Metabarcoding of soil environmental DNA to estimate plant diversity globally

. 2023 ; 14 () : 1106617. [epub] 20230418

Status PubMed-not-MEDLINE Jazyk angličtina Země Švýcarsko Médium electronic-ecollection

Typ dokumentu časopisecké články

Perzistentní odkaz   https://www.medvik.cz/link/pmid37143888

INTRODUCTION: Traditional approaches to collecting large-scale biodiversity data pose huge logistical and technical challenges. We aimed to assess how a comparatively simple method based on sequencing environmental DNA (eDNA) characterises global variation in plant diversity and community composition compared with data derived from traditional plant inventory methods. METHODS: We sequenced a short fragment (P6 loop) of the chloroplast trnL intron from from 325 globally distributed soil samples and compared estimates of diversity and composition with those derived from traditional sources based on empirical (GBIF) or extrapolated plant distribution and diversity data. RESULTS: Large-scale plant diversity and community composition patterns revealed by sequencing eDNA were broadly in accordance with those derived from traditional sources. The success of the eDNA taxonomy assignment, and the overlap of taxon lists between eDNA and GBIF, was greatest at moderate to high latitudes of the northern hemisphere. On average, around half (mean: 51.5% SD 17.6) of local GBIF records were represented in eDNA databases at the species level, depending on the geographic region. DISCUSSION: eDNA trnL gene sequencing data accurately represent global patterns in plant diversity and composition and thus can provide a basis for large-scale vegetation studies. Important experimental considerations for plant eDNA studies include using a sampling volume and design to maximise the number of taxa detected and optimising the sequencing depth. However, increasing the coverage of reference sequence databases would yield the most significant improvements in the accuracy of taxonomic assignments made using the P6 loop of the trnL region.

Center of Mycology and Microbiology University of Tartu Tartu Estonia

Departamento de Biología Agrícola Facultad de Agronomía y Veterinaria Universidad Nacional de Río Cuarto Córdoba Argentina

Department of Biology Nakhon Phanom University Nakhon Phanom Thailand

Department of Botany University of Tartu Tartu Estonia

Department of Ecology Swedish University of Agricultural Sciences Uppsala Sweden

Department of Geography and Environmental Studies Stellenbosch University Stellenbosch South Africa

Department of Natural Resource Sciences Thompson Rivers University Kamloops BC Canada

Department of Wildlife Management and Ecotourism University of Namibia Katima Mulilo Namibia

Ecologie et Dynamique des Systèmes Anthropisés Jules Verne University of Picardie Amiens France

Faculty of Science University of South Bohemia České Budějovice Czechia

Grupo de Microbiología Ambiental y Grupo BioMicro Escuela de Microbiología Universidad de Antioquia UdeA Medellín Colombia

Iluka Chair in Vegetation Science and Biogeography Harry Butler Institute Murdoch University Perth WA Australia

Institute of Botany The Czech Academy of Sciences Třeboň Czechia

Institute of Ecology and Earth Sciences University of Tartu Tartu Estonia

Institute of Forestry and Engineering Estonian University of Life Sciences Tartu Estonia

Instituto de Biología Universidad Nacional Autónoma de México Ciudad de México Mexico

Instituto Multidisciplinario de Biología Vegetal Universidad Nacional de Córdoba CONICET Córdoba Argentina

Zoology Department College of Science King Saud University Riyadh Saudi Arabia

Zobrazit více v PubMed

Banchi E., Ametrano C. G., Greco S., Stanković D., Muggia L., Pallavicini A. (2020). PLANiTS: a curated sequence reference dataset for plant ITS DNA metabarcoding. Database 2020. doi: 10.1093/database/baz155 PubMed DOI PMC

Barnes C. J., Nielsen I. B., Aagaard A., Ejrnæs R., Hansen A. J., Frøslev T. G. (2022). Metabarcoding of soil environmental DNA replicates plant community variation but not specificity. Environ. DNA 4 (4), 732–746. doi: 10.1002/edn3.287 DOI

