in soil organisms with metabarcoding, but the limitations of metabarcoding should be acknowledged.

<figure><img src="image_16.png" /></figure>

<figure><img src="image_17.png" /></figure>

<figure><img src="image_18.png" /></figure>

<figure><img src="image_19.png" /></figure>

Regarding our second question, there was a correlation between prevalence in metabarcoding samples and abundance in traditional samples, but the proportion of explained variance was relatively low. A partial explanation may be, in the first place, the biases inherent in metabarcoding workflow, as pointed out above. Nonetheless, while similarity in faunal composition between

<figure><img src="image_20.png" /></figure>

<figure><img src="image_21.png" /></figure>

<figure><img src="image_22.png" /></figure>

<figure><img src="image_23.png" /></figure>

traditional and metabarcoding samples was rather low, our approach to Question 3 showed that the methods is sensitive enough to reveal a good correspondence in the seasonal shifts of species in traditional and metabarcoding samples. This issue is relevant to long-term monitoring by indicating that metabarcoding is sufficiently sensitive to replicate ecological patterns through time in the soil fauna. This pattern has been demonstrated in multi-year analysis of other systems such as

<figure><img src="image_24.png" /></figure>

<figure><img src="image_25.png" /></figure>

<figure><img src="image_26.png" /></figure>

<figure><img src="image_27.png" /></figure>

a supprimé: worker

a supprimé: to be
macroinvertebrates from wetlands [52]. Hence, related to Questions 2 and 3, we tentatively conclude that it may be possible to use prevalence in metabarcoding samples to derive an estimate of, for example, the annual rate of change in the abundance of species that are well recorded (common) in metabarcoding samples.

**Biomass and read frequencies.** With respect to Question 4, we observed that the most common species of ants with high total number of reads are soil nesting species that are common on BCI. However, the relation between ant's body weight and read frequency or prevalence in metabarcoding samples was either weak or non-existent. For social insects, colony size may be more relevant than worker size or biomass, but we did not have pertinent data to test this relationship. While good correlation between read frequencies and species biomass has been reported for nematodes [75], this relationship is less clear-cut, weak or absent for arthropods [24,25,27,73,76,77]. Hence, with the current technology available to us, biomass data are unlikely to refine estimation of species prevalence (or biomass) among metabarcoding samples. Bista et al. [77] showed that shotgun mitogenomic sequencing provides better relationships between reads and biomass than metabarcoding, a finding later confirmed and developed by Ji et al. [78]. Hence, PCR-free methods may potentially offer an alternative avenue towards improved quantification.

**False positives and negatives.** The prevalence of false positive species is always a risk, because of contamination or errors during PCR and sequencing [40]. For long-term monitoring this is not an acute problem, as monitoring is often restricted to common species well amenable to statistical analysis [44]. “Probable false positives” as defined in our study can also sometimes be ruled out with a priori knowledge of the local fauna. In evidence of this, *Ectatomma* ACH3273 ruidum and *Cylindrotermes macrognathus* Snyder, 1929 were most likely missed in traditional samples, being part of a complex of cryptic species that can only be delineated with BINs (*Ectatomma* spp.) or difficult identification (*C. macrognathus*). The prevalence of *Atlantitermes* sp. 4 on BCI and of
Collembola ADU7662 may be plausible, but the other three species, including the two *Tetramorium* which are invasive and urban pests from Asia [79,80], are most likely errors (Table 3).

625

626

Explanations for the prevalence of “probable false negative species” are challenging. These species

a supprimé: were

627

were reasonably well collected on BCI by the ForestGEO Arthropod Initiative during the period

628

2008-2019, and they all have BINs included in the reference libraries used. Thus, their absence in

629

metabarcoding samples is unlikely to be due to local scarcity. The prevalence of probable false

630

negative species is more problematic for long-term monitoring [23]. These errors may be generated

631

by primer biases [39], but this appears less likely in our study, because metabarcoding samples

632

recorded congeneric species well. Further, the *in silico* evaluation revealed the primers to be

633

capable of amplifying the COI barcodes of the probable false negative species. Another source of

634

error may occur when genuine metabarcodes are assigned to an incorrect taxon due to incomplete or

635

inappropriate reference databases [39], or to the use of inefficient assignment methods [41]. In our

