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Soil type and precipitation level have a greater influence on fungal than bacterial diversity in serpentine and non-serpentine biological soil crusts

July 2024
Ecological Research 39(6)

DOI:10.1111/1440-1703.12500

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CC BY-NC-ND 4.0

Authors:

Danielle Botha

North-West University

Sandra Barnard

North-West University

Nishanta Rajakaruna

California Polytechnic State University

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Serpentine soils are characterized by nutrient imbalances and high levels of potentially toxic metals (PTMs). These soils host depauperate plant communities of species with specialized adaptations. Initial studies showed that South African serpentine soils harbor distinct biocrust algal and cyanobacterial species compared to adjacent non-serpentine soils, with these communities further differing based on high and low precipitation levels. Here, we investigated the bacterial and fungal diversity of biological soil crusts from serpentine and non-serpentine soils at two precipitation levels. The bacterial and fungal communities were characterized using 16S rDNA and ITS metabarcoding, respectively. No significant differences could be found in bacterial richness and community structure. Nevertheless, bacterial taxa such as Archangium, Candidatus Solibacter, Chthoniobacter, and Microvirga were more abundant in serpentine biocrusts or biocrusts receiving lower precipitation. The fungal community structure was distinct between serpentine and non-serpentine soils (p = 0.027) and between high and low precipitation (p = 0.018). Furthermore, fungal diversity was lowest in the drier, serpentine biocrusts compared to non-serpentine (p = 0.001) and serpentine crusts receiving higher precipitation (p = 0.002). The fungal genera, Ramimonilia and Vishniacozyma, which are known to be resistant or tolerant to PTMs and other environmental extremes, were significantly more abundant (p = 0.036 and p = 0.016, respectively) in serpentine biocrusts, with the latter indicating serpentine habitats. This study concluded that soil type influenced the fungal alpha diversity, specifically in the serpentine soil, resulting in a decrease in fungal species richness. Furthermore, precipitation influenced fungal beta diversity by shaping distinct fungal communities found in the biocrusts of serpentine and non-serpentine soils.

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SPECIAL FEATURE

Ultramafic Ecology: Proceedings of the 10th International Conference on Serpentine Ecology

Soil type and precipitation level have a greater influence

on fungal than bacterial diversity in serpentine and

non-serpentine biological soil crusts

Danielle Botha

| Sandra Barnard

| Sarina Claassens

1,2

Nishanta Rajakaruna

1,3

| Arthurita Venter

| Arshad Ismail

4,5,6

Mushal Allam

4,7

| Stefan J. Siebert

Unit for Environmental Sciences and Management, North-West University, Potchefstroom, South Africa

School of Molecular and Life Sciences, Curtin University, Bentley, Western Australia, Australia

Biological Sciences Department, California Polytechnic State University, San Luis Obispo, California, USA

Sequencing Core Facility, National Institute for Communicable Diseases, A Division of the National Health Laboratory Service, Johannesburg,

South Africa

Department of Biochemistry and Microbiology, Faculty of Science, Engineering and Agriculture, University of Venda, Thohoyandou, South Africa

Institute for Water and Wastewater Technology, Durban University of Technology, Durban, South Africa

College of Medicine and Health Sciences, United Arab Emirates University, Al Ain, United Arab Emirates

Correspondence

Danielle Botha, Unit for Environmental

Sciences and Management, North-West

University, Potchefstroom, South Africa.

Email: daniellebotha3@gmail.com

Funding information

National Geographic Society,

Grant/Award Number: 9774-15;

Fulbright Program

Abstract

Serpentine soils are characterized by nutrient imbalances and high levels of

potentially toxic metals (PTMs). These soils host depauperate plant communi-

ties of species with specialized adaptations. Initial studies showed that

South African serpentine soils harbor distinct biocrust algal and cyanobacterial

species compared to adjacent non-serpentine soils, with these communities fur-

ther differing based on high and low precipitation levels. Here, we investigated

the bacterial and fungal diversity of biological soil crusts from serpentine and

non-serpentine soils at two precipitation levels. The bacterial and fungal com-

munities were characterized using 16S rDNA and ITS metabarcoding, respec-

tively. No significant differences could be found in bacterial richness and

community structure. Nevertheless, bacterial taxa such as Archangium,Candi-

datus Solibacter,Chthoniobacter, and Microvirga were more abundant in ser-

pentine biocrusts or biocrusts receiving lower precipitation. The fungal

community structure was distinct between serpentine and non-serpentine soils

(p=0.027) and between high and low precipitation (p=0.018). Furthermore,

fungal diversity was lowest in the drier, serpentine biocrusts compared to non-

serpentine (p=0.001) and serpentine crusts receiving higher precipitation

Received: 16 October 2023 Revised: 20 May 2024 Accepted: 26 May 2024

DOI: 10.1111/1440-1703.12500

This is an open access article under the terms of the Creative Commons Attribution-NonCommercial-NoDerivs License, which permits use and distribution in any

medium, provided the original work is properly cited, the use is non-commercial and no modifications or adaptations are made.

Ecological Research. 2024;1–17. wileyonlinelibrary.com/journal/ere 1

(p=0.002). The fungal genera, Ramimonilia and Vishniacozyma, which are

known to be resistant or tolerant to PTMs and other environmental extremes,

were significantly more abundant (p=0.036 and p=0.016, respectively) in

serpentine biocrusts, with the latter indicating serpentine habitats. This study

concluded that soil type influenced the fungal alpha diversity, specifically in

the serpentine soil, resulting in a decrease in fungal species richness. Further-

more, precipitation influenced fungal beta diversity by shaping distinct fungal

communities found in the biocrusts of serpentine and non-serpentine soils.

KEYWORDS

biocrust, biological soil crust, metabarcoding, microbial diversity, serpentine geoecology

1|INTRODUCTION

Serpentine soils, characterized by their nutrient imbal-

ances (Ca:Mg molar ratio of <1, generally low levels of P,

N, and K) and potentially toxic metals (PTMs) which

include high levels of Cr, Cd, and Ni, are known to har-

bor specialized plant communities in many parts of the

world. They often shape the diversity of soil biota by

influencing both the colonization and persistence of spe-

cies (Rajakaruna & Boyd, 2008). These harsh soils are

known for the stressors they impose on plants, collec-

tively called “serpentine syndrome”(Bini & Maleci, 2014;

Jenny, 1980), resulting in low ecosystem productivity,

high levels of endemism, and plants with lower competi-

tive ability (Anacker, 2014). Serpentine soils are also of

special interest for their potential to model solutions to

degraded ecosystems, especially in mine restoration

(Rajkumar et al., 2009; Robinson et al., 1999).

Despite the extensive work done on serpentine-soil-

vascular-plant relations worldwide (Alexander et al., 2007;

Galey et al., 2017; Rajakaruna et al., 2009;Teptina

et al., 2018), research on serpentine substrate-microbe rela-

tionsislimitedtoafewstudiesonmycorrhizalfungi

(Schechter & Branco, 2014;Southworthetal.,2013), soil-

dwelling bacteria (Ma et al., 2015; Oline, 2006), and saxico-

lous lichens (Favero-Longo et al., 2018;Mulroyetal.,2022;

Rajakaruna et al., 2012). Some important distinctions

between the microbial diversity of serpentine and non-

serpentinesoilshavecometolightintheseandotherstud-

ies. Microbial community structure may differ based on the

presence of different plants in serpentine and non-

serpentine soils (Pessoa-Filho et al., 2015), and local hetero-

geneity in soil characteristics and microclimatic gradients

can impact the diversity of cyanobacteria and algae in ser-

pentine soils (Venter et al., 2018). Actinobacteria is the most

commonly isolated bacterial phylum from serpentine soils

(Abou-Shanab et al., 2009; Khilyas et al., 2019;Mengoni

et al., 2001; Turgay et al., 2012; Visioli et al., 2019)and

exhibits K-strategist attributes that allow these species to

thrive in resource-limited and high-competition environ-

ments, making them more abundant in soils low in organic

matterandhighinNiconcentrations(Brzeszczetal.,2016;

Visioli et al., 2019).

According to Muller and Hilger (2015), the richness of

fungal communities in serpentine soils is not influenced by

edaphic factors, but the community structure may differ sig-

nificantly between serpentine and non-serpentine soils. This

was supported by Branco and Ree (2010), concluding that

serpentine soils host rich fungal communities with repre-

sentatives from all fungal lineages and that these environ-

ments are not extreme for ectomycorrhizal fungi. Husna

et al. (2017)alsoreportedthatarbuscular mycorrhizal fungi

are influenced by soil chemical properties such as metal

content, Ni, and Ca:Mg ratio. Therefore, serpentine soils are

not necessarily depauperate and may host a rich assemblage

of fungal species, with the structure of these communities

strongly influenced by soil chemical and physical proper-

ties.Ortizetal.(

2020) furthermore found a significant influ-

ence of climate on rhizosphere microbial communities and

Naidoo et al. (2022) found that precipitation is a key cli-

matic factor that shapes the taxonomy and resulting ecosys-

tem services of arid soil microbiomes.

However, serpentine soils are not only studied for their

unique chemical and physical attributes and the consequen-

tial impacts on soil biota, but also for the microbial commu-

nities living in the upper millimeters of these and other

harsh soils, namely biological soil crusts, or biocrusts.

Biocrusts constitute communities of photoautotrophic cya-

nobacteria, algae, lichens, and bryophytes growing along-

side heterotrophic fungi, bacteria, and archaea (Weber

et al., 2016). They are an essential component of dryland

and desert ecosystems and contribute to a range of ecosys-

tem functions. Biocrusts play a fundamental role in soil

aggregation and erosion resistance (Belnap et al., 2003;

Chamizo et al., 2016), while also enhancing soil nutrient

levels through N-fixation (Elbert et al., 2012), dust trapping

(Reynolds et al., 2001), and nutrient cycling (Strauss

et al., 2012). They further influence the hydrological

2BOTHA ET AL.

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(Chamizo et al., 2016) and thermal properties of soil

(Couradeau et al., 2016;Rutherfordetal.,2017). Given that

serpentine soils are often low in essential nutrients,

especially N, P, and K, enriched with PTMs (e.g., Cd, Cr,

and Ni), and generally water stressed due to poor soil tex-

ture and structure, as well as habitat openness/bareness

(Rajakaruna & Boyd, 2014), biocrusts are likely to enrich

serpentine soil with limited nutrients (such as N), minimize

PTM stress by changing soil pH and increasing soil mois-

ture content. Significant knowledge gaps in the composition

of biocrusts for certain regions persist with data for African

serpentine soils being very limited (Venter et al., 2015,

2018). Venter et al. (2015) characterized microbial commu-

nities using an isolation approach but no unique algal flora

for serpentine soils was confirmed. Venter et al. (2018)then

demonstrated, using a metabarcoding approach, that ser-

pentine soils harbor distinct biocrust algal and cyanobacter-

ial species compared to adjacent non-serpentine soils at

different precipitation levels. Their study also documented,

for the first time, nine genera of cyanobacteria from

South African serpentine soils.

These findings prompted us to further survey the com-

position of biocrusts of serpentine soils in South Africa

(Venter et al., 2015,2018) across varying precipitation

levels, via a DNA metabarcoding approach. Here, we inves-

tigate the differences and changes in the (i) bacterial and

fungal biocrust community diversity and composition

between serpentine and non-serpentine soils and (ii) bacte-

rial and fungal diversity of biocrusts according to differences

in precipitation. We hypothesize that the microbial diversity

within biocrusts occurring on serpentine soils is signifi-

cantly influenced by the combined effects of PTMs, low

nutrients, and varying precipitation levels. Specifically, we

predict that serpentine biocrusts will exhibit distinct micro-

bial community composition relative to non-serpentine,

with biocrusts of serpentine soils to be enriched with metal-

tolerant or resistant microorganisms. Furthermore, we

expect that the microbial diversity within serpentine bio-

crusts would be influenced by the interaction between the

extreme soil conditions and the contrasting precipitation

levels. Higher precipitation levels may potentially promote

greater species richness and more even microbial commu-

nity structures, while lower precipitation levels may lead to

decreased diversity and increased abundance of stress-

tolerant microbial taxa.

2|MATERIALS AND METHODS

2.1 |Sampling sites

Biological soil crusts were sampledateightdifferentlocalities

along the Barberton Greenstone belt in Mpumalanga,

South Africa, paired with one on serpentine soil and one off

serpentine to give 16 sampling sites in total (for more details,

see Venter et al., 2018 and Table S1). Half of the localities

(four serpentine and four non-serpentine) were sampled

along the temperate Highveld escarpment with a cool mean

annual temperature of 17Cwithfrequentfogandprecipita-

tion levels of more than 1000 mm per annum. The datasets

resulting from the analysis of these biocrust samples are

referred to as serpentine wet (SW: four sites), and non-

serpentine wet (NSW: four sites). The other half of the paired

samples (four serpentine and four non-serpentine) were

obtained in subtropical Lowveld with higher mean annual

temperature (19.7C) and lower precipitation (<800 mm).