Bell K. L., Loeffler V. M., Brosi B. J. (2017). An rbcL reference library to aid in the identification of plant species mixtures by DNA metabarcoding. Appl. Plant Sci. 5 (3), 1600110. doi: 10.3732/apps.1600110 PubMed DOI PMC

Bolger A. M., Lohse M., Usadel B. (2014). Trimmomatic: a flexible trimmer for illumina sequence data. Bioinformatics 30 (15), 2114–2120. doi: 10.1093/bioinformatics/btu170 PubMed DOI PMC

Bousquin J. (2021). Discrete global grid systems as scalable geospatial frameworks for characterizing coastal environments. Environ. Model. Software 146, 105210. doi: 10.1016/j.envsoft.2021.105210 PubMed DOI PMC

Brummitt N., Araújo A. C., Harris T. (2021). Areas of plant diversity–what do we know? Plants People Planet 3 (1), 33–44. doi: 10.1002/ppp3.10110 DOI

Cai L., Kreft H., Taylor A., Denelle P., Schrader J., Essl F., et al. . (2022). Machine learning improves global models of plant diversity. bioRxiv. doi: 10.1101/2022.04.08.487610 PubMed DOI

Cai L., Kreft H., Taylor A., Denelle P., Schrader J., Essl F., et al. . (2023). Global models and predictions of plant diversity based on advanced machine learning techniques. New Phytologist. 237 (4), 1432–1445. doi: 10.1111/nph.18533 PubMed DOI

Calderón-Sanou I., Munkemuller T., Boyer F., Zinger L., Thuiller W. (2020). From environmental DNA sequences to ecological conclusions: how strong is the influence of methodological choices? J. Biogeogr. 47, 193–206. doi: 10.1111/jbi.13681 DOI

Camacho C., Coulouris G., Avagyan V., Ma N., Papadopoulos J., Bealer K., et al. . (2009). BLAST+: architecture and applications. BMC Bioinf. 10 (1), 1–9. doi: 10.1186/1471-2105-10-421 PubMed DOI PMC

Carini P., Marsden P. J., Leff J. W., Morgan E. E., Strickland M. S., Fierer N. (2016). Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2 (3), 16242. doi: 10.1038/nmicrobiol.2016.242 PubMed DOI

Cristescu M. E., Hebert P. D. N. (2018). Uses and misuses of environmental DNA in biodiversity science and conservation. Annual Review of Ecology, Evolution, and Systematics 49, 209–230. doi: 10.1146/annurev-ecolsys-110617-062306 DOI

Davison J., Vasar M., Sepp S. K., Oja J., Al-Quraishy S., Bueno C. G., et al. . (2022). Dominance, diversity, and niche breadth in arbuscular mycorrhizal fungal communities. Ecology 103 (9), e3761. doi: 10.1002/ecy.3761 PubMed DOI

Dutilleul P., Clifford P., Richardson S., Hemon D. (1993). Modifying the t test for assessing the correlation between two spatial processes. Biometrics 49, 305–314. doi: 10.2307/2532625 PubMed DOI

Edwards M. E., Alsos I. G., Yoccoz N., Coissac E., Goslar T., Gielly L., et al. . (2018). Metabarcoding of modern soil DNA gives a highly local vegetation signal in Svalbard tundra. Holocene 28 (12), 2006–2016. doi: 10.1177/0959683618798095 DOI

Foster N. R., van Dijk K.-j., Biffin E., Young J. M., Thomson V. A., Gillanders B. M., et al. . (2021). A multi-gene region targeted capture approach to detect plant DNA in environmental samples: a case study from coastal environments. Front. Ecol. Evol. 9. doi: 10.3389/fevo.2021.735744 DOI

Hiiesalu I., Klimešová J., Doležal J., Mudrák O., Götzenberger L., Horník J., et al. . (2021). Hidden below-ground plant diversity buffers against species loss during land-use change in species-rich grasslands. J. Vegetation Sci. 32 (1), e12971. doi: 10.1111/jvs.12971 DOI