636

study, false negatives may result mostly from inappropriate referencing of metabarcodes,

637

particularly for species complexes. To reduce the number of false negatives, Ficetola et al. [40]

638

advised to perform multiple PCR extractions and to use species occupancy models to refine the

639

results. Unfortunately, the *occupancy probability of* false negative species cannot be estimated by

a supprimé: In our study

640

such models, since these species were never encountered in *our* metabarcoding samples.

a supprimé: true species occupancy of probable

641

**Conclusions.** Targeting Question 6, we observed different patterns in the distribution of ants,

642

springtails, and termites in traditional and metabarcoding samples. While Berlese-Tullgren is an

643

efficient method for extracting soil springtails, termites are best surveyed with transects [81], and

644

ants with Winkler extraction [34]. Thus, the efficiency of metabarcoding may ultimately be

645

improved by choosing a more selective sampling method appropriate for *each* target taxa.

a supprimé: the

646

Our study indicates that metabarcoding of samples extracted with Berlese-Tullgren may be well

647

suited for the long-term monitoring of springtails and termites in tropical rainforests, *provided* that

a supprimé: over time

a supprimé: providing
baseline data for area, including DNA reference libraries, exist [43]. Vouchered reference libraries are indispensable in this context, as they allow compilation of species traits and delineation of functional groups for a sound interpretation of time-series [32]. For ants, this issue is less clear-cut because many species lacked in metabarcoding samples. Technological improvements that may provide longer sequences and improved detection in reference to DNA libraries may improve ant detection. Irrespective of this issue, monitoring ants with metabarcoding may be possible for certain species with samples obtained from Berlese-Tullgren, and this may be complemented with metabarcoding samples obtained with other collecting methods such as Winkler, Malaise or light traps targeting alates [82]. Metabarcoding with Berlese-Tullgren may also provide additional data about non-target soil taxa not considered in this study. Sample processing can be simplified by obtaining bulk soil samples without extracting arthropods, although in this case the validity of analyses at the species level would need to be ascertained [20]. Overall, metabarcoding emerges as a method of choice for the long-term monitoring of the elusive biodiversity in tropical soils.

a supprimé: studies of the surveyed

667

668 **Data Accessibility**

The field protocol was deposited at protocols.io (dx.doi.org/10.17504/protocols.io.bj9gkr3w).
Sequences and BINs used as reference libraries are publicly accessible in BOLD projects as
indicated in the text, and also deposited in GenBank databases under accession numbers KP845288-
KP849461, KU745531-KU745532, KX072335-KX072563, MF922335-MF970719, MG030727,
MK758129-MK770080, MN345316-MN621065 and MT357731. Details about sampling locations
and a full list of species and their BINs are available in [33]. Raw sequences were deposited at the
Sequence Read Archive (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA668155).

a supprimé: The whole procedure

676

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865
866 Acknowledgments

867 We thank ForestGEO and the Smithsonian Tropical Research Institute for logistical support. This
868 study was supported by SENACYT (FID16-070 to YB and HB), the Czech Science Foundation
869 (GAČR 20-31295S to YB) and ForestGEO. Grants from the Smithsonian Institution Barcoding
870 Opportunity FY012, FY015 and FY018 (to YB) and in-kind help from the Canadian Centre for
871 DNA Barcoding allowed to sequence specimens. YB and HB were supported by the Sistema
872 Nacional de Investigación, SENACYT, Panama, and GPAL by a Junior GAČR grant (19-15645Y)
873 and a European Research Council Grant (No. 669609). Arturo García-Gómez and Blanca E. Mejía-
874 Recamier helped refining identifications of Collembola.

875
876 Author Contributions

877 The experimental design was originally conceived by Y.B., H.B. and L.D.L., with additional input
878 from M.H., D.A.D., S.T.S., D.S.V., T.R., M.A.S., G.P.A.L. and T.D. M.W., A.M.C. and D.Y.S.
879 helped with laboratory and bioinformatic analyses. Insect data were contributed by J.G.P.V.,
880 G.C.M., R.H.S., M.R., F.P., R.B., Y.B., J.A.R., M.M.C. and A.A.G. Analyses were performed by
881 Y.B. and D.S.V. All authors contributed to the writing of the manuscript.

