Effects of soil preservation for biodiversity monitoring using environmental DNA

Environmental DNA (eDNA) metabarcoding is becoming a key tool for biodiversity monitoring over large geographical or taxonomic scales and for elusive taxa such as soil organisms. Increasing sample sizes and interest in remote or extreme areas often require the preservation of soil samples and thus deviations from optimal standardized protocols. However, we still ignore the impact of different methods of soil sample preservation on the results of metabarcoding studies and there is no guideline for best practices so far. Here, we assessed the impact of four methods of soil sample preservation that can be conveniently used also in metabarcoding studies targeting remote or difficult to access areas. Tested methods include: preservation at room temperature for 6 hr, preservation at 4°C for 3 days, desiccation immediately after sampling and preservation for 21 days, and desiccation after 6 hr at room temperature and preservation for 21 days. For each preservation method, we benchmarked resulting estimates of taxon diversity and community composition of three different taxonomic groups (bacteria, fungi and eukaryotes) in three different habitats (forest, river bank and grassland) against results obtained under ideal conditions (i.e., extraction of eDNA immediately after sampling). Overall, the different preservation methods only marginally impaired results and only under certain conditions. When rare taxa were considered, we detected small but significant changes in molecular operational taxonomic units (MOTU) richness of bacteria, fungi and eukaryotes across treatments, but MOTU richness was similar across preservation methods if rare taxa were not considered. All the approaches were able to identify differences in community structure among habitats, and the communities retrieved using the different preservation conditions were extremely similar. We propose guidelines on the selection of the optimal soil sample preservation conditions for metabarcoding studies, depending on the practical constraints, costs and ultimate research goals.

[1]  F. Denoeud,et al.  The Treasure Vault Can be Opened: Large-Scale Genome Skimming Works Well Using Herbarium and Silica Gel Dried Material , 2020, Plants.

[2]  J. Bever,et al.  Keeping it cool: Soil sample cold pack storage and DNA shipment up to 1 month does not impact metabarcoding results , 2020, Ecology and evolution.

[3]  Richard C. Edmunds,et al.  Enhancing tropical conservation and ecology research with aquatic environmental DNA methods: an introduction for non‐environmental DNA specialists , 2020, Animal Conservation.

[4]  G. Ficetola,et al.  Numerical methods for sedimentary‐ancient‐DNA‐based study on past biodiversity and ecosystem functioning , 2020, Environmental DNA.

[5]  V. Elbrecht.  Environmental DNA: For Biodiversity Research and Monitoring. By Pierre Taberlet, Aurélie Bonin, Lucie Zinger, and Eric Coissac. Oxford and New York: Oxford University Press. $80.00 (hardcover); $39.95 (paper). xiii + 253 p.; ill.; index. ISBN: 978-0-19-876722-0 (hc); 978-0-19-876728-2 (pb). 2018. , 2020 .

[6]  M. Gaither,et al.  A practical guide to sample preservation and pre‐PCR processing of aquatic environmental DNA , 2020, Molecular ecology resources.

[7]  E. Coissac,et al.  Assessing the shared variation among high-dimensional data matrices: a modified version of the Procrustean correlation coefficient , 2019, bioRxiv.

[8]  W. Thuiller,et al.  From environmental DNA sequences to ecological conclusions: How strong is the influence of methodological choices? , 2019, Journal of Biogeography.

[9]  Noah Fierer,et al.  DNA metabarcoding—Need for robust experimental designs to draw sound ecological conclusions , 2019, Molecular ecology.

[10]  P. Taberlet,et al.  Optimizing environmental DNA sampling effort for fish inventories in tropical streams and rivers , 2019, Scientific Reports.

[11]  P. Taberlet,et al.  Body size determines soil community assembly in a tropical forest. , 2018, Molecular ecology.

[12]  P. Legendre,et al.  Box–Cox‐chord transformations for community composition data prior to beta diversity analysis , 2018 .

[13]  Beth Shapiro,et al.  Minimizing polymerase biases in metabarcoding. , 2018, Molecular ecology resources.

[14]  R. Halvorsen,et al.  Sequence clustering threshold has little effect on the recovery of microbial community structure , 2018, Molecular ecology resources.

[15]  S. Collins,et al.  Drought consistently alters the composition of soil fungal and bacterial communities in grasslands from two continents , 2018, Global change biology.

[16]  H. Buckley,et al.  Towards robust and repeatable sampling methods in eDNA‐based studies , 2018, Molecular ecology resources.

[17]  Dezhu Li,et al.  A comparison of different methods for preserving plant molecular materials and the effect of degraded DNA on ddRAD sequencing , 2018, Plant diversity.

[18]  O. Diserud,et al.  Environmental DNA filtration techniques affect recovered biodiversity , 2018, Scientific Reports.

[19]  Basten L. Snoek,et al.  Drought Legacy Effects on the Composition of Soil Fungal and Prokaryote Communities , 2018, Front. Microbiol..

[20]  P. Taberlet,et al.  Environmental DNA: For Biodiversity Research and Monitoring , 2018 .

[21]  N. Fierer,et al.  A global atlas of the dominant bacteria found in soil , 2018, Science.

[22]  Casper W. Berg,et al.  glmmTMB Balances Speed and Flexibility Among Packages for Zero-inflated Generalized Linear Mixed Modeling , 2017, R J..