The datasets resulting in the analysis of these biocrusts will

be referred to as serpentine dry (SD: four sites) and

non-serpentine dry (NSD: four sites). These defined substrate-

climate groups (SW, NSW, NSD, and SD) were designated as

“treatments”in subsequent statistical comparisons.

2.2 |Soil and biocrust sampling

Nine subsamples of soil and biocrusts were obtained ran-

domly at each of the 16 sampling localities by placing three

20 20 m plots at each locality and taking three subsam-

ples from each plot. Biocrust samples were collected using a

sterile spatula to a depth of 3 mm, placed into sterile screw-

cap falcon tubes, kept on ice during field collection and

then stored at 80C until DNA extraction. Soil samples

for physical and chemical analyses were collected with a

soil auger to a depth of 10 cm. The nine subsamples col-

lected at each site were combined to obtain one composite

sample per sampling site for soil and biocrusts, respectively.

Because this is a continuation of the study of Venter et al.

(2018), using the same study sites and samples, the chemi-

cal and physical soil parameters of the serpentine and non-

serpentine soils analyzed in this study were already known

(Tables S2 and S3).

Some important distinctions to note from Venter et al.

(2018) include that biocrusts of serpentine soils had an over-

all higher chlorophyll-acontent, and serpentine soils also

had higher electrical conductivity (EC), higher Mg, and lower

Ca concentrations than non-serpentine soils. Serpentine soils

further had higher concentrations of PTMs such as Co, Cr,

Fe, Mn, and Ni. There were no significant differences

between nutrient levels of serpentine and non-serpentine

soilswhichincludedN,S,andCpercentagesaswellascon-

centrations (mg L

1

or ppm) of Cl, Na, N, P, K, and S.

2.3 |DNA extraction

DNA was extracted from 250 mg of each of the 16 com-

posite biocrust samples (four independent replicates per

treatment) using the Power

Soil DNA extraction kit

BOTHA ET AL.3

(MO-BIO Laboratories, Carlsbad, CA) according to the

manufacturer's instructions. An additional step of a Pro-

teinase K treatment was added. The DNA was eluted in

100 μL of the eluent buffer provided by the PowerMax

Soil DNA kit. DNA samples were subsequently quantified

using a Qubit 3.0 Fluorometer (Life Technologies, Ther-

moFisher Scientific Inc.).

2.4 |16S rRNA and ITS gene

amplification and MiSeq sequencing

This study employed 16S rRNA gene sequencing for bac-

terial community exploration and the nuclear ribosomal

internal transcribed spacer (ITS) for fungi. The MiS-

eq341F (50-TCGTCGGCAGCGTCAGATGTGTATAAGA

GACAGCCTACGGGNGGCWGCAG-30) and MiSeq785R

(50-GTCTCGTGGGCTCGGAGATGTGTATAAGAGACAG

GACTACHVGGGTATCTAATCC-30) primer pair (Klindworth

et al., 2012) was used to amplify the variable V3–V4

region of the 16S ribosomal RNA gene which resulted in

amplicon lengths of 464 bp. Fungal library preparation

was performed using the ITS1 (50-50TCGTCGGCAGCG

TCAGATGTGTATAAGAGACAGTCCGTAGGTGAACCTGC

GG-30)andITS4(5

0-50GTCTCGTGGGCTCGGAGATGTGTAT

AAGAGACAGTCCTCCGCTTATTGATATGC-30) primer

set (White et al., 1990) that spans the whole ITS region

(ITS1, 5.8S rDNA, and ITS2) creating amplicons of

350–880 bp in length. Both 16S and ITS primer pairs

incorporated the Illumina overhang adaptor. Library

preparation and pooling were done per the methods

described by Venter et al. (2018).

2.5 |Sequencing data processing

CutAdapt (v.4: Martin, 2011) was used to remove Illu-

mina overhang adapters and sequencing primers. The

Divisive Amplicon Denoising Algorithm 2 (DADA2,

v.1.24: Callahan et al., 2016) was used to analyze the 16S

and ITS Illumina amplicon sequence data. This analysis

encompasses denoising (removal of sequencing errors

and PCR artifacts), quality control (removal of low-

quality reads and sequences with ambiguous bases), error

correction via a learned error model, inference of ampli-

con sequence variants (ASVs) and the creation of an ASV

table that summarizes the abundance of each identified

variant across samples. During the DADA2 analysis, for-

ward and reverse reads are merged to create a consensus

sequence, but this could only be done for the 16S dataset

due to low-quality reverse reads for the ITS sequences.

Additionally, the sequences of the ITS region targeted for

fungal community exploration were too long for MiSeq

sequencing since only 301 bp from each end could be

sequenced, therefore, sequences could not be generated

with a sufficient overlap to create a consensus sequence.

Consequently, only forward ITS reads were represented

in the analysis. The ASVs and the ASV tables created for

16S and ITS were subsequently used to explore the micro-

bial diversity and community structure of the biocrust

samples.

To determine the sum of branch lengths (SBL) of the

bacterial and fungal datasets respectively, the ASVs were

aligned in the Multiple alignment program for amino

acid or nucleotide sequences (MAFFT, v.7: Katoh &

Standley, 2013) and then used to reconstruct neighbor-

joining (NJ) phylogenetic trees for the 16S and ITS data-

sets, as implemented by the R package ape (v.5.6.2:

Paradis & Schliep, 2018) with bootstrap testing of 1000

replicates using the Jukes–Cantor model. The taxonomy

of the ASVs was assigned according to the SILVA (Quast

et al., 2012) and NCBI genomic databases for bacteria

(http://www.ncbi.nlm.nih.gov) and the UNITE genomic

database (Nilsson et al., 2019) for fungi. Taxonomic

assignments were followed by visualization in R (v.4.3.1:

R Core Team, 2023) via bar plots to enable a broad per-

spective of the fungal and bacterial datasets. These bar

plots showed the relative abundance of ASVs detected for

each marker dataset per treatment by the R package, phy-

loseq (v.1.4: McMurdie & Holmes, 2013).

2.6 |Statistical analyses

Alpha-diversity metrics calculated species richness based

on the number of distinct taxa (ASVs) observed in a sam-

ple, which were then grouped according to treatment,

soil type (serpentine and non-serpentine) and precipita-

tion level (high and low). This calculation was facilitated

by the estimate_richness function of the phyloseq package.

This metric provided insights into the diversity of microbial

communities: higher observed species richness indicated a

greater diversity of taxa in a sample. Statistical analyses

were performed to assess the significant differences in the

observed ASV richness between groups. The normality of

the data distribution was assessed by the Shapiro–Wilk test.

In the case of normally distributed data, parametric tests

such as ANOVA (Analysis of Variance) were used. Con-

versely, if the data was not normally distributed, non-

parametric tests such as the Kruskal–Wallis test were used.

Inthecaseofsignificance,post-hoctestssuchasTukey's

HSD (Honestly SignificantDifference)ortheMann–

Whitney Utest were used to facilitate pairwise compari-

sons, respectively. All statistical analyses and subsequent

visualizations were performedinRandthesignificance

level was set at p=0.05.

4BOTHA ET AL.

To assess the dissimilarities in microbial community

structure between treatments, soil types and precipitation

levels, beta-diversity analyses, including non-metric mul-

tidimensional scaling (NMDS), Permutational Multivari-

ate Analysis of Variance (PerMANOVA), and canonical

correspondence analysis (CCA) were conducted using

presence-absence matrices based on fungal and bacterial

ASV tables by various functions of the R package, vegan

(v.2.3: Oksanen et al., 2022). NMDS ordinations were per-

formed on entire, unfiltered datasets. This approach

allowed for the representation of the entire microbial

community present in the biocrust samples, including

ASVs that could not be taxonomically assigned in geno-

mic reference databases. NMDS analysis was executed

via the metaMDS function based on Bray–Curtis dissimi-

larities calculated with the vegdist function. To determine

the statistical significance of environmental variables in

structuring microbial communities, the non-parametric

PerMANOVA was employed via the adonis2 function.

PerMANOVA tests the association between groups and

co-variates using permutation procedures, providing

robust statistical support for the observed differences.

This analysis assessed the association of environmental

variables with microbial community structure within the

different treatments. The analysis included the separate

consideration of soil type and precipitation level.

CCA was performed using filtered datasets that only

represented taxa occurring at least five times in 25% of

the samples. This filtering step was included to focus the

analysis of beta diversity on the most robust and abun-

dant taxa, thereby reducing the introduction of noise and

potential biases by rare or spurious ASVs (e.g., Gabay

et al., 2023; Pombubpa et al., 2020). CCA was then con-

ducted at the order level to identify key environmental

variables that drive the community structure of bacteria

and fungi biocrusts across serpentine (sites 1, 3, 5, 7,

9,11,13,and15)andnon-serpentine(sites2,4,6,8,10,12,

14, and 16) localities using CANOCO software for windows,

v.4.5 (Ter Braak & Smilauer, 2002). To determine which

environmental variables to include in the CCA, Kruskal–

Wallis tests and inspections of variance inflation factors

(VIFs) were used to identify those variables that signifi-

cantly differed between serpentine and non-serpentine sites

and to remove environmental variables that may be inflat-

ing the correlations. Monte Carlo permutation tests were

conducted using 499 random permutations to determine

the statistical validity of the CCAs.

Following the CCA, further investigations were con-

ducted to determine the ecological relevance of the identi-

fied environmental variables. Specifically, genera whose

abundances significantly differed between treatments, soil

type and precipitation levels were examined to identify taxa

that may be especially sensitive to specific environmental

conditions. Kruskal–Wallis tests were used to determine

which genera differed significantly in abundance across dif-

ferent treatments and environmental conditions. Only gen-

era with p-values lower than 0.05 were represented in

boxplots showing their abundance. An analysis of indicator

species was also conducted via the multipatt function in the

indicspecies package (De C

aceres & Legendre, 2009)tofind

taxa that function as markers of environmental conditions

and preferred habitats.

The analysis of significantly abundant genera and

indicator species relied on the same filtered dataset as the

CCA, however, instead of presence-absence data, abun-

dance data was used. The centered log-ratio (CLR) trans-

formation was applied to the abundance data to address

PCR bias, which refers to the selective or disproportion-

ate amplification of specific taxa. The CLR transforma-

tion was applied via the transform_sample_counts

function of the phyloseq package (Barlow et al., 2020;

Silverman et al., 2021). This transformation addresses the

compositional nature of the sequencing data (Gloor

et al., 2017), that is, that the observed relative abundance

of certain taxa is dependent on the relative abundances

of all other taxa within a sample. CLR transformation

describes an approach which uses the geometric mean of

the read counts of all taxa within a sample as a denomi-

nator for that sample. The total taxon read counts are

then divided by this denominator and the log fold

changes in this ratio between samples are compared

(Aitchison & Greenacre, 2002). A reliable framework for

the evaluation of abundance data was therefore created

that allowed for meaningful comparisons among sam-

ples. To further mitigate PCR bias, genera that had signif-

icant differences in abundance among treatments, soil

types and precipitation levels, were compared across

these environmental groupings and not within them,

assuming that PCR biases are consistent within a taxon.

Unless stated otherwise all statistical analyses were per-

formed in R with R-base packages and visualized with

ggplot2 (Wickham, 2016).

3|RESULTS

3.1 |Microbial abundance

The 16S rRNA metabarcoding yielded 5989 ASVs of which

5973 could be assigned to phylum level. Overall, the bacte-

rial dataset consisted of 30 phyla, 65 classes, 134 orders,

179 families, 295 genera and 72 species with the most abun-

dant phyla being Actinobacteria (31.21%), Proteobacteria

(21%), and Acidobacteriota (13.51%). These phyla and

others that had a relative abundance of more than 2%

across the dataset were included in a stacked bar plot

BOTHA ET AL.5

(Figure 1a), including Cyanobacteria, Verrumicrobiota,

Planctomycetota, Chloroflexi, Bacteroidota, and Gemmati-

monadota. The remaining 21 phyla were grouped into

“Other.”The same phyla that were most abundant in the

overall dataset were also most abundant in NSD, SD, and

SW, respectively. NSW did not follow this trend, with Cya-

nobacteria being the second most abundant (18.98%) after

Actinobacteria (30.27%), followed by Proteobacteria

(18.58%) and then Acidobacteriota (10.87%) (Figure 1a).