882
883 Competing interests
884 The authors declare no competing interests.

885

886 **Additional information**

887 Appendix S1. Supplementary methods.

888 Appendix S2. Supplementary methodological considerations.

889 Table S1. Analytical parameters used in the mBRAVE platform.

890 Table S2. Sensitivity analysis with mBRAVE parameters and the data of the present study.

891 Table S3. Distribution of post filter reads.

892 Table S4. Summary of run results.

893 Table S5. Numbers of reads in BINs, of unique BINs and reads in OTUs, detailed by arthropod

894 orders and sampling location.

895 Table S6. Number of OTUs and complexes of focal species detected for three treatments.

896 Table S7. In silico PCR results using EcoPCR.

897 Fig. S1. Euler diagrams indicating the number of species with BINs detected by traditional and

898 metabarcoding samples, and by both methods for ants, springtails and termites.

899 Fig. S2. Accumulation of species richness vs. the number of samples for common species of ants,

900 springtails and termites.

901 Fig. S3. Plot of sample locations in the first two axes of the Procrustes rotation for all species of

902 Formicidae, Collembola and Isoptera and for common species of the same taxa.

903

904

905
906 FIGURE LEGENDS

907

908

909 **Fig. 1.** Summary diagram illustrating the workflow of samples for springtails. The black square in the BCI map indicates the location of the 50ha plot. Traditional samples of ants and termites were processed at STRI in Panama.

912

913 **Fig. 2.** Accumulation of species richness vs. the number of samples for ants, springtails and termites, detailed for traditional and metabarcoding samples. CollMeta, CollTrad = Collembola in metabarcoding and traditional samples; FormMeta, FormTrad = Formicidae in metabarcoding and traditional samples; TermMeta, TermTrad = termites in metabarcoding and traditional samples.

917

918 **Fig. 3.** Taxonomic comparison between identification methods for the three focal groups. The size of each node represents the number of BINs for each hierarchical classification, while differences in color correspond to sampling methods (purple = traditional samples; brown-orange = metabarcoding samples; gray = occurring in both).

922

923 **Fig. 4.** Species ranked by, first, prevalence in traditional samples (blue bars) and, second, prevalence in metabarcoding samples (orange bars) for (a) Formicidae, (b) Collembola and (c) Isoptera. Species indicated by "*" represent common species as defined in this study.

926

927 **Fig. 5.** Plot of the difference in species' prevalence between wet and dry season in traditional samples vs. that in metabarcoding samples.

929

930
Table 1. Number of individuals and species of ants, springtails and termites (with BINs; traditional samples) and number of reads, reads in BINs, reads in OTUs and species (metabarcoding samples) recorded in samples grouped by location.

934

<table><thead><tr><th rowspan="2">Location</th><th colspan="2">Taxonomic samples</th><th colspan="4">Metabarcoding samples</th></tr><tr><th>No. ind.</th><th>No. Spp.</th><th>Total reads<sup>1</sup></th><th>Reads in BINs<sup>2</sup></th><th>Reads in OTUs<sup>3</sup></th><th>No. Spp.</th></tr></thead><tbody><tr><td>ARM1</td><td>401</td><td>38</td><td>2959781</td><td>707227</td><td>736268</td><td>43</td></tr><tr><td>ARM2</td><td>441</td><td>40</td><td>3253108</td><td>646202</td><td>1073925</td><td>41</td></tr><tr><td>ARM3</td><td>656</td><td>65</td><td>3650551</td><td>929015</td><td>856161</td><td>56</td></tr><tr><td>ARM4</td><td>774</td><td>57</td><td>3337128</td><td>135814</td><td>1397542</td><td>42</td></tr><tr><td>BAL1</td><td>274</td><td>42</td><td>2804034</td><td>622318</td><td>876746</td><td>38</td></tr><tr><td>DRA1</td><td>344</td><td>39</td><td>2623464</td><td>345515</td><td>934815</td><td>42</td></tr><tr><td>WHE1</td><td>361</td><td>42</td><td>2351194</td><td>627690</td><td>574774</td><td>36</td></tr><tr><td>WHE2</td><td>63</td><td>24</td><td>2424845</td><td>813851</td><td>660620</td><td>49</td></tr><tr><td>ZET1</td><td>688</td><td>48</td><td>3805815</td><td>635410</td><td>1543644</td><td>42</td></tr><tr><td>ZET2</td><td>845</td><td>58</td><td>3252451</td><td>725566</td><td>1118475</td><td>32</td></tr></tbody></table>

