Lavelle, P. et al. Soil invertebrates and ecosystem services. Eur. J. Soil Biol. 42, S3–S15 (2006).
Google Scholar
André, H. M., Noti, M. I. & Lebrun, P. The soil fauna: The other last biotic frontier. Biodiv. Conserv. 3, 45–56 (1994).
Google Scholar
Decaëns, T. Macroecological patterns in soil communities. Glob. Ecol. Biogeogr. 19, 287–302 (2010).
Google Scholar
IPCC. Global Warming of 1.5 °C. Summary for Policymakers. (World Meteorological Organization, 2018).
Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59–67 (2012).
Google Scholar
Kardol, P., Reynolds, W. N., Norby, R. J. & Classen, A. T. Climate change effects on soil microarthropod abundance and community structure. Appl. Soil Ecol. 47, 37–44 (2011).
Google Scholar
Kaspari, M., Clay, N. A., Lucas, J., Yanoviak, S. P. & Kay, A. Thermal adaptation generates a diversity of thermal limits in a rainforest ant community. Glob. Change Biol. 21, 1092–1102 (2015).
Google Scholar
Baird, D. J. & Hajibabaei, M. Biomonitoring 2.0: A new paradigm in ecosystem assessment made possible by next-generation DNA sequencing. Mol. Ecol. 21, 2039–2044 (2012).
Google Scholar
Leray, M. & Knowlton, N. DNA barcoding and metabarcoding of standardized samples reveal patterns of marine benthic diversity. PNAS 112, 2076–2081 (2015).
Google Scholar
Beng, K. C. et al. The utility of DNA metabarcoding for studying the response of arthropod diversity and composition to land-use change in the tropics. Sci. Rep. 6, 1–13. https://doi.org/10.1038/srep24965 (2016).
Google Scholar
Zhang, K. et al. Plant diversity accurately predicts insect diversity in two tropical landscapes. Mol. Ecol. 25, 4407–4419 (2016).
Google Scholar
Hebert, P. D., Cywinska, A., Ball, S. L. & Dewaard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321 (2003).
Google Scholar
Hajibabaei, M., Janzen, D. H., Burns, J. M., Hallwachs, W. & Hebert, P. D. DNA barcodes distinguish species of tropical Lepidoptera. PNAS 103, 968–971 (2006).
Google Scholar
Ratnasingham, S. & Hebert, P. D. N. A DNA-based registry for all animal species: The Barcode Index Number (BIN) system. PLoS ONE 8, e66213. https://doi.org/10.1371/journal.pone.0066213 (2013).
Google Scholar
Shendure, J. & Ji, H. Next-generation DNA sequencing. Nat. Biotechnol. 26, 1135–1145 (2008).
Google Scholar
Porter, T. M. & Hajibabaei, M. Scaling up: A guide to high-throughput genomic approaches for biodiversity analysis. Mol. Ecol. 27, 313–338 (2018).
Google Scholar
Tang, M. et al. High-throughput monitoring of wild bee diversity and abundance via mitogenomics. Methods Ecol. Evol. 6, 1034–1043 (2015).
Google Scholar
Arribas, P., Andújar, C., Hopkins, K., Shepherd, M. & Vogler, A. P. Metabarcoding and mitochondrial metagenomics of endogean arthropods to unveil the mesofauna of the soil. Methods Ecol. Evol. 7, 1071–1081 (2016).
Google Scholar
Arribas, P., Andújar, C., Salces-Castellano, A., Emerson, B. C. & Vogler, A. P. The limited spatial scale of dispersal in soil arthropods revealed with whole-community haplotype-level metabarcoding. Mol. Ecol. 30, 48–61 (2021).
Google Scholar
Oliverio, A. M., Gan, H., Wickings, K. & Fierer, N. A DNA metabarcoding approach to characterize soil arthropod communities. Soil Biol. Biochem. 125, 37–43 (2018).
Google Scholar
Zinger, L. et al. Body size determines soil community assembly in a tropical forest. Mol. Ecol. 28, 528–543 (2019).