The other treatments had lower relative abundances of Cya-

nobacteria with NSD having 9.82%, SD having 8.39%, and

SW having 9.49%.

Of the 1433 ASVs created for the ITS metabarcoding

dataset, 1373 could be assigned to phylum level. Overall,

the fungal dataset consisted of 8 phyla, 28 classes,

73 orders, 157 families, 286 genera, and 191 species with

the most abundant phyla being Ascomycota (83.5%),

Basidiomycota (8.53%), and Fungi_phy_Incertae_sedis

(7.11%) (Figure 1b). The treatments receiving lower pre-

cipitation (NSD and SD) had the same most abundant

phyla as the overall dataset, but the treatments with

higher precipitation (NSW and SW) had Ascomycota

(88.5% and 93.37%, respectively), Fungi_phy_Incertae_se-

dis (7.59% and 3.24%, respectively), and then Basidiomy-

cota (3.68% and 2.51%, respectively). From Figure 1b,itis

also clear that the serpentine treatments (SW and SD)

had a higher abundance of Rozellomycota and NSD had

a lower abundance of Ascomycota and a higher abun-

dance of Basidiomycota than the other treatments.

3.2 |Community diversity

To estimate the phylogenetic distances of communities

sampled within each treatment, the SBL of the con-

structed NJ trees from each treatment for the 16S and the

ITS ASVs were compared. The phylogenetic distances

(SBL) decreased for ITS ASVs as follows: NSW (69)

> NSD (48) > SW (27) > SD (22) and 16S ASVs as

(a)

(b)

FIGURE 1 Relative abundance of

(a) phyla constituting more than 2% of

the bacterial dataset, and (b) all fungal

phyla present in the different treatments

of serpentine biocrusts receiving high

and low levels of precipitation (SW and

SD, respectively) and non-serpentine

biocrusts receiving high and low levels

of precipitation (NSW and NSD,

respectively). Relative taxa abundances

were calculated per treatment. NSD, dry,

non-serpentine soils with low rainfall;

NSW, cool, non-serpentine soils with

high rainfall; SD, dry, serpentine soils

with low rainfall; SW, cool serpentine

soils with high rainfall.

TABLE 1 The sum of branch lengths (SBL) of the neighbor-

joining trees constructed for bacterial (16S) and fungal (ITS)

amplicon sequence variants for serpentine biocrusts receiving high

and low frequencies of precipitation (SW and SD, respectively) and

non-serpentine biocrusts receiving high and low precipitation

frequencies (NSW and NSD, respectively).

Treatment SBL

16S

SW 35.51

SD 45.72

NSW 37.94

NSD 42.54

ITS

SW 27.46

SD 22.44

NSW 69.00

NSD 48.45

6BOTHA ET AL.

follows: SD (46) > NSD (43) > NSW (38) > SW

(36) (Table 1).

3.3 |Alpha diversity

Alpha diversity metrics were calculated to assess the

observed species richness in microbial communities

(Figure 2). The observed species richness was highest in

non-serpentine biocrusts, biocrusts receiving higher pre-

cipitation, and particularly high in NSW for fungal com-

munities (Figure 2b). An inverse trend was observed for

bacterial communities since diversity was higher in ser-

pentine biocrusts, lower precipitation and especially low

in SD (Figure 2a). The Shapiro–Wilk test confirmed that

the 16S data was not normally distributed and the subse-

quent Kruskal–Wallis analysis showed that there was no

significant difference in the observed species richness

between treatments, soil types or precipitation levels. The

Shapiro–Wilk tests showed that the ITS data is normally

distributed and the subsequent ANOVA results revealed

that there was a significant difference in fungal species

richness for treatments (precipitation and soil type)

(p=0.002) and soil type (p=0.001). Pairwise compari-

sons by the Tukey HSD test showed that NSW is signifi-

cantly different from SD (p=0.003) and SW (p=0.004)

in terms of observed species richness. This test also

showed that the observed fungal species richness of ser-

pentine and non-serpentine soils was significantly differ-

ent (p=0.001).

3.4 |Beta diversity

Microbial communities in the different treatments could

be differentiated using metabarcoding data. NMDS ordi-

nations constructed based on Bray–Curtis dissimilarities

confirmed that the community structure differed between

the different serpentine and non-serpentine treatments.

However, the PerMANOVA indicated that the difference

observed was only a result of the difference in the fungal

communities present in different treatments (Figure 3a)

and precipitation levels (Figure 3b). This difference in the

community structure of the fungi for the different

(a)

(b)

FIGURE 2 Bar plots representing the number of unique ASVs (observed species richness) for high and low precipitation frequencies

(n=8 ± std), non-serpentine and serpentine soils (n=8 ± std), and treatments, (n=4 ± std), for the (a) bacterial, and (b) fungal datasets.

The whiskers indicate the standard deviation. Observed ASV richness in the bacterial dataset (a) was assessed for differences across

precipitation levels, soil type and treatment using the Kruskal–Wallis test. For comparisons of fungal ASV richness (b), different letters

indicate statistical differences between treatments according to a one-way ANOVA combined with Tukey post-hoc tests (p< 0.05). ASV,

amplicon sequence variant; NS, non-serpentine soil; NSD, dry, non-serpentine soils with low rainfall; NSW, cool, non-serpentine soils with

high rainfall; S, serpentine soil; SD, dry, serpentine soils with low rainfall; SW, cool serpentine soils with high rainfall.

BOTHA ET AL.7

treatments was statistically supported by a PerMANOVA

with p=0.027, and p=0.018 for the differences in fun-

gal community structures observed with higher and

lower precipitation.

CCA was employed to visualize the species distribu-

tions concerning different sites and to represent how dif-

ferent edaphic factors correlated with bacterial (Figure 4)

and fungal (Figure 5) taxa. The environmental variables

chosen for the ordinations were based on Kruskal–Wallis

tests and analysis of VIFs. The Kruskal–Wallis analysis

showed that serpentine and non-serpentine biocrusts can

be differentiated based on soil metal content, with ser-

pentine soil being enriched with Cr (p=0.023), Mn

(p=0.015), Fe (p=0.017), Co (p=0.012), and Ni

(p=0.017). The Ca:Mg ratio of serpentine soil was also

significantly lower than that of their non-serpentine

counterparts (p=0.031). Other environmental variables

were included/excluded based on the VIFs and the final

environmental variables were chosen to explain the vari-

ation in microbial composition across the different sam-

pling sites were soil texture (percentage of sand, silt, and

clay), PTM concentrations (Mn, Ni, and Fe), pH, EC, soil

(a)

(b)

FIGURE 3 Non-metric multidimensional scaling (NMDS)

ordination plot of Bray–Curtis dissimilarities for fungal

communities across (a) treatments and (b) precipitation

frequencies. Analyses were based on presence-absence amplicon

sequence variant data matrices. The stress value for the ordination

is 0.172. NSD, dry, non-serpentine soils with low rainfall; NSW,

cool, non-serpentine soils with high rainfall; SD, dry, serpentine

soils with low rainfall; SW, cool serpentine soils with high rainfall.

FIGURE 4 Environmental factors driving the bacterial

composition variation. Canonical correspondence analysis (CCA)

triplot based on amplicon sequence variants assigned to bacterial

orders (blue triangles) concerning the environmental variables (red

arrows) and serpentine and non-serpentine soils. The sites are

represented as follows: SW (cool serpentine soils with high rainfall)

includes sites 1, 11, 13, and 15 (black circles); NSW (cool, non-

serpentine soils with high rainfall) includes sites 2, 12, 14, and

16 (purple squares); SD (dry, serpentine soils with low rainfall)

includes sites 3, 5, 7, and 9 (green diamonds); NSD (dry, non-

serpentine soils with low rainfall) includes sites 4, 6, 8, and

10 (yellow rectangles). Azo, Azospirillales; Blasto, Blastocatellales;

Bryo, Bryobacterales; Burk, Burkholderiales; Caulo,

Caulobacterales; Chitin, Chitinophagales; Chloro, Chloroplast;

Chthon, Chthoniobacterales; Cyano, Cyanobacteriales; Defluvi,

Defluviicoccales; Elster, Elsterales; Frank, Frankiales; Gaiel,

Gaiellales; Gemma, Gemmatimonadales; Iso, Isosphaerales; Kineo,

Kineosporiales; Microc, Micrococcales; Microm,

Micromonosporales; Microp, Micropepsales; Myxoc, Myxococcales;

Nitros, Nitrospirales; Propion, Propionibacteriales; Pseudo,

Pseudonocardiales; Pyrino, Pyrinomonadales; Rhizo, Rhizobiales;

Rhodo, Rhodobacterales; Rubro, Rubrobacterales; Solibac,

Solibacterales; Solirub, Solirubrobacterales; Sphingo,

Sphingomonadales; Strepto, Streptomycetales; Tepidis,

Tepidisphaerales.

8BOTHA ET AL.

nutrients (P and the nitrogen nutrient factor represented

by “NO”which is the sum of NO



and NO



concentra-

tions) and the Ca:Mg ratio.

The CCA for the bacterial dataset was performed for

the 11 environmental variables and 34 bacterial orders

left after filtering (Figure 4). A summary of Eigenvalues

for the first four axes of the CCA is displayed in Table 2.

The environmental variables accounted for 43.94% of the

total variability in the occurrence of bacterial taxa across

biocrust samples. The most important environmental var-

iables shaping the bacterial community structure across

different sites were clay, EC and sand. Furthermore, the

ordination plot showed EC to be most strongly, and posi-

tively associated with the first ordination axis (r=0.668),

whereas clay was most strongly, and negatively related to

the second axis (r=0.397). The analysis revealed that

PTMs (Mn and Ni) were positively associated with each

other and the presence of silt and high Ca:Mg ratios was

strongly associated with high pH values and high

EC. The biocrust samples were also scattered across the

ordination, making it challenging to identify clear

groups. Efforts to link specific species to serpentine and

non-serpentine sites were thus hampered by the lack of

cohesion in the ordination. Some species associated

closely with serpentine sites and some serpentine condi-

tions, including Burkholderiales, Gemmatimonadales,

Myxococcales to SD site 9, high silt percentages, high

nitrogen nutrient concentrations, and high Mn concen-

trations, as well as Propionibacteriales, Solibacterales,

Chitinophagales, and Defluviicoccales to SW site 11 and

Kineosporiales, Elsterales, Micropepsales which also

associated closely with serpentine sites (9 and 12) as well

as high concentrations of Ni. Bryobacterales and Gaiel-

lales were closely associated with NSD site 12 and Micro-

monosporales and C0119 were closely associated with

NSW sites 16 and 14, respectively. Taxa that associated

closely with high Ca:Mg ratios, EC, pH, and P concentra-

tions were Blastocatellales, Azospirillales, Pyrinomona-

dales, and Rubrobacterales. Cyanobacteriales was found

very close to the origin of axes in the CCA ordination

demonstrating ubiquitous distribution across environ-

mental variables and sites. However, caution should be

exercised when establishing direct associations between

these microbial taxa and specific environmental condi-

tions and sites solely based on the ordination results.

CCA analysis was also performed for the fungal data-

set for all samples and 11 statistically significant different

environmental variables for six taxa (Figure 5). A sum-

mary of Eigenvalues for the first four axes of the CCA for

the fungal dataset is displayed in Table 3. The environ-

mental variables, soil texture, PTM concentrations, pH,

electrical conductivity, soil nutrients, and the Ca:Mg

ratio, could explain 70.36% of the total variability in the

occurrence of fungal taxa in the dataset. Silt, Fe, and the

Ca:Mg ratio were identified as the most important vari-

ables in shaping fungal community structure. The Ca:Mg

ratio is furthermore shown to be most strongly, and

negatively associated with the first ordination axis

(r=0.51), whereas the percentage of silt was most

strongly, and negatively associated with the second ordi-

nation axis (r=0.47).

SD sites (5, 7, and 11) were characterized by the pres-

ence of high percentages of silt and clay, low pH values

and Ca:Mg ratios, as well as a close correlation with high

concentrations of PTMs and soil nutrients. The SD site

3, while also positively correlated with high percentages

of silt, was also associated with sandy soil texture and

low clay percentages. This site was also negatively

FIGURE 5 Environmental factors driving the fungal

community composition variation. Canonical correspondence

analysis (CCA) triplot based on amplicon sequence variants

assigned to fungal orders (blue triangles) concerning the

environmental variables (red arrows) and serpentine and non-

serpentine soils. The sites are represented as follows: SW (cool

serpentine soils with high rainfall) includes sites 1, 11, 13, and

15 (black circles); NSW (cool, non-serpentine soils with high

rainfall) includes sites 2, 12, 14, and 16 (purple squares); SD (dry,

serpentine soils with low rainfall) includes sites 3, 5, 7, and 9 (green

diamonds); NSD (dry, non-serpentine soils with low rainfall)

includes sites 4, 6, 8, and 10 (yellow rectangles). Botryos,

Botryosphaeriales; Capno, Capnodiales; Pleosp, Pleosporales;

Sordar, Sordariales; Tremel, Tremellales; Tubeuf, Tubeufiales.

