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Soil microbial communities associated with giant sequoia: How does the world’s largest tree affect some of the world’s smallest organisms?

Chelsea J. Carey1*, Sydney I. Glassman2, Thomas D. Bruns3, Emma L. Aronson2,

Stephen C. Hart4

Author Affiliations: 1Point Blue Conservation Science, Petaluma, CA 94954 2Department of Microbiology and Plant Pathology, University of California, Riverside, CA 92521 3Department of Plant and Microbial Biology, University of California, Berkeley, CA 94720 4 Department of Life & Environmental Sciences and the Sierra Nevada Research Institute, University of California, Merced, CA 95343, USA

Corresponding author: [email protected]

Keywords: California, next-generation sequencing, Pinus lambertiana, plant-soil interactions, Sequoiadendron giganteum, Sierra Nevada

1 bioRxiv preprint doi: https://doi.org/10.1101/807040; this version posted October 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

1 Abstract 2 3 Giant sequoia (Sequoiadendron giganteum) is an iconic conifer that lives in relic

4 populations on the western slopes of the California Sierra Nevada. In these settings it is

5 unusual among the dominant trees in that it associates with arbuscular mycorrhizal fungi

6 rather than ectomycorrhizal fungi. However, it is unclear whether differences in

7 microbial associations extends more broadly to non-mycorrhizal components of the soil

8 microbial community. To address this question we characterized microbiomes associated

9 with giant sequoia and co-occurring sugar pine (Pinus lambertiana) by sequencing 16S

10 and ITS1 of the bulk soil community at two groves with distinct parent material. We

11 found tree-associated differences were apparent despite a strong grove effect.

12 Bacterial/archaeal richness was greater beneath giant sequoia than sugar pine, with a

13 unique core community that was double the size. The tree species also harbored

14 compositionally distinct fungal communities. This pattern depended on grove but was

15 associated with a consistently elevated relative abundance of Hygrocybe species beneath

16 giant sequoia. Compositional differences between host trees correlated with soil pH,

17 calcium availability, and soil moisture. We conclude that the effects of giant sequoia

18 extend beyond mycorrhizal mutualists to include the broader community, and that some

19 but not all host tree differences are grove-dependent.

20

21

22

23

24

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25 Introduction

26 There is increasing evidence that tree species influence a combination of soil

27 chemical, physical, and biological properties (Hobbie et al., 2007; Mitchell et al., 2010;

28 Langenbruch et al., 2012). For example, variation in litter chemistry, patterns of nutrient

29 uptake, root exudation, and microclimate among tree species can alter rates of

30 decomposition, soil nitrogen (N) and carbon (C) availability, and pH (Binkley &

31 Giardina, 1998). Tree-induced differences in resource availability and microclimate can,

32 in turn, modify soil bacterial, archaeal, and fungal community composition (Ushio et al.,

33 2008; Prescott & Grayston, 2013) by encouraging microorganisms with compatible

34 resource acquisition strategies and growth optima (Ayres et al., 2009). In addition, under

35 some circumstances, direct association with host-specific symbiotic microorganisms such

36 as mycorrhizal fungi can further promote distinct soil fungal communities (Gao et al.,

37 2013; Urbanova et al., 2015). Such plant-induced changes to microbial communities can

38 become more or less pronounced with time since plant establishment (Strayer et al.,

39 2006), and can feedback on soil chemical and physical properties (Falkowski et al.,

40 2008), plant performance (Bever et al., 2010; Aponte et al., 2013), plant phenology

41 (Wagner et al., 2014), and plant community composition (van der Heijden et al., 2008).

42 Although soil microorganisms can directly and indirectly influence plant

43 dynamics (Abbott et al., 2015) and may mediate how plant communities respond to

44 anthropogenic threats (Malcolm et al., 2006; Stephens et al., 2014), information on soil

45 microbial communities associated with many rare or endemic tree species is limited. One

46 such tree is the giant sequoia (Sequoiadendron giganteum)—a species that epitomizes

47 charismatic mega-flora (Hall et al., 2011). Endemic to the western slope of the Sierra

3 bioRxiv preprint doi: https://doi.org/10.1101/807040; this version posted October 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

48 Nevada (California, USA), giant sequoia harbor a number of traits that make it unique,

49 ecologically interesting, and of conservation concern. Most conspicuously, giant sequoia

50 are the world’s largest trees, growing up to 87 m in height and reaching a bole volume of

51 1,500 m3 (Stephenson, 2000). They are also one of the longest-lived trees, with an

52 estimated average age of 2,230 years and a maximum known age of 3,266 years

53 (Stephenson, 2000). Over the last century, fire suppression has threatened giant sequoia

54 regeneration by minimizing canopy gaps and exposure of mineral soil, two important

55 factors for germination of this shade intolerant tree (York et al., 2011). Future conditions

56 marked by increased and prolonged drought are expected to put additional stress on giant

57 sequoia (Su et al. 2017). Fortunately, their charisma has deemed them one of the seven

58 natural wonders of the United States (DeFries, 2013), and together the ecological and

59 cultural importance of the giant sequoia has promoted their current protection by both

60 state and national agencies (Leisz, 1994; Lindenmayer et al., 2014).

61 Despite their iconic status, very little is known about how giant sequoia influence

62 the soil and even less is known about how these titans of the tree world interact with

63 some of the smallest yet most important organisms on the planet: microorganisms. The

64 few prior studies that have been conducted suggest that, similar to other members of the

65 Cupressaceae family (Cross & Perakis, 2011a,b), giant sequoia accumulate relatively

66 large amounts of base cations such as calcium in their leaf litter resulting in high soil base

67 saturation compared to other mixed-conifer species (Zinke, 1962; Zinke &

68 Stangenberger, 1994). In those same studies, giant sequoia was also reported to maintain

69 relatively high values of soil pH and soil organic matter. From a microbial perspective,

70 giant sequoia are known to associate with arbuscular mycorrhizal fungi (AMF),

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71 symbiotic fungi that form relationships with the vast majority of herbaceous plants

72 (Fahey et al., 2012). This contrasts with most other trees in the Sierra Nevada, such as

73 those of the Pinaceae and the Fagaceae families, whose woody roots instead associate

74 with symbiotic ectomycorrhizal fungi (EMF; Brundrett & Tedersoo, 2018). Few studies

75 have probed the mycorrhizal dynamics of giant sequoia (Kough et al., 1985; Molina,

76 1994; Fahey et al., 2012), and none have intensively assessed soil bacterial/archaeal and

77 fungal community structure (composition and diversity) using molecular techniques.