Google Scholar
McGee, K. M., Porter, T. M., Wright, M. & Hajibabaei, M. Drivers of tropical soil invertebrate community composition and richness across tropical secondary forests using DNA metasystematics. Sci. Rep. 10, 18429. https://doi.org/10.1038/s41598-020-75452-4 (2020).
Google Scholar
Hajibabaei, M., Spall, J. L., Shokralla, S. & van Konynenburg, S. Assessing biodiversity of a freshwater benthic macroinvertebrate community through non-destructive environmental barcoding of DNA from preservative ethanol. BMC Ecol. 12, 28. https://doi.org/10.1186/1472-6785-12-28 (2012).
Google Scholar
Gibson, J. et al. Simultaneous assessment of the macrobiome and microbiome in a bulk sample of tropical arthropods through DNA metasystematics. PNAS 111, 8007–8012 (2014).
Google Scholar
Lamb, P. D. et al. How quantitative is metabarcoding: A meta-analytical approach. Mol. Ecol. 28, 420–430 (2019).
Google Scholar
Piñol, J., Senar, M. A. & Symondson, W. O. The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. Mol. Ecol. 28, 407–419 (2019).
Google Scholar
Creedy, T. J., Ng, W. S. & Vogler, A. P. Toward accurate species-level metabarcoding of arthropod communities from the tropical forest canopy. Ecol. Evol. 9, 3105–3116 (2019).
Google Scholar
Lach, L., Parr, C., Abbott, K. Ant Ecology (Oxford University Press, 2010).
Palacios-Vargas, J. G. & Castaño-Meneses, G. Seasonality and community composition of springtails in Mexican forest. In Arthropods of Tropical Forests. Spatio-Temporal Dynamics and Resource Use in the Canopy (eds. Basset, Y. et al.) 159–169 (Cambridge University Press, 2003).
Bignell, D. E. & Eggleton, P. Termites in ecosystems. In Termites: Evolution, Sociality, Symbiosis, Ecology (eds Abe, T., Bignell, D. E. & Higashi, M.) 363–387 (Kluwer Academic Publishers, 2000).
Anderson-Teixeira, K. J. et al. CTFS-Forest GEO: A worldwide network monitoring forests in an era of global change. Glob. Change Biol. 21, 528–549 (2015).
Google Scholar
Lamarre, G. P. et al. Monitoring tropical insects in the 21st century. Adv. Ecol. Res. 62, 295–330 (2020).
Google Scholar
Basset, Y. et al. Enemy-free space and the distribution of ants, springtails and termites in the soil of one tropical rainforest. Eur. J. Soil Biol. 99, 103193. https://doi.org/10.1016/j.ejsobi.2020.103193 (2020).
Google Scholar
Agosti, D., Majer, J. D., Alonso, L. E. & Schultz, T. R. Ants. Standards Methods for Measuring and Monitoring Biodiversity (Smithsonian Institution Press, 2000).
Bourguignon, T., Leponce, M. & Roisin, Y. Insights into the termite assemblage of a neotropical rainforest from the spatio-temporal distribution of flying alates. Insect. Conserv. Divers. 2, 153–162 (2009).
Google Scholar
Yu, D. W. et al. Biodiversity soup: Metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. Methods Ecol. Evol. 3, 613–623 (2012).
Google Scholar
Gaston, K. J. & Lawton, J. H. Patterns in the distribution and abundance of insect populations. Nature 331, 709–712 (1988).
Google Scholar
Liu, M., Clarke, L. J., Baker, S. C., Jordan, G. J. & Burridge, C. P. A practical guide to DNA metabarcoding for entomological ecologists. Ecol. Entomol. 45, 373–385 (2019).
Google Scholar
Zinger, L. et al. DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions. Mol. Ecol. 28, 1857–1862 (2019).
Google Scholar
Ficetola, G. F. et al. Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data. Mol. Ecol. Res. 15, 543–556 (2015).
Google Scholar
Porter, T. M. & Hajibabaei, M. Automated high throughput animal CO1 metabarcode classification. Sci. Rep. 8, 1–10. https://doi.org/10.1038/s41598-018-22505-4 (2018).
