Illumina 16S rRNA Gene Sequencing Dataset of Bacterial Communities of Soil Associated with Ironwood Trees (Casuarina equisetifolia) in Guam
1
Department of Entomology, Louisiana State University Agricultural Center, Baton Rouge, LA 70803, USA
2
College of Natural & Applied Sciences, University of Guam, Cooperative Extension Service, Mangilao 96923, Guam
*
Author to whom correspondence should be addressed.
Data 2024, 9(4), 54; https://doi.org/10.3390/data9040054
Submission received: 29 January 2024
/
Revised: 20 March 2024
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Accepted: 1 April 2024
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Published: 7 April 2024
(This article belongs to the Section Computational Biology, Bioinformatics, and Biomedical Data Science)
Abstract
:Ironwood trees, which are of great importance for the economy and environment of tropical areas, were first discovered to suffer from a slow progressive dieback in Guam in 2002, later referred to as ironwood tree decline (IWTD). A variety of biotic factors have been shown to be associated with IWTD, including putative bacterial pathogens Ralstonia solanacearum and Klebsiella species (K. variicola and K. oxytoca), the fungus Ganoderma australe, and termites. Due to the soilborne nature of these pathogens, soil microbiomes have been suggested to be a significant factor influencing tree health. In this project, we sequenced the microbiome in the soil collected from the root region of healthy ironwood trees and those showing signs of IWTD to evaluate the association between the bacterial community in soil and IWTD. This dataset contains 4,782,728 raw sequencing reads present in soil samples collected from thirty-nine ironwood trees with varying scales of decline severity in Guam obtained via sequencing the V1–V3 region of the 16S rRNA gene on the Illumina NovaSeq (2 × 250 bp) platform. Sequences were taxonomically assigned in QIIME2 using the SILVA 132 database. Firmicutes and Actinobacteria were the most dominant phyla in soil. Differences in soil microbiomes were detected between limestone and sand soil parent materials. No putative plant pathogens of the genera Ralstonia or Klebsiella were found in the samples. Bacterial diversity was not linked to parameters of IWTD. The dataset has been made publicly available through NCBI GenBank under BioProject ID PRJNA883256. This dataset can be used to compare the bacterial taxa present in soil associated with ironwood trees in Guam to bacteria communities of other geographical locations to identify microbial signatures of IWTD. In addition, this dataset can also be used to investigate the relationship between soil microbiomes and the microbiomes of ironwood trees as well as those of the termites which attack ironwood trees.
Dataset: Repository name: National Center for Biotechnology Information. Data identification number: BioProject ID PRJNA883256 [Accession Numbers: SRX20942017-SRX20942059]. Direct URL to data: https://www.ncbi.nlm.nih.gov/sra?LinkName=bioproject_sra_all&from_uid=883256 (accessed on 1 September 2023).
1. Summary
The decline of ironwood trees (Casuarina equisetifolia) in Guam has been investigated extensively since it was first detected in 2002 [1]. A complex of factors was found to be associated with IWTD including the presence of the wilt-causing bacteria Ralstonia solanacearum, wetwood bacteria of the genus Klebsiella, the fungus Ganoderma australe, as well as attacks by numerous termite species [2,3]. Soilborne pathogens and soil microbiome composition have been previously reported to be linked to the decline of trees other than ironwood [4,5]. This project aims at the assessment of bacterial compositions in soil collected from the root region of healthy and declining ironwood trees in Guam using metataxanomics. Bacterial communities were described from soil samples that were collected from thirty-nine ironwood trees at seventeen different locations on the island of Guam. The climate of Guam is tropical marine. The collection dates were in February and March, 2022, which is the dry season of the island with one-third of annual rainfall of 84–116 inches [6]. The temperature range during collection was 76–87 °F. The collection sites were spread over the entire island, which encompasses five distinct soil orders. Entisols are prevalent in the soil type called Guam series on Northern Guam, which is shallow soil covering coral limestone, while a combination of Oxisols, Mollisols, and Alfisols characterizes the volcanic terrain in Southern Guam, composing Pulantat, Atate and Akina series. Inceptisols are