Wenhao LI,Mingshuo QIN,Xianglei HOU,et al.Application of eDNA Metabarcoding for Detecting Anura in North China[J].Asian Herpetological Research(AHR),2022,13(4):224-231.[doi:10.16373/j.cnki.ahr.220021]
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Application of eDNA Metabarcoding for Detecting Anura in North China
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Asian Herpetological Research[ISSN:2095-0357/CN:51-1735/Q]

Issue:
2022 VoI.13 No.4
Page:
224-231
Research Field:
Publishing date:
2022-12-20

Info

Title:
Application of eDNA Metabarcoding for Detecting Anura in North China
Author(s):
Wenhao LI2;  Mingshuo QIN2; 3;  Xianglei HOU2; 3;  Jiaqi ZHANG2; 3;  Siqi WANG2; 3;  Yu LI2; 3;  Zexu LUO2; 3;  Teng DENG2; 3;  Tianjian SONG2; 3;  Chunxia XU2; 3;  Xuan LIU2; 3;  Xuyu WANG4 and Yiming LI1; 2; 3*
1 School of Life Sciences, Institute of Life Sciences and Green Development, Hebei University, Baoding 071002, Hebei, China
2 Key Laboratory of Animal Ecology and Conservation Biology, Institute of Zoology, Chinese Academy of Sciences, Beijing 100101, China
3 University of Chinese Academy of Sciences, Beijing 100049, China
4 College of Ecology, Lanzhou University, Lanzhou 730000, Gansu, China
Keywords:
anura;  biodiversity;  eDNA;  metabarcoding
PACS:
-
DOI:
10.16373/j.cnki.ahr.220021
Abstract:
eDNA metabarcoding is an advanced method for monitoring biodiversity proposed in recent years. By analyzing DNA in water, soil and sediment samples, the technology obtains species distribution and population quantity information. It was found that macrobarcode technology is more accurate than the traditional method in measuring the species richness of some groups. In Europe, America and South America, the reliability of this technology in monitoring amphibian diversity in the wild was studied, and it was found to be better than traditional biodiversity monitoring methods in detecting species diversity. At present, amphibian monitoring mainly depends on various traditional methods, such as transects, drift fence traps, artificial shelters and mark-recapture. These monitoring techniques have many shortcomings, such as low accuracy and strong subjectivity of study results. These technologies have poor effects on rare, invasive and endangered species with strong concealment ability, low density and strong seasonality and are difficult to implement in sites inaccessible to people. Traditional monitoring technology also requires considerable investment of human and material resources, and the economic cost is relatively high, while eDNA metabarcoding ismore efficient and less costly, so it is important to use eDNA metabarcoding in amphibian monitoring in China. In this study, the eDNA metabarcoding and traditional line transect method (TLTM) were used to study the characteristics of the two methods in the Beijing-Tianjin-Hebei region. Repeated sampling was conducted on 58 waterbodies in July 2019 and June 2020. After sequencing the samples using high-throughput sequencing technology, the differences between metabarcoding and commonly used TLTM surveys in detecting the diversity of four amphibians in North China were assessed. Our results showed that eDNA metabarcoding is more sensitive to the detection of the four amphibian species in the sampling area, and the combined use of eDNA metabarcoding and TLTM can improve the survey results of amphibians in the survey area to the greatest extent. In addition, in the process of species classification and identification of metabarcoding results, 7 species of reptiles were detected, indicating that eDNA metabarcoding is also useful to detect reptiles. The results of this study indicate that metabarcoding in combination with TLTM can accurately estimate the diversity of amphibians in a short-term survey in North China and is also useful in reptile species detection.