78 Given that giant sequoia occur on soils derived from various rock types—including

79 granite, diorite, and andesite (Weatherspoon, 1990)—it is important to evaluate whether

80 microbial dynamics beneath giant sequoia remain consistent or if they vary across groves

81 with different parent material. Parent material exerts a strong influence on soil properties,

82 and differences in underlying geology often interact with trees to shape soil microbial

83 community structure (Ulrich & Becker, 2006; Carletti et al., 2009; Wagai et al., 2011).

84 For example, parent material and vegetation type interacted to affect soil macroaggregate

85 size, and both factors also shaped microbial community structure following 30 years of

86 surface exposure of reclaimed surface mining sites (Yarwood et al., 2015). The degree to

87 which this occurs with the iconic giant sequoia, however, remains unknown.

88 In this paper, we sequenced bacterial, archaeal, and fungal communities from beneath

89 giant sequoia and a co-dominant ectomycorrhizal tree, sugar pine (Pinus lambertiana

90 Dougl.). Sugar pine are prevalent on the western slope of the Sierra Nevada and are

91 second only to giant sequoia in total volume, with individuals reaching 76 m in height

92 and living up to 600 years (Hardin et al., 2001). We sampled soil from 32 individuals of

93 each tree species across two groves with contrasting geologic substrates in Yosemite

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94 National Park, USA. By comparing these two tree species within and between groves, our

95 experimental design allows us to evaluate for the first time the relative impact of these

96 tree hosts and parent material on soil microbial structure. Using subsamples of soil

97 collected for physiochemical analysis in a companion study (Hart et al., unpublished

98 data), we sought to address the following questions: (Q1) what bacterial, archaeal, and

99 fungal members comprise the soil microbial community beneath giant sequoia

100 individuals?; (Q2) how do these microbial communities compare to those beneath co-

101 occurring sugar pine individuals?; (Q3) are giant sequoia and sugar pine-associated

102 microbial communities consistent across groves with differing geological substrates?; and

103 (Q4) which soil characteristics, if any, correlate with tree-associated changes in microbial

104 richness and composition?

105

106 Methods

107 Site Description

108 We sampled from two of the three giant sequoia groves contained within

109 Yosemite National Park (YNP): Mariposa Grove (N 37.51539°, W 119.60435°) and

110 Merced Grove (N 37.74982°, W 119.84061°). Both groves are located on the western

111 slope of the Sierra Nevada in the rain-snow transition zone, and experience a

112 Mediterranean-type climate with warm-dry summers and cool-wet winters. In addition to

113 giant sequoia and sugar pine, other common trees in these groves are the ectomycorrhizal

114 (EM) trees white fir (Abies concolor) and ponderosa pine (Pinus ponderosa), and the

115 arbuscular mycorrhizal (AM) tree incense cedar (Calocedrus decurrens; Wang & Qiu,

116 2006, Allen & Kitajima, 2014). Both groves have relatively sparse understory vegetation

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117 composed primarily of AM-associated Ceanothus spp. and broadleaf lupine (Lupinus

118 latifolius), as well seedlings and saplings of the EM-associated white fir (Abies concolor).

119 Soils in the Mariposa Grove are derived from residuum and colluvium of metavolcanic

120 (andesite and hornfels) with minor amounts of intermediate granitoid rock. Soils in the

121 Merced Grove are derived from residuum and colluvium of quart-rich metasedimentary

122 rock (USDA NRCS 2007). Despite contrasting parent materials, soils from both sites are

123 classified within the same Soil Taxonomic Family: coarse-loamy, isotic, frigid Ultic

124 Haploxeralfs.

125 Experimental Design

126 Within each grove, we sampled from beneath 16 mature giant sequoia individuals

127 and 16 co-occurring mature sugar pine individuals. Because mature giant sequoia trees

128 were generally the limiting experimental unit (especially in the Merced Grove), we first

129 selected mature giant sequoia individuals whose crowns did not overlap with adjacent

130 trees. We then selected the closest mature sugar pine trees to each giant sequoia

131 individual that shared similar aspect, slope, landscape position, and understory species

132 (when present). Relatively little understory vegetation occurred beneath each focal tree,

133 and areas containing nitrogen-fixing species such as Ceanothus spp. were avoided. As

134 with giant sequoia, we ensured selection of sugar pine individuals whose crowns did not

135 overlap with adjacent trees. This selection procedure was designed to minimize any

136 confounding influences that may affect soil properties besides tree species. Our

137 experimental design resulted in 32 total giant sequoia-sugar pine pairs that were typically

138 within 30 m of each other.

139 Soil Sampling

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140 In August 2013, we sampled bulk surface soil from beneath each tree individual

141 (i.e., we did not directly target rhizosphere soil surrounding plant roots). We selected

142 sampling locations mid-crown and downslope of the tree bole with the assumption that

143 aboveground litter would accumulate most at these locations and therefore the influence

144 of trees would be maximal (Zinke, 1962). Five replicate soil cores (0-5 cm depth of

145 mineral soil) per tree were taken within an approximately 20 cm x 20 cm area using an

146 Oakfield corer (1.9 cm diameter; Oakfield Apparatus Co, Fond du Lac, WI, USA), and

147 composited into a single sample within a sterile plastic bag (Whirl-Pak®, Nasco, Fort

148 Atkinson, WI, USA). The soil corer was sanitized after each composite sample using a

149 rinse of 10% bleach followed by a rinse of 95% ethanol. Soil samples were stored at 4

150 °C, transported to University of California (UC) Merced, sieved (< 2 mm; sanitized

151 between samples as described above), and subsampled for microbial analysis.

152 DNA extraction

153 Each subsample was extracted immediately upon returning to the laboratory

154 (within 24 hours), and two separate DNA aliquots per subsample were stored at -20 °C

155 for subsequent analysis. Specifically, we extracted microbial DNA from 0.250 g soil (±

156 0.025) using a MO BIO PowerSoil Isolation Kit (Mo Bio Laboratories, Inc., Carlsbad,

157 CA, USA) following the manufacturer’s instructions. One 50 µL DNA aliquot was

158 shipped on dry ice to UC Riverside for bacterial/archaeal 16S analysis and one was

159 delivered on ice to UC Berkeley for fungal ITS analysis.