typically formed in the bottom lands of southern Guam [7]. The soil moistures and parent materials (PM) are listed in Table 1. Briefly, the soil of collection locations on limestone consists of clay, sand, and silt, with a cation exchange capacity (CEC) of 20–45 meq/100 g, pH values of 6.6–7.8, and organic matters (OM) of 8–15 Pct; the soil belonging to the sand PM category is dominant in sand and alkaline, with the lowest CEC < 10 meq/100 g and the lowest OM < 5 Pct; and the soil belonging to the tuff PM category is rich in clay and acidic, with a CEC of 10–50 meq/100 g, and a OM of 5–10 Pct [8]. Bacterial community composition and diversity were determined via amplicon sequencing using the V1–V3 region of the 16S rRNA gene on the Illumina NovaSeq (2 × 250 bp) platform following the Illumina Nextera Dilute library protocol and evaluated using QIIME 2 version 2022.2 with SILVA 132 as the taxonomic reference database and iNEXT. The dataset in the present study was generated to test whether the soil collected from under ironwood trees in Guam contained putative pathogenic bacteria, such as the genera Ralstonia and Klebsiella, and whether factors, such as Ralstonia presence and decline severity of ironwood trees had an effect on bacterial diversity.
2. Data Description
The V1–V3 variable region of the 16S rRNA gene was amplified using the Illumina NovaSeq platform to identify the bacterial taxa and diversity present in soil around ironwood trees. The links and accession numbers to the fastq files in this dataset are provided in Table 2. The bacterial sequences present in the 39 samples were taxonomically assigned using the Quantitative Insights into Microbial Ecology (QIIME2 version 2022.2) pipeline [9].
A total of 4,782,728 raw sequencing reads were obtained with 27F as the forward primer and 519Rmod and 519Rmodbio as reverse primers [10] to capture a broad array of bacterial diversity across soil samples collected from the 39 ironwood trees in Guam. A total of 3,337,420 sequence reads represented by 28,563 unique Amplicon Sequence Variants (ASVs) remained after quality-filtering using DADA2 and merging of amplicons obtained with both reverse primer sets for each sample. The ASVs with no taxonomical assignment, i.e., sequences with less than 99% identity to references in the SILVA 132 database, were removed, resulting in 200,499 sequence reads and 2303 ASVs with taxonomic assignment.
Alpha-rarefaction in QIIME2 and iNEXT was used to assess whether sequencing depth, sample numbers, and coverage were sufficient to capture the majority of bacterial diversity present in the soil samples. The sequence-depth-based rarefaction curves for ASV richness and Faith’s phylogenetic distance (PD) (Figure 1a) showed that diversity in many samples started to level off but did not reach an asymptote in some of the samples, indicating that at least in highly diverse samples, the soil microbiome richness and phylogenetic diversity were not completely captured. In contrast, Shannon diversity plateaued at a sequencing depth of around 1000 reads indicating that overall bacteria diversity was sufficiently captured, and the continued increase in richness and phylogenetic distance was likely based on rare species as it would be expected in microbiome-rich environments like soil.
In addition, rarefaction curves were created using sample-based and coverage-based approaches (Figure 1b,c) to depict the relationship between effective diversity and the number of samples or the estimated sample coverage. Effective diversity considers both the relative abundance and richness of the data and is quantified using Hill numbers (q) with q = 0, 1, and 2 representing ASV richness, Shannon diversity, and Simpson Inverse, respectively. The sample- and coverage-based rarefaction curves were extrapolated to twice the sample size to allow for the computation of effective diversity values beyond the original sample size.
The sample-based rarefaction curves (Figure 1b) for Shannon diversity and Simpson Inverse mostly had levelled out at the actual sample size reaching an effective diversity of approximately 500 and 250, respectively. Extrapolation of these two curves did not result in significant increases in these values. On the other hand, the rarefaction curve for ASV richness continued to rise even after extrapolation. However, this increase in ASV richness seemed to be driven by rare ASVs since there was no corresponding increase in Shannon and Simpson Inverse. The 39 samples in this dataset provided over 80% coverage and doubling the sample size would increase the coverage to 95% (Figure 1c).