Hiiesalu I., Öpik M., Metsis M., Lilje L., Davison J., Vasar M., et al. . (2012). Plant species richness belowground: higher richness and new patterns revealed by next-generation sequencing. Mol. Ecol. 21 (8), 2004–2016. doi: 10.1111/j.1365-294X.2011.05390.x PubMed DOI

Hiiesalu I., Pärtel M., Davison J., Gerhold P., Metsis M., Moora M., et al. . (2014). Species richness of arbuscular mycorrhizal fungi: associations with grassland plant richness and biomass. New Phytol. 203 (1), 233–244. doi: 10.1111/nph.12765 PubMed DOI

Karger D. N., Conrad O., Böhner J., Kawohl T., Kreft H., Soria-Auza R. W., et al. . (2017). Climatologies at high resolution for the earth’s land surface areas. Sci. Data 4 (1), 1–20. doi: 10.1038/sdata.2017.122 PubMed DOI PMC

Keppel G., Craven D., Weigelt P., Smith S. A., van der Sande M. T., Sandel B., et al. . (2021). Synthesizing tree biodiversity data to understand global patterns and processes of vegetation. J. Vegetation Sci. 32, e13021. doi: 10.1111/jvs.13021 DOI

Kolter A., Gemeinholzer B. (2021). Plant DNA barcoding necessitates marker-specific efforts to establish more comprehensive reference databases. Genome 64 (3), 265–298. doi: 10.1139/gen-2019-0198 PubMed DOI

Kreft H., Jetz W. (2007). Global patterns and determinants of vascular plant diversity. Proc. Natl. Acad. Sci. United States America 104 (14), 5925–5930. doi: 10.1073/pnas.0608361104 PubMed DOI PMC

Leff J. W., Bardgett R. D., Wilkinson A., Jackson B. G., Pritchard W. J., De Long J. R., et al. . (2018). Predicting the structure of soil communities from plant community taxonomy, phylogeny, and traits. ISME J. 12 (7), 1794–1805. doi: 10.1038/s41396-018-0089-x PubMed DOI PMC

Magoč T., Salzberg S. L. (2011). FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963. doi: 10.1093/bioinformatics/btr507 PubMed DOI PMC

Mallott E. K., Garber P. A., Malhi R. S. (2018). trnL outperforms rbcL as a DNA metabarcoding marker when compared with the observed plant component of the diet of wild white-faced capuchins (Cebus capucinus, primates). PloS One 13 (6), e0199556. doi: 10.1371/journal.pone.0199556 PubMed DOI PMC

McMurdie P. J., Holmes S. (2014). Waste not, want not: why rarefying microbiome data is inadmissible. PloS Comput. Biol. 10 (4), e1003531. doi: 10.1371/journal.pcbi.1003531 PubMed DOI PMC

Mokany K., McCarthy J. K., Falster D. S., Gallagher R. V., Harwood T. D., Kooyman R., et al. . (2022). Patterns and drivers of plant diversity across Australia. Ecography 11, e06426. doi: 10.1111/ecog.06426 DOI

Oksanen J., Blanchet F. G., Kindt R., Legendre P., Minchin P. R., O’hara R. B., et al. . (2013). Package ‘vegan’. Community Ecol. Package version 2, 1–295.

Olson D. M., Dinerstein E., Wikramanayake E. D., Burgess N. D., Powell G. V. N., Underwood E. C., et al. . (2001). Terrestrial ecoregions of the world: a new map of life on earth. BioScience 51, 933–938. doi: 10.1641/0006-3568(2001)051[0933:TEOTWA]2.0.CO;2 DOI

Rijal D. P., Heintzman P. D., Lammers Y., Yoccoz N. G., Lorberau K. E., Pitelkova I., et al. . (2021). Sedimentary ancient DNA shows terrestrial plant richness continuously increased over the Holocene in northern fennoscandia. Sci. Adv. 7 (31), eabf9557. doi: 10.1126/sciadv.abf9557 PubMed DOI PMC

