MANS S., KAMKE J., LEE C. K., MACDIARMID R., MORALES S. E., ORLOVICH D. A., SMISSEN R., WOOD J. & HOLDAWAY R. 2018. — Methods for the extraction, storage, amplification and sequencing of DNA from environmental samples. *New Zealand Journal of Ecology* 42 (1): 10-50A

LEE P.-S., GAN H. M., CLEMENTS G. R. & WILSON J.-J. 2016. — Field calibration of blowfly-derived DNA against traditional methods for assessing mammal diversity in tropical forests. *Genome* 59 (11): 1008-1022. https://doi.org/10.1139/gen-2015-0193

LEESE F., ALTERMATT F., BOUCHEZ A., EKREM T., HERING D., MEISSNER K., MERGEN P., PAWLOWSKI J., PIGGOTT J., RIMET F., STEINKE D., TABERLET P., WEIGAND A., ABARENKOV K., BEJA P., BERVOETS L., BJÖRNSDÖTTIR S., BOETS P., BOGGERO A., BONES A., BORJA Á., BRUCE K., BURSIĆ V., CARLSSON J., ČIAMPOR F., ČIAMPOROVÁ-ZATOVIČOVÁ Z., COISSAC E., COSTA F., COSTACHE M., CREER S., CSABAI Z., DEINER K., DELVALLS Á., DRAKARE S., DUARTE S., ELERŠEK T., FAZI S., FIŠER C., FLOT J.-F., FONSECA V., FONTANETO D., GRABOWSKI M., GRAF W., GUDBRANDSSON J., HELLSTRÖM M., HERSKOVITZ Y., HOLLINGSWORTH P., JAPOSHVILI B., JONES J., KAHLERT M., STROIL B.K., KASAPIDIS P., KELLY M., KELLY-QUINN M., KESKIN E., KÖLJALG U., LJUBEŠIĆ Z., MAČEK I., MÄCHLER E., MAHON A., MAREČKOVÁ M., MEJDANDZIC M., MIRCHEVA G., MONTAGNA M., MORITZ C., MULK V., NAUMOSKI A., NAVODARU I., PADISÁK J., PALSSON S., PANKSEP K., PENEV L., PETRUSEK A., PFANNKUCHEN M., PRIMER C., RINKEVICH B., ROTTER A., SCHMIDT-KLOIBER A., SEGU RADO P., SPEKNISJIDER A., STOEV P., STRAND M., ŠÚLČIUS S., SUNDBERG P., TRAUGOTT M., TSIGENOPOULOS C., TURON X., VALENTINI A., HOORN B. VAN DER, VÅRBÍRØ G., HADJILYRA M.V., VIGURI J., VITONYTÉ I., VOGLER A., VRÅLSTAD T., WÄGELE W., WENNE R., WINDING A., WOODWARD G., ZEGURA B. & ZIMMERMANN J. 2016. — DNAqua-Net: developing new genetic tools for bioassessment and monitoring of aquatic ecosystems in Europe. *Research Ideas and Outcomes* 2: e11321. https://doi.org/10.3897/rio.2.e11321

LEONARD J. A. 2008. — Ancient DNA applications for wildlife conservation. *Molecular Ecology* 17 (19): 4186-4196. https://doi.org/10.1111/j.1365-294X.2008.03891.x

LEONHARDT F., KELLER A., ARRANZ AVECES C. & ERNST R. 2023. — From alien species to alien communities: host- and habitat-associated microbiomes in an alien amphibian. *Microbial Ecology* 86: 2373-2385. https://doi.org/10.1007/s00248-023-02227-5

LEONTIDOU K., VOKOU D., SANDIONIGI A., BRUNO A., LAZARINA M., DE GROEVE J., LI M., VAROTTO C., GIRARDI M., CASIRAGHI M. & CRISTOFORI A. 2021. — Plant biodiversity assessment through pollen DNA metabarcoding in Natura 2000 habitats (Italian Alps). *Scientific Reports* 11 (1): 18226. https://doi.org/10.1038/s41598-021-97619-3

LI W., SONG T., HOU X., QIN M., XU C. & LI Y. 2021. — Application of eDNA metabarcoding for detecting anura on a Tropical Island. *Diversity* 13 (9): 440. https://doi.org/10.3390/d13090440

LI W., QIN M., HOU X., ZHANG J., WANG S., LI Y., LUO Z., DENG T., SONG T., XU C., LIU X., WANG X. & LI Y. 2022. — Application of eDNA metabarcoding for detecting anura in North China. *Asian Herpetological Research* 13 (4): 224-232. https://doi.org/10.16373/j.cnki.ahr.220021

LIU M., CLARKE L. J., BAKER S. C., JORDAN G. J. & C. P. 2020. — A practical guide to DNA metabarcoding for entomological ecologists. *Ecological Entomology* 45 (3): 373-385. https://doi.org/10.1111/een.12831

LOBO J., SHOKRALLA S., COSTA M. H., HAJIBABAEI M. & COSTA F. O. 2017. — DNA metabarcoding for high-throughput monitoring of estuarine macrobenthic communities. *Scientific Reports* 7 (1): 15618. https://doi.org/10.1038/s41598-017-15823-6

LOPES C. M., SASSO T., VALENTINI A., DEJEAN T., MARTINS M., ZAMUDIO K. R. & HADDAD C. F. B. 2017. — eDNA metabarcoding: a promising method for anuran surveys in highly diverse tropical forests. *Molecular Ecology Resources* 17 (5): 904-914. https://doi.org/10.1111/1755-0998.12643

LOPES C. M., BAËTA D., VALENTINI A., LYRA M. L., SABBAG A. F., GASPARINI J. L., DEJEAN T., HADDAD C. F. B. & ZAMUDIO K. R. 2021. — Lost and found: frogs in a biodiversity hotspot rediscovered with environmental DNA. *Molecular Ecology* 30 (13): 3289-3298. https://doi.org/10.1111/mec.15594

LOW L. & TAMMI M. T. 2017. — Introduction to next generation sequencing technologies, in LOW L. & TAMMI M. T. (éd.s), *Bioinformatics. A Practical Handbook of Next Generation Sequencing and Its Applications*. World Scientific, Singapour: 1-21 https://doi.org/10.1142/9789813144750_0001