160 At the same time that DNA was extracted, additional soil subsamples were taken

161 for 22 physiochemical analyses (Hart et al., unpublished data). These analyses were

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162 conducted on fresh or air-dried soils, as appropriate for the assay (Supplemental

163 Information Methods S1).

164 16S rRNA amplicon preparation

165 After quantifying the extracted DNA using a NanoDrop 2000 (Thermo Fisher

166 Scientific Inc., Wilmington, DE, USA), we amplified each sample in duplicate using

167 primers targeting the V3-V4 region of the 16S rRNA gene (S-D-Bact-0341-b-S-17 and S-

168 D-Bact-0785-a-A-21; Klindworth et al., 2012). We conducted polymerase chain reaction

169 (PCR) by combining 2.5 µL DNA template, 5 µL each of 1 µM forward and reverse

170 primers, and 12.5 µL KAPA HiFi HotStart ReadyMix (KAPA Biosystems, Inc.,

171 Wilmington, MA, USA), totaling a 25 µL reaction. Thermocycler conditions were: 95 °C

172 for 3 min., followed by 25 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s,

173 followed by an extension step for 5 min. at 72 °C. After amplification, we combined and

174 purified the duplicate PCR products using Agencourt AMPure XP Beads (Beckman

175 Coulter Genomics, Danvers, MA, USA). A second round of PCR was subsequently

176 conducted to attach dual indices to each sample using the Nextera XT Index Kit (Illumina

177 Inc., San Diego, CA, USA). Briefly, 5 µL DNA, 5 µL each of 1 µM forward and reverse

178 index primers, 25 µL KAPA HiFi HotStart ReadyMix, and 10 µL PCR grade water were

179 combined to create a 50 µL mixture. Thermocycler conditions were: 95 °C for 3 min.,

180 followed by 8 cycles of 95 °C for 30 s, 55 °C for 30 s, 72 °C for 30 s, followed by an

181 extension step for 5 min. at 72 °C. We then conducted a second AMPure purification step

182 on the indexed amplicons, and quantified the products using the Quant-iTTM PicoGreen®

183 dsDNA assay kit (Life Technologies Inc., Grand Island, NY, USA). As a final step, we

184 pooled the samples together in equimolar concentrations and sequenced them in one

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185 Illumina MiSeq PE 2x300 run at the UC Riverside Genomics Core Facility, Riverside,

186 CA, USA.

187 ITS1 amplicon preparation

188 We PCR amplified the ITS1 spacer, a part of the internal transcribed spacer

189 region (ITS) that serves as the universal DNA barcode for fungi, from each sample using

190 the ITS1F-2 primer pair with Illumina MiSeq primers designed by Smith and Peay

191 (2014). We conducted PCR by combining 1 µl DNA template, 0.50 µl of 10 µM forward

192 primer, 1 µl of the 10µM bar-coded reverse primer, 0.130 µl of HotStar Taq Plus (5

193 units/µl) DNA polymerase (Qiagen, Valencia, CA, USA), 2.5 µl of 10 x PCR buffer

194 supplied by the manufacturer, and 0.50 µl 10mM each dNTPs; the 25 µl reaction was

195 brought to volume with water. Thermocycler conditions were: 95 °C for 5 min., followed

196 by 29 amplification cycles of 94 °C for 30 s, 51 °C for 30 s, 72 °C for 1 min., followed

197 by a 10 min. final extension at 72 °C. Amplifications for each barcoded sample were

198 cleaned with Agencourt AMPure XP Beads, quantified with Qubit, pooled to equimolar

199 concentration, and quality checked as described previously (Glassman et al., 2016). The

200 library was sequenced with Illumina MiSeq PE 2x250 at the Vincent J Coates Genomic

201 Sequencing Laboratory, UC Berkeley, Berkeley, CA, USA.

202 16S Sequence analysis

203 We obtained the 16S rRNA gene sequences already demultiplexed from the UC

204 Riverside Genomics Core Facility and processed them using Quantitative Insights into

205 Microbial Ecology (QIIME; Caporaso et al., 2010). After we joined the forward and

206 reverse reads (allowing for 20% maximum difference within the region of overlap), we

207 used default parameters to conduct quality control: reads were excluded if the length was

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208 less than 75 bases, if there were more than three consecutive low-quality base calls, if

209 less than 75% of the read length was consecutive high-quality base calls, if a Phred score

210 was below three, or if one or more ambiguous calls were present (Bokulich et al., 2013).

211 After quality filtering, 3.9 M sequence reads remained.

212 Operational taxonomic units (OTU) with 97% similarity were picked using open

213 reference UCLUST against the 13_8 release of the Greengenes database (DeSantis et al.,

214 2006). Reads that did not match any sequences in the database were clustered de novo

215 and singletons were filtered out. The median number of sequences per samples was

216 60,494, the mean was 61,422, and the range was from 18,378 to 114,657 sequences per

217 sample. After removing unassigned OTUs (1% of sequences), the OTU table was rarefied

218 to 36,345 reads per sample, and as a result, one sugar pine sample was dropped.

219 ITS Sequence analysis

220 We obtained the ITS gene sequences already demultiplexed from the UC

221 Berkeley Vincent J Coates Genomic Sequencing Laboratory and processed them as in

222 (Glassman et al., 2016) using UPARSE (Edgar, 2013). We removed distal

223 priming/adapter sites, trimmed the remaining untrimmed, low-quality regions from the

224 ends, and then joined the forward and reverse reads. Paired reads were then quality

225 filtered using the fastq_filter command in usearch and employing a maximum expected

226 number of errors of 0.25. After quality filtering, 2.9 M sequence read pairs remained. We

227 picked 97% OTUs, then reference based chimera detection was employed using usearch

228 and referencing against the UNITE database accessed on 10.09.2014 (Kõljalg et al.,

229 2005). After making the OTU table in usearch, we assigned taxonomy in QIIME

230 (Caporaso et al., 2010) using the same UNITE database. The resulting OTU table yielded

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231 2,173 total OTUs. The median number of sequences per samples was 41,801 the mean

232 was 39,946, and the range was from 4 to 75,202 sequences per sample. After removing

233 all OTUs with a No Blast Hit, the OTU table was rarefied to 13,904 reads per sample; as

234 a result, one giant sequoia and two sugar pine samples were dropped.