The taxonomic assignment of ASVs revealed seventeen different phyla in soil collected around ironwood trees in Guam. The dominant phyla and their relative abundances were as follows: Firmicutes (50.67%), Actinobacteria (31.52%), Proteobacteria (10.57%), Acidobacteria (3.96%), and others (3.28%) (Figure 2). The twenty most abundant ASVs with the highest number of reads were assigned to phyla Firmicutes, Actinobacteria, Proteobacteria, and Acidobacteria (Table 3). The ASV with the highest number of reads belonged to the genus Bacillus, which is typically predominant in soil (Table 3). None of the ASVs from the 39 samples were assigned to putative pathogens associated with IWTD in Guam such as Ralstonia spp. and Klebsiella spp. However, the positive controls that were spiked with R. solanacearum did show Ralstonia presence, confirming that DNA extraction methods and primers were appropriate for detection.
Four indices, i.e., ASV richness, Pielou’s evenness, Shannon diversity, and Faith’s PD, were used to perform alpha-diversity analysis on the dataset after rarefaction to a common sequencing depth of 3200 per sample. Kruskal–Wallis ANOVA with Benjamini–Hochberg correction was used to determine the group significance for factors with categorical data: location, presence of Ralstonia in trees as measured with the Agdia Strip test, tree decline severity as measured by visual inspection of fullness and dieback of branches (symptomless, slightly damaged, distinctly damaged, heavily damaged, and nearly dead), altitude classification (high ≥ 100 m and low < 100 m above sea level), parent material (limestone, sand, and tuff), and site management (no maintenance, moderately managed, and highly managed) [10,11]. Spearman rank tests were used to assess the correlations for factors with numerical data including altitude, percentage of dead trees in plot, and percentage of trees with termites in plot (for a detailed description of these factors, see [10,11]). However, no significant effects on alpha-diversity were detected for any of these factors.
For beta-diversity, single-factorial Permutational Multivariate Analysis of Variance (PERMANOVA) at 1000 permutations based on both of the weighted and unweighted Unifrac distance metrics [12,13] was used to evaluate the differentiation of the microbial composition among soil samples grouped by the categorical factors listed above. The weighted Unifrac distance metric takes both abundance and phylogenetic distance into account; however, no significant influences on bacterial community differentiation were found for any of these factors. For unweighted Unifrac, which only considers presence/absence and phylogenetic distance of ASVs, altitude classification (p = 0.00999, pseudo-F = 1.26, n = 39) and parent material (p = 0.04995) showed significant effects on the beta-diversity of the bacterial communities. The effects of the parent material on the soil microbiome were driven by significant differences between limestone and sand (p = 0.047952, pseudo-F = 1.16, n = 30); however, no significant differences in the microbiota were observed in other factors.
3. Methods
3.1. Sample Collection and Processing
Soil samples were collected by the team from the University of Guam from February to March in 2022 from under 39 different ironwood trees in Guam using heat-sterilized equipment (Table 1 and Table 4). The trees with different scales of IWTD severity (symptomless, slightly damaged, distinctly damaged, heavily damaged, and nearly dead) were distributed across seventeen different locations. Approximately 100 mL of soil was collected from 5 to 6 soil cores around each tree. Samples were collected between 1 and 1.5 m from the base of the tree at a depth of 2–10 cm. The cores collected from the same tree were mixed and an aliquot of the soil was transferred to a 15 mL plastic ultra-high-performance centrifuge tube containing 8 mL of 95% ETOH until a volume of 11 mL was reached. In addition, 10 g of six soil samples were spiked with ooze from slices of tissue from trees marked positive for Ralstonia solanacearum species complex by Agdia Strip tests and also preserved in 95% ethanol for use as positive control.