LYET A., PELLISSIER L., VALENTINI A., DEJEAN T., HEHMEYER A. & NAIDOO R. 2021. — eDNA sampled from stream networks correlates with camera trap detection rates of terrestrial mammals. *Scientific Reports* 11 (1): 11362. https://doi.org/10.1038/s41598-021-90598-5

LYMAN J. A., SANCHEZ D. E., HERSHAUER S. N., SOBEK C. J., CHAMBERS C. L., ZAHRATKA J. & WALKER F. M. 2022. — Mammalian eDNA on herbaceous vegetation? Validating a qPCR assay for detection of an endangered rodent. *Environmental DNA* 11: 11362. https://doi.org/10.1002/edn3.331

LYNGGAARD C., BERTELSEN M. F., JENSEN C. V., JOHNSON M. S., FRØSLEV T. G., OLSEN M. T. & BOHMANN K. 2022. — Airborne environmental DNA for terrestrial vertebrate community monitoring. *Current Biology* 32 (3): 701-707.e5. https://doi.org/10.1016/j.cub.2021.12.014

LYNGGAARD C., NIELSEN M., SANTOS-BAY L., GASTAUER M., OLIVEIRA G. & BOHMANN K. 2019. — Vertebrate diversity revealed by metabarcoding of bulk arthropod samples from tropical forests. *Environmental DNA* 1 (4): 329-341. https://doi.org/10.1002/edn3.34

MAESTRI S., COSENTINO E., PATERNO M., FREITAG H., GARCES J. M., MARCOLUNGO L., ALFANO M., NJUNJIĆ I., SCHILTHUIZEN M., SLIK F., MENEGON M., ROSSATO M. & DELLEDONNE M. 2019. — A rapid and accurate MinION-Based workflow for tracking species biodiversity in the field. *Genes* 10 (6): 1-11. https://doi.org/10.3390/genes10060468

MARIZZI C., FLORIO A., LEE M., KHALFAN M., GHIBAN C., NASH B., DOREY J., MCKENZIE S., MAZZA C., CELLINI F., BARIA C., BEPAT R., COSENTINO L., DVORAK A., GACEVIC A., GUZMAN-MOUMTZIS C., HELLER F., HOLT N. A., HORENSTEIN J., JORALEMON V., KAUR M., KAUR T., KHAN A., KUPPAN J., LAVERTY S., LOCK C., PENA M., PETRYCHYN I., PUTHENKALAM I., RAM D., RAMOS A., SCOCA N., SIN R., GONZALEZ I., THAKUR A., USMANOV H., HAN K., WU A., ZHU T. & MICKLOS D.A. 2018. — DNA barcoding Brooklyn (New York): a first assessment of biodiversity in Marine Park by citizen scientists. *PLOS ONE* 13 (7): e0199015. https://doi.org/10.1371/journal.pone.0199015

MARQUINA D., ESPARZA-SALAS R., ROSLIN T. & RONQUIST F. 2019. — Establishing arthropod community composition using metabarcoding: Surprising inconsistencies between soil samples and preservative ethanol and homogenate from Malaise trap catches. *Molecular Ecology Resources* 19 (6): 1516-1530. https://doi.org/10.1111/1755-0998.13071

MARQUINA D., ROSLIN T., ŁUKASIK P. & RONQUIST F. 2022. — Evaluation of non-destructive DNA extraction protocols for insect metabarcoding: gentler and shorter is better. *Metabarcoding and Metagenomics* 6: e78871. https://doi.org/10.3897/mbmg.6.78871

MARSHALL N. T., VANDERPLOEG H. A. & CHAGANTI S. R. 2021. — Environmental (e)RNA advances the reliability of eDNA by predicting its age. *Scientific Reports* 11 (1): 2769. https://doi.org/10.1038/s41598-021-82205-4

MARTINS F. M. S., GALHARDO M., FILIPE A. F., TEIXEIRA A., PINHEIRO P., PAUPÉRIO J., ALVES P. C. & BEJA P. 2019. — Have the cake and eat it: Optimizing nondestructive DNA metabarcoding of macroinvertebrate samples for freshwater biomonitoring: a promising method for anuran surveys in highly diverse tropical forests. *Molecular Ecology Resources* 17 (5): 904-914. https://doi.org/10.1111/1755-0998.12643
ring. *Molecular Ecology Resources* 19 (4): 863-876. https://doi.org/10.1111/1755-0998.13012

MARTONI F., PIPER A. M., RODONI B. C. & BLACKET M. J. 2022. — Disentangling bias for non-destructive insect metabarcoding. *PeerJ* 10: e12981. https://doi.org/10.7717/peerj.12981

MATHON L., VALENTINI A., GUÉRIN P.-E., NORMANDEAU E., NOEL C., LIONNET C., BOULANGER E., THUILLER W., BERNATCHEZ L., MOUILLOT D., DEJEAN T. & MANEL S. 2021. — Benchmarking bioinformatic tools for fast and accurate eDNA metabarcoding species identification. *Molecular Ecology Resources* 21 (7): 2565-2579. https://doi.org/10.1111/1755-0998.13430

MAUVISSEAU Q., COIGNET A., DELAUNAY C., PINET F., BOUCHON D. & SOUTY-GROSSET C. 2018. — Environmental DNA as an efficient tool for detecting invasive crayfishes in freshwater ponds. *Hydrobiologia* 805 (1): 163-175. https://doi.org/10.1007/s10750-017-3288-y

MAUVISSEAU Q., DAVY-BOWKER J., BULLING M., BRYS R., NEYRINCK S., TROTH C. & SWEET M. 2019. — Combining ddPCR and environmental DNA to improve detection capabilities of a critically endangered freshwater invertebrate. *Scientific Reports* 9 (1): 14064. https://doi.org/10.1038/s41598-019-50571-9

MAUVISSEAU Q., HARPER L. R., SANDER M., HANNER R. H., KLEYER H. & DEINER K. 2022. — The multiple states of environmental dna and what is known about their persistence in aquatic environments. *Environmental Science & Technology* 56 (9): 5322-5333. https://doi.org/10.1021/acs.est.1c07638

MCDONALD R., BATEMAN P. W., COOPER C., VAN DER HEYDE M., MOUSAVI-DERAZMAHALLEH M., HEDGES B. A., GUZIK M. T. & NEVILL P. 2023. — Detection of vertebrates from natural and artificial inland water bodies in a semi-arid habitat using eDNA from filtered, swept, and sediment samples. *Ecology and Evolution* 13 (4): e10014. https://doi.org/10.1002/ece3.10014

