DOI: 10.5285/d93d9ae6-4eff-4d9a-9997-d0ed06c3df1b
title: Richness and abundance of plant and animal pathogenic fungi in Patagonian, sub-Antarctic and Maritime Antarctic soil samples collected January-February 2018

description: 57 soil samples were collected from 12 sites across Patagonia, the sub-Antarctic and the Maritime Antarctic between January and February 2018. The dataset presented here comprises two tables. The first shows the numbers of DNA reads of 105 genera of plant or animal pathogenic fungi in the soil samples. In the second table, for each of the 57 soil samples, seven bioclimatic variable predictors, six measures of soil chemistry, the total number of fungi reads and plant and animal pathogen richness and abundance are given, as well as the relative abundance of each fungal genus in each sample. The data in this second table were inputted into LASSO regression models and species indicator analyses.

This research was jointly funded by the NERC-CONICYT award (NE/P003079/1 and PII20150126, respectively) and Danish National Research Foundation award (VOLT, DNRF 168).
description: Soil samples were collected in January-February 2018 from 12 sites between Torres del Paine (Chile) and Lagotellerie Island (Maritime Antarctica). Four to five samples of barren soils (c. 20 g fresh weight) were sampled 1-2 m from Colobanthus quitensis plants into sterile containers that were stored at c. 4 °C before being frozen at -20 °C, prior to transfer to the lab, also at -20 °C.

The samples were defrosted and mixed. Soil pH value was measured in a suspension of 5 g soil and 20 ml of distilled water. Soil C/N ratio was calculated following measurement of total C and N in freeze-dried soils using a TruSpec CN analyser. Water-soluble carbon (C) and nitrogen (N) were extracted from 5 g of fresh soil in 25 ml distilled water for 60 min. After filtration through filter paper, the concentrations of total dissolved organic C were measured using a Shimadzu TOC-L CSH/CSN total organic C analyzer. Total dissolved N was measured with a FIAstar 5000 flow injection analyser after digesting extracts in sulphuric acid with selenium as the catalyst. Dissolved concentrations of NO3--N and NH4+-N were measured using a FIAstar 5000 flow injection analyser using cadmium reduction and indophenol blue and molybdenum blue methods, respectively.

DNA was extracted from 0.25 g subsamples of freeze-dried soil using a FastDNA Spin Kit for Soil. The internal transcribed spacer (ITS) 2 region of fungal DNA was amplified using the primers ITS4 and ITS7. PCR amplifications consisted of two steps: (i) primers and template DNA (1 µl) were added to Illustra puReTaq Ready-To-Go PCR Beads and were thermocycled at 94 °C for 2 min, then 35 cycles at 94 °C for 30 s, 56 °C for 30 s and 72 °C for 30 s, followed by an extension step at 72 °C for 5 min and (ii) 2 µl of the diluted PCR product was added to a reaction mixture consisting of 0.15 µl DNA polymerase (AccuPrimeTM Taq DNA Polymerase High Fidelity, Invitrogen), 2 µl 10 x AccuPrimeTM PCR Buffer II, 1 µl (770 nM) of custom-tagged forward and reverse primers and 13.85 µl sterile water. Mixtures were thermocycled at 94 °C for 2 min, then 14 cycles at 94 °C for 30 s, 56 °C for 30 s and 72 °C for 30 s, followed by an extension step at 72 °C for 5 min. Amplicons were purified in 1% agarose gels using a Montage Gel Extraction Kit. The concentrations of amplicons were measured using Qubit dsDNA HS assays and fluorescence was measured using a Qubit fluorometer. Samples, pooled in equimolar amounts, were paired-end sequenced over two Illumina MiSeq flowcells.

Demultiplexed sequences from each sample were trimmed of primers using cutadapt 1.18. DADA2 v1.22.0 was subsequently used in R v 4.1.1 for quality filtering, amplicon sequence variant inference and taxon assignment. Quality filtering removed all sequences with ambiguous bases (trncQ=11), &gt;2 expected errors and lengths &lt;50 bp. Error rates were estimated for forward and reverse sequences and amplicon sequence variants inferred before merging with a 30 base pair minimum overlap. Chimeras were removed and taxonomies assigned to resulting amplicon sequence variants using the RDP classifier with the sh_general_release-dynamimc_all_25.07.2023 reference from UNITE. The decontam v1.14.0 package was then used to remove putative contaminants identified in negative controls (threshold value 1.0). Taxonomies and occurrences of the removed amplicon sequence variants were cross-checked through manual inspection. The taxonomy-assigned, decontaminated amplicon sequence variants were clustered into operational taxonomic units (OTUs) at 98% similarity over 90% of sequence length using the BLASTCLUST algorithm in the package blast-legacy v2.2.26. For each OTU, the taxonomies for its constituent amplicon sequence variants were compared to validate the OTU taxonomy used for all subsequent analyses.

Less than half of the OTUs could be assigned to species, and hence assignments of taxa to plant or animal pathogenic fungal guilds were made at the genus level. Manual searches were made in Web of Science using all genus names and 'pathogen*' (with the asterisk representing a wild card) or 'disease' as terms, with a genus being assigned to a guild when there was evidence that members of it cause, or are associated with, diseases of plants or animals. Eight of the genera were assigned to both guilds.

Seven climatic predictors for each site, based on a 30 arc sec resolution for 1981-2010, were extracted from the CHELSA database v. 2.1 using the terra v 1.6-47 package in the R environment v 4.1.3. The predictors were BIO1 (mean annual air temperature, in °C), BIO4 (air temperature seasonality, unitless), BIO5 (maximum air temperature of the warmest month, in °C), BIO6 (minimum air temperature of the coldest month, in °C), BIO7 (annual range in air temperature, in °C), BIO12 (annual precipitation, in m yr-1) and BIO15 (precipitation seasonality, unitless).

Projected values of BIO1, BIO5, BIO6 and BIO12 for 2071-2100 were extracted from the CHELSA-BIOCLIM+ database, again based on a 30 arc sec resolution, using predictions from five Earth system models (mri-esm2-0, ukesm1-0-ll, gfdl-esm4, ipsl-cm6a-lr and mpi-esm1-2-hr) for three shared socioeconomic pathway (SSP) scenarios (SSP5-8.5, SSP3-7.0 and SSP1-2.6), which assume very high, high and low greenhouse gas emissions to the atmosphere, respectively.
description: Instruments 

Qubit fluorometer (Thermofisher, USA)
Illumina MiSeq System (Illumina, San Diego, California, USA)
TruSpec CN analyser Leco (St Joseph, MI, USA)
Shimadzu TOC-L CSH/CSN total organic C analyzer (Shimadzu, Kyoto, Japan)
FIAstar 5000 flow injection analyser (Foss Tecator, Hoganas, Sweden)

Software

R versions 4.1.1 and 4.1.3
R packages: terra v 1.6-47, glmnet v4.1-8, DADA2 v1.22.0, decontam v1.14.0 and indicspecies v1.7.15
Python: cutadapt 1.18
UNITE: RDP classifier with sh_general_release-dynamimc_all_25.07.2023
blast-legacy v2.2.26
Web of Science
CHELSA database v. 2.1
CHELSA-BIOCLIM+ database
iNEXT package
description: Raw data are shown. The data have not been cleaned.