1 Total no. of reads uploaded in mBRAVE

2 Matched BINs, mostly arthropods

3 Arthropods and non-arthropods, the later including vertebrates, invertebrates and plants

938

939

940
<table><caption>**Table 2.** Linear regressions including Formicidae, Collembola, Isoptera species and the following independent and dependent variables: AbTaxo: abundance in traditional samples; PrTaxo: prevalence in traditional samples; PrMeta: prevalence in metabarcoding samples; PrTaxo: prevalence in traditional samples. Regressions for common species of termites could not be tested because of small sample size.</caption><thead><tr><th>Group</th><th>Independent</th><th>Dependent</th><th>n</th><th>R<sup>2</sup></th><th>Constant (s.e.)</th><th>Coefficient (s.e.)</th><th>F</th><th>p</th></tr></thead><tbody><tr><td>All species</td><td>AbTaxo</td><td>PrTaxo</td><td>134</td><td>0.676</td><td>2.408 (0.548)</td><td>0.120 (0.007)</td><td>275.4</td><td>&lt; 0.001</td></tr><tr><td>All species</td><td>AbTaxo</td><td>PrMeta</td><td>134</td><td>0.139</td><td>2.853 (0.922)</td><td>0.056 (0.012)</td><td>21.2</td><td>&lt; 0.001</td></tr><tr><td>All species</td><td>PrTaxo</td><td>PrMeta</td><td>193</td><td>0.144</td><td>2.803 (0.695)</td><td>0.400 (0.070)</td><td>32.2</td><td>&lt; 0.001</td></tr><tr><td>All common spp.</td><td>AbTaxo</td><td>PrTaxo</td><td>34</td><td>0.491</td><td>8.076 (2.349)</td><td>0.093 (0.017)</td><td>30.8</td><td>&lt; 0.001</td></tr><tr><td>All common spp.</td><td>AbTaxo</td><td>PrMeta</td><td>34</td><td>0.08</td><td>n.s.</td><td>n.s.</td><td>2.8</td><td>0.106</td></tr><tr><td>All common spp.</td><td>PrTaxo</td><td>PrMeta</td><td>34</td><td>0.144</td><td>1.633 (4.368)</td><td>0.465 (0.201)</td><td>5.6</td><td>0.027</td></tr><tr><td>Formicidae</td><td>AbTaxo</td><td>PrTaxo</td><td>78</td><td>0.694</td><td>2.372 (0.428)</td><td>0.084 (0.006)</td><td>172.3</td><td>&lt; 0.001</td></tr><tr><td>Formicidae</td><td>AbTaxo</td><td>PrMeta</td><td>78</td><td>0.240</td><td>0.974 (0.876)</td><td>0.064 (0.013)</td><td>24.0</td><td>&lt; 0.001</td></tr><tr><td>Formicidae</td><td>PrTaxo</td><td>PrMeta</td><td>101</td><td>0.190</td><td>0.902 (0.796)</td><td>0.568 (0.118)</td><td>23.3</td><td>&lt; 0.001</td></tr><tr><td>Formicidae common spp.</td><td>AbTaxo</td><td>PrTaxo</td><td>20</td><td>0.617</td><td>6.904 (1.558)</td><td>0.064 (0.012)</td><td>29.0</td><td>&lt; 0.001</td></tr><tr><td>Formicidae common spp.</td><td>AbTaxo</td><td>PrMeta</td><td>20</td><td>0.271</td><td>-0.779 (3.663)</td><td>0.073 (0.028)</td><td>7.0</td><td>0.019</td></tr><tr><td>Formicidae common spp.</td><td>PrTaxo</td><td>PrMeta</td><td>20</td><td>0.398</td><td>-7.919 (4.592)</td><td>1.076 (0.312)</td><td>11.9</td><td>0.003</td></tr><tr><td>Collembola</td><td>AbTaxo</td><td>PrTaxo</td><td>41</td><td>0.717</td><td>3.170 (1.492)</td><td>0.149 (0.015)</td><td>99.0</td><td>&lt; 0.001</td></tr><tr><td>Collembola</td><td>AbTaxo</td><td>PrMeta</td><td>41</td><td>0.156</td><td>3.153 (1.887)</td><td>0.051 (0.019)</td><td>7.2</td><td>0.011</td></tr><tr><td>Collembola</td><td>PrTaxo</td><td>PrMeta</td><td>52</td><td>0.289</td><td>2.218 (1.371)</td><td>0.376 (0.084)</td><td>20.3</td><td>&lt; 0.001</td></tr><tr><td>Collembola common spp.</td><td>AbTaxo</td><td>PrTaxo</td><td>10</td><td>0</td><td>n.s.</td><td>n.s.</td><td>0.0</td><td>0.965</td></tr><tr><td>Collembola common spp.</td><td>AbTaxo</td><td>PrMeta</td><td>10</td><td>0.026</td><td>n.s.</td><td>n.s.</td><td>0.2</td><td>0.659</td></tr><tr><td>Collembola common spp.</td><td>PrTaxo</td><td>PrMeta</td><td>10</td><td>0.223</td><td>n.s.</td><td>n.s.</td><td>2.3</td><td>0.168</td></tr><tr><td>Isoptera</td><td>AbTaxo</td><td>PrTaxo</td><td>15</td><td>0.039</td><td>n.s.</td><td>n.s.</td><td>0.5</td><td>0.48</td></tr><tr><td>Isoptera</td><td>AbTaxo</td><td>PrMeta</td><td>15</td><td>0.035</td><td>n.s.</td><td>n.s.</td><td>0.5</td><td>0.507</td></tr><tr><td>Isoptera</td><td>PrTaxo</td><td>PrMeta</td><td>40</td><td>0.351</td><td>4.052 (1.686)</td><td>4.341 (0.957)</td><td>20.6</td><td>&lt; 0.001</td></tr></tbody></table>