MCHUGO G. P., DOVER M. J. & MACHUGH D. E. 2019. — Unlocking the origins and biology of domestic animals using ancient DNA and paleogenomics. *BMC Biology* 17 (1): 98. https://doi.org/10.1186/s12915-019-0724-7

MENA J. L., YAGUI H., TEJEDA V., BONIFAZ E., BELLEMAIN E., VALENTINI A., TOBLER M. W., SÁNCHEZ-VENDIZÚ P. & LYET A. 2021. — Environmental DNA metabarcoding as a useful tool for evaluating terrestrial mammal diversity in tropical forests. *Ecological Applications* 31 (5): e02335. https://doi.org/10.1002/eap.2335

MÉTRIS K. L. & MÉTRIS J. 2023. — Aircraft surveys for air eDNA: probing biodiversity in the sky. *PeerJ* 11: e15171. https://doi.org/10.7717/peerj.15171

MEYER R., RAMOS M., LIN M., SCHWEIZER T., GOLD Z., RAMOS D., SHIRAZI S., KANDLIKAR G., KWAN W., CURD E., FREISE A., PARKER J., SEXTON J., WETZER R., PENTCHEFF N., WALL A., PIPES L., GARCIA-VEDRENNE A., MEJIA M., MOORE T., ORLAND C., BALLARE K., WORTH A., BERAUT E., ARONSON E., NIELSEN R., LEWIN H., BARBER P., WALL J., KRAFT N., SHAPIRO B. & WAYNE R. 2021. — The CALEDNA program: Citizen scientists and researchers inventory California's biodiversity. *California Agriculture* 75 (1): 20-32. https://doi.org/10.3733/ca.2021a0001

MILIAN-GARCÍA Y., YOUNG R., MADDEN M., BULLAS-APPLETON E. & HANNER R. H. 2021. — Optimization and validation of a cost-effective protocol for biosurveillance of invasive alien species. *Ecology and Evolution* 11 (5): 1999-2014. https://doi.org/10.1002/ece3.7139

MORI E., BAERI A., SFORZI A., VITALE A. & GALIMBERTI A. 2017. — From accidental citizen-science observations to genetic confirmation: how to spot new hidden invaders. *Hystrix, the Italian Journal of Mammalogy* 28 (2): 284-287. https://doi.org/10.4404/hystrix-28.2-12421

NELL C. S., PRATT R., BURGER J., PRESTON K. L., TRESEDER K. K., KAMADA D., MOORE K. & MOONEY K. A. 2023. — Consequences of arthropod community structure for an at-risk insectivorous bird. *PLOS ONE* 18 (2): e0281081. https://doi.org/10.1371/journal.pone.0281081

NEWTON J. P., BATEMAN P. W., HEYDENRYCH M. J., MOUSAVI-DERAZMAHALLEH M. & NEVILL P. 2022. — Home is where the hollow is: revealing vertebrate tree hollow user biodiversity with eDNA metabarcoding. *Environmental DNA* 4 (5): 1078-1091. https://doi.org/10.1002/edn3.313

NICHOLS R. V., KÖNIGSSON H., DANELL K. & SPONG G. 2012. — Browsed twig environmental DNA: diagnostic PCR to identify ungulate species. *Molecular Ecology Resources* 12 (6): 983-989. https://doi.org/10.1111/j.1755-0998.2012.03172.x

NORDSTROM B., MITCHELL N., BYRNE M. & JARMAN S. 2022. — A review of applications of environmental DNA for reptile conservation and management. *Ecology and Evolution* 12 (6): e8995. https://doi.org/10.1002/ece3.8995

OLIVERIO A.M., GAN H., WICKINGS K. & FIERER N. 2018. — A DNA metabarcoding approach to characterize soil arthropod communities. *Soil Biology and Biochemistry* 125: 37-43. https://doi.org/10.1016/j.soilbio.2018.06.026

ORLANDO L., ALLABY R., SKOGLUND P., DER SARKISSIAN C., STOCKHAMMER P. W., ÁVILA-ARCOS M. C., FU Q., KRAUSE J., WILLERSLEV E., STONE A. C. & WARINNER C. 2021. — Ancient DNA analysis. *Nature Reviews Methods Primers* 1 (1): 1-26. https://doi.org/10.1038/s43586-020-00011-0

PACIONI C., HUNT H., ALLENTOFT M. E., VAUGHAN T. G., WAYNE A. F., BAYNES A., HAOUCCHAR D., DORTCH J. & BUNCE M. 2015. — Genetic diversity loss in a biodiversity hotspot: ancient DNA quantifies genetic decline and former connectivity in a critically endangered marsupial. *Molecular Ecology* 24 (23): 5813-5828. https://doi.org/10.1111/mec.13430

PASCHER K., ŠVARA V. & JUNGMEIER M. 2022. — Environmental DNA-based methods in biodiversity monitoring of protected areas: application range, limitations, and needs. *Diversity* 14 (6): 463. https://doi.org/10.3390/d14060463

PAWLOWSKI J., APOTHÉLOZ-PERRET-GENTIL L., MÄCHLER E. & ALTERMATT F. 2020. — Environmental DNA applications For Biomonitoring and Bioassessment in Aquatic Ecosystems. Federal Office for the Environment (FOEN), Bern, 71 p. https://doi.org/10.5167/UZH-187800

PAWLOWSKI J., BRUCE K., PANKSEP K., AGUIRRE F. I., AMALFITANO S., APOTHÉLOZ-PERRET-GENTIL L., BAUSSANT T., BOUCHEZ A., CARUGATI L., CERMAKOVA K., CORDIER T., CORINALDESI C., COSTA F. O., DANOVARO R., DELL'ANNO A., DUARTE S., EISENDLE U., FERRARI B. J. D., FRONTALINI F., FRÜHE L., HAEGERBAEUMER A., KISAND V., KROLICKA A., LANZÉN A., LEESE F., LEJZEROWICZ F., LYAUTEY E., MAČEK I., SAGOVA-MAREČKOVÁ M., PEARMAN J. K., POCHON X., STOECK T., VIVIEN R., WEIGAND A. & FAZI S. 2022. — Environmental DNA metabarcoding for benthic monitoring: a review of sediment sampling and DNA extraction methods. *Science of The Total Environment* 818: 151783. https://doi.org/10.1016/j.scitotenv.2021.151783