Rognes T., Flouri T., Nichols B., Quince C., Mahé F. (2016). VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584. doi: 10.7717/peerj.2584 PubMed DOI PMC

Sabatini F. M., Jiménez-Alfaro B., Jandt U., Chytrý M., Field R., Kessler M., et al. . (2022). Global patterns of vascular plant alpha diversity. Nat. Commun. 13 (1), 1–16. doi: 10.1038/s41467-022-32063-z PubMed DOI PMC

Sepp S.-K., Davison J., Moora M., Neuenkamp L., Oja J., Roslin T., et al. . (2021). Woody encroachment in grassland elicits complex changes in the functional structure of above- and belowground biota. Ecosphere 12 (5), e03512. doi: 10.1002/ecs2.3512 DOI

Taberlet P., Coissac E., Pompanon F., Gielly L., Miquel C., Valentini A., et al. . (2007). Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 35 (3), e14. doi: 10.1093/nar/gkl938 PubMed DOI PMC

Taberlet P., Prud’Homme S. M., Campione E., Roy J., Miquel C., Shehzad W., et al. . (2012). Soil sampling and isolation of extracellular DNA from large amount of starting material suitable for metabarcoding studies. Mol. Ecol. 21 (8), 1816–1820. doi: 10.1111/j.1365-294X.2011.05317.x PubMed DOI

Tamme R., Pärtel M., Kõljalg U., Laanisto L., Liira J., Mander Ü., et al. . (2021). Global macroecology of nitrogen-fixing plants. Global Ecol. Biogeogr. 30 (2), 514–526. doi: 10.1111/geb.13236 DOI

Träger S., Öpik M., Vasar M., Wilson S. D. (2019). Belowground plant parts are crucial for comprehensively estimating total plant richness in herbaceous and woody habitats. Ecology 100 (2), e02575. doi: 10.1002/ecy.2575 PubMed DOI

Vallejos R., Osorio F., Bevilacqua M. (2020). Spatial relationships between two georeferenced variables: with applications in R (Berlin/Heidelberg, Germany: Springer Nature; ). doi: 10.1007/978-3-030-56681-4 DOI

Vasar M., Davison J., Neuenkamp L., Sepp S.-K., Young J. P. W., Moora M., et al. . (2021). User-friendly bioinformatics pipeline gDAT (graphical downstream analysis tool) for analysing rDNA sequences. Mol. Ecol. Resour. 21 (4), 1380–1392. doi: 10.1111/1755-0998.13340 PubMed DOI

Vasar M., Davison J., Sepp S.-K., Mucina L., Oja J., Al-Quraishy S., et al. . (2022). Global soil microbiomes: a new frontline of biome-ecology research. Global Ecol. Biogeogr. 31 (6), 1120–1132. doi: 10.1111/geb.13487 DOI

Wang Y., Pedersen M. W., Alsos I. G., De Sanctis B., Racimo F., Prohaska A., et al. . (2021). Late quaternary dynamics of Arctic biota from ancient environmental genomics. Nature 600 (7887), 86–92. doi: 10.1038/s41586-021-04016-x PubMed DOI PMC

Willerslev E., Davison J., Moora M., Zobel M., Coissac E., Edwards M. E., et al. . (2014). Fifty thousand years of Arctic vegetation and megafaunal diet. Nature 506 (7486), 47–51. doi: 10.1038/nature12921 PubMed DOI

Wood S. N. (2003). Thin plate regression splines. J. R. Stat. Society: Ser. B (Statistical Methodology) 65, 95–114. doi: 10.1111/1467-9868.00374 DOI

Yoccoz N. G., Brathen K. A., Gielly L., Haile J., Edwards M. E., Goslar T., et al. . (2012). DNA From soil mirrors plant taxonomic and growth form diversity. Mol. Ecol. 21 (15), 3647–3655. doi: 10.1111/j.1365-294X.2012.05545.x PubMed DOI

Najít záznam

Citační ukazatele

Nahrávání dat ...

Možnosti archivace

Nahrávání dat ...