3.2. DNA Extraction and Sequencing
Samples were sent to Louisiana State University Agricultural Center where DNA was extracted from 250 mg of dried soil of each sample using the DNeasy PowerSoil Pro kit (Qiagen, Germantown, MA, USA). All the procedures were conducted using sterile techniques and supplies to eliminate contamination. The DNA concentrations were quantified on a Qubit 4 Fluorometer (Thermo Fisher Scientific, Wilmington, DE, USA) with the Qubit dsDNA BR Assay Kit (Invitrogen, Thermo Fisher Scientific). The samples were then sent to the University of New Hampshire Hubbard Center for Genome Studies for subsequent sequencing. The V1–V3 region of the 16S rRNA gene of the bacterial DNA was amplified using the 27F as the forward primer and 519Rmod and 519Rmodbio as the reverse primers [14,15] and sequenced on the Illumina NovaSeq (2 × 250 bp) platform using Illumina Nextera Dilute library protocol with a spike-in of 1% Phi X (Illumina, San Diego, CA, USA).
3.3. Data Analysis
Bioinformatic analysis was performed using the QIIME2 pipeline version 2022.2 [9]. Demultiplexed fastq sequences were obtained after sequencing from the University of New Hampshire Hubbard Center for Genome Studies. The primers and chimera sequences were removed and denoising was performed using DADA2 [16]. Sequence reads of 251-nucleotide lengths were obtained. The amplicons obtained by the two sets of reverse primers were combined for each sample. Due to a lack of overlap between some of the forward and reverse reads, only forward sequences were used for the analysis. To assess diversity, sequence-, sample size- and coverage-based rarefaction curves were generated with the QIIME2 alpha-rarefaction plugin and the R package iNEXT version 3.0.0 (iNterpolation/EXTrapolation) [17]. Taxonomic assignments were performed by comparing the sequence reads or ASVs obtained through DADA2 quality filtering against the SILVA 132 reference database [18] using the consensus method in BLAST, with a 99% pairwise identity cutoff. ASVs that could not be assigned to any taxonomic group were excluded from the dataset prior to generating taxa bar plots, which illustrated the relative abundance of ASVs at the phylum level. Alpha-diversity analyses based on the four indices, ASV richness, Pielou’s evenness, Shannon diversity, and Faith’s PD, were performed using alpha-correlation and alpha-group-significance plugins in QIIME2. The beta-diversity was calculated using beta-group-significance method of qiime diversity plugin in QIIME2.
4. User Notes
- Bacteria Ralstonia solanacearum and Klebsiella species (K. variicola and K. oxytoca) were previously isolated from diseased ironwood trees [2]. This dataset could be used to assess whether soilborne microbiomes are the source of these or other putative pathogens;
- This dataset describes bacterial communities present in soil collected from under ironwood trees with varying scales of decline severity in Guam. Microbiologists can use this dataset to compare the bacteria in soil with those found in other geographic regions that are affected by IWTD;
Author Contributions
C.H.: funding acquisition, conceptualization, supervision, writing—review and editing; R.L.S.: project administration, funding acquisition, sample collection, writing—review and editing; T.J.: formal analysis, writing—original draft, data analysis. All authors have read and agreed to the published version of the manuscript.
Funding
This research was funded by the National Institute of Food and Agriculture, U.S. Department of Agriculture, under award number 2018-38640-28418 through the Western Sustainable Agriculture Research and Education program under project number SW19-906.
Institutional Review Board Statement
Not applicable.
Informed Consent Statement
Not applicable.
Data Availability Statement
The original data presented in the study are openly available in National Center for Biotechnology Information (NCBI) at https://www.ncbi.nlm.nih.gov/sra?LinkName=bioproject_sra_all&from_uid=883256 (accessed on 1 September 2023).
Acknowledgments
We express our thanks to the lab members at the University of Guam for their assistance in gathering the soil samples, as well as Garima Setia and Junyan Chen for their help in the bioinformatics analysis. We also express thanks to Kelley Thomas and his team from the University of New Hampshire Hubbard Center for Genomic Studies for their support in DNA sequencing and for providing the server for data analysis.
Conflicts of Interest
The authors declare no conflicts of interest.
References
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Figure 1.