PEDERSEN M. W., OVERBALLE-PETERSEN S., ERMINI L., SARKISSIAN C. D., HAILE J., HELLSTROM M., SPENS J., THOMSEN P. F., BOHMANN K., CAPPELLINI E., SCHNELL I. B., WALES N. A., CARØE C., CAMPOS P. F., SCHMIDT A. M. Z., GILBERT M. T. P., HANSEN A. J., ORLANDO L. & WILLERSLEV E. 2015. — Ancient and modern environmental DNA. *Philosophical Transactions of the Royal Society B: Biological Sciences* 370 (1660): 20130383. https://doi.org/10.1098/rstb.2013.0383

PERSAUD S. F., COTTENIE K. & GLEASON J. E. 2021. — Ethanol eDNA reveals unique community composition of aquatic macroinvertebrates compared to bulk tissue metabarcoding in a biomonitoring sampling scheme. *Diversity* 13 (1): 34. https://doi.org/10.3390/d13010034

Piñol J., SENAR M. A. & SYMONDSON W. O. C. 2019. — The choice of universal primers and the characteristics of the species mixture determine when DNA metabarcoding can be quantitative. *Molecular Ecology* 28 (2): 407-419. https://doi.org/10.1111/mec.14776
POLAND J. A. & RIFE T. W. 2012. — Genotyping-by-sequencing for plant breeding and genetics. *The Plant Genome* 5 (3): 92-102. https://doi.org/10.3835/plantgenome2012.05.0005

PONT D., ROCLE M., VALENTINI A., CIVADE R., JEAN P., MAIRE A., ROSET N., SCHABUSS M., ZORNIG H. & DEJEAN T. 2018. — Environmental DNA reveals quantitative patterns of fish biodiversity in large rivers despite its downstream transportation. *Scientific Reports* 8 (1): 10361. https://doi.org/10.1038/s41598-018-28424-8

PORNON A., ANDALO C., BURRUS M. & ESCARAVAGE N. 2017. — DNA metabarcoding data unveils invisible pollination networks. *Scientific Reports* 7 (1): 16828. https://doi.org/10.1038/s41598-017-16785-5

PORT J. A., O'DONNELL J. L., ROMERO-MARACCINI O. C., LEARY P. R., LITVIN S. Y., NICKOLS K. J., YAMAHARA K. M. & KELLY R. P. 2016. — Assessing vertebrate biodiversity in a kelp forest ecosystem using environmental DNA. *Molecular Ecology* 25 (2): 527-541. https://doi.org/10.1111/mec.13481

PRIÉ V., VALENTINI A., LOPES-LIMA M., FROUFE E., ROCLE M., POULET N., TABERLET P. & DEJEAN T. 2021. — Environmental DNA metabarcoding for freshwater bivalves biodiversity assessment: methods and results for the Western Palearctic (European sub-region). *Hydrobiologia* 848 (12): 2931-2950. https://doi.org/10.1007/s10750-020-04260-8

PUENTE-SÁNCHEZ F., ARCE-RODRÍGUEZ A., OGGERIN M., GARCÍA-VILLADANGOS M., MORENO-PAZ M., BLANCO Y., RODRÍGUEZ N., BIRD L., LINCOLN S. A., TORNOS F., PRIETO-BALLESTEROS O., FREEMAN K. H., PIEPER D. H., TIMMIS K.N., AMILS R. & PARRO V. 2018. — Viable cyanobacteria in the deep continental subsurface. *Proceedings of the National Academy of Sciences* 115 (42): 10702-10707. https://doi.org/10.1073/pnas.1808176115

PUMKAEO P., TAKAHASHI J. & IWAHASHI H. 2021. — Detection and monitoring of insect traces in bioaerosols. *PeerJ* 9: e10862. https://doi.org/10.7717/peerj.10862

RAEMY M. & URSENBACHER S. 2018. — Detection of the European pond turtle (*Emys orbicularis*) by environmental DNA: is eDNA adequate for reptiles? *Amphibia-Reptilia* 39 (2): 135-143. https://doi.org/10.1163/15685381-17000025

RATNASINGHAM S. & HEBERT P. D. N. 2007. — BOLD: the Barcode of Life Data System (http://www.barcodinglife.org). *Molecular Ecology Notes* 7 (3): 355-364. https://doi.org/10.1111/j.1471-8286.2007.01678.x

RITTER C. D., HÄGGQVIST S., KARLSSON D., SÄÄKSJÄRVI I. E., MUASYA A. M., NILSSON R.H. & ANTONELLI A. 2019. — Biodiversity assessments in the 21 st century: the potential of insect traps to complement environmental samples for estimating eukaryotic and prokaryotic diversity using high-throughput DNA metabarcoding. *Génome* 62 (3): 147-159. https://doi.org/10.1139/gen-2018-0096

ROCHA R. G., JUSTINO J., LEITE Y. L. R. & COSTA L. P. 2015. — DNA from owl pellet bones uncovers hidden biodiversity. *Systematics and Biodiversity* 13 (4): 403-412. https://doi.org/10.1007/s10477-015-10440-8

RODGERS T. W. & JANEČKA J. E. 2013. — Applications and techniques for non-invasive faecal genetics research in felid conservation. *European Journal of Wildlife Research* 59 (1): 1-16. https://doi.org/10.1007/s10344-012-0675-6

ROGER F., GHANAVI H. R., DANIELSSON N., WAHLBERG N., LÖNDAHL J., PETTERSSON L. B., ANDERSSON G. K. S., BOKE OLÉN N. & CLOUGH Y. 2022. — Airborne environmental DNA metabarcoding for the monitoring of terrestrial insects – A proof of concept from the field. *Environmental DNA* 4 (4): 790-807. https://doi.org/10.1002/edn3.290

ROSA L. H., PINTO O. H. B., ŠANTL-TEMKIV T., CONVEY P., CARVALHO-SILVA M., ROSA C. A. & CÂMARA P. E. A. S. 2020. — DNA metabarcoding of fungal diversity in air and snow of Livingston Island, South Shetland Islands, Antarctica. *Scientific Reports* 10 (1): 21793. https://doi.org/10.1038/s41598-020-78630-6

ROGERIE R., DECAËNS T., DEHARVENG L., PORCO D., JAMES S. W., CHANG C.-H., RICHARD B., POTAPOV M., SUHARDJONO Y. & HEBERT P. D. N. 2009. — DNA barcodes for soil animal taxonomy. *Pesquisa Agropecuária Brasileira* 44 (8): 789-802. https://doi.org/10.1590/S0100-204X2009000800002