References:

Bai C., Liu X., Fisher M. C., Garner T. W. J., Li Y. 2012. Global and endemic Asian lineages of the emerging pathogenic fungus Batrachochytrium dendrobatidis widely infect amphibians in China. Divers Distrib, 18: 307–318
Balint M., Nowak C., Marton O., Pauls S. U., Wittwer C., Aramayo J. L., Schulze A., Chambert T., Cocchiararo B., Jansen M. 2018. Accuracy, limitations and cost efficiency of eDNA-based community survey in tropical frogs. Mol Ecol Resour, 18: 1415–1426
Boessenkool S., Epp L. S., Haile J., Bellemain E., Edwards M., Coissac E., Willerslev E., Brochmann C. 2012. Blocking human contaminant DNA during PCR allows amplification of rare mammal species from sedimentary ancient DNA. Mol Ecol, 21: 1806–1815
Bohmann K., Evans A., Gilbert M., Carvalho G. R., Creer S., Knapp M., Yu D. W., Bruyn M. D. 2014. Environmental DNA for wildlife biology and biodiversity monitoring. Trends Ecol Evol, 29: 358–367
Boyer F., Mercier C., Bonin A., Bras Y. L., Taberlet P., Coissac E. 2016. Obitools: A unix‐inspired software package for DNA metabarcoding, Mol Ecol Resour. 16: 176–182
Burnham K. P., Anderson D. R., Laake J. L. 1980. Estimation of density from line transect sampling of biological populations. Wildlife monogr: 3–202
Calvignac-Spencer S., Merkel K., Kutzner N., Kühl H., Boesch C., Kappeler P. M., Metzger S., Schubert G., Leendertz F. H. 2013. Carrion fly-derived DNA as a tool for comprehensive and cost-effective assessment of mammalian biodiversity. Mol Ecol, 22: 915–924
Cilleros K., Valentini A., Allard L., Dejean T., Etienne R., Grenouillet G., Iribar A., Taberlet P., Vigouroux R., Brosse S. 2019. Unlocking biodiversity and conservation studies in high-diversity environments using environmental DNA (eDNA): A test with Guianese freshwater fishes. Mol Ecol Resour, 19: 27–46
Cristescu M. E., Hebert P. D. N. 2018. Uses and misuses of environmental dna in biodiversity science and conservation. Annu Rev Ecol Evol Syst, 49: 209–230
Deiner K., Bik H. M., Machler E., Seymour M., Lacoursiere-Roussel A., Altermatt F., Creer S., Bista I., Lodge D. M., Vere N., Pfrender M. E., Bernatchez L. 2017. Environmental DNA metabarcoding: Transforming how we survey animal and plant communities. Mol Ecol, 26: 5872–5895
Deiner K., Fronhofer E. A., Machler E., Walser E., Altermatt F. 2016. Environmental DNA reveals that rivers are conveyer belts of biodiversity information. Nat Commun, 7: 12544
Kristy D., Walser J. C., M?chler E., Altermatt F. 2015. Choice of capture and extraction methods affect detection of freshwater biodiversity from environmental DNA. Biol Conserv, 183: 53–63
Dejean T., Alice V., Christian M., Pierre T., Bellemain E., Miaud C. 2012. Improved detection of an alien invasive species through environmental DNA barcoding: The example of the American bullfrog Lithobates catesbeianus. J Appl Ecol, 49: 953–959
Aylagas E., ?ngel B., Rodríguez E. 2015. DNA metabarcoding as a novel technique for assessing good environmental status by measuring benthic biodiversity
Ficetola G. F., Coissac E., Zundel S., Riaz T., Shehzad W., Bessiere J., Taberlet P., Pompanon F. 2010. An in silico approach for the uation of DNA barcodes. BMC Genom, 11: 434
Ficetola G. F., Taberlet P., Coissac E. 2016. How to limit false positives in environmental DNA and metabarcoding? Mol Ecol Resour, 16: 604–607
Fonseca V. G. 2018. Pitfalls in relative abundance estimation using eDNA metabarcoding. Mol Ecol Resour, 18: 923–926
Gibson J. F., Shadi S., Colin C., Baird D. J., Monk W. A., Ian K., Mehrdad H., Diego F. 2015. Large-scale biomonitoring of remote and threatened ecosystems via high-throughput sequencing. PLoS One, 10: e0138432