235 Statistical Analysis

236 We used a multifaceted approach to assess microbial community structure of

237 giant sequoia soils and to compare the structure of these communities with those beneath

238 sugar pine. We first tested the main and interactive effects of plant species and grove on

239 bacterial/archaeal and fungal OTU richness (alpha diversity) by performing a two-way

240 analysis of variance (ANOVA, ɑ = 0.05), transforming the data for normality and

241 homogeneity of variance when necessary. We then visualized similarities in microbial

242 community composition between tree species and grove using non-metric

243 multidimensional scaling (NMDS) of the Jaccard (presence-absence) and Bray-Curtis

244 (relative abundance) dissimilarity metrics. To determine if beta diversity differed

245 significantly between tree species and grove, we performed the multivariate permutation

246 test perMANOVA using the ‘adonis’ function in the R VEGAN package (permutations =

247 999; Oksanen et al., 2012). Because an assumption of perMANOVA is equal variance

248 between groups, we also performed an analysis of multivariate homogeneity of group

249 dispersions (permDISP; Supplemental Information Table S1).

250 In addition to running beta diversity analyses that included all taxa, we ran

251 separate analyses for two subsets of fungal taxa that included only EMF or AMF. This

252 allowed us to determine whether these root symbionts differed significantly between

253 samples (by tree and grove). Specifically, EMF taxa were bioinformatically parsed as

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254 previously established (Glassman et al., 2015) and AMF were bioinformatically parsed to

255 include only individuals of Glomeromycotina. The resultant OTU tables were rarefied to

256 even sampling depths (EMF = 3022; AMF = 40), visualized using NMDS, and analyzed

257 using perMANOVA in the same way as described above.

258 As a complement to these multivariate tests, we compared the relative abundance

259 of bacterial/archaeal and fungal phyla within each grove using non-parametric Mann-

260 Whitney U test on ranks. Given that abundant taxa tend to contribute significantly to

261 ecosystem functioning (Dai et al., 2016), we also analyzed the frequency and relative

262 abundance of dominant OTUs across species and grove. Specifically, we filtered both

263 bacterial/archaeal and fungal OTU tables to include only those OTUs that comprised

264 >1% of the total sequences. While 28 fungal OTUs met this criterion, only one

265 bacterial/archaeal OTU met it (a Bradyrhizobium species); therefore, we summarized the

266 results of the fungal OTUs only. Specifically, we visualized OTU frequency (presented as

267 a percentage of the total number of samples) and relative abundance, and assessed

268 significant differences in OTU relative abundance within each grove using non-

269 parametric Mann-Whitney U tests.

270 Finally, we identified core OTUs that were unique to each tree species across both

271 groves, where a core OTU was defined as an OTU that occurred in 100% of the samples

272 recovered beneath a tree species. This enabled us to capture and identify the microbial

273 members that were shared among all giant sequoia or sugar pine soils, but not both (i.e.,

274 the unique or host-specific core microbiome of each tree species). Core OTUs were

275 identified using the compute_core_microbiome.py script in QIIME and graphically

276 represented using Venn diagrams. Core bacterial/archaeal OTUs that were unique to each

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277 tree species were summarized at the phylum level. In addition, we determined the

278 taxonomy of core bacterial/archaeal OTUs that were significantly more frequent (> 20%

279 difference) beneath giant sequoia than sugar pine using EzTaxon (Kim et al., 2012), and

280 illustrated the presence/absence of these OTUs from all samples with the heatmap3

281 package (Zhao et al., 2014). We provide no such summary for fungi, as neither giant

282 sequoia nor sugar pine had a core microbiome based on our 100% occurrence definition.

283 In addition to determining differences in microbial community structure, we

284 aimed to identify whether any structural differences may be attributed to tree-induced

285 changes in soil parameters. To that end, univariate Spearman rank correlations were

286 conducted to determine if any of the measured physiochemical parameters correlated

287 with microbial richness. In addition, we used simple and partial Mantel tests to examine

288 correlations between each physiochemical parameter and microbial community

289 composition. A simple Mantel test is a non-parametric method that compares two

290 distance matrices, and which calculates a correlation coefficient and p-value using

291 permutations. It determines whether samples that are similar in one measure (e.g., soil

292 pH) are similar in another (e.g., microbial composition). A partial Mantel test determines

293 the relationship between two variables while holding the effects of other, potentially

294 confounding, variables constant. We performed these Mantel tests using Jaccard

295 measures of dissimilarity for microbial composition, and Euclidean distances for each

296 soil parameter. Because we were particularly interested in understanding if and how giant

297 sequoia influence microbial communities via changes in the soil, we only included

298 physiochemical parameters that were found to differ significantly by tree species in our

299 analyses (Hart et al., unpublished data), and we conducted separate analyses for each

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300 grove. Namely, we determined whether bacterial/archaeal and fungal community

301 composition correlated with soil pH, extractable aluminum (Al), sum of the base cations

302 (composed of 76-91% calcium, 5-16% magnesium, 2-11% potassium, and 0-3% sodium),

303 and gravimetric soil moisture. All analyses were conducted in R version 3.2.1 (R Core

304 Team, 2017).

305

306 Results

307 Characterizing Giant Sequoia Bacterial/Archaeal Communities (Q1)

308 Averaged across both the Mariposa and Merced groves, Proteobacteria comprised the

309 majority of the 16S sequences recovered beneath giant sequoia (31.5% ± SE 0.5%),

310 followed by Acidobacteria (15.9% ± SE 0.5%), Actinobacteria (15.6% ± SE 0.4%),

311 Planctomycetes (11.2% ± SE 0.3%), and Verrucomicrobia (9.0% ± SE 0.2%;

312 Supplemental Information Figure S1A). Together, these five phyla accounted for greater

313 than 80% of the sequences. Forty-one less abundant phyla were also recovered from giant

314 sequoia soils, three of which were archaeal.

315

316 Characterizing Giant Sequoia Fungal Communities (Q1)

317 Averaged across both the Mariposa and Merced groves, Basidiomycota comprised the

318 majority of ITS sequences recovered beneath giant sequoia (82.1% ± SE 2.4%), followed

319 by Ascomycota (12.5% ± SE 1.5%), and Zygomycota (2.1% ± SE 1.4%). Less than 2%

320 were Glomeromycota or unidentified fungi (Supplemental Information Figure S1B).

321 Hygrocybe was the most dominant Basidiomycota genus recovered, comprising 21.5% (±

322 SE 5.8%) of all sequences. Wilcoxina was the most relatively abundant Ascomycota

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323 genus recovered from beneath giant sequoia, comprising 4.9% (± SE 1.1%) of all

324 sequences.