RUNTUWENE L. R., TUDA J. S. B., MONGAN A. E. & SUZUKI Y. 2019. — On-Site MinION sequencing, in *SUZUKI Y. (éd.), Single Molecule and Single Cell Sequencing*. Springer, Singapore: 143-150. https://doi.org/10.1007/978-981-13-6037-4_10

RUPPERT K. M., KLINE R. J. & RAHMAN M. S. 2019. — Past, present, and future perspectives of environmental DNA (eDNA) metabarcoding: a systematic review in methods, monitoring, and applications of global eDNA. *Global Ecology and Conservation* 17: e00547. https://doi.org/10.1016/j.gecco.2019.e00547

SALES N. G., KAIZER M. DA C., COSCIA I., PERKINS J. C., HIGHLANDS A., BOUBLI J. P., MAGNUSSON W. E., DA SILVA M. N. F., BENVENUTO C. & MCDEVITT A. D. 2020. — Assessing the potential of environmental DNA metabarcoding for monitoring Neotropical mammals: a case study in the Amazon and Atlantic forest, Brazil. *Mammal Review* 50 (3): 221-225. https://doi.org/10.1111/mam.12183

SASSO T., LOPES C. M., VALENTINI A., DEJEAN T., ZAMUDIO K. R., HADDAD C. F. B. & MARTINS M. 2017. — Environmental DNA characterization of amphibian communities in the Brazilian Atlantic forest: potential application for conservation of a rich and threatened fauna. *Biological Conservation* 215: 225-232. https://doi.org/10.1016/j.biocon.2017.09.015

SAYERS E. W., CAVANAUGH M., CLARK K., OSTELL J., PRUITT K. D. & KARSCH-MIZRACHI I. 2019. — GenBank. *Nucleic Acids Research* 47 (D1): D94-D99. https://doi.org/10.1093/nar/gky989

SCHNELL I. B., THOMSEN P. F., WILKINSON N., RASMUSSEN M., JENSEN L. R. D., WILLERSLEV E., BERTELSEN M. F. & THOMAS M. GILBERT P. 2012. — Screening mammal biodiversity using DNA from leeches. *Current Biology* 22 (8): R262-R263. https://doi.org/10.1016/j.cub.2012.02.058

SCHNELL I. B., SOLLMANN R., CALVIGNAC-SPENCER S., SIDDALL M. E., YU D. W., WILTING A. & THOMAS M. GILBERT P. 2015. — iDNA from terrestrial haematophagous leeches as a wildlife surveying and monitoring tool — prospects, pitfalls and avenues to be developed. *Frontiers in Zoology* 12 (1): 24. https://doi.org/10.1186/s12983-015-0115-z

SCHULZ F., ALTEIO L., GOUDEAU D., RYAN E. M., YU F. B., MALMSTROM R. R., BLANCHARD J. & WOYKE T. 2018. — Hidden diversity of soil giant viruses. *Nature Communications* 9 (1): 4881. https://doi.org/10.1038/s41467-018-07335-2

SEEBER P. A. & EPP L. S. 2022. — Environmental DNA and metagenomics of terrestrial mammals as keystone taxa of recent and past ecosystems. *Mammal Review* 52 (4): 538-553. https://doi.org/10.1111/mam.12302

SEEBER P. A., VON HIPPEL B., KAUSERUD H., LÖBER U., STOOF-LEICHSENRING K. R., HERZSCHUH U. & EPP L. S. 2022. — Evaluation of lake sedimentary ancient DNA metabarcoding to assess fungal biodiversity in Arctic paleoecosystems. *Environmental DNA* 4 (5): 1150-1163. https://doi.org/10.1002/edn3.315

SEERSHOLM F. V., COLE T. L., GREALY A., RAWLENCE N. J., GREIG K., KNAPP M., STAT M., HANSEN A. J., EASTON L. J., SHEPHERD L., TENNYSON A. J. D., SCOFIELD R. P., WALTER R. & BUNCE M. 2018. — Subsistence practices, past biodiversity, and anthropogenic impacts revealed by New Zealand-wide ancient DNA survey. *Proceedings of the National Academy of Sciences* 115 (30): 7771-7776. https://doi.org/10.1073/pnas.1803573115

SELLERS G. S., MURI C. D., GÓMEZ A. & HÄNFLING B. 2018. — Mu-DNA: a modular universal DNA extraction method adaptable for a wide range of sample types. *Metabarcoding and Metagenomics* 2: e24556. https://doi.org/10.3897/mbmg.2.24556

SHEPHERD L. D. & LAMBERT D. M. 2008. — Ancient DNA and conservation: lessons from the endangered kiwi of New Zealand. *Molecular Ecology* 17 (9): 2174-2184. https://doi.org/10.1111/j.1365-294X.2008.03749.x
<figure><img src="image_4.png" /></figure>

SIMS D., SUDBERY I., ILOTT N. E., HEGER A. & PONTING C. P. 2014. — Sequencing depth and coverage: key considerations in genomic analyses. *Nature Reviews Genetics* 15 (2): 121-132. https://doi.org/10.1038/nrg3642 Smith J. A., Thomas A. C., Levi T., Wang Y. & Wilmers C. C. 2018. — Human activity reduces niche partitioning among three widespread mesocarnivores. *Oikos* 127 (6): 890-901. https://doi.org/10.1111/oik.04592

SMITH J. A., THOMAS A. C., LEVI T., WANG Y. & WILMERS C. C. 2018. — Human activity reduces niche partitioning among three widespread mesocarnivores. *Oikos* 127 (6): 890-901. https://doi.org/10.1111/oik.04592

SPECCHIA V., TZAFESTA E., MARINI G., SCARCELLA S., D'ATTIS S. & PINNA M. 2020. — Gap analysis for DNA barcode reference libraries for aquatic macroinvertebrate species in the apulia region (Southeast of Italy). *Journal of Marine Science and Engineering* 8 (7): 538. https://doi.org/10.3390/jmse8070538

SRIVATHSAN A., SHA J. C. M., VOGLER A. P. & MEIER R. 2015. — Comparing the effectiveness of metagenomics and metabarcoding for diet analysis of a leaf-feeding monkey (*Pygathrix nemaeus*). *Molecular Ecology Resources* 15 (2): 250-261. https://doi.org/10.1111/1755-0998.12302