The rarefaction curves based on (a) sequencing depth of each sample depict the ASV richness, Faith’s phylogenetic distance, and Shannon diversity indices of bacteria diversity in 39 soil samples; (b) the number of samples depict effective bacterial diversity for richness, Shannon and Simpson Inverse metrics; and (c) estimated sample coverage depict effective diversity for the same three metrics. Solid lines were drawn up to the actual sample size; dashed lines represent extrapolation to twice the sample size. Rarefaction was conducted across the entire bacteria diversity (including ASVs without taxonomical assignment). Sequencing depth was assessed by the number of reads.
Figure 1.
The rarefaction curves based on (a) sequencing depth of each sample depict the ASV richness, Faith’s phylogenetic distance, and Shannon diversity indices of bacteria diversity in 39 soil samples; (b) the number of samples depict effective bacterial diversity for richness, Shannon and Simpson Inverse metrics; and (c) estimated sample coverage depict effective diversity for the same three metrics. Solid lines were drawn up to the actual sample size; dashed lines represent extrapolation to twice the sample size. Rarefaction was conducted across the entire bacteria diversity (including ASVs without taxonomical assignment). Sequencing depth was assessed by the number of reads.
Figure 2.
Relative abundance of bacterial phyla associated with 39 samples of soil collected from ironwood trees in Guam. The legend contains each phylum with its percent abundance and number of samples in which that phylum was found.
Figure 2.
Relative abundance of bacterial phyla associated with 39 samples of soil collected from ironwood trees in Guam. The legend contains each phylum with its percent abundance and number of samples in which that phylum was found.
Table 1.
Metadata describing the locations where 39 soil samples used for bacterial diversity analysis were collected.
Table 1.
Metadata describing the locations where 39 soil samples used for bacterial diversity analysis were collected.
| Sample ID | Location | Tree GPS | Altitude Classification (m) | Parent Material | Site Management | Percentage of Soil Moisture (%) | Depth of Soil Cores Taken (cm) |
|---|---|---|---|---|---|---|---|
| 22-122 | UOG, Mangilao | 13.43109, 144.80084 | low (67) | limestone | none | 25.57 | 4, 7, 4, 10, 10, 4 |
| 22-126 | Mangilao Golf Course | 13.47111, 144.84528 | high (129) | limestone | high | 16.26 | 10, 10, 10, 10, 10 |
| 22-127 | UOG Yigo Station | 13.53309, 144.87161 | high (178) | limestone | moderate | 34.33 | 10, 10, 10, 10, 10 |
| 22-128 | UOG Yigo Station | 13.53327, 144.87158 | High (144) | limestone | high | 40 | 10, 10, 10, 10, 10 |
| 22-129 | UOG Yigo Station | 13.53297, 144.87364 | high (142) | limestone | moderate | 29.87 | 10, 10, 10, 10, 10 |
| 22-131 | UOG Yigo Station | 13.53231, 144.87267 | high (173) | limestone | moderate | 38.99 | 10, 10, 10, 10, 10 |
| 22-133 | Mangilao Golf Course | 13.47082, 144.84503 | high (127) | limestone | high | 39.19 | 10, 10, 10, 10, 10 |
| 22-137 | Watson’s Farm, Yigo | 13.56456, 144.87746 | high (168) | limestone | moderate | 34.46 | 10, 6, 8, 10, 4 |
| 22-139 | Watson’s Farm, Yigo | 13.56660, 144.87416 | high (161) | limestone | moderate | 40.84 | 8, 10, 10, 6, 10 |