BOTHA ET AL.9

correlated with P. SW sites (11, 13, and 15) were, like SD

sites, characterized by the absence of clay, with site

1 associating with high silt percentages and 9 and 25 with

sand. All SW sites were characterized by high concentra-

tions of Fe and Mn and sites 1 and 13 further correlated

with high Ni concentrations and high nitrogen. SW sites

9 and 15 also showed a weak positive association with

high pH values and Ca:Mg ratios. Overall, serpentine

sites were associated with low Ca:Mg ratios, low pH

values, the silt/sand soil texture, high concentrations of

soil nutrients, especially nitrogen, as well as high concen-

trations of PTMs. Furthermore, serpentine sites with the

above-mentioned characteristics were positively associ-

ated with the presence of fungal orders Tremellales and

Pleosporales (SW sites 9 and 15: high EC, high concentra-

tions of Mn and Fe) and Botryosphaeriales (SD sites

7 and 5: high concentrations of soil nutrients and high

percentages of clay). All non-serpentine soils were posi-

tively associated with high percentages of sand (except

NSW site 16, which was associated with high silt percent-

ages), high Ca:Mg ratios (except NSD site 4 and NSW site

16) and high pH values (except NSW site 16). The fungal

orders Capnodiales and Sordariales were also positively

associated with these characteristics by being near NSD

sites 8 and 12 in the CCA ordination. NSW sites 10 and

16 and NSD site 6 were further positively associated with

high concentrations of soil nutrients. The fungal order

Tubeufiales was closely associated with NSW site

10 (sand/clay soil texture, high Ca:Mg ratios, high pH

values and high concentrations of soil nutrients). NSW

site 16 and NSD site 4 were also associated with high EC

and high concentrations of Mn and Fe.

The analysis of significant abundant genera, across

treatments, soil type and precipitation level revealed

some bacterial and fungal species that may be sensitive to

certain environmental conditions (Figure 6). Eight bacte-

rial genera showed significant abundance across treat-

ments, soil types (serpentine and non-serpentine) and

precipitation levels (high and low) (Figure 6a). Archan-

gium (Order: Myxococcales) was significantly more abun-

dant in “harsh”treatments: NSD, SD, and SW (p=0.48),

and biocrusts receiving lower precipitation (p=0.021).

This genus was also identified as an indicator species of

biocrusts receiving lower precipitation (p=0.019).

TABLE 2 Summary of results from canonical correspondence analysis (CCA) of the bacterial taxa-environment relation.

Axes 1 2 3 4

Eigenvalues 0.094 0.069 0.052 0.041

Species-environment correlations 0.998 0.934 0.981 0.894

Cumulative percentage of variance of species data 18.4 31.8 41.9 49.8

Cumulative percentage variance of species-environment relation 25.4 44.1 58.0 69.0

Sum of all eigenvalues =0.513

Sum of all canonical eigenvalues =0.371

Total inertia =0.513

Test of significance of all canonical axes: Trace =0.371

F-ratio =1.301

p-Value =0.0600

TABLE 3 Summary of results from canonical correspondence analysis (CCA) of the fungal taxa-environment relation.

Axes 1 2 3 4

Eigenvalues 0.169 0.142 0.083 0.033

Species-environment correlations 0.873 0.853 0.791 0.707

Cumulative percentage of variance of species data 26.4 48.6 61.4 66.6

Cumulative percentage variance of species-environment relation 38.2 70.3 89.0 96.5

Sum of all eigenvalues =0.641

Sum of all canonical eigenvalues =0.442

Total inertia =0.641

Test of significance of all canonical axes: Trace =0.442

F-ratio =1.116

p-Value =0.3400

10 BOTHA ET AL.

Methylobacterium-Methylorubrum (Order: Rhizobiales)

was significantly more abundant in NSW (p=0.028) and

was also identified as an indicator species of biocrusts

belonging to this treatment (p=0.013). Other genera

belonging to this order that were identified as significantly

abundant, were Psychroglaciecola and Microvirga,withthe

former being more abundant in biocrusts receiving higher

precipitation (p=0.021) whereas Microvirga was more

abundant in biocrusts receiving lower precipitation

(p=0.036). Microvirga was also an indicator species of bio-

crusts receiving lower precipitation (p=0.026). Genera that

were significantly more abundant in biocrusts of serpentine

soils were Candidatus Solibacter (Order: Solibacterales)

(p=0.036) and Chthoniobacter (Order: Chthoniobacterales)

(p=0.027). Pseudarthrobacter (Order: Microccoccales) was

more abundant in biocrusts of non-serpentine soils

(p=0.046). Candidatus Udeobacter (Order: Chthoniobacter)

and Sphingomonas (Order: Sphingomonadales) were more

abundant in biocrusts receiving higher precipitation.

Six fungal genera showed significant abundance

across treatments, soil type, and precipitation levels

(Figure 6b). The genera Epicoccum and Pseudopithomyces

(order: Pleosporales) were significantly more abundant in

NSW (p=0.042 and p=0.008, respectively), and

Pseudopithomyces was also an indicator of biocrusts of

non-serpentines (p=0.046). Papiliotrema (Order: Tre-

mellales) was significantly more abundant in non-

serpentine biocrusts (p=0.046) and was also an indica-

tor of NSD (p=0.038). Ramimonilia (order: Botryo-

sphaeriales) was more abundant in biocrusts of

serpentine soils (p=0.036) and another genus from Tre-

mellales, Vishniacozyma, was also significantly more

abundant in serpentine biocrusts (p=0.016) and was

also an indicator of this condition (p=0.031). The only

(a)

(b)

FIGURE 6 Boxplots illustrating the centered log-ratio (CLR) transformed abundance of significant (a) bacterial and (b) fungal genera

(p< 0.05), stratified by precipitation levels (high and low) (n=8 ± std), soil type (serpentine and non-serpentine) (n=8 ± std), and

treatment (n=4 ± std). NS, non-serpentine soil; NSD, dry, non-serpentine soils with low rainfall; NSW, cool, non-serpentine soils with high

rainfall; S, serpentine soil; SD, dry, serpentine soils with low rainfall; SW, cool serpentine soils with high rainfall. For the bacterial dataset

(a), the genera appear in the following order from left to right: for precipitation: Archangium,Candidatus Udeobacter,Microvirga,

Psychroglaciecola, and Sphingomonas; for soil: Candidatus Solibacter,Chthoniobacter, and Pseudarthrobacter; for treatment: Archangium and

Methylobacterium-Methylorubrum. For the fungal dataset (b), the genera appear in the following order from left to right: for precipitation:

Helicoma; for soil: Papiliotrema,Pseudopithomyces,Ramimonilia, and Vishniacozyma; for treatment: Epicoccum and Pseudopithomyces.

BOTHA ET AL.11

genus more abundant across precipitation levels was

Helicoma (Order: Tubeufiales), whose abundance was

significantly higher for biocrusts receiving lower precipi-

tation (p=0.036). The genus Pyronchaetopsis (Order:

Pleosporales), although not significantly more abundant

in biocrusts receiving higher precipitation, was an indica-

tor of this condition (p=0.033).

4|DISCUSSION

The serpentine and non-serpentine soil harboring bio-

crusts differed significantly in terms of PTM content,

with serpentine soils being enriched with Cr, Mn, Fe, Co,

and Ni as well as having characteristically lower Ca:Mg

ratios than their non-serpentine counterparts. The bacte-

rial phyla Actinobacteria, Proteobacteria and Acidobac-

teriota were most abundant for NSD, SW and SD, which

is consistent with the results of dominant phyla from ser-

pentine and non-serpentine soils from a previous study

(Khilyas et al., 2019). Cyanobacteria was less abundant in

the serpentine soils since this phylum is sensitive to

increased concentrations of metals (Lu et al., 2000), but

not entirely intolerant to such conditions in the presence

of moisture (Venter et al., 2018), as also reflected in the

CCA ordination where it plotted close to the origin of

axes, demonstrating ubiquitous distribution.

Studies by Branco and Ree (2010), Urban et al. (2008),

and Venter et al. (2015,2018) indicate that microbial

communities in serpentine soils do not follow the general

pattern of low diversity and high specialization seen

among higher plants (Harrison & Rajakaruna, 2011), but

are rich in species and phylogenetic distance compared

to non-serpentine communities. The serpentine dry

(SD) treatment showed the highest diversity of bacterial

species richness and phylogenetic diversity. However, the

non-significant statistics for the alpha and beta diversity

of the bacterial community composition suggest several

implications. First, the absence of significant differences

in the observed alpha diversity across treatments, precipi-

tation level, and soil type suggest that bacterial diversity

may not be strongly influenced by the specific conditions

considered in this study. This could be explained by the

ubiquitous nature of bacteria, which are generally less

specialized (Chen et al., 2022) and more adaptable com-

pared to fungi. Fungi often exhibit distinct ecological

niches and show specialized adaptations, while bacteria

may be less affected by dispersal limitations and less

prone to differentiation based on environmental

conditions alone. Second, the lack of differentiation in

bacterial community structure between serpentine and

non-serpentine treatments and precipitation levels sug-

gests that other unmeasured variables or ecological

processes may play a more important role in shaping

these communities. Additionally, the lack of distinguish-

able patterns and unexpected correlations observed in the

CCA could indicate that the environmental variables

considered in this study (concentrations of soil nutrients,

PTM concentrations, pH, electrical conductivity, and the

Ca: Mg ratio), do not fully capture the complexity of fac-

tors that drive bacterial community structure across ser-

pentine and non-serpentine biocrusts and implies a

complex and potentially multifaceted relationship

between bacterial communities and the environmental

conditions.

While the non-significant statistical results suggest

that the considered environmental conditions and vari-

ables do not fully explain the bacterial community com-

position, specific taxa correlations were identified in the

CCA that may provide insights into the ecological

dynamics of these communities. For instance, Myxococ-

cales and Solibacterales, which were closely associated

with serpentine treatments had genera, Archangium and

Candidatus Solibacter, respectively, known to be signifi-

cantly abundant in, or indicators of, harsh environmental

conditions. Candidatus Solibacter was significantly abun-

dant in serpentine biocrusts and Archangium in NSD,

SD, and SW and was also an indicator of biocrusts receiv-

ing lower precipitation. Candidatus Solibacter produces

enzymes that can break down organic carbon (Ward

et al., 2009; Dedysh & Yilmaz, 2018), is adapted to low-

nutrient environments (Eichorst et al., 2011) and pro-

vides conditions to benefit other bacteria that degrade

organic compounds (Rime et al., 2015). Myxococcales, or

Myxobacteria, can survive under extremes such as broad

pH and temperature ranges. Members of this order also

have highly tolerant fruiting bodies when stressed by

starvation (Findlay, 2016). This may explain why Archan-

gium was significantly more abundant and an indicator

of sites receiving lower precipitation. Other genera that

were significantly abundant in harsh environments (ser-

pentine and/or lower precipitation) were Chthoniobacter

and Microvirga.Chthoniobacter flavus is the sole member

of the genus Chthoniobacter and can grow at pH values of

4.0 up to 7.5, plays an important role in the decomposi-

tion of plant material and transformation of organic car-

bon compounds in the soil (aids in preventing nutrient

leaching and soil erosion) and is unable to grow on

amino acids or organic acids other than pyruvate

(Sangwan et al., 2004). Chthoniobacter belongs to the

class Spartobacteria which has members that are highly

active and abundant in soil habitats (Sangwan

et al., 2004). Species contributing to the Microvirga genus

are versatile and widely distributed. These members have

been distinguished into two clades, categorized by isola-

tion from roots or soil (Li et al., 2020). Members

12 BOTHA ET AL.

belonging to the soil clade are characterized by a high

abundance of heat- or radiation-resistant genes. Bacterial

taxa that correlate closely with serpentine environments

or were found to be more abundant in serpentine than

non-serpentine treatments were also commonly associ-

ated with having adaptations to extreme environments.