SRIVATHSAN A., LEE L., KATOH K., HARTOP E., KUTTY S. N., WONG J., YEO D. & MEIER R. 2021. — ONTbarcoder and MinION barcodes aid biodiversity discovery and identification by everyone, for everyone. *BMC Biology* 19 (1): 217. https://doi.org/10.1186/s12915-021-01141-x

SRIVATHSAN A., LOH R. K., ONG E. J., LEE L., ANG Y., KUTTY S. N. & MEIER R. 2022. — Network analysis with either Illumina or MinION reveals that detecting vertebrate species requires metabarcoding of iDNA from a diverse fly community. *Molecular Ecology* 32 (23): 6418-6435. https://doi.org/10.1111/mec.16767

STOECKLE M. Y. & THALER D. S. 2018. — Why should mitochon-
dria define species? *Human Evolution* 33 (1-2): 1-30. https://doi.
org/10.14673/HE2018121037

SUCHAN T., TALAVERA G., SÁEZ L., RONIKIER M. & VILA R. 2019. — Pollen metabarcoding as a tool for tracking long-distance insect migrations. *Molecular Ecology Resources* 19 (1): 149-162. https://doi.org/10.1111/1755-0998.12948

TABERLET P., COISSAC E., HAJIBABAEI M. & RIESEBERG L. H. 2012a. — Environmental DNA. *Molecular Ecology* 21 (8): 1789-1793. https://doi.org/10.1111/j.1365-294X.2012.05542.x

TABERLET P., PRUD'HOMME S. M., CAMPIONE E., ROY J., MIQUEL C., SHEHZAD W., GIELLY L., RIOUX D., CHOLER P., CLÉMENT J.-C., MELODELIMA C., POMPANON F. & COISSAC E. 2012b. — Soil sampling and isolation of extracellular DNA from large amount of starting material suitable for metabarcoding studies. *Molecular Ecology* 21 (8): 1816-1820. https://doi.org/10.1111/j.1365-294X.2011.05317.x

TAKAHASHI M., SACCO M., KESTEL J. H., NESTER G., CAMPBELL M. A., VAN DER HEYDE M., HEYDENRYCH M. J., JUSZKIEWICZ D. J., NEVILL P., DAWKINS K. L., BESSEY C., FERNANDES K., MILLER H., POWER M., MOUSAVI-DERAZMAHALLEH M., NEWTON J. P., WHITE N. E., RICHARDS Z. T. & ALLENTOFT M. E. 2023. — Aquatic environmental DNA: a review of the macro-organismal biomonitoring revolution. *Science of the Total Environment* 873: 162322. https://doi.org/10.1016/j.scitotenv.2023.162322

TANG M., HARDMAN C. J., JI Y., MENG G., LIU S., TAN M., YANG S., MOSS E. D., WANG J., YANG C., BRUCE C., NEVARD T., POTTS S. G., ZHOU X. & YU D. W. 2015. — High-throughput monitoring of wild bee diversity and abundance via mitogenomics. *Methods in Ecology and Evolution* 6 (9): 1034-1043. https://doi.org/10.1111/2041-210X.12416

THOMSEN P. F., KIELGAST J., IVERSEN L. L., WIUF C., RASMUS-
SEN M., THOMAS M. GILBERT P., ORLANDO L. & WILLERSLEV E.
2012. — Monitoring endangered freshwater biodiversity using
environmental DNA. *Molecular Ecology* 21 (11): 2565-2573.
https://doi.org/10.1111/j.1365-294X.2011.05418.x

THOMSEN P. F. & SIGSGAARD E. E. 2019. — Environmental DNA metabarcoding of wild flowers reveals diverse communities of

terrestrial arthropods. *Ecology and Evolution* 9 (4): 1665-1679. https://doi.org/10.1002/ece3.4809

TINGLEY R., GREENLEES M., OERTLEES S., VAN ROOYEN A. R. & WEEKS A. R. 2019. — Environmental DNA sampling as a surveillance tool for cane toad *Rhinella marina* introductions on offshore islands. *Biological Invasions* 21 (1): 1-6. https://doi.org/10.1007/s10530-018-1810-4

TORDONI E., AMETRANO C. G., BANCHI E., ONGARO S., PALLAVI-
CINI A., BACARO G. & MUGGIA L. 2021. — Integrated eDNA
metabarcoding and morphological analyses assess spatio-temporal
patterns of airborne fungal spores. *Ecological Indicators* 121:
107032. https://doi.org/10.1016/j.ecolind.2020.107032

TSUJI S., TAKAHARA T., DOI H., SHIBATA N. & YAMANAKA H. 2019. — The detection of aquatic macroorganisms using environmental DNA analysis – a review of methods for collection, extraction, and detection. *Environmental DNA* 1 (2): 99-108. https://doi.org/10.1002/edn3.21

USHIO M., FUKUDA H., INOUE T., MAKOTO K., KISHIDA O., SATO K., MURATA K., NIKAIDO M., SADO T., SATO Y., TAKESHITA M., IWASAKI W., YAMANAKA H., KONDOH M. & MIYA M. 2017. — Environmental DNA enables detection of terrestrial mammals from forest pond water. *Molecular Ecology Resources* 17 (6): e63-e75. https://doi.org/10.1111/1755-0998.12690

VALENTINI A., TABERLET P., MIAUD C., CIVADE R., HERDER J., THOMSEN P. F., BELLEMAIN E., BESNARD A., COISSAC E., BOYER F., GABORIAUD C., JEAN P., POULET N., ROSET N., COPP G. H., GENIEZ P., PONT D., ARGILLIER C., BAUDOIN J.-M., PEROUX T., CRIVELLI A. J., OLIVIER A., ACQUEBERGE M., BRUN M. L., MØLLER P. R., WILLERSLEV E. & DEJEAN T. 2016. — Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. *Molecular Ecology* 25 (4): 929-942. https://doi.org/10.1111/mec.13428

VAN DER HEYDE M., BATEMAN P. W., BUNCE M., WARDELL-
JOHNSON G., WHITE N. E. & NEVILL P. 2021. — Scat DNA
provides important data for effective monitoring of mammal and
bird biodiversity. *Biodiversity and Conservation* 30 (12): 3585-
3602. https://doi.org/10.1007/s10531-021-02264-x