325

326 Comparing giant sequoia and sugar pine microbial communities across both groves:

327 Alpha and beta diversity (Q2 & Q3)

328 Bacterial/archaeal communities were most strongly structured by grove effects,

329 followed by host tree differences (Figure 1A, Table 1A). Bacterial/archaeal richness was

330 greater under giant sequoia (mean richness ± SE, Mariposa Grove: 5,924.6 ± 129.2,

331 Merced Grove: 6,164.6 ± 104.6) compared to sugar pine (mean richness ± SE, Mariposa

332 Grove: 5,327.7 ± 178.5, Merced Grove: 5,875.4 ± 221.5; Figure 2A). These differences

333 remained constant across grove (no significant tree x grove interaction, P > 0.1). At the

334 phylum level, Proteobacteria, Actinobacteria, and Gemmatimonadetes were relatively

335 more abundant—and Acidobacteria, Armatimonadetes, and TM7 were relatively less

336 abundant—in giant sequoia compared to sugar pine soils (Supplemental Information

337 Figure S1A). However, these differences were not consistent across groves and some

338 were only marginally significant (P = 0.05-0.10).

339 Fungal communities were also structured most strongly by grove effects, followed

340 by host tree differences (Figure 1B; Table 1B). However, in contrast to bacteria/archaea,

341 there was a significant tree by grove interaction (P < 0.05), and fungal richness did not

342 significantly differ between tree species (giant sequoia mean richness ± SE, Mariposa

343 Grove: 160.7 ± 12.7, Merced Grove: 200.1 ± 13.1; sugar pine mean richness ± SE,

344 Mariposa Grove: 187.9 ± 17.7, Merced Grove: 187.3 ± 14.8; Figure 2B). At the phylum

345 level, Basidiomycota and Glomeromycota were relatively more abundant—and

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346 Ascomycota and Zygomycota were relatively less abundant—in giant sequoia compared

347 to sugar pine soils (Supplemental Information Figure S1B). These phylum-level

348 differences were not consistent across groves and some were marginally significant (P =

349 0.05-0.1). In addition, the composition of EMF and AMF communities showed small but

350 significant effects of tree host and grove with no interaction (Supplemental Information

351 Figure S2)—and AMF had significantly more OTUs beneath giant sequoia (giant sequoia

352 mean richness ± SE = 8.5 ± 1.09; sugar pine mean richness = 5.4 ± 0.96; P = 0.009).

353

354 Comparing giant sequoia and sugar pine microbial communities across both groves:

355 Dominant taxa and core members (Q2 & Q3)

356 Only one bacterial OTU, a Bradyrhizobium (phylum Proteobacteria), comprised more

357 than 1% of the total bacterial sequences. In contrast, 28 fungal taxa each comprised more

358 than 1% of the total fungal taxa (Figure 3). Of these 28 fungal taxa, 64% were EMF and

359 18% belong to the genus Hygrocybe. We observed a number of these dominant OTUs

360 whose frequency, relative abundance, or both differed consistently between tree species.

361 For example, in both Mariposa and Merced groves, an unidentified Cryptococcus species

362 was recovered from 100% of samples, but was relatively more abundant beneath sugar

363 pine. An unidentified Byssocorticium species, an EMF taxon, also showed a consistent

364 trend across groves, where it was more frequent and relatively abundant beneath sugar

365 pine compared to giant sequoia. In contrast, species of Hygrocybe, which is generally

366 considered saprophytic (although see discussion below), were almost always more

367 frequent and relatively more abundant in giant sequoia soils, although these differences

368 were not always statistically significant (Figure 3). Finally, some OTUs differed

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369 significantly between tree species in one grove but not the other. This included Russula

370 acrifolia , another EMF taxon (Tedersoo et al., 2010), which was more frequent and

371 relatively abundant beneath sugar pine than giant sequoia in the Mariposa grove, and an

372 unidentified Geminibasidium species, which is a xerotolerant basidiomycete yeast

373 (Nguyen et al., 2013), was relatively more abundant beneath sugar pine than giant

374 sequoia in the Merced grove.

375 There were very few fungal OTUs that were recovered from 100% of giant

376 sequoia or sugar pine samples in either grove. Accordingly, neither giant sequoia nor

377 sugar pine had a core fungal microbiome comprised of OTUs that were recovered from

378 all samples (Figure 4A). Even when we relaxed the definition of a core to require only

379 80% frequency, giant sequoia soils contained only two unique core fungal OTUs and

380 sugar pine soils contained one—both of which were Zygomycota. In contrast,

381 bacterial/archaeal communities beneath giant sequoia and sugar pine harbored a number

382 of OTUs that made up a unique core (i.e., were recovered from 100% of giant sequoia

383 samples or 100% of sugar pine samples, but not 100% of both; Figure 4B). Both tree

384 species contained the phyla Proteobacteria, Acidobacteria, Planctomycetes,

385 Actinobacteria, Bacteroidetes, and Verrucomicrobia in their core community (Figure 4C

386 & D). Each core also contained a number of OTUs representing phyla unique to that tree

387 species. For instance, giant sequoia contained core members from Chloroflexi,

388 Gemmatimonadetes, and TM7, while sugar pine did not (Figure 4C). Similarly, only

389 sugar pine contained core members from Armatimonadetes, Chlorobi, and OD1 (Figure

390 4D). In addition to these phylum-level differences, the size of the core for giant sequoia

391 and sugar pine differed, with giant sequoia containing substantially more core OTUs (101

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392 OTUs) than sugar pine (50 OTUs; Figure 4B). However, of the 101 core OTUs beneath

393 giant sequoia, only 13% were notably less frequent (frequency < 80%) in sugar pine soils

394 (Figure 4E; Supplemental Information Table S2). Similarly, of the 50 core OTUs beneath

395 sugar pine, only 10% were notably less frequent in giant sequoia soils (Supplemental

396 Information Table S2). In all other cases, OTUs that comprised the core of one tree

397 community were often missing from only a few samples in the other tree community (80-

398 95% frequency).