| 22-140 | Watson’s Farm, Yigo | 13.56598, 144.87462 | high (164) | limestone | moderate | 31.2 | 10, 10, 10, 10, 10 |
| 22-141 | Watson’s Farm, Yigo | 13.56583, 144.87688 | high (170) | limestone | moderate | 33.83 | 8, 10, 10, 10, 10 |
| 22-143 | Watson’s Farm, Yigo | 13.56707, 144.87654 | high (171) | limestone | none | 49.4 | 8, 8, 10, 8, 4 |
| 22-145 | Watson’s Farm, Yigo | 13.56692, 144.87740 | high (163) | limestone | moderate | 38.84 | 10, 10, 10, 8, 10 |
| 22-149 | UOG Ija Station | 13.26595, 144.71707 | high (110) | tuff | moderate | 50.52 | 10, 10, 10, 10, 10 |
| 22-155 | AAFB | 13.56314, 144.93079 | high (160) | limestone | moderate | 28.31 | 4, 8, 10, 8, 10 |
| 22-156 | AAFB | 13.56830, 144.93179 | high (164) | limestone | high | 56.23 | 10, 10, 10, 10, 9.5 |
| 22-157 | AAFB | 13.56132, 144.93056 | high (155) | limestone | moderate | 54.76 | 8, 10, 8, 10, 10 |
| 22-158 | AAFB | 13.55782, 144.93009 | high (155) | limestone | high | 38.51 | 10, 10, 10, 10, 6 |
| 22-159 | Paseo Park, Hagatna | 13.47956, 144.75429 | low (6) | limestone | high | 7.51 | 4, 4, 4, 8, 4 |
| 22-160 | Governor’s Complex, Aniquia | 13.47870, 144.73200 | low (6) | tuff | high | 17.76 | 6, 10, 6, 10, 10 |
| 22-161 | Apaca Point, Agat | 13.40239, 144.66307 | low (6) | tuff | moderate | 52.65 | 10, 10, 10, 10, 10 |
| 22-164 | Windward Hills Golf Course | 13.37788, 144.74162 | high (126) | tuff | high | 35.97 | 6, 10, 8, 8, 10 |
| 22-165 | Windward Hills Golf Course | 13.37650, 144.73789 | high (115) | tuff | high | 36.79 | 8, 8, 10, 6, 4 |
| 22-166 | Country Club of the Pacific | 13.37163, 144.76817 | low (26) | tuff | high | 17.59 | 4, 8, 6, 8, 8, 4 |
| 22-168 | Duenas Beach | 13.25978, 144.73688 | low (11) | sand | moderate | 7.24 | 10, 10, 10, 10, 10 |
| 22-169 | Duenas Beach | 13.25963, 144.73735 | low (12) | sand | moderate | 15.08 | 10, 10, 10, 10, 10 |
| 22-170 | Ysrael Beach | 13.24797, 144.72690 | low (5) | sand | none | 11.23 | 4, 10, 10, 10, 10 |
| 22-171 | Ysrael Beach | 13.24741, 144.72708 | low (7) | sand | none | 22.46 | 8, 10, 8, 8, 4 |
| 22-173 | Tarague Beach | 13.62518, 144.89525 | low (9) | sand | high | 7.87 | 10, 10, 10, 10, 10 |
| 22-174 | Tarague Beach | 13.62340, 144.89664 | low (21) | sand | high | 6.36 | 10, 10, 10, 10, 8 |
| 22-187 | Ritidian | 13.64889, 144.85289 | low (12) | sand | moderate | 17.69 | 10, 10, 10, 10, 10 |
| 22-188 | Watson’s Farm, Yigo | 13.56741, 144.87413 | high (163) | limestone | none | 53.9 | 4, 6, 6, 8, 10 |
| 22-189 | Watson’s Farm, Yigo | 13.56864, 144.87701 | high (173) | limestone | none | 43.11 | 4, 4, 6, 6, 6, 10 |
| 22-190 | Watson’s Farm, Yigo | 13.56594, 144.87816 | high (168) | limestone | none | 39.51 | 2, 4, 4, 4, 4, 8 |
| 22-191 | Watson’s Farm, Yigo | 13.56553, 144.87749 | high (159) | limestone | moderate | 20.74 | 10, 10, 10, 10, 6 |
| 22-192 | Watson’s Farm, Yigo | 13.56528, 144.87704 | high (159) | limestone | moderate | 31.58 | 6, 8, 10, 10, 10 |
| 22-208 | Cocos Island | 13.23476, 144.64574 | low (20) | sand | none | 24.59 | 10, 4, 8, 10, 10 |
| 22-209 | Cocos Island | 13.23701, 144.65102 | low (12) | sand | none | 24.98 | 2, 4, 10, 4, 10, 6 |
| 22-223 | Sagan Kotturan Chamoru | 13.50311, 144.78416 | low (45) | limestone | none | 8.8 | 10, 10, 10, 10, 8 |
Table 2.