<table><caption>**Table 3.** List of species considered as "probable false positive" (i.e., high prevalence in metabarcoding samples but absent in traditional samples) and as "probable false negative" (i.e., high prevalence in traditional samples but absent in metabarcoding samples). Prev, Prevalence in samples; Rec, no. of records on BCI, years 2008-2019; Spm, no. of specimens with BINs. Sources: BOLD (https://www.boldsystems.org/); Termite catalog (http://164.41.140.9/catal/); antwiki (https://www.antwiki.org).</caption><thead><tr><th>Species</th><th>Family</th><th>BIN</th><th>Prev</th><th>BCI</th><th>Distribution</th><th>Notes</th></tr></thead><tbody><tr><td colspan="7"><strong>(a) Probable false positive</strong></td></tr><tr><td>Atlantitermes sp. 4</td><td>Termitidae</td><td>TAX:674460</td><td>40</td><td>No</td><td>Ecuador, Peru</td><td>Atlantitermes spp. Neotropical; A. kirby on BCI</td></tr><tr><td>Tetramorium tsushimae</td><td>Formicidae</td><td>BOLD:ACV4760</td><td>21</td><td>No</td><td>Japan, Korea, China</td><td>Tetramorium bicarinatum on BCI (BIN BOLD:AAA5578)</td></tr><tr><td>Cylindrotermes macrognathus</td><td>Termitidae</td><td>BOLD:AAP9583</td><td>19</td><td>Yes</td><td>Costa Rica, Panama</td><td>156 specimens collected on BCI</td></tr><tr><td>Isotomidae ABA4127</td><td>Isotomidae</td><td>BOLD:ABA4127</td><td>18</td><td>No</td><td>Africa, Indonesia</td><td>Information minimal</td></tr><tr><td>Ectatomma ACH3273 ruidum</td><td>Formicidae</td><td>BOLD:ACH3273</td><td>16</td><td>Yes</td><td>Neotropical</td><td>Four cryptic species of "E. ruidum" are present on BCI</td></tr><tr><td>Collembola ADU7662</td><td>Entomobryidae?</td><td>BOLD:ADU7662</td><td>13</td><td>Yes</td><td>Panama</td><td>44 specimens collected from Malaise trap on BCI</td></tr><tr><td>Tetramorium ADR2002 caespitum</td><td>Formicidae</td><td>BOLD:ADR2002</td><td>11</td><td>No</td><td>China</td><td>Tetramorium bicarinatum on BCI (BIN BOLD:AAA5578)</td></tr><tr><td colspan="7"><strong>b) Probable false negative</strong></td></tr><tr><td>Solenopsis AAP9748</td><td>Formicidae</td><td>BOLD:AAP9748</td><td>34</td><td>Yes</td><td>Panama</td><td>Rec 1008, Spm 12</td></tr><tr><td>Folsomia sensibilis</td><td>Isotomidae</td><td>BOLD:ADS5886</td><td>27</td><td>Yes</td><td>Panama</td><td>Rec 41, Spm 1</td></tr><tr><td>Isotomodes falsus</td><td>Isotomidae</td><td>BOLD:ADT5082</td><td>26</td><td>Yes</td><td>Panama</td><td>Rec 32, Spm 1</td></tr><tr><td>Mesaphorura yosiii</td><td>Tullbergiidae</td><td>BOLD:ADS1278</td><td>19</td><td>Yes</td><td>Panama</td><td>Rec 26, Spm 2</td></tr><tr><td>Solenopsis AAZ7574</td><td>Formicidae</td><td>BOLD:AAZ7574</td><td>17</td><td>Yes</td><td>Panama</td><td>Rec 109, Spm11</td></tr><tr><td>Octostruma amrishi</td><td>Formicidae</td><td>BOLD:ABX5315†</td><td>12</td><td>Yes</td><td>Panama</td><td>Rec 630, Spm 19; two BINs: ABX5315/AAP3374</td></tr><tr><td>Willemia ADU3782 panamensis</td><td>Hypogastruridae</td><td>BOLD:ADU3782</td><td>12</td><td>Yes</td><td>Panama</td><td>Rec 17, Spm 1; one close species with BIN ADU3783 on BCI</td></tr><tr><td>Americanura basseti</td><td>Neanuridae</td><td>BOLD:ADS4642</td><td>12</td><td>Yes</td><td>Panama</td><td>Rec 16, Spm 3</td></tr><tr><td>Solenopsis AAN9169</td><td>Formicidae</td><td>BOLD:AAN9169</td><td>11</td><td>Yes</td><td>Neotropical</td><td>Rec 560, Spm 19</td></tr><tr><td>Pseudachorutes parvulus</td><td>Neanuridae</td><td>BOLD:ADV9385†</td><td>10</td><td>Yes</td><td>Panama</td><td>Rec 16, Spm 5; two BINs: ADT9797/ADV9385</td></tr></tbody></table>