399

400 Determining the indirect effects of tree species on microbial diversity and composition

401 (Q4)

402 Giant sequoia and sugar pine soils differed significantly in a number of measured

403 physiochemical parameters (Supplemental Information Table S3). Of these parameters,

404 bacterial/archaeal and fungal community composition correlated most strongly and

405 consistently with soil pH (Figure 5); these relationships with soil pH remained strong

406 even when the effects of other soil parameters were statistically controlled for using a

407 partial Mantel test. The composition of bacterial/archaeal and fungal communities also

408 tended to be strongly related to differences in soil moisture (gravimetric water content) in

409 both the simple and partial Mantel tests. In addition, bacterial/archaeal and fungal

410 community composition correlated with extractable aluminum and the sum of base

411 cations; however, these relationships were inconsistent between groves for fungi, and

412 often disappeared for both microbial groups when the effects of other soil parameters

413 were accounted for (Figure 5).

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414 In contrast to microbial composition, bacterial/archaeal and fungal richness were

415 largely unrelated to the measured physiochemical parameters (Supplemental Information

416 Table S3). Only in the Merced Grove did bacterial/archaeal richness correlate positively

417 with pH, and negatively with soil moisture and extractable aluminum. Similarly, of all the

418 physiochemical parameters, fungal richness only correlated (positively) with bulk density

419 in the Mariposa Grove, and this relationship was only marginally significant (P = 0.05-

420 0.1).

421

422 Discussion

423 Giant sequoia are the largest, and some of the longest-lived, trees in the world. An

424 iconic species with great ecological and cultural importance, little is known about how

425 giant sequoia individuals interact with belowground communities. In this study, we used

426 next-generation sequencing techniques to describe for the first time the soil microbiome

427 associated with this charismatic megaflora. We compared giant sequoia microbiomes to

428 those of co-occurring sugar pine trees across two groves on contrasting geological

429 substrates in Yosemite National Park, USA, and determined whether host tree differences

430 in microbial communities were related to differences in soil physiochemical parameters.

431 While this kind of observational study design—which is largely unavoidable given the

432 long-lived nature of the host trees under study—limits our power of causal inference,

433 these limitations can be partly addressed by future work that tracks giant sequoia soil

434 microbiomes from time since establishment and across additional groves with (dis)similar

435 parent materials.

436

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437 Characterizing giant sequoia soil microbiomes and comparing them to sugar pine

438 microbiomes across both groves (Q1 - Q3)

439 Differences in bacterial/archaeal community composition between groves tended

440 to be greater than those differences associated with tree species. Still, there was evidence

441 that giant sequoia influenced underlying bacteria and archaea in unique ways compared

442 to sugar pine. Specifically, we found that bacterial and archaeal richness was greater

443 beneath giant sequoia than sugar pine (Figure 2A), possibly because giant sequoia are

444 larger and create more niche space within the soil (Glassman et al., 2017). In addition,

445 communities of bacteria/archaea were compositionally distinct from those beneath sugar

446 pine (Figure 1A). These differences remained constant across the two groves, despite the

447 fact that soils from each grove were derived from geochemically distinct substrates.

448 Fungal community composition also differed between giant sequoia and sugar pine;

449 however, the specific ways that fungi differed between tree species depended on the

450 grove (Figure 1B). Overall, these findings—which agree with a number of other studies

451 where bulk soil microbial communities differed between contrasting tree species (Ayres

452 et al., 2009; Thoms et al., 2010; Scheibe et al., 2015; Sun et al., 2016)—suggest that,

453 despite relatively large grove differences, a “host signal” can still be observed.

454 Giant sequoia and sugar pine are known to associate with two contrasting groups

455 of mycorrhizal fungi. The former with AMF (Fahey et al., 2012) and the latter with EMF

456 (Walker, 2001). While AMF and EMF were recovered from beneath both tree hosts—

457 possibly because of understory influences or root overlap—communities associated with

458 giant sequoia differed from those associated with sugar pine (Supplemental Information

459 Figure S2). AMF communities were considerably more diverse beneath giant sequoia,

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460 and EMF taxa such as an unidentified Byssocorticium species and Russula acrifolia were

461 more frequent and relatively abundant beneath sugar pine. While this is to be expected,

462 our findings show that differences in mycorrhizal communities in mixed stands of AMF

463 and EMF trees can be seen at the bulk soil scale, differences that can have cascading

464 effects on global scale biogeochemical processes including the cycling of carbon (Averill

465 et al., 2018) and nitrogen (Mushinski et al., 2019).

466 Microbial taxa that are abundant within the community can contribute

467 significantly to ecosystem function (Dai et al., 2016). To discern patterns in frequent and

468 abundant taxa between trees, we filtered both the bacterial/archaeal and fungal OTU

469 tables to include only those OTUs that comprised greater than 1% of the total sequences.

470 In accordance with the idea that prokaryotic microbial communities often include a very

471 small number of dominant members, and are instead comprised of many rare members

472 (Sogin et al., 2006), only one bacterium passed our high abundance filter. This bacterial

473 OTU, which was of the genus Bradyrhizobium, comprised 2.1% of the total 16S rRNA

474 sequences recovered, making it the dominant bacterial OTU at both groves. Recent work

475 employing quantitative population genomics suggests that free-living Bradyrhozbia are

476 unexpectedly incapable of fixing atmospheric N and may instead metabolize aromatic

477 carbon sources (VanInsberghe et al., 2015), which may explain why this genus tends to

478 dominate microbial communities of forest bulk soil (Uroz et al., 2010; Hartmann et al.,

479 2012; VanInsberghe et al., 2015).

480 In contrast to bacteria/archaea, we recovered 28 abundant OTUs that each

481 comprised greater than 1% of the total ITS sequences (Figure 3). Notably, 18% of these

482 OTUs were from the genus Hygrocybe (waxcaps). Hygrocybe are widespread and can be

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483 found in a variety of habitats worldwide (Halbwachs et al., 2013a), including forests of

484 the Sequoiadendron sister genus Sequoia (Enright et al., 2019). For example, Hygrocybe

485 was the dominant taxon comprising 13% of ITS1 Illumina MiSeq sequences in a

486 California coastal redwood (Sequoia sempervirens) forest (Enright et al., 2019). These

487 fungi are frequently considered to be saprotrophic, although recent work has implicated

488 some Hygrocybe species, such as H. virginea, as being endophytic (Halbwachs et al.,

489 2013b; Persoh, 2013; Tello et al., 2014). In our study, Hygrocybe were rarely recovered

490 from beneath sugar pine, indicating a preference for giant sequoia soils. Indeed, a

491 canonical discriminant analysis (CDA) illustrated that differences in Hygrocybe drove

492 much of the fungal compositional variability between the two trees (data not shown).