Google Scholar
Marquina, D., Esparza-Salas, R., Roslin, T. & Ronquist, F. Establishing arthropod community composition using metabarcoding: Surprising inconsistencies between soil samples and preservative ethanol and homogenate from Malaise trap catches. Mol. Ecol. Res. 19, 1516–1530 (2019).
Google Scholar
Porter, T. M. et al. Variations in terrestrial arthropod DNA metabarcoding methods recovers robust beta diversity but variable richness and site indicators. Sci. Rep. 9, 1–11. https://doi.org/10.1038/s41598-019-54532-0 (2019).
Google Scholar
Basset, Y. et al. Cross-continental comparisons of butterfly assemblages in tropical rainforests: Implications for biological monitoring. Insect. Conserv. Divers 6, 223–233 (2013).
Google Scholar
Ryder Wilkie, K. T., Mertl, A. L. & Traniello, J. F. A. Biodiversity below ground: Probing the subterranean ant fauna of Amazonia. Naturwissenschaften 94, 725–731 (2007).
Google Scholar
André, H. M., Ducarme, X. & Lebrun, P. Soil biodiversity: Myth, reality or conning?. Oikos 96, 3–24 (2002).
Google Scholar
Wilson, J. J. DNA barcodes for insects. In DNA Barcodes: Methods and Protocols (eds Kress, W. J. & Erickson, D. L.) 17–46 (Springer, 2012).
Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).
Google Scholar
Gibson, J. F. et al. Large-scale biomonitoring of remote and threatened ecosystems via high-throughput sequencing. PLoS ONE 10, e0138432. https://doi.org/10.1371/journal.pone.0138432 (2015).
Google Scholar
Hajibabaei, M., Porter, T. M., Wright, M. & Rudar, J. COI metabarcoding primer choice affects richness and recovery of indicator taxa in freshwater systems. PLoS One 14, e0220953. https://doi.org/10.1371/journal.pone.0220953 (2019).
Google Scholar
Bush, A. et al. DNA metabarcoding reveals metacommunity dynamics in a threatened boreal wetland wilderness. PNAS 117, 8539–8545 (2020).
Google Scholar
Calderón-Sanou, I. et al. From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices?. J. Biogeogr. 47, 193–206 (2020).
Google Scholar
Schloss, P. D. Reintroducing mothur: 10 years later. Appl. Env. Microbiol. 86, e02343-19. https://doi.org/10.1128/AEM.02343-19 (2020).
Google Scholar
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
Google Scholar
Boyer, F. et al. Obitools: A unix-inspired software package for DNA metabarcoding. Mol. Ecol. Res. 16, 176–182 (2016).
Google Scholar
Ratnasingham, S. mBRAVE: The multiplex barcode research and visualization environment. Biodivers. Inf. Sci. Stand. 3, e37986. https://doi.org/10.3897/biss.3.37986 (2019).
Google Scholar
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: A versatile open source tool for metagenomics. PeerJ 4, e2584. https://doi.org/10.7717/peerj.2584 (2016).
Google Scholar
Edgar, R. C. UPARSE: Highly accurate OTU sequences from microbial amplicon reads. Nat. Methods 10, 996–998 (2013).
Google Scholar
Gaston, K. J. Rarity (Springer, 1994).
Kaspari, M. Litter ant patchiness at the 1–m2 scale: Disturbance dynamics in three Neotropical forests. Oecologia 107, 265–273 (1996).
Google Scholar
Hsieh, T. C., Ma, K. H. & Chao, A. iNEXT: An R package for rarefaction and extrapolation of species diversity (Hill numbers). Methods Ecol. Evol. 7, 1451–1456 (2016).
Google Scholar
Foster, Z. S. L., Sharpton, T. J. & Grünwald, N. J. Metacoder: An R package for visualization and manipulation of community taxonomic diversity data. PLoS Comput. Biol. 13, e1005404. https://doi.org/10.1371/journal.pcbi.1005404 (2017).
Google Scholar
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-3 (2018).
Hyams, D. G. CurveExpert Professional. A Comprehensive Data Analysis Software System for Windows, Mac, and Linux. Version 1.2.2. www.curveexpert.net (2011). Accessed 1 Jan 2022.