[23]  S. Knudsen,et al.  Comparison of capture and storage methods for aqueous macrobial eDNA using an optimized extraction protocol: advantage of enclosed filter , 2017 .

[24]  R. Standish,et al.  Sample storage conditions alter colonisation structures of arbuscular mycorrhizal fungi and, particularly, fine root endophyte , 2017, Plant and Soil.

[25]  R. Kulkarni,et al.  Corrigendum: IRS1 deficiency protects β-cells against ER stress-induced apoptosis by modulating sXBP-1 stability and protein translation , 2016, Scientific Reports.

[26]  I. Schöning,et al.  Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests , 2016, Scientific Reports.

[27]  Karoline Faust,et al.  Millions of reads, thousands of taxa: microbial community structure and associations analyzed via marker genes. , 2016, FEMS microbiology reviews.

[28]  O. Paliy,et al.  Application of multivariate statistical techniques in microbial ecology , 2016, Molecular ecology.

[29]  G. Schubert,et al.  Aquatic biodiversity assessment for the lazy , 2016, Molecular ecology.

[30]  P. Taberlet,et al.  obitools: a unix‐inspired software package for DNA metabarcoding , 2016, Molecular ecology resources.

[31]  M. Úriz,et al.  Deep-Sea, Deep-Sequencing: Metabarcoding Extracellular DNA from Sediments of Marine Canyons , 2015, PloS one.

[32]  P. Bork,et al.  Eukaryotic plankton diversity in the sunlit ocean , 2015, Science.

[33]  P. Taberlet,et al.  Replication levels, false presences and the estimation of the presence/absence from eDNA metabarcoding data , 2015, Molecular ecology resources.

[34]  A. Jumpponen,et al.  Scraping the bottom of the barrel: are rare high throughput sequences artifacts? , 2015 .

[35]  C. Takacs-Vesbach,et al.  Bacterial community composition of divergent soil habitats in a polar desert. , 2014, FEMS microbiology ecology.

[36]  R. Ambrosini,et al.  Effect of preservation method on the assessment of bacterial community structure in soil and water samples. , 2014, FEMS Microbiology Letters.

[37]  I. Burke,et al.  Is bacterial moisture niche a good predictor of shifts in community composition under long-term drought? , 2014, Ecology.

[38]  Douglas W. Yu,et al.  Reliable, verifiable and efficient monitoring of biodiversity via metabarcoding. , 2013, Ecology letters.

[39]  Fernando Azpiroz,et al.  Storage conditions of intestinal microbiota matter in metagenomic analysis , 2012, BMC Microbiology.

[40]  P. Taberlet,et al.  Environmental DNA , 2012, Molecular ecology.

[41]  V. I. Gusarov,et al.  New environmental metabarcodes for analysing soil DNA: potential for studying past and present ecosystems , 2012, Molecular ecology.

[42]  P. Taberlet,et al.  Towards next‐generation biodiversity assessment using DNA metabarcoding , 2012, Molecular ecology.

[43]  Eric Coissac,et al.  OligoTag: a program for designing sets of tags for next-generation sequencing of multiplexed samples. , 2012, Methods in molecular biology.

[44]  Pierre Legendre,et al.  Numerical Ecology with R , 2011 .

[45]  Tommi Aho,et al.  Storage of environmental samples for guaranteeing nucleic acid yields for molecular microbiological studies , 2010, Applied Microbiology and Biotechnology.

[46]  E. Bååth,et al.  Growth response of the bacterial community to pH in soils differing in pH. , 2010, FEMS microbiology ecology.

[47]  Rob Knight,et al.  Effect of storage conditions on the assessment of bacterial community structure in soil and human-associated samples. , 2010, FEMS microbiology letters.

[48]  François Pompanon,et al.  An In silico approach for the evaluation of DNA barcodes , 2010, BMC Genomics.

[49]  Korbinian Strimmer,et al.  fdrtool: a versatile R package for estimating local and tail area-based false discovery rates , 2008, Bioinform..

[50]  K. Scow,et al.  Soil Water Content and Organic Carbon Availability Are Major Determinants of Soil Microbial Community Composition , 2004, Microbial Ecology.

[51]  E. Bååth,et al.  Temperature-dependent changes in the soil bacterial community in limed and unlimed soil. , 2003, FEMS microbiology ecology.

[52]  Y. Benjamini,et al.  Controlling the false discovery rate: a practical and powerful approach to multiple testing , 1995 .

[53]  E. Bååth,et al.  Growth rate and response of bacterial communities to pH in limed and ash treated forest soils , 1994 .

[54]  K. R. Clarke,et al.  Non‐parametric multivariate analyses of changes in community structure , 1993 .

[55]  F. Famoye,et al.  Generalized poisson regression model , 1992 .

[56]  M. Chase,et al.  Silica gel: An ideal material for field preservation of leaf samples for DNA studies , 1991 .

[57]  D. M. Ward,et al.  16S rRNA sequences reveal numerous uncultured microorganisms in a natural community , 1990, Nature.

[58]  S. Giovannoni,et al.  Genetic diversity in Sargasso Sea bacterioplankton , 1990, Nature.

[59]  V. Okore,et al.  EFFECT OF' STORAGE CONDITIONS ON THE , 1990 .

[60]  C. Braak.  Canonical Correspondence Analysis: A New Eigenvector Technique for Multivariate Direct Gradient Analysis , 1986 .