Goldberg C. 2016. Critical considerations for the application of environmental DNA methods to detect aquatic species. Methods Ecol Evol, 7: 1299–1307
He L., Miao X., Lv G., Yang P., Wu W., Jia L. 2011. Nutritional rehabilitation of mitochondrial aberrations in aplastic anaemia. Br J Nutr, 105: 1180–1187
Hoffmann F. G., Storz J. F., Gorr T. A., Opazo J. C. 2010. Lineage-specific patterns of functional diversification in the alpha- and beta-globin gene families of tetrapod vertebrates. Mol Biol Evol, 27: 1126–1138
Li J., Hatton‐Ellis T. W., Handley L. L., Kimbell H. S., Benucci M., Peirson G., H?nfling B., Paiva V. 2019. Ground‐truthing of a fish‐based environmental DNA metabarcoding method for assessing the quality of lakes. J Appl Ecol, 56: 1232–1244
Li W., Hou X., Xu C., Qin M., Wang S., Wei L., Wang Y., Liu X., Li Y. 2021a. Validating eDNA measurements of the richness and abundance of anurans at a large scale. J Anim Ecol, 90: 1466–1479
Li W., Song T., Hou X., Qin M., Xu C., Li Y. 2021b. Application of eDNA Metabarcoding for Detecting Anura on a Tropical Island. diversity, 13(9): 440
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. Sci Rep, 8: 10361
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. Mol Ecol, 25: 527–541
R Core Team. 2015. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing: Vienna, Austria, Available online: http://www.R-project.org/
Rapha C., Tony D., Alice V., Nicolas R., Jean-Claude R., Aurélie B., Pierre T., Didier P., Carlos G. 2016. Spatial representativeness of environmental DNA metabarcoding signal for fish biodiversity assessment in a natural freshwater system. PLoS One, 11: e0157366
Ribeiro-Júnior M. A., Gardner T. A., ?vila-Pires T. C. S. 2008. uating the effectiveness of herpetofaunal sampling techniques across a gradient of habitat change in a tropical forest landscape. J Herpetol, 42: 733–749
Rosenzweig M. L. 1995. Species diversity in space and time. Cambridge: Cambridge University Press
Singer G. A. C., Fahner N. A., Barnes J. G., McCarthy A., Hajibabaei M. 2019. Comprehensive biodiversity analysis via ultra-deep patterned flow cell technology: A case study of eDNA metabarcoding seawater. Sci Rep, 9: 5991
Stat M., John J., DiBattista J. D., Newman S. J., Bunce M., Harvey E. S. 2019. Combined use of eDNA metabarcoding and video surveillance for the assessment of fish biodiversity. Conserv Biol, 33: 196–205
Thomsen P. F., Willerslev E. 2015. Environmental DNA–An emerging tool in conservation for monitoring past and present biodiversity. Biol Conserv, 183: 4–18
ntin R. E., Maslo B., Lockwood J. L., Pote J., Fonseca D. M. 2016. Real-time PCR assay to detect brown marmorated stink bug, Halyomorpha halys (Stal), in environmental DNA. Pest Manag Sci, 72: 1854–1861
ntini 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., Le Brun M., Moller P. R., Willerslev E., Dejean T. 2016. Next-generation monitoring of aquatic biodiversity using environmental DNA metabarcoding. Mol Ecol, 25: 929–942
Wang J., Liu P., Chang J., Li C., Xie F., Jiang J. P. 2022. Development of an eDNA metabarcoding tool for surveying the world’s largest amphibian. Curr Zool, 68: 608–614
Wang S., Zhu W., Gao X., Li X., Yan S., Liu X., Yang J., Gao Z., Li Y. 2014. Population size and time since island isolation determine genetic diversity loss in insular frog populations. Mol Ecol, 23: 637–648
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. Environ DNA, 1: 5–13

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Last Update: 2022-12-25