493 While the exact trophic lifestyle of this genus remains uncertain (Seitzman et al., 2010),

494 it is possible that—given the preponderance of Hygrocybe species in giant sequoia soils

495 and their apparent ability to act as root and systemic endophytes—these fungi may form

496 ecologically significant symbiotic relationships with giant sequoia (Zarraonaindia et al.,

497 2015). Future research should therefore identify whether such a relationship exists and, if

498 so, what the ecological implications may be for giant sequoia growth and survival.

499 In addition to identifying common and abundant OTUs, it can be useful to

500 distinguish core members of a microbial community that remain constant across space or

501 time (Shade & Handelsman, 2012). Doing so helps define a healthy (or alternatively a

502 degraded) community, and can improve our understanding how that community will

503 respond to future perturbations. In contrast to fungi, which had no discernable core

504 community, we identified considerable core bacterial/archaeal communities associated

505 with both tree species (Figure 4A & B). Interestingly, giant sequoia’s unique core was

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506 double the size of sugar pine’s, indicating that this giant, long-lived tree maintains a

507 relatively large and consistent set of prokaryotic OTUs in its surrounding soil. The larger

508 community associated with giant sequoia could be due to its age and size, as larger trees

509 are known to host more microbial taxa (Glassman et al., 2017). Of these OTUs, thirteen

510 were also considerably (at least 20%) less frequent in sugar pine soils. However, there

511 were no clear trends in the taxonomy or ecology of these thirteen OTUs, with family

512 associations ranging from Norcardioidaceae (contains endophytes and species capable of

513 degrading organic matter; Tóth & Borsodi, 2014) to Mycobacteriaceae (contains animal

514 pathogens and species capable of degrading hydrocarbons; Lory, 2014). Metagenomic

515 data could provide a more complete picture of core community dynamics, as some

516 evidence suggests that communities assemble at the functional rather than the

517 phylogenetic level (Burke et al., 2011). Regardless, our data contribute to a small set of

518 previously published work that explicitly identify core communities in bulk soil (Andrew

519 et al., 2012; Orgiazzi et al., 2013), and indicate that giant sequoia and sugar pine each

520 harbor unique core communities of bacteria/archaea, but lack consistent core fungal

521 OTUs at the spatial scale studied here.

522 It is possible that having a large and diverse core community of bacteria/archaea

523 aids in the long-term success of giant sequoia individuals. In addition to providing critical

524 biogeochemical functions (e.g., decomposition of organic matter and nutrient cycling),

525 core and abundant microorganisms within soil may act as a source that “seeds”

526 rhizospheric and endospheric communities, ultimately contributing directly to plant

527 health. Indeed, it has been proposed that some foliar endophytes persist through the

528 winter as saprobes of litter only to re-invade host leaves in the spring (Unterseher et al.,

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529 2013; Baldrian, 2017). A recent study assessing foliar bacterial endophytic communities

530 of giant sequoia and coastal redwoods found that giant sequoia contained a diverse

531 endophytic community, with major phyla including Acidobacteria, Actinobacteria,

532 Bacteroidetes, Firmicutes, Fusobacteria, Proteobacteria, and TM7 (Carrell & Frank,

533 2015). Notably, five of the twenty dominant orders recovered from giant sequoia foliage

534 samples were represented in our core bacterial/archaeal community dataset

535 (Actinomycetales, Burkholderiales, Rhizobiales, Rhodospirillales, and

536 Sphingobacteriales). It is conceivable that at least some of these endophytes are derived

537 from the soil community; however, (at minimum) comparative sampling of giant sequoia

538 microbial communities within the same site, and (at maximum) use of more advanced

539 tracing techniques (e.g., isotopes or quantum dots), will be required to assess this. Future

540 studies that focus on connecting the giant sequoia holobiont with soil communities should

541 provide promising insights to the stability of this tree species over millennia.

542

543 Determining the indirect effects of tree species on microbial diversity and composition

544 (Q4)

545 Trees can affect the composition and diversity of soil microbial communities

546 through a variety of indirect mechanisms. Some of these mechanisms include changes in

547 root exudation, nutrient uptake, soil microclimate, pH, and the amount and quality of

548 litter inputs (Binkley & Giardina, 1998). In our study, a number of notable soil

549 parameters differed between giant sequoia and sugar pine (Hart et al., unpublished data;

550 Supplemental Information Methods S1)—and while bacterial/archaeal and fungal

551 richness were generally insensitive to these parameters, community composition

25 bioRxiv preprint doi: https://doi.org/10.1101/807040; this version posted October 17, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license.

552 correlated with a number of them. Most strongly and consistently, bacterial/archaeal and

553 fungal community composition related to soil pH and soil moisture (Figure 5). Soil

554 moisture dynamics are coupled to water stress, oxygen diffusion, and substrate supply;

555 differences in soil moisture can therefore alter metabolic activity of microbial functional

556 groups, the occurrence of aerobic/anaerobic processes (e.g., aerobic

557 decomposition/denitrification), and ultimately microbial community composition

558 (Manzoni et al., 2012). Soil pH is a well-known driver of bacterial/archaeal community

559 composition and diversity, a driver that is often unrivaled by other soil parameters due to

560 the narrow tolerance range of prokaryotic microorganisms (Lauber et al., 2009; Collins et

561 al. , 2016). While fungi are thought to exhibit wider pH ranges for optimal growth (Rousk

562 et al., 2010), a recent study indicates that soil pH can also be a strong mediator of fungal

563 community composition—a finding consistent with ours (Glassman et al., 2017).

564 There is growing recognition that calcium availability also regulates soil

565 microbial (Grayston et al., 2004; Allison et al., 2007; Sun et al., 2016; Glassman et al.,

566 2017) and faunal (Reich et al., 2005) community composition, organic matter

567 decomposition (Hobbie et al., 2006; Hobbie et al., 2007), and overall forest health

568 (Battles et al., 2014). Calcium provides structure to many cell walls, and the cycling of

569 this cation in soil depends largely on decomposition (Chapin et al., 2011). In our study,

570 giant sequoia litter was significantly enriched in base cations compared to sugar pine, and

571 this resulted in increased availability of exchangeable calcium (in addition to Mg2+, K+,

572 Na+) in the upper mineral soil. Accordingly, Mantel tests revealed that bacterial/archaeal

573 and fungal community composition correlated significantly with the sum of the base

574 cations, a composite measure that was composed primarily of calcium. However, this

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575 relationship almost always disappeared when the effects of the other variables were

576 accounted for (Figure 5), suggesting that the influence of calcium was mediated by other

577 soil parameters—namely soil pH. Base cations compete with H+ and Al3+ for exchange

578 sites on soil particle surfaces; therefore, the concentration of calcium in soil mediates soil

579 pH, such that higher amounts of exchangeable calcium (and other base cations) create

580 less acidic soils (Reich et al., 2005). The idea that soil calcium indirectly mediates

581 microbial composition via soil pH agrees with the conclusions of some other studies

582 (Grayston et al., 2004; Reich et al., 2005); however, Allison et al. (2007) suggested that

583 calcium may influence the soil community primarily via calcium effects on soil

584 aggregation. Taken together, these data indicate that—by maintaining high soil pH,

585 nutrient availability (i.e., calcium and other base cations), and moisture compared to

586 sugar pine—giant sequoia indirectly influence the composition of underlying microbial

587 communities.