Accession numbers and links for raw fastq sequences of soil samples collected from thirty-nine ironwood trees in Guam.
Table 2.
Accession numbers and links for raw fastq sequences of soil samples collected from thirty-nine ironwood trees in Guam.
Table 3.
The 20 most abundant ASVs in soil samples according to total numbers of reads with their assignment in SILVA and the number of samples in which they were detected.
Table 3.
The 20 most abundant ASVs in soil samples according to total numbers of reads with their assignment in SILVA and the number of samples in which they were detected.
| Phylum | Order | Lowest SILVA Assignment | Number of Reads | Number of Samples | Average Number of Reads per Sample | Standard Deviation |
|---|---|---|---|---|---|---|
| Firmicutes | Bacillales | Bacillus | 25,369 | 34 | 650 | 1420.2 |
| Firmicutes | Bacillales | Bacillus | 24,899 | 33 | 638 | 1473.3 |
| Actinobacteria | Gaiellales | Gaiellales | 15,500 | 33 | 397 | 1110.6 |
| Actinobacteria | Solirubrobacterales | Solirubrobacterales | 9381 | 29 | 241 | 703.1 |
| Firmicutes | Bacillales | Bacillus | 8560 | 31 | 219 | 434.7 |
| Firmicutes | Bacillales | Bacillus abyssalis | 6286 | 17 | 161 | 557.7 |
| Firmicutes | Bacillales | Lysinibacillus | 6281 | 15 | 161 | 457.8 |
| Proteobacteria | Rhizobiales | Xanthobacteraceae | 3607 | 28 | 92 | 170.2 |
| Firmicutes | Bacillales | Bacillus sp. A-10 | 3312 | 29 | 85 | 105.9 |
| Actinobacteria | Propionibacteriales | Microlunatus | 3197 | 21 | 82 | 221.2 |
| Actinobacteria | Unknown | Thermoleophilia | 3195 | 19 | 82 | 228.3 |
| Firmicutes | Bacillales | Cohnella | 2959 | 19 | 76 | 187.6 |
| Actinobacteria | Streptomycetales | Streptomyces | 2841 | 26 | 73 | 120.3 |
| Actinobacteria | Gaiellales | Gaiella | 2719 | 23 | 70 | 183.4 |
| Proteobacteria | Rhizobiales | Pedomicrobium | 2503 | 23 | 64 | 118.3 |
| Actinobacteria | Micromonosporales | Micromonospora | 2496 | 20 | 64 | 144.2 |
| Firmicutes | Bacillales | Paenibacillus | 2429 | 20 | 62 | 156.4 |
| Actinobacteria | Micromonosporales | Micromonosporaceae | 2401 | 15 | 62 | 193.1 |
| Acidobacteria | Unknown | Subgroup 6 | 2214 | 20 | 57 | 161.5 |
| Actinobacteria | Microtrichales | Microtrichales | 2022 | 17 | 52 | 160.6 |
Table 4.
Metadata indicating the health of the trees at the sample location. Note that some metadata were not available for all samples (NA = not applicable).
Table 4.
Metadata indicating the health of the trees at the sample location. Note that some metadata were not available for all samples (NA = not applicable).