† See notes

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FIGURES

<figure><img src="image_28.png" /><figcaption>**Fig. 1.** Summary diagram illustrating the workflow of samples for springtails. The black square in the BCI map indicates the location of the 50ha plot. Traditional samples of ants and termites were processed at STRI in Panama.</figcaption></figure>
<figure><img src="image_29.png" /><figcaption>**Fig. 2.** Accumulation of species richness vs. the number of samples for ants, springtails and termites, detailed for traditional and metabarcoding samples. CollMeta, CollTrad = Collembola in metabarcoding and traditional samples; FormMeta, FormTrad = Formicidae in metabarcoding and traditional samples; TermMeta, TermTrad = termites in metabarcoding and traditional samples.</figcaption></figure>
<figure><img src="image_30.png" /><figcaption>**Fig. 3.** Taxonomic comparison between identification methods for the three focal groups. The size of each node represents the number of BINs for each hierarchical classification, while differences in color correspond to sampling methods (purple = traditional samples; brown-orange = metabarcoding samples; gray = occurring in both).</figcaption></figure>
<figure><img src="image_31.png" /><figcaption>**Fig. 4.** Species ranked by, first, prevalence in traditional samples (blue bars) and, second, prevalence in metabarcoding samples (orange bars) for (a) Formicidae, (b) Collembola and (c) Isoptera. Species indicated by "*" represent common species as defined in this study.</figcaption></figure>
<figure><img src="image_32.png" /><figcaption>**Fig. 5.** Plot of the difference in species' prevalence between wet and dry season in traditional samples vs. that in metabarcoding samples.</figcaption></figure>