Deagle, B. E. et al. Counting with DNA in metabarcoding studies: How should we convert sequence reads to dietary data?. Mol. Ecol. 28, 391–406 (2019).
Google Scholar
Ficetola, G. F. et al. An In Silico approach for the evaluation of DNA barcodes. BMC Genom. 11, 434. https://doi.org/10.1186/1471-2164-11-434 (2010).
Google Scholar
Auer, L., Mariadassou, M., O’Donohue, M., Klopp, C. & Hernandez-Raquet, G. Analysis of large 16S rRNA Illumina data sets: Impact of singleton read filtering on microbial community description. Mol. Ecol. Res. 17, e122–e132. https://doi.org/10.1111/1755-0998.12700 (2017).
Google Scholar
Novotný, V. & Basset, Y. Rare species in communities of tropical insect herbivores: Pondering the mystery of singletons. Oikos 89, 564–572 (2000).
Google Scholar
Seifert, B. & Goropashnaya, A. V. Ideal phenotypes and mismatching haplotypes-errors of mtDNA treeing in ants (Hymenoptera: Formicidae) detected by standardized morphometry. Org. Divers. Evol. 4, 295–305 (2004).
Google Scholar
Gotzek, D., Clarke, J. & Shoemaker, D. Mitochondrial genome evolution in fire ants (Hymenoptera: Formicidae). BMC Evol. Biol. 10, 300. https://doi.org/10.1186/1471-2148-10-300 (2010).
Google Scholar
Meza-Lázaro, R. N., Poteaux, C., Bayona-Vásquez, N. J., Branstetter, M. G. & Zaldívar-Riverón, A. Extensive mitochondrial heteroplasmy in the neotropical ants of the Ectatomma ruidum complex (Formicidae: Ectatomminae). Mit. DNA Part A 29, 1203–1214 (2018).
Google Scholar
Saitoh, S. et al. A quantitative protocol for DNA metabarcoding of springtails (Collembola). Genome 59, 705–723 (2016).
Google Scholar
Elbrecht, V. et al. Validation of COI metabarcoding primers for terrestrial arthropods. PeerJ 7, e7745. https://doi.org/10.7717/peerj.7745 (2019).
Google Scholar
Schenk, J., Geisen, S., Kleinbölting, N. & Traunspurger, W. Metabarcoding data allow for reliable biomass estimates in the most abundant animals on earth. Metabarcoding Metagenom. 3, e46704. https://doi.org/10.3897/mbmg.3.46704 (2019).
Google Scholar
Elbrecht, V. & Leese, F. Can DNA-based ecosystem assessments quantify species abundance? Testing primer bias and biomass—Sequence relationships with an innovative metabarcoding protocol. PLoS One 10, e0130324. https://doi.org/10.1371/journal.pone.0130324 (2015).
Google Scholar
Bista, I. et al. Performance of amplicon and shotgun sequencing for accurate biomass estimation in invertebrate community samples. Mol. Ecol. Res. 18, 1020–1034 (2018).
Google Scholar
Ji, Y. et al. SPIKEPIPE: A metagenomic pipeline for the accurate quantification of eukaryotic species occurrences and intraspecific abundance change using DNA barcodes or mitogenomes. Mol. Ecol. Res. 20, 256–267 (2020).
Google Scholar
Steiner, F. M. et al. Tetramorium tsushimae, a new invasive ant in North America. Biol. Invasions 8, 117–123 (2006).
Google Scholar
Wetterer, J. K. Worldwide spread of the penny ant, Tetramorium bicarinatum (Hymenoptera: Formicidae). Sociobiology 54, 811–830 (2009).
Roisin, Y. et al. Vertical stratification of the termite assemblage in a neotropical forest. Oecologia 149, 301–311 (2006).
Google Scholar
Basset, Y. et al. Methodological considerations for monitoring soil/litter arthropods in tropical rainforests using DNA metabarcoding, with a special emphasis on ants, springtails and termites. Metabarcoding Metagenom. 4, 151–163. https://doi.org/10.3897/mbmg.4.58572 (2020).
Google Scholar
Source: Ecology - nature.com