VAN DER HEYDE M., BUNCE M. & NEVILL P. 2022. — Key factors to consider in the use of environmental DNA metabarcoding to monitor terrestrial ecological restoration. *Science of the Total Environment* 848: 157617. https://doi.org/10.1016/j.scitotenv.2022.157617

VAROTTO C., PINDO M., BERTONI E., CASAROTTO C., CAMIN F., GIRARDI M., MAGGI V. & CRISTOFORI A. 2021. — A pilot study of eDNA metabarcoding to estimate plant biodiversity by an alpine glacier core (Adamello glacier, North Italy). *Scientific Reports* 11 (1): 1208. https://doi.org/10.1038/s41598-020-79738-5

VASAR M., DAVISON J., MOORA M., SEPP S.-K., ANSLAN S., ALQURAISHY S., BAHRAM M., BUENO C. G., CANTERO J. J., FABIANO E. C., DECOCQ G., DRENKHAN R., FRASER L., OJA J., GARIBAY-ORIJEL R., HIIESALU I., KOOREM K., MUCINA L., ÖPIK M., PÖLME S., PÄRTEL M., PHOSRI C., SEMCHENKO M., VAHTER T., DOLEŽAL J., PALACIOS A. M. V., TEDERSOO L. & ZOBEL M. 2023. — Metabarcoding of soil environmental DNA to estimate plant diversity globally. *Frontiers in Plant Science* 14: 1-9. https://doi.org/10.3389/fpls.2023.1106617

VILLSEN K., CORSE E., MEGLÉCZ E., ARCHAMBAUD-SUARD G., VIGNES H., ERESKOVSKY A. V., CHAPPAZ R. & DUBUT V. 2022. — DNA metabarcoding suggests adaptive seasonal variation of individual trophic traits in a critically endangered fish. *Molecular Ecology* 31 (22): 5889-5908. https://doi.org/10.1111/mec.16698

VIOTTI C., BACH C., MAILLARD F., ZIEGLER-DEVIN I., MIESZ-
KIN S. & BUÉE M. 2021. — Sapwood and heartwood affect
differentially bacterial and fungal community structure and
successional dynamics during *Quercus petraea* decomposition.
*Environmental Microbiology* 23 (10): 6177-6193. https://doi.
org/10.1111/1462-2920.15522
WÄGELE J. W., BODESHEIM P., BOURLAT S. J., DENZLER J., DIEPENBROEK M., FONSECA V., FROMMOLT K.-H., GEIGER M. F., GEMEINHOLZER B., GLÖCKNER F. O., HAUCKE T., KIRSE A., KÖLPIN A., KOSTADINOV I., KÜHL H. S., KURTH F., LASSECK M., LIEDKE S., LOSCH F., MÜLLER S., PETROVSKAYA N., PIOTROWSKI K., RADIG B., SCHERBER C., SCHOPPMANN L., SCHULZ J., STEINHAGE V., TSCHAN G. F., VAUTZ W., VELOTTO D., WEIGEND M. & WILDERMANN S. 2022. — Towards a multisensor station for automated biodiversity monitoring. *Basic and Applied Ecology* 59: 105-138. https://doi.org/10.1016/j.baae.2022.01.003

WATTS C., DOPHEIDE A., HOLDAWAY R., DAVIS C., WOOD J., THORNBURROW D. & DICKIE I. A. 2019. — DNA metabarcoding as a tool for invertebrate community monitoring: a case study comparison with conventional techniques. *Austral Entomology* 58 (3): 675-686. https://doi.org/10.1111/aen.12384

WEIGAND H., BEERMANN A. J., ČIAMPOR F., COSTA F.O., CSABAI Z., DUARTE S., GEIGER M. F., GRABOWSKI M., RIMET F., RULIK B., STRAND M., SZUCSICH N., WEIGAND A. M., WILLASSEN E., WYLER S. A., BOUCHEZ A., BORJA A., ČIAMPOROVÁZAŤOVIČOVÁ Z., FERREIRA S., DIJKSTRA K.-D. B., EISENDLE U., FREYHOF J., GADAWSKI P., GRAF W., HAEGERBAEUMER A., VAN DER HOORN B. B., JAPOSHVILI B., KERESZTES L., KESKIN E., LEESE F., MACHER J. N., MAMOS T., PAZ G., PEŠIĆ V., PFANNKUCHEN D. M., PFANNKUCHEN M. A., PRICE B.W., RINKEVICH B., TEIXEIRA M. A. L., VÁRBÍRÓ G. & EKREM T. 2019. — DNA barcode reference libraries for the monitoring of aquatic biota in Europe: gap-analysis and recommendations for future work. *Science of the Total Environment* 678: 499-524. https://doi.org/10.1016/j.scitotenv.2019.04.247

WILSON J.-J., JISMING-SEE S.-W., BRANDON-MONG G.-J., LIM A.-H., LIM V.-C., LEE P.-S. & SING K.-W. 2015. — Citizen Science: the first peninsular Malaysia butterfly count. *Biodiversity Data Journal* 3: e7159. https://doi.org/10.3897/BDJ.3.e7159

XU J. 2016. — Fungal DNA barcoding. *Génome* 59 (11): 913-932. https://doi.org/10.1139/gen-2016-0046

YANG C., BOHMANN K., WANG X., CAI W., WALES N., DING Z., GOPALAKRISHNAN S. & YU D. W. 2021. — Biodiversity Soup II: a bulk-sample metabarcoding pipeline emphasizing error reduction. *Methods in Ecology and Evolution* 12 (7): 1252-1264. https://doi.org/10.1111/2041-210X.13602

YATES M. C., FRASER D. J. & DERRY A. M. 2019. — Meta-analysis supports further refinement of eDNA for monitoring aquatic species-specific abundance in nature. *Environmental DNA* 1 (1): 5-13. https://doi.org/10.1002/edn3.7

YATES M. C., DERRY A. M. & CRISTESCU M. E. 2021. — Environmental RNA: a revolution in ecological resolution? *Trends in Ecology & Evolution* 36 (7): 601-609. https://doi.org/10.1016/j.tree.2021.03.001

YOCCOZ N. G., BRÄTHEN K. A., GIELLY L., HAILE J., EDWARDS M. E., GOSLAR T., STEDINGK H. V., BRYSTING A. K., COISSAC E., POMPANON F., SØNSTEBØ J. H., MIQUEL C., VALENTINI A., BELLO F. D., CHAVE J., THUILLER W., WINCKER P., CRUAUD C., GAVORY F., RASMUSSEN M., THOMAS M. GILBERT P., ORLANDO L., BROCHMANN C., WILLERSLEV E. & TABERLET P. 2012. — DNA from soil mirrors plant taxonomic and growth form diversity. *Molecular Ecology* 21 (15): 3647-3655. https://doi.org/10.1111/j.1365-294X.2012.05545.x