Genera that were abundant in non-serpentine soils or

sites receiving higher precipitation were two genera from

the order Rhizobiales, Methylobacterium-Methylorubrum,

and Psychroglaciecola, as well as a genus from the order

Sphingomonadales, Sphingomonas. Members belonging

to both Methylobacterium and Sphingomonas are anoxy-

genic phototrophs and several of their isolates have been

reported in biocrusts (Csotonyi et al., 2010). Methylobac-

terium-Methylorubrum is a methanotrophic Alphaproteo-

bacteria that lives freely in water, soil or air and they are

especially associated with the phyllosphere (Delmotte

et al., 2009; Knief et al., 2008) and are known plant colo-

nizers (Knief et al., 2010). Members of the family Beijer-

enckiaceae, Microvirga and Psychroglaciecola, are

described as symbionts of bryophytes and they could con-

tribute to biocrust formation in environments of lower

and higher precipitation levels, respectively. Environ-

mental conditions such as elevated moisture levels could

lead to the enrichment of Sphingomonas in biocrust since

this genus is known to increase with biocrust develop-

ment (Zhang et al., 2016). Candidatus_Udaeobacter

(order: Chthoniobacterales) was significantly abundant

in sites receiving higher precipitation and is an organic

matter-degrading bacterium that has been observed in

non-serpentine soil (Böhmer et al., 2020).

In contrast to Branco and Ree's (2010) finding that

fungal diversity is not limited by the chemical character-

istics of serpentine soils, we found that serpentine soils

have a distinct composition and are less diverse when

compared to non-serpentine soils. Serpentine environ-

ments can have reduced leaf litter and soil organic matter

that may lead to greater extremes of temperature, less

water holding capacity and soil aeration, and more lim-

ited nutrient cycling and therefore less favorable condi-

tions for fungal growth (Southworth et al., 2013) The

fungal community structure also differed according to

higher and lower precipitation levels with greater species

richness observed for biocrusts receiving higher precipita-

tion. According to Chen et al. (2019), drought stress

increases the relative abundance of Ascomycota, which is

reflected in the present study by this phylum being most

abundant in SD and NSD. Wang et al. (2014) also note

that changes in soil total nitrogen and pH due to precipi-

tation played important roles in shaping the fungal com-

munity structure in a temperate forest. The most

prevalent fungal orders across the serpentine and

non-serpentine treatments, as represented in the CCA

ordination (Figure 5), were Botryospirales, Capnodiales,

Pleosporales, Sordariales, Tremellales, and Tubeufiales.

The Botryospirales were associated with high concentra-

tions of soil nutrients (phosphorous) and clay. Botryospir-

ales species are important pathogens of woody plants but

have been found on lichens (Denman et al., 2000) and in

lichen biocrusts growing on limestone and dolomite out-

crops on high elevations (2026 m) of the Al-Jabal Al-

Akhdar mountain range (Abed et al., 2013). A genus

belonging to this order, Ramimonilia, was found to be

significantly more abundant in serpentine soils.

R. apicalis is the sole member of this genus which is a

rock-inhabiting fungus first isolated from Patones, Cen-

tral Mountain System, Spain, that tolerates harsh condi-

tions on rock surfaces (Ruibal et al., 2009). Capnodiales

and Sordariales were closely associated with sites 8 and

10 (NSD) and soil nutrients. The order Capnodiales

includes fungi known as the sooty molds and, like Sor-

dariales, encompasses a wide array of metabolically

diverse species (Crous et al., 2007; Kruys et al., 2015). The

fungal orders Pleosporales and Tremellales were associ-

ated with serpentine soils with high concentrations of Fe

and Mn and high electrical conductivity. However, signif-

icantly abundant genera belonging to Pleosporales, Epi-

coccum and Pseudopithomyces, favored NSW and

Pseudopithomyces was also an indicator of non-serpentine

biocrusts. Other studies have shown that members of

Pleosporales can be present in serpentine and non-

serpentine soils (Daghino et al., 2012). Genera belonging

to Tremellales was significantly abundant across serpen-

tine and non-serpentine treatments with Papiliotrema

being significantly abundant in non-serpentine biocrusts

and an indicator of NSD and Vishniacozyma being signif-

icantly abundant in serpentine biocrusts. Papiliotrema is

a yeast whose species are associated with nutrient-rich

habitats (Ladino et al., 2019) as well as being an indicator

of bare soils sampled next to moss biocrusts and is there-

fore exposed to environmental stress (García-Carmona

et al., 2022). Vishniacozyma is highly enriched in root

rhizospheric samples from acid mine drainage sites, which

are extreme environments rich in PTMs (Kalu et al., 2021).

5|CONCLUSION

The bacterial and fungal community composition of ser-

pentine and non-serpentine soils differ significantly and

serpentine soils were found to be enriched with metal-

tolerant or resistant microorganisms. Bacterial commu-

nity structures were not statistically distinguishable

between serpentine and non-serpentine soil. Nonetheless,

taxa resistant to extreme environments were more abun-

dant in serpentine biocrusts or those receiving lower pre-

cipitation and included the genera Archangium,

Candidatus Solibacter, Chthoniobacter, and Microvirga.

BOTHA ET AL.13

Fungal species richness, however, was higher at the

non-serpentine localities than at serpentine localities.

A significant difference was found between the fungal

community structure of serpentine and non-serpentine

treatments (beta diversity) as well as biocrusts receiv-

ing higher and lower precipitation. Fungal biocrusts of

serpentine soils exhibited a notable abundance of gen-

era adapted to harsh environmental conditions and

high concentrations of PTMs. Ramimonilia and

Vishniacozyma were significantly more abundant in

serpentine biocrusts than their non-serpentine coun-

terparts and Vishniacozyma was also identified as an

indicator species specifically associated with serpentine

habitats. In summary, our findings highlight two key

influences on fungal communities in the studied envi-

ronments. First, soil type determines the richness of

fungal species within biocrusts. We observed a

decrease in fungal species richness (alpha diversity) on

serpentine soils, suggesting a selective filter effect for

specific fungal species adapted to harsh soil environ-

ments. Second, precipitation levels played a crucial role

in species turnover (beta diversity), influencing the

differentiation of fungal biocrust communities between

sites. Distinct fungal community structure was

observed for serpentine and non-serpentine biocrusts.

We conclude that a complex interplay exists between

serpentine soil characteristics and precipitation to

shape fungal diversity within and across habitats.

ACKNOWLEDGMENTS

The authors wish to thank the National Geographic Soci-

ety (NGS Grant #9774-15) for financial support. Nishanta

Rajakaruna was supported by a Fulbright Program

(2022-2023). We also thank the two anonymous reviewers

for their constructive comments, which helped us

improve this manuscript.

CONFLICT OF INTEREST STATEMENT

The authors declare that there is no conflict of interest.

ORCID

Danielle Botha https://orcid.org/0000-0002-7321-5382

Sarina Claassens https://orcid.org/0000-0003-3955-

4361

Arshad Ismail https://orcid.org/0000-0003-4672-5915

Mushal Allam https://orcid.org/0000-0002-9875-6716

REFERENCES

Abed, R. M. M., Al-Sadi, A. M., Al-Shehi, M., Al-Hinai, S., &

Robinson, M. D. (2013). Diversity of free-living and lichenized

fungal communities in biological soil crusts of the Sultanate of

Oman and their role in improving soil properties. Soil Biology

and Biochemistry,57, 695–705. https://doi.org/10.1016/j.soilbio.

2012.07.023

Abou-Shanab, R. A. I., Van Berkum, P., Angle, J. S., Delorme, T. A.,

Chaney, R. L., Ghozlan, H. A., Ghanem, K., & Moawad, H.

(2009). Characterization of Ni-resistant bacteria in the rhizo-

sphere of the hyperaccumulator Alyssum murale by 16S rRNA

gene sequence analysis. World Journal of Microbiology and Bio-

technology,26, 101–108. https://doi.org/10.1007/s11274-009-

0148-6

Aitchison, J., & Greenacre, M. (2002). Biplots of compositional data.

Journal of the Royal Statistical Society: Series c (Applied Statis-

tics),51, 375–392. https://doi.org/10.1111/1467-9876.00275

Alexander, E. B., Coleman, R. G., Keeler-Wolfe, T., &

Harrison, S. P. (2007). Serpentine Geoecology of Western North

America: Geology, soils, and vegetation. Oxford University Press.

Anacker, B. L. (2014). The nature of serpentine endemism. Ameri-

can Journal of Botany,101, 219–224. https://doi.org/10.3732/

ajb.1300349

Barlow, J. T., Bogatyrev, S. R., & Ismagilov, R. F. (2020). A quantitative

sequencing framework for absolute abundance measurements of

mucosal and lumenal microbial communities. Nature Communi-

cations,11, 2590. https://doi.org/10.1038/s41467-020-16224-6

Belnap, J., Büdel, B., & Lange, O. L. (2003). Biological soil crusts:

Characteristics and distribution. In O. L. Lange (Ed.), Biological

soil crusts: Structure, function and management (pp. 3–30).

Springer.

Bini, C., & Maleci, L. (2014). The “Serpentine Syndrome”

(H. Jenny, 1980): A proxy for soil remediation. DOAJ (DOAJ:

Directory of Open Access Journals),15,1–13. https://doi.org/10.

6092/issn.2281-4485/4547

Böhmer, M., Ozdín, D., Raˇ

cko, M., Lichv

ar, M., Budiˇ

s, J., &

Szemes, T. (2020). Identification of bacterial and fungal com-

munities in the roots of orchids and surrounding soil in heavy

metal contaminated area of mining heaps. Applied Sciences,10,

7367. https://doi.org/10.3390/app10207367

Branco, S., & Ree, R. H. (2010). Serpentine soils do not limit mycor-

rhizal fungal diversity. PLoS One,5, e11757. https://doi.org/10.

1371/journal.pone.0011757

Brzeszcz, J., Steliga, T., Kapusta, P., Turkiewicz, A., & Kaszycki, P.

(2016). R-strategist versus K-strategist for the application in bio-

remediation of hydrocarbon-contaminated soils. International

Biodeterioration & Biodegradation,106,41–52. https://doi.org/

10.1016/j.ibiod.2015.10.001

Callahan, B. J., McMurdie, P. J., Rosen, M. J., Han, A. W.,

Johnson,A.J.A.,&Holmes,S.P.(2016).DADA2:High-

resolution sample inference from Illumina amplicon data. Nature

Methods,13,581–583. https://doi.org/10.1038/nmeth.3869

Chamizo, S., Cant

on, Y., Rodríguez-Caballero, E., & Domingo, F.

(2016). Biocrusts positively affect the soil water balance in semi-

arid ecosystems. Ecohydrology,9, 1208–1221. https://doi.org/10.

1002/eco.1719

Chen, H., Zhao, X., Lin, Q., Li, G., & Kong, W. (2019). Using a com-

bination of PLFA and DNA-based sequencing analyses to

detect shifts in the soil microbial community composition after

a simulated spring precipitation in a semi-arid grassland in

China. Science of the Total Environment,657, 1237–1245.

https://doi.org/10.1016/j.scitotenv.2018.12.126

Chen, Y., Xi, J., Xiao, M., Wang, S., Chen, W., Liu, F., Shao, Y., &

Yuan, Z. (2022). Soil fungal communities show more specificity

14 BOTHA ET AL.

than bacteria for plant species composition in a temperate for-

est in China. BMC Microbiology,22, 208. https://doi.org/10.

1186/s12866-022-02591-1

Couradeau, E., Karaoz, U., Lim, H. C., Nunes, U., Northen, T. R.,

Brodie, E. L., & Garcia-Pichel, F. (2016). Bacteria increase arid-

land soil surface temperature through the production of sun-

screens. Nature Communications,7,1–7. https://doi.org/10.

1038/ncomms10373

Crous, P. W., Braun, U., & Groenewald, J. Z. (2007). Mycosphaer-

ella is polyphyletic. Studies in Mycology,58,1–32. https://doi.

org/10.3114/sim.2007.58.01

Csotonyi, J. T., Swiderski, J., Stackebrandt, E., & Yurkov, V. (2010).

A new environment for aerobic anoxygenic phototrophic bacte-

ria: Biological soil crusts. Environmental Microbiology Reports,

2, 651–656. https://doi.org/10.1111/j.1758-2229.2010.00151.x

Daghino, S., Murat, C., Sizzano, E., Girlanda, M., & Perotto, S.