588

589 Conclusion

590 Using next-generation sequencing techniques, we show for the first time that

591 microbial communities of bulk soil differ between giant sequoia and a co-occurring

592 conifer, sugar pine. Namely, giant sequoia supported unique bacterial/archaeal and fungal

593 community composition, greater bacterial/archaeal richness, and a large unique core

594 community of bacteria/archaea. These host tree differences, which were at least partially

595 driven by soil pH, calcium availability, and soil moisture, were discernible despite

596 concurrently large grove effects. In some cases, the influence of host tree differed

597 between the two groves under study, which were close in proximity but had contrasting

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598 parent material. Our findings suggest that the effects of giant sequoia extend beyond

599 mycorrhizal mutualists to include the broader community at the bulk soil scale.

600

601 Acknowledgements

602 The authors declare that they have no conflict of interest. We thank Teresa L. Fukuda for

603 field and laboratory assistance and Judy Chung for help with ITS1 sequencing. We also

604 thank the National Park Service for allowing us to sample soils within Yosemite National

605 Park. This research was supported by grants from the NSF Research Experience for

606 Undergraduates (DBI-1263407) and the Critical Zone Observatory Programs (EAR-

607 1331939), as well as a USDA National Institute of Food and Agriculture Seed grant (CA-

608 R-PPA-5101-CG) and RSAP grant (CA-R-PPA-5093-H).

609

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Table 1. Results of the PERMANOVA for (A) bacteria/archaea and (B) fungi. Results are presented for both Jaccard and Bray-Curtis dissimilarity matrices. Because there was a significant tree x site interaction for fungi, we present the effects of tree species separated by grove. Site = Mariposa versus Merced Grove. Tree Species = giant sequoia versus sugar pine.

2 (A) Bacteria/Archaea Fmodel R P value Jaccard Site 3.663 0.057 0.001 Tree Species 2.29 0.036 0.001 Site x Tree 1.16 0.018 0.088

Bray-curtis Site 9.427 0.134 0.001 Tree Species 4.09 0.063 0.001 Site x Tree 1.296 0.017 0.145

2 (B) Fungi Fmodel R P value Jaccard Site 4.06 0.064 0.001 Tree Species 2.32 0.038 0.001 Site x Tree 1.34 0.021 0.016 Tree Species in Merced Grove 1.98 0.062 0.001 Tree Species in Mariposa Grove 1.83 0.064 0.002 Bray-curtis Site 3.589 0.047 0.001 Tree Species 1.62 0.027 0.007 Site x Tree 1.38 0.022 0.032 Tree Species in Merced Grove 1.79 0.056 0.001 Tree Species in Mariposa Grove 1.26 0.045 0.096

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Figure 1. Influence of tree species (giant sequoia and sugar pine) and grove (Mariposa and Merced Grove) on (A) bacterial/archaeal and (B) fungal community composition. Left panel = non-metric multidimensional scaling (NMDS) of Jaccard (presence-absence) dissimilarity metric. Right panel = NMDS of Bray-Curtis (relative abundance) dissimilarity metric. Each symbol corresponds to a sample collected from one of two groves, and each color corresponds to a tree species. Points that are close together represent samples with similar community composition, and the dashed ovals represent 95% confidence intervals of sample ordination grouped by unique tree x grove combinations. The stress values for the bacterial/archaeal ordinations were 0.06 (Jaccard) and 0.07 (Bray-Curtis); the stress values for the fungal ordinations were 0.14 (Jaccard) and 0.18 (Bray-Curtis).

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Figure 2. Influence of tree species (giant sequoia and sugar pine) and grove (Mariposa and Merced Grove) on alpha diversity for (A) bacteria/archaea and (B) fungi. Observed OTUs = number of observed operational taxonomic units, presented as mean ± 1 standard error (n =16). In (A), an asterisk denotes significant differences (P < 0.05) between tree species. P-values were derived from a two-way ANOVA.

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Figure 3. The relative abundance of the most abundant fungal OTUs in giant sequoia and sugar pine soils across both groves. Error bars = 1 standard error of the mean (n = 16). The size of each point is scaled by the frequency of an OTU (how many samples it was recovered from), with larger circles corresponding to greater frequency. Significant differences in OTU relative abundance between tree species were assessed using Mann-Whitney U test on ranks (* p < 0.05, ** p < 0.01, *** p < 0.001).

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Figure 4. Shared OTUs of the (A) fungal and (B) bacterial/archaeal core microbiomes associated with giant sequoia and sugar pine. The Venn diagrams show absolute number of OTUs shared between core microbiomes of each tree species across two groves. Phylum level taxonomic information is also provided for the OTUs comprising the (C) giant sequoia and (D) sugar pine core bacterial/archaeal communities. (E) Heatmap illustrating presence/absence and taxonomy of giant sequoia’s core OTUs that were considerably less frequent (20% difference) in sugar pine soils. Taxonomic information was derived from EzTaxon, and % similarity is the sequence similarity between the OTU and its nearest cultured match. Colors of the bar beneath the heatmap correspond to tree type (pink = sugar pine, blue = giant sequoia).

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Figure 5. Results of (top panel) simple (r) and (bottom panel) partial (ρ) Mantel tests relating soil parameters that varied between tree types to bacterial/archaeal and fungal community composition. Only those parameters that correlated significantly with composition at least once are presented. pH = soil pH; Al = soil xtractable aluminum; Sum BC = sum of the base cations (Ca2+, Mg2+, K+, and Na+); soil moisture = soil gravimetric water content. N.S. = non-significant (p > 0.05) correlation.