| Sample ID | Presence of Ralstonia 1 | Tree Health Ranking | Tree Decline Severity | Percentage of Dead Trees in Plot (%) | Percentage of Trees with Termites in Plot (%) | Plot Average Health |
|---|---|---|---|---|---|---|
| 22-122 | + | sick | nearly dead | 13.33 | 26.67 | sick |
| 22-126 | − | healthy | symptomless | 2.94 | 55.88 | sick |
| 22-127 | − | healthy | symptomless | 16.67 | 56.67 | sick |
| 22-128 | + | sick | nearly dead | NA | NA | NA |
| 22-129 | − | healthy | slightly damaged | 0 | 56.56 | sick |
| 22-131 | + | sick | nearly dead | 9.09 | 36.36 | sick |
| 22-133 | + | sick | nearly dead | 0 | 40 | sick |
| 22-137 | − | healthy | slightly damaged | 0 | 61.76 | sick |
| 22-139 | − | healthy | symptomless | 2.86 | 65.71 | sick |
| 22-140 | − | sick | nearly dead | 3.03 | 39.39 | healthy |
| 22-141 | + | sick | heavily damaged | 53.33 | 33.33 | sick |
| 22-143 | − | healthy | symptomless | 8.33 | 8.33 | sick |
| 22-145 | + | sick | heavily damaged | 38.1 | 23.81 | sick |
| 22-149 | + | sick | nearly dead | 58.33 | 20.83 | sick |
| 22-155 | − | healthy | slightly damaged | 14.29 | 71.43 | sick |
| 22-156 | − | sick | heavily damaged | 0 | 60 | sick |
| 22-157 | − | healthy | slightly damaged | NA | NA | NA |
| 22-158 | − | sick | nearly dead | NA | NA | NA |
| 22-159 | − | healthy | symptomless | NA | NA | NA |
| 22-160 | − | healthy | symptomless | NA | NA | NA |
| 22-161 | − | healthy | symptomless | NA | NA | NA |
| 22-164 | + | healthy | slightly damaged | 0 | 100 | sick |
| 22-165 | + | sick | nearly dead | NA | NA | NA |
| 22-166 | − | sick | heavily damaged | NA | NA | NA |
| 22-168 | − | healthy | symptomless | 10 | 60 | sick |
| 22-169 | − | healthy | slightly damaged | 12.94 | 38.82 | sick |
| 22-170 | − | sick | nearly dead | 7.59 | 16.46 | healthy |
| 22-171 | − | healthy | slightly damaged | 9.09 | 16.97 | sick |
| 22-173 | + | sick | nearly dead | NA | NA | NA |
| 22-174 | − | healthy | symptomless | 8.11 | 21.62 | sick |
| 22-187 | − | healthy | slightly damaged | 0 | 100 | sick |
| 22-188 | − | healthy | slightly damaged | 2.78 | 36.11 | sick |
| 22-189 | − | healthy | slightly damaged | 0 | 33.33 | sick |
| 22-190 | − | healthy | slightly damaged | 25.71 | 28.57 | sick |
| 22-191 | + | sick | nearly dead | NA | NA | NA |
| 22-192 | + | sick | heavily damaged | NA | NA | NA |
| 22-208 | − | sick | distinctly damaged | 7.61 | 21.74 | sick |
| 22-209 | − | healthy | slightly damaged | 7.56 | 25.58 | sick |
| 22-223 | − | healthy | slightly damaged | 8.57 | 51.43 | sick |
1 Positive (+) means presence of Ralstonia detected and negative (−) means absence of Ralstonia.
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MDPI and ACS Style
Jin, T.; Schlub, R.L.; Husseneder, C. Illumina 16S rRNA Gene Sequencing Dataset of Bacterial Communities of Soil Associated with Ironwood Trees (Casuarina equisetifolia) in Guam. Data 2024, 9, 54. https://doi.org/10.3390/data9040054
AMA Style
Jin T, Schlub RL, Husseneder C. Illumina 16S rRNA Gene Sequencing Dataset of Bacterial Communities of Soil Associated with Ironwood Trees (Casuarina equisetifolia) in Guam. Data. 2024; 9(4):54. https://doi.org/10.3390/data9040054
Chicago/Turabian StyleJin, Tao, Robert L. Schlub, and Claudia Husseneder. 2024. "Illumina 16S rRNA Gene Sequencing Dataset of Bacterial Communities of Soil Associated with Ironwood Trees (Casuarina equisetifolia) in Guam" Data 9, no. 4: 54. https://doi.org/10.3390/data9040054