(2012). Fungal diversity is not determined by mineral and

chemical differences in serpentine substrates. PLoS One,7,

e44233. https://doi.org/10.1371/journal.pone.0044233

De C

aceres, M., & Legendre, P. (2009). Associations between spe-

cies and groups of sites: Indices and statistical inference. Ecol-

ogy,90, 3566–3574. https://doi.org/10.1890/08-1823.1

Dedysh, S. N., & Yilmaz, P. (2018). Refining the taxonomic struc-

ture of the phylum Acidobacteria.International Journal of Sys-

tematic and Evolutionary Microbiology,68, 3796–3806. https://

doi.org/10.1099/ijsem.0.003062

Delmotte, N., Knief, C., Chaffron, S., Innerebner, G., Roschitzki, B.,

Schlapbach, R., Von Mering, C., & Vorholt, J. A. (2009). Com-

munity proteogenomics reveals insights into the physiology of

phyllosphere bacteria. Proceedings of the National Academy

of Sciences,106, 16428–16433. https://doi.org/10.1073/pnas.

0905240106

Denman, S., Crous, P. W., Taylor, J. E., Kang, J. C., Pascoe, L., &

Wingfield, M. J. (2000). An overview of the taxonomic history

of Botryosphaeria, and a re-evaluation of its anamorphs based

on morphology and ITS rDNA phylogeny. Studies in Mycology,

45, 129–140.

Eichorst, S. A., Kuske, C. R., & Schmidt, T. M. (2011). Influence of

plant polymers on the distribution and cultivation of bacteria

in the phylum Acidobacteria. Applied and Environmental

Microbiology,77, 586–596. https://doi.org/10.1128/aem.

01080-10

Elbert, W., Weber, B., Burrows, S., Steinkamp, J., Büdel, B.,

Andreae, M. O., & Pöschl, U. (2012). Contribution of crypto-

gamic covers to the global cycles of carbon and nitrogen.

Nature Geoscience,5, 459–462. https://doi.org/10.1038/

ngeo1486

Favero-Longo, S. E., Matteucci, E., Giordani, P., Paukov, A., &

Rajakaruna, N. (2018). Diversity and functional traits of lichens

in ultramafic areas: A literature-based worldwide analysis inte-

grated by field data at the regional scale. Ecological Research,

33, 593–608. https://doi.org/10.1007/s11284-018-1573-5

Findlay, B. (2016). The chemical ecology of predatory soil bacteria.

ACS Chemical Biology,11, 1502–1510. https://doi.org/10.1021/

acschembio.6b00176

Gabay, T., Petrova, E., Gillor, O., Ziv, Y., & Angel, R. (2023). Only a

minority of bacteria grow after wetting in both natural and

post-mining biocrusts in a hyperarid phosphate mine. The Soil,

9, 231–242. https://doi.org/10.5194/soil-9-231-2023

Galey, M. L., Van der Ent, A., Iqbal, M. C. M., & Rajakaruna, N.

(2017). Ultramafic geoecology of South and Southeast Asia.

Botanical Studies,58, 18. https://doi.org/10.1186/s40529-017-

0167-9

García-Carmona, M., Lepinay, C., García-Orenes, F., Baldrian, P.,

Arcenegui, V., Cajthaml, T., & Mataix-Solera, J. (2022). Moss

biocrust accelerates the recovery and resilience of soil microbial

communities in fire-affected semi-arid Mediterranean soils. Sci-

ence of the Total Environment,846, 157467. https://doi.org/10.

1016/j.scitotenv.2022.157467

Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V., &

Egozcue, J. J. (2017). Microbiome datasets are compositional:

And this is not optional. Frontiers in Microbiology,8,1–6.

https://doi.org/10.3389/fmicb.2017.02224

Harrison, S., & Rajakaruna, N. (2011). Serpentine: The evolution and

ecology of a model system. University Of California Press.

Husna, Tuheteru, F. D., & Arif, A. (2017). Arbuscular mycorrhizal

fungi and plant growth on serpentine soils (pp. 293–303).

Springer EBooks. https://doi.org/10.1007/978-981-10-4115-0_12

Jenny, H. (1980). The soil resource: Origin and behavior. Springer

Science & Business Media.

Kalu, C. M., Oduor Ogola, H. J., Selvarajan, R., Tekere, M., &

Ntushelo, K. (2021). Fungal and metabolome diversity of the

rhizosphere and endosphere of Phragmites australis in an

AMD-polluted environment. Heliyon,7, e06399. https://doi.

org/10.1016/j.heliyon.2021.e06399

Katoh, K., & Standley, D. M. (2013). MAFFT multiple sequence

alignment software version 7: Improvements in performance

and usability. Molecular Biology and Evolution,30, 772–780.

https://doi.org/10.1093/molbev/mst010

Khilyas, I. V., Sorokina, A. V., Elistratova, A. A., Markelova, M.,

Siniagina, M., Шарипова,М.Р., Shcherbakova, T. A.,

D'Errico, M. E., & Cohen, M. F. (2019). Microbial diversity and

mineral composition of weathered serpentine rock of the Khali-

lovsky massif. PLoS One,14, e0225929. https://doi.org/10.1371/

journal.pone.0225929

Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C.,

Horn, M., & Glöckner, F. O. (2012). Evaluation of general 16S

ribosomal RNA gene PCR primers for classical and next-

generation sequencing-based diversity studies. Nucleic Acids

Research,41, e1. https://doi.org/10.1093/nar/gks808

Knief, C., Frances, L., Cantet, F., & Vorholt, J. A. (2008). Cultiva-

tion-independent characterization of Methylobacterium popula-

tions in the plant phyllosphere by automated ribosomal

intergenic spacer analysis. Applied and Environmental Microbi-

ology,74, 2218–2228. https://doi.org/10.1128/aem.02532-07

Knief, C., Ramette, A., Frances, L., Alonso-Blanco, C., &

Vorholt, J. A. (2010). Site and plant species are important deter-

minants of the Methylobacterium community composition in

the plant phyllosphere. The ISME Journal,4, 719–728. https://

doi.org/10.1038/ismej.2010.9

Kruys, Å., Huhndorf, S. M., & Miller, A. N. (2015). Coprophilous

contributions to the phylogeny of Lasiosphaeriaceae and allied

taxa within Sordariales (Ascomycota, Fungi). Fungal Diversity,

70, 101–113. https://doi.org/10.1007/s13225-014-0296-3

Ladino, G., Ospina-Bautista, F., Estévez Var

on, J., Jerabkova, L., &

Kratina, P. (2019). Ecosystem services provided by bromeliad

plants: A systematic review. Ecology and Evolution,9, 7360–

7372. https://doi.org/10.1002/ece3.5296

BOTHA ET AL.15

Li, J., Gao, R., Chen, Y., Xue, D., Han, J., Wang, J., Dai, Q., Lin, M.,

Ke, X., & Zhang, W. (2020). Isolation and identification of

Microvirga thermotolerans HR1, a novel thermo-tolerant bacte-

rium, and comparative genomics among Microvirga species.

Microorganisms,8, 101. https://doi.org/10.3390/

microorganisms8010101

Lu, C. M., Chau, C. W., & Zhang, J. H. (2000). Acute toxicity of

excess mercury on the photosynthetic performance of cyano-

bacterium, S. platensis –Assessment by chlorophyll fluores-

cence analysis. Chemosphere,41, 191–196. https://doi.org/10.

1016/s0045-6535(99)00411-7

Ma, Y., Rajkumar, M., Rocha, I., Oliveira, R. S., & Freitas, H.

(2015). Serpentine bacteria influence metal translocation and

bioconcentration of Brassica juncea and Ricinus communis

grown in multi-metal polluted soils. Frontiers in Plant Science,

5,1–13. https://doi.org/10.3389/fpls.2014.00757

Martin, M. (2011). Cutadapt removes adapter sequences from high-

throughput sequencing reads. EMBnet Journal,17, 10. https://

doi.org/10.14806/ej.17.1.200

McMurdie, P. J., & Holmes, S. (2013). Phyloseq: An r package for

reproducible interactive analysis and graphics of microbiome

census data. PLoS One,8, e61217. https://doi.org/10.1371/

journal.pone.0061217

Mengoni,A.,Barzanti,R.,Gonnelli,C.,Gabbrielli,R.,&

Bazzicalupo, M. (2001). Characterization of nickel-resistant bacte-

ria isolated from serpentine soil. Environmental Microbiology,3,

691–698. https://doi.org/10.1046/j.1462-2920.2001.00243.x

Muller, L. A. H., & Hilger, H. H. (2015). Insights into the effects of

serpentine soil conditions on the community composition

of fungal symbionts in the roots of Onosma echioides.Soil Biol-

ogy and Biochemistry,81,1–8. https://doi.org/10.1016/j.soilbio.

2014.10.027

Mulroy, M., Fryday, A. M., Gersoff, A., Dart, J., Reese

Næsborg, R., & Rajakaruna, N. (2022). Lichens of ultramafic

substrates in North America: A review. Botany,100, 593–617.

https://doi.org/10.1139/cjb-2021-0187

Naidoo, Y., Valverde, A., Pierneef, R. E., & Cowan, D. A. (2022).

Differences in precipitation regime shape microbial community

composition and functional potential in Namib Desert soils.

Microbial Ecology,83, 689–701. https://doi.org/10.1007/s00248-

021-01785-w

Nilsson, R. H., Larsson, K.-H., Taylor, A. F., Bengtsson-Palme, J.,

Jeppesen, T. S., Schigel, D., Kennedy, P., Picard, K.,

Glöckner, F. O., Tedersoo, L., Saar, I., Kõljalg, U., &

Abarenkov, K. (2019). The UNITE database for molecular iden-

tification of fungi: Handling dark taxa and parallel taxonomic

classifications. Nucleic Acids Research,47, D259–D264. https://

doi.org/10.1093/nar/gky1022

Oksanen, J., Blanchet, F. G., Kindt, R., Legendre, P., Minchin, P. R.,

O'Hara, R. B., Simpson, G. L., Solymos, P., Stevens, M. H. H.,

Wagner, H., & Oksanen, M. J. (2022). Vegan: Community ecol-

ogy package. R Package Version 2.6-4. https://cran.r-project.

org/package=vegan

Oline, D. K. (2006). Phylogenetic comparisons of bacterial commu-

nities from serpentine and nonserpentine soils. Applied and

Environmental Microbiology,72, 6965–6971. https://doi.org/10.

1128/aem.00690-06

Ortiz, Y., Restrepo, C., Vilanova-Cuevas, B., Santiago-Valentin, E.,

Tringe, S. G., & Godoy-Vitorino, F. (2020). Geology and climate

influence rhizobiome composition of the phenotypically diverse

tropical tree Tabebuia heterophylla.PLoS One,15, e0231083.

https://doi.org/10.1371/journal.pone.0231083

Paradis, E., & Schliep, K. (2018). Ape 5.0: An environment for mod-

ern phylogenetics and evolutionary analyses in R. Bioinformat-

ics,35, 526–528. https://doi.org/10.1093/bioinformatics/bty633

Pessoa-Filho, M., Barreto, C. C., dos Reis Junior, F. B.,

Fragoso, R. R., Costa, F. S., de Carvalho Mendes, I., & de

Andrade, L. R. M. (2015). Microbiological functioning, diver-

sity, and structure of bacterial communities in ultramafic soils

from a tropical savanna. Antonie Van Leeuwenhoek,107, 935–

949. https://doi.org/10.1007/s10482-015-0386-6

Pombubpa, N., Pietrasiak, N., De Ley, P., & Stajich, J. E. (2020).

Insights into dryland biocrust microbiome: Geography, soil

depth and crust type affect biocrust microbial communities and

networks in Mojave Desert, USA. FEMS Microbiology Ecology,

96,1–16. https://doi.org/10.1093/femsec/fiaa125

Quast, C., Pruesse, E., Yilmaz, P., Gerken, J., Schweer, T., Yarza, P.,

Peplies, J., & Glöckner, F. O. (2012). The SILVA ribosomal

RNA gene database project: Improved data processing and

web-based tools. Nucleic Acids Research,41, D590–D596.

https://doi.org/10.1093/nar/gks1219

R Core Team. (2023). R: A language and environment for statistical

computing. R-Project.org; R Foundation for Statistical Comput-

ing. http://www.r-project.org/

Rajakaruna, N., & Boyd, R. S. (2008). Edaphic factor. In S. E.

Jørgensen & D. D. Fath (Eds.), Encyclopedia of ecology

(pp. 1201–1207). Elsevier Science.