YOO K., LEE T. K., CHOI E. J., YANG J., SHUKLA S. K., HWANG S. & PARK J. 2017. — Molecular approaches for the detection and monitoring of microbial communities in bioaerosols: a review. *Journal of Environmental Sciences* 51: 234-247. https://doi.org/10.1016/j.jes.2016.07.002

YOON S.-H., HA S.-M., KWON S., LIM J., KIM Y., SEO H. & CHUN J. 2017. — Introducing EzBioCloud: a taxonomically united database of 16S rRNA gene sequences and whole-genome assemblies. *International Journal of Systematic and Evolutionary Microbiology* 67 (5): 1613-1617. https://doi.org/10.1099/ijsem.0.001755

YOUNG R. G., MILIÁN-GARCÍA Y., YU J., BULLAS-APPLETON E. & HANNER R. H. 2021. — Biosurveillance for invasive insect pest species using an environmental DNA metabarcoding approach and a high salt trap collection fluid. *Ecology and Evolution* 11 (4): 1558-1569. https://doi.org/10.1002/ece3.7113

YU D. W., JI Y., EMERSON B. C., WANG X., YE C., YANG C. & DING Z. 2012. — Biodiversity soup: metabarcoding of arthropods for rapid biodiversity assessment and biomonitoring. *Methods in Ecology and Evolution* 3 (4): 613-623. https://doi.org/10.1111/j.2041-210X.2012.00198.x

ZAFEIROPOULOS H., VIET H. Q., VASILEIADOU K., POTIRAKIS A., ARVANITIDIS C., TOPALIS P., PAVLOUDI C. & PAFILIS E. 2020. — PEMA: a flexible Pipeline for Environmental DNA Metabarcoding Analysis of the 16S/18S ribosomal RNA, ITS, and COI marker genes. *GigaScience* 9 (3): 1-12. https://doi.org/10.1093/gigascience/giaa022

ZENKER M. M., SPECHT A. & FONSECA V. G. 2020. — Assessing insect biodiversity with automatic light traps in Brazil: pearls and pitfalls of metabarcoding samples in preservative ethanol. *Ecology and Evolution* 10 (5): 2352-2366. https://doi.org/10.1002/ece3.6042

ZHOU J.-L., XU J., JIAO A.-G., YANG L., CHEN J., CALLAC P., LIU Y. & WANG S.-X. 2019. — Patterns of PCR amplification artifacts of the fungal barcode marker in a hybrid mushroom. *Frontiers in Microbiology* 10 (2686): 1-12. https://doi.org/10.3389/fmicb.2019.02686

ZIZKA V. M. A., LEESE F., PEINERT B. & GEIGER M. F. 2018. — DNA metabarcoding from sample fixative as a quick and voucher-preserving biodiversity assessment method. *Génome* 62: 122-136. https://doi.org/10.1139/gen-2018-0048

Soumis le 12 septembre 2022;
accepté le 18 juillet 2023;
publié le 14 février 2024.
ANNEXE 1. — Glossaire (d'après Creer et al. 2016; Deiner et al. 2017; Liu et al. 2020)

**ADN communautaire** : l'ADN extrait d'un mélange d'organismes représentant plusieurs espèces de spécimens collectés ensemble (bulk sample ou échantillon mixte) et séparés de l'échantillon environnemental.

**ADN environnemental (ADNe)** : ADN extrait d'échantillons environnementaux, tels que le sol, l'eau ou l'air, sans isolement préalable des organismes cibles, qui ont relâché de l'ADN dans leur environnement.

**Amplification par PCR** : utilisation de l'amplification en chaîne par polymérase ou PCR pour créer de nombreuses copies d'un fragment d'ADN cible avec une enzyme polymérase. Le fragment est ciblé à l'aide d'une paire d'amorces (avant et arrière) qui se lient au fragment flanquant d'ADN d'intérêt.

**Analyse bioinformatique** : application d'approches informatics pour le traitement de données biologiques complexes.

**Barcoding ADN** : identification des espèces grâce à un marqueur ADN consensuel couramment utilisé (e.g., le gène mitochondrial du cytochrome c oxydase I [COI] est le codebarres ADN standard pour identifier les espèces animales).

**Chimère** : artefact ADN constitué de deux séquences (ou plus) combinées au cours de l'étape d'amplification PCR.

**Échantillon mixte ou composite (i.e. bulk sample)** : échantillon contenant plusieurs spécimens collectés ensemble et non triés.

**Marqueur ADN** : tout gène ou fragment d'ADN ciblé dans le séquençage pour identifier une espèce, un individu ou un génotype.

**Metabarcoding ADN** : méthode d'identification taxonomique de plusieurs espèces, extraites à partir d'un échantillon d'ADN en vrac des spécimens du mélange d'espèces ou d'ADN environnemental, qui ont a été amplifiées par PCR et séquencées sur une plateforme à haut débit.

**Profondeur de séquençage** : le nombre de séquences d'ADN récupérées (généralement par échantillon) lors du séquençage à haut débit. La profondeur de séquençage est le nombre de lectures uniques qui incluent un nucléotide donné dans la séquence ou nombre de « reads » pour chaque position.

**Read ou lecture** : succession de paires bases lues par le séquenceur correspondant à tout ou à une partie d'un fragment d'ADN séquencé (la longueur varie selon la technologie).

**Séquençage à haut débit ou séquençage de nouvelle génération** : technique de séquençage permettant l'analyse simultané de millions de fragments d'ADN.

**Séquençage d'amplicons** : séquençage ciblé de fragments (i.e. gène marqueur) d'ADN amplifiés.

**Unité taxonomique opérationnelle moléculaire (MOTU)** : les MOTUs sont souvent utilisées comme indicateur des espèces taxonomiques dans les études de *metabarcoding* lorsqu'il n'est pas possible de déterminer avec précision les limites des espèces. Les groupes sont identifiés grâce à l'utilisation d'algorithmes de regroupement (clustering) fondé sur un pourcentage prédefini de similitude des séquences ADN de clusters et de la similitude des séquences ADN en pourcentage prédéfinie.