Rajakaruna, N., & Boyd, R. S. (2014). Serpentine soils. In D. Gibson

(Ed.), Oxford Bibliographies in Ecology. Oxford University Press.

https://doi.org/10.1093/obo/9780199830060-0055

Rajakaruna, N., Harris, T. B., & Alexander, E. B. (2009). Serpentine

geoecology of eastern north america: A review. Rhodora,111,

21–108. https://doi.org/10.3119/07-23.1

Rajakaruna, N., Knudsen, K., Fryday, A. M., O'Dell, R. E.,

Pope, N. S., Olday, F. C., & Woolhouse, S. (2012). Investigation

of the importance of rock chemistry for saxicolous lichen com-

munities of the New Idria serpentinite mass, San Benito

County, California, USA. The Lichenologist,44, 695–714.

https://doi.org/10.1017/s0024282912000205

Rajkumar, M., Vara Prasad, M. N., Freitas, H., & Ae, N. (2009). Bio-

technological applications of serpentine soil bacteria for phytor-

emediation of trace metals. Critical Reviews in Biotechnology,

29, 120–130. https://doi.org/10.1080/07388550902913772

Reynolds, R., Belnap, J., Reheis, M., Lamothe, P., & Luiszer, F.

(2001). Aeolian dust in Colorado Plateau soils: Nutrient inputs

and recent change in source. Proceedings of the National Acad-

emy of Sciences,98, 7123–7127. https://doi.org/10.1073/pnas.

121094298

Rime, T., Hartmann, M., Brunner, I., Widmer, F., Zeyer, J., &

Frey, B. (2015). Vertical distribution of the soil microbiota

along a successional gradient in a glacier forefield. Molecular

Ecology,24, 1091–1108. https://doi.org/10.1111/mec.13051

Robinson, B., Brooks, R. R., & Clothier, B. E. (1999). Soil amend-

ments affecting nickel and cobalt uptake by Berkheya coddii:

Potential use for phytomining and phytoremediation. Annals of

Botany,84, 689–694. https://doi.org/10.1006/anbo.1999.0970

Ruibal, C., Gueidan, C., Selbmann, L., Gorbushina, A. A.,

Crous, P. W., Groenewald, J. Z., Muggia, L., Grube, M.,

16 BOTHA ET AL.

Isola, D., Schoch, C. L., Staley, J. T., Lutzoni, F., & de

Hoog, G. S. (2009). Phylogeny of rock-inhabiting fungi related

to Dothideomycetes. Studies in Mycology,64, 123–133. https://

doi.org/10.3114/sim.2009.64.06

Rutherford, W. A., Painter, T. H., Ferrenberg, S., Belnap, J.,

Okin, G. S., Flagg, C., & Reed, S. C. (2017). Albedo feedbacks to

future climate via climate change impacts on dryland biocrusts.

Scientific Reports,7,1–9. https://doi.org/10.1038/srep44188

Sangwan, P., Chen, X., Hugenholtz, P., & Janssen, P. H. (2004).

Chthoniobacter flavus gen. nov., sp. nov., the first pure-culture

representative of subdivision two, Spartobacteria classis nov., of

the phylum Verrucomicrobia. Applied and Environmental

Microbiology,70, 5875–5881. https://doi.org/10.1128/aem.70.10.

5875-5881.2004

Schechter, S. P., & Branco, S. (2014). The ecology and evolution of

mycorrhizal fungi in extreme soils. In N. Rajakaruna, R. S.

Boyd, & T. B. Harris (Eds.), Plant ecology and evolution in harsh

environments (pp. 33–53). Nova Publishers.

Silverman, J. D., Bloom, R. J., Jiang, S., Durand, H. K., Dallow, E.,

Mukherjee, S., & David, L. A. (2021). Measuring and mitigating

PCR bias in microbiota datasets. PLoS Computational Biology,

17, e1009113. https://doi.org/10.1371/journal.pcbi.1009113

Southworth, D., Tackaberry, L. E., & Massicotte, H. B. (2013).

Mycorrhizal ecology on serpentine soils. Plant Ecology & Diver-

sity,7, 445–455. https://doi.org/10.1080/17550874.2013.848950

Strauss, S. L., Day, T. A., & Garcia-Pichel, F. (2012). Nitrogen

cycling in desert biological soil crusts across biogeographic

regions in the Southwestern United States. Biogeochemistry,

108, 171–182. https://doi.org/10.1007/s10533-011-9587-x

Teptina, A., Paukov, A., & Rajakaruna, N. (2018). Ultramafic vege-

tation and soils in the circumboreal region of the Northern

Hemisphere. Ecological Research,33, 609–628. https://doi.org/

10.1007/s11284-018-1577-1

Ter Braak, C. J. F., & Smilauer, P. (2002). CANOCO Reference Man-

ual and CanoDraw for Windows User's Guide: Software for

Canonical Community Ordination (version 4.5).https://edepot.

wur.nl/405659

Turgay, O. C., Görmez, A., & Bilen, S. (2012). Isolation and characteri-

zation of metal resistant-tolerant rhizosphere bacteria from the

serpentine soils in Turkey. Environmental Monitoring and Assess-

ment,184,515–526. https://doi.org/10.1007/s10661-011-1984-z

Urban, A., Puschenreiter, M., Strauss, J., & Gorfer, M. (2008). Diver-

sity and structure of ectomycorrhizal and co-associated fungal

communities in a serpentine soil. Mycorrhiza,18, 339–354.

https://doi.org/10.1007/s00572-008-0189-y

Venter, A., Levanets, A., Siebert, S. J., & Rajakaruna, N. (2015). A

preliminary survey of the diversity of soil algae and cyanopro-

karyotes on mafic and ultramafic substrates in South Africa.

Australian Journal of Botany,63, 341. https://doi.org/10.1071/

bt14207

Venter, A., Siebert, S., Rajakaruna, N., Barnard, S., Levanets, A.,

Ismail, A., Allam, M., Peterson, B., & Sanko, T. (2018). Biologi-

cal crusts of serpentine and non-serpentine soils from the

Barberton Greenstone Belt of South Africa. Ecological Research,

33, 629–640. https://doi.org/10.1007/s11284-017-1546-0

Visioli, G., Sanangelantoni, A. M., Conti, F. D., Bonati, B.,

Gardi, C., & Menta, C. (2019). Above and belowground biodi-

versity in adjacent and distinct serpentine soils. Applied Soil

Ecology,133,98–103. https://doi.org/10.1016/j.apsoil.2018.

09.013

Wang, M., Shi, S., Lin, F., & Jiang, P. (2014). Response of the soil

fungal community to multi-factor environmental changes in a

temperate forest. Applied Soil Ecology,81,45–56. https://doi.

org/10.1016/j.apsoil.2014.04.008

Ward, N. L., Challacombe, J. F., Janssen, P. H., Henrissat, B.,

Coutinho, P. M., Wu, M., Xie, G., Haft, D. H., Sait, M.,

Badger, J., Barabote, R. D., Bradley, B., Brettin, T. S.,

Brinkac, L. M., Bruce, D., Creasy, T., Daugherty, S. C.,

Davidsen, T. M., DeBoy, R. T., …Kuske, C. R. (2009). Three

genomes from the phylum Acidobacteria provide insight into

the lifestyles of these microorganisms in soils. Applied and

Environmental Microbiology,75, 2046–2056. https://doi.org/10.

1128/aem.02294-08

Weber, B., Büdel, B., & Belnap, J. (2016). Biological soil crusts: An

organizing principle in drylands (pp. 3–13). Springer Interna-

tional Publishing.

White, T. J., Bruns, T. D., Lee, S. B., & Taylor, J. W. (1990). Amplifi-

cation and direct sequencing of fungal ribosomal RNA genes

for phylogenetics. In M. A. Innis, D. H. Gelfand, J. J. Sninsky, &

T. J. White (Eds.), PCR protocols: A guide to methods and appli-

cations (pp. 315–322). Elsevier Science.

Wickham, H. (2016). ggplot2: Elegant graphics for data analysis.

Springer-Verlag. https://ggplot2.tidyverse.org

Zhang, B., Kong, W., Wu, N., & Zhang, Y. (2016). Bacterial diversity

and community along the succession of biological soil crusts in

the Gurbantunggut Desert, Northern China. Journal of Basic

Microbiology,56, 670–679. https://doi.org/10.1002/jobm.

201500751

SUPPORTING INFORMATION

Additional supporting information can be found online

in the Supporting Information section at the end of this

article.

How to cite this article: Botha, D., Barnard, S.,

Claassens, S., Rajakaruna, N., Venter, A., Ismail,

A., Allam, M., & Siebert, S. J. (2024). Soil type and

precipitation level have a greater influence on

fungal than bacterial diversity in serpentine and

non-serpentine biological soil crusts. Ecological

Research,1–17. https://doi.org/10.1111/1440-1703.

12500

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Fungal Frontiers in Toxic Terrain: Revealing Culturable Fungal Communities in Taiwan’s Serpentine Paddy Fields

Preprint

Full-text available

Apr 2025

Serpentine soils are predominantly distributed along the Circum-Pacific margin and the Mediterranean, including eastern Taiwan. These soils are characterized by high levels of heavy metals, including nickel and chromium, and a low calcium-to-magnesium ratio, creating a unique environment that fosters microorganisms with specialized traits. In this study, culture-dependent isolation methods were used to elucidate the composition of culturable fungal communities in serpentine-characterized paddy fields in eastern Taiwan. A total of 154 fungal strains were isolated from serpentine paddy fields in eastern Taiwan. These strains were grouped into 79 morphotypes based on colony morphology and subsequently evaluated through morphological and multi-locus phylogenetic analyses. The results revealed that 60% of the strains belong to class Dothideomycetes, followed by 21% in Sordariomycetes and 19% in Eurotiomycetes. At the genus level, Westerdykella was the dominant genus, accounting for 35% of the total isolated strains, followed by Pyrenochaetopsis (20%), Talaromyces (19%), and Pseudorhypophila (8%). The study reports 11 novel species: Dimorphiseta formosana sp. nov. , D. serpentinicola sp. nov. , Parasarocladium formosum sp. nov. , Phialoparvum formosanum sp. nov. , Poaceascoma serpentinum sp. nov. , Pseudorhypophila formosana sp. nov. , Reticulascus formosana sp. nov. , Sarocladium formosum sp. nov. , S. serpentinicola sp. nov. , Talaromyces taiwanensis sp. nov. , and Westerdykella formosana sp. nov . Additionally, 11 species are reported for the first time in Taiwan: Pseudothielavia terricola , Pseudoxylomyces aquaticus , Pyrenochaetopsis oryzicola , Py. paucisetosa , Setophaeosphaeria microspora , Talaromyces adpressus , T. thailandensis , Westerdykella aquatica , W. capitulum , W. dispersa , and W. globosa . In addition, this study presents the first documented asexual morph within the genus Poaceascoma , represented by P. serpentinum . These discoveries will be valuable for future evaluations of the potential uses and functions of these species as bioremediation agents.

Recent advances in the study of serpentine plants and ecosystems: Perspectives from the 10th International Conference on Serpentine Ecology, France: Part II

Article

Oct 2024
ECOL RES

The 10th International Conference on Serpentine Ecology was held in Nancy, France on June 12–16, 2023. The main goals of the conference were to create a platform for the exchange of ideas and experiences and to promote scientific dialogue among scientists from numerous fields who share expertise in the study of ultramafic habitats worldwide. The proceedings of the conference are being published as two Special Issues of Ecological Research , of which this is the second. In this article, we present the major topics and provide some highlights of the contributions to the 10th International Conference on Serpentine Ecology.

The Edaphic Factor in Ecology

Chapter

Dec 2024

The edaphic factor encompasses the physical, chemical, and biological properties of soil that result from biological and geological phenomena or anthropogenic activities. Variations in the edaphic factor contribute to the intriguing patterns of diversity observed in the biotic world. Chemical and physical features of soil greatly influence the ecology and evolution of plants and their associated biota. Extreme soil conditions, such as those found on serpentine outcrops, limestone and gypsum deposits, and even mine tailings, have led to the formation of unique plant communities characterized by both rarity and endemism. Such sites have also provided model organisms for examining the processes of divergence due to adaptation, reproductive isolation, and subsequent genetic differentiation, in some cases resulting in speciation. Ever-expanding agriculture and forestry, mining activity, urbanization, and rapidly changing climate, pose severe threats to the world’s unusual edaphic habitats and their biota. While some areas have been preserved for their unique biota associated with unusual edaphic conditions, many more are rapidly being impacted by human activities and climate change. Without strict conservation measures, many edaphic specialist species, including metal hyperaccumulating plants that are useful for innovative, green technologies such as phytoremediation and agromining, may soon be lost.

Only a minority of bacteria grow after wetting in both natural and post-mining biocrusts in a hyperarid phosphate mine

Article

Full-text available

May 2023

Biological soil crusts (biocrusts) are key contributors to desert ecosystem functions, therefore, biocrust restoration following mechanical disturbances is imperative. In the Negev Desert hyperarid regions, phosphate mining has been practiced for over 60 years, destroying soil habitats and fragmenting the landscape. In this study, we selected one mining site restored in 2007, and we used DNA stable isotope probing (DNA-SIP) to identify which bacteria grow in post-mining and adjacent natural biocrusts. Since biocrust communities activate only after wetting, we incubated the biocrusts with H218O for 96 h under ambient conditions. We then evaluated the physicochemical soil properties, chlorophyll a concentrations, activation, and functional potential of the biocrusts. The DNA-SIP assay revealed low bacterial activity in both plot types and no significant differences in the proliferated communities' composition when comparing post-mining and natural biocrusts. We further found no significant differences in the microbial functional potential, photosynthetic rates, or soil properties. Our results suggest that growth of hyperarid biocrust bacteria after wetting is minimal. We hypothesize that due to the harsh climatic conditions, during wetting, bacteria devote their meager resources to prepare for the coming drought, by focusing on damage repair and organic compound synthesis and storage rather than on growth. These low growth rates contribute to the sluggish recovery of desert biocrusts following major disturbances such as mining. Therefore, our findings highlight the need for implementing active restoration practices following mining.

Soil fungal communities show more specificity than bacteria for plant species composition in a temperate forest in China

Article

Full-text available

Aug 2022
BMC MICROBIOL

Background Soil microbiome is an important part of the forest ecosystem and participates in forest ecological restoration and reconstruction. Niche differentiation with respect to resources is a prominent hypothesis to account for the maintenance of species diversity in forest ecosystems. Resource-based niche differentiation has driven ecological specialization. Plants influence soil microbial diversity and distribution by affecting the soil environment. However, with the change in plant population type, whether the distribution of soil microbes is random or follows an ecologically specialized manner remains to be further studied. We characterized the soil microbiome (bacteria and fungi) in different plant populations to assess the effects of phytophysiognomy on the distribution patterns of soil microbial communities in a temperate forest in China. Results Our results showed that the distribution of most soil microbes in different types of plant populations is not random but specialized in these temperate forests. The distribution patterns of bacteria and fungi were related to the composition of plant communities. Fungal species (32%) showed higher specialization than bacterial species (15%) for different types of plant populations. Light was the main driving factor of the fungal community, and soil physicochemical factors were the main driving factor of the bacterial community. Conclusion These findings suggest that ecological specialization is important in maintaining local diversity in soil microbial communities in this forest. Fungi are more specialized than bacteria in the face of changes in plant population types. Changes in plant community composition could have important effects on soil microbial communities by potentially influencing the stability and stress resistance of forest ecosystems.

Moss biocrust accelerates the recovery and resilience of soil microbial communities in fire-affected semi-arid Mediterranean soils

Article

Full-text available

Jul 2022
SCI TOTAL ENVIRON

After wildfires in Mediterranean ecosystems, ruderal mosses are pioneer species, stabilizing the soil surface previous to the establishment of vascular vegetation. However, little is known about the implication of pioneer moss biocrusts for the recovery and resilience of soils in early post-fire stages in semi-arid areas. Therefore, we studied the effects of the burgeoning biocrust on soil physicochemical and biochemical properties and the diversity and composition of microbial communities after a moderate-to-high wildfire severity. Seven months after the wildfire, the biocrust softened the strong impact of the fire in soils, affecting the diversity and composition of bacteria and fungi community compared to the uncrusted soils exposed to unfavourable environmental stress. Soil moisture, phosphorous, and enzyme activities representing the altered biogeochemical cycles after the fire, were the main explanatory variables for biocrust microbial community composition under the semi-arid conditions. High bacterial diversity was found in soils under mosses, while long-lasting legacies are expected in the fungal community, which showed greater sensitivity to the fire. The composition of bacterial and fungal communities at several taxonomical levels was profoundly altered by the presence of the moss biocrust, showing a rapid successional transition toward the unburned soil community. Pioneer moss biocrust play an important role improving the resilience of soil microbial communities. In the context of increasing fire intensity, studying the moss biocrust effects on the recovery of soils microbiome is essential to understanding the resistance and resilience of Mediterranean forests to wildfires.

Lichens of ultramafic substrates in North America: a review

Article

Full-text available

Mar 2022

Lichens are among the most prominent and successful life forms of metal-rich habitats, including ultramafic rocks and soils; however, research on lichens of ultramafic habitats is limited, especially on the North American continent. This review examines geographic and ecological patterns of ultramafic lichen assemblages by synthesizing published reports of lichens of ultramafic substrates in North America, and by creating a database characterizing the ecology and habitat (substrate type, pH affinity, geographic distribution) for all taxa recorded in the literature. This effort yielded a total of 437 lichen species and infraspecific taxa reported from ultramafic substrates in the published literature. Lichen assemblages of ultramafic substrates vary in composition and are dominated by acidophytic taxa with a minor, but consistent, basiphytic component. Species lists from ultramafic habitats in different geographic regions varied widely, suggesting that factors unrelated to substrate, such as climate, have a large effect on lichen assemblage composition. However, several studies showed clear differentiation between lichen composition on nearby or adjacent ultramafic and nonultramafic habitats, suggesting that ultramafic substrates harbor regionally unique lichen assemblages.

Measuring and mitigating PCR bias in microbiota datasets

Article

Full-text available

Jul 2021
PLOS COMPUT BIOL

PCR amplification plays an integral role in the measurement of mixed microbial communities via high-throughput DNA sequencing of the 16S ribosomal RNA (rRNA) gene. Yet PCR is also known to introduce multiple forms of bias in 16S rRNA studies. Here we present a paired modeling and experimental approach to characterize and mitigate PCR NPM-bias (PCR bias from non-primer-mismatch sources) in microbiota surveys. We use experimental data from mock bacterial communities to validate our approach and human gut microbiota samples to characterize PCR NPM-bias under real-world conditions. Our results suggest that PCR NPM-bias can skew estimates of microbial relative abundances by a factor of 4 or more, but that this bias can be mitigated using log-ratio linear models.

Differences in Precipitation Regime Shape Microbial Community Composition and Functional Potential in Namib Desert Soils

Article

Full-text available

Apr 2022
MICROB ECOL

Precipitation is one of the major constraints influencing the diversity, structure, and activity of soil microbial communities in desert ecosystems. However, the effect of changes in precipitation on soil microbial communities in arid soil microbiomes remains unresolved. In this study, using 16S rRNA gene high-throughput sequencing and shotgun metagenome sequencing, we explored changes in taxonomic composition and functional potential across two zones in the Namib Desert with contrasting precipitation regime. We found that precipitation regime had no effect on taxonomic and functional alpha-diversity, but that microbial community composition and functional potential (beta-diversity) changed with increased precipitation. For instance, Acidobacteriota and ‘resistance to antibiotics and toxic compounds’ related genes were relatively more abundant in the high-rainfall zone. These changes were largely due to a small set of microbial taxa, some of which were present in low abundance (i.e. members of the rare biosphere). Overall, these results indicate that key climatic factors (i.e. precipitation) shape the taxonomic and functional attributes of the arid soil microbiome. This research provides insight into how changes in precipitation patterns associated with global climate change may impact microbial community structure and function in desert soils.

Fungal and metabolome diversity of the rhizosphere and endosphere of Phragmites australis in an AMD-polluted environment

Article

Full-text available

Mar 2021

Symbiotic associations with rhizospheric microbial communities coupled with the production of metabolites are key adaptive mechanisms by metallophytes to overcome metal stress. However, little is known about pseudo-metallophyte Phragmites australis interactions with fungal community despite commonly being applied in wetland phytoremediation of acid mine drainage (AMD). In this study, fungal community diversity and metabolomes production by rhizosphere and root endosphere of P. australis growing under three different AMD pollution gradient were analyzed. Our results highlight the following: 1) Ascomycota and Basidiomycota were dominant phyla, but the diversity and richness of taxa were lower within AMD sites with Penicillium, Candida, Saccha-romycetales, Vishniacozyma, Trichoderma, Didymellaceae, and Cladosporium being enriched in the root endosphere and rhizosphere in AMD sites than non-AMD site; 2) non-metric multidimensional scaling (NMDS) of 73 metabolomes revealed spatially defined metabolite exudation by distinct root parts (rhizosphere vs endosphere) rather than AMD sites, with significant variability occurring within the rhizosphere correlating to pH, TDS, Fe, Cr, Cu and Zn content changes; 3) canonical correspondence analysis (CCA) confirmed specific rhizospheric fungal taxonomic changes are driven by pH, TDS, heavy metals, and stress-related metabolomes produced. This is the first report that gives a snapshot on the complex endophytic and rhizospheric fungal community structure and metabolites perturbations that may be key in the adaptability and metal phytoremediation by P. australis under AMD environment.

Identification of Bacterial and Fungal Communities in the Roots of Orchids and Surrounding Soil in Heavy Metal Contaminated Area of Mining Heaps

Article

Full-text available

Oct 2020

Orchids represent a unique group of plants that are well adapted to extreme conditions. In our study, we aimed to determine if different soil contamination and pH significantly change fungal and bacterial composition. We identified bacterial and fungal communities from the roots and the surrounding soil of the family Orchidaceae growing on different mining sites in Slovakia. These communities were detected from the samples of Cephalanthera longifolia and Epipactis pontica from Fe deposit Sirk, E. atrorubens from Ni-Co deposit Dobšiná and Pb-Zn deposit Jasenie and Platanthera bifolia by 16S rRNA gene and ITS next-generation sequencing method. A total of 171 species of fungi and 30 species of bacteria were detected from five samples of orchids. In summary, slight differences in pH of the initial soils do not significantly affect the presence of fungi and bacteria and thus the presence of the studied orchids in these localities. Similarly, the toxic elements in the studied localities, do not affect the occurrence of fungi, bacteria, and orchids. Moreover, Cortinarius saturatus, as a dominant fungus, and Candidatus Udaeobacter as a dominant bacterium were present in all soil samples and some root samples. Finally, many of these fungal and bacterial communities have the potential to be used in the bioremediation of the mining areas.

Serpentine Geoecology of Western North America: Geology, Soils, and VegetationGeology, Soils, and Vegetation

Book

Mar 2007

Geoecology is a fruitful interdisciplinary field, relating rocks to soils to plant and animal communities and studying the interactions between them. Modern geoecology especially concentrates on showing how geology and soils affect the structure, composition, and distribution of plant communities in a certain research area. This book applies the principles of geoecology to Western North America, and to a specific kind of rock, the fascinating serpentine belts that run along the continental margins of the West Coast from Alaska to Baja. The authors come from different disciplines: Alexander is a soil scientist, Coleman a geologist, Harrison a biological researcher, and Keeler-Wolfe a vegetation ecologist. It begins with an overview of the geology of this rock and this region, covering mineralogy, petrology, and stratigraphy of West Coast serpentine. It will continue with serpentine soils and their development and distribution, and serpentine effects on plants and vegetation and animals. The serpentine geoecology of the different regions of Western North America, concentrating on California, will conclude the study. So, this academic book should appeal to plant ecologists, soil scientists, researchers in geoecology, and students in advanced courses in soil science.

Insights into dryland biocrust microbiome: geography, soil depth and crust type affect biocrust microbial communities and networks in Mojave Desert, USA

Article

Jun 2020
FEMS MICROBIOL ECOL

Biocrusts are the living skin of drylands, comprising diverse microbial communities that are essential to desert ecosystems. Despite extensive knowledge on biocrust ecosystem functions and lichen and moss biodiversity, little is known about factors structuring diversity among their microbial communities. We used amplicon-based metabarcode sequencing to survey microbial communities from biocrust surface and subsurface soils at 4 sites located within the Mojave Desert. Five biocrust types were examined: Light-algal/Cyanobacteria, Cyano-lichen, Green-algal lichen, Smooth-moss, and Rough-moss crust types. Microbial diversity in biocrusts was structured by several characteristics 1) central versus southern Mojave sites displayed different community signatures, 2) indicator taxa of plant associated fungi (plant pathogens and wood saprotrophs) were identified at each site, 3) surface and subsurface microbial communities were distinct, and 4) crust types had distinct indicator taxa. Network analysis ranked bacteria-bacteria interactions as the most connected of all within-domain and cross-domain interaction networks in biocrust surface samples, with Actinobacteria, Proteobacteria, Cyanobacteria, and Ascomycota as hubs among all phyla. Specifically, the genera with highest node degree was Pseudonocardia sp. (Pseudonocardiales, Actinobacteria) in bacteria and Alternaria sp. (Pleosporales, Ascomycota) among fungal genera. Our findings provide crucial insights for dryland microbial community ecology, conservation, and sustainable management.

Soil type and precipitation level have a greater influence on fungal than bacterial diversity in serpentine and non-serpentine biological soil crusts

Abstract

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