GEOTHERMAL SOIL ECOLOGY IN YELLOWSTONE NATIONAL PARK

by

James Francis Meadow

A dissertation submitted in partial fulfillment
of the requirements for the degree

of

Doctor of Philosophy

in

Land Resources and Environmental Sciences

MONTANA STATE UNIVERSITY
Bozeman, Montana

April 2012



©COPYRIGHT

by

James Francis Meadow

2012

All Rights Reserved



ii

APPROVAL

of a dissertation submitted by

James Francis Meadow

This dissertation has been read by each member of the dissertation committee and
has been found to be satisfactory regarding content, English usage, format, citations,
bibliographic style, and consistency, and is ready for submission to The Graduate
School.

Dr. Catherine A. Zabinski

Approved for the Department of Land Resources and Environmental Sciences

Dr. Tracy M. Sterling

Approved for The Graduate School

Dr. Carl A. Fox



iii

STATEMENT OF PERMISSION TO USE

In presenting this dissertation in partial fulfillment of the requirements for a

doctoral degree at Montana State University, I agree that the Library shall make it

available to borrowers under rules of the Library. I further agree that copying of

this dissertation is allowable only for scholarly purposes, consistent with “fair use” as

prescribed in the U. S. Copyright Law. Requests for extensive copying or reproduction

of this dissertation should be referred to ProQuest Information and Learning, 300

North Zeeb Road, Ann Arbor, Michigan 48106, to whom I have granted “the exclusive

right to reproduce and distribute my dissertation in and from microform along with

the non-exclusive right to reproduce and distribute my abstract in any format in

whole or in part.”

James Francis Meadow

April 2012



iv

ACKNOWLEDGEMENTS

Funding was provided by a GK-12 Graduate Fellowship from the NSF and the Big

Sky Institute, a Boyd Evison Graduate Fellowship from the Grand Teton Association,

a dissertation fellowship from the Institute on Ecosystems, and also from a USDA

NRICG to Dr. Zabinski. I would like to thank Rosie Wallander, Galena Ackerman,

John Dore, Christian Klatt, Dana Skorupa, Scott Klingenpeel, Jake Beam and Mark

Kozubal for endless help with sample processing, analysis, supplies, and dumb ques-

tions. Thanks to Dr. Ylva Lekberg for helping me to step into the world I am now

in, and for introducing me to molecular biology. Thanks to the incredibly patient

YNP Permitting Staff. Thanks to my graduate committee, Dr. Tim McDermott, Dr.

David Roberts, Dr. Cathy Cripps, Dr. Tom Deluca, Dr. Billie Kerans and Dr. Rick

Engel for support throughout the process. I especially have my major advisor, Dr.

Cathy Zabinski, to thank for helping me maintain balance and perspective during

my five years at MSU, and for being a terrific example of quiet stability in academia.

Thanks to my lab mates for well-timed discussions on everything but science, and to

my great friend, Adam Sigler, who will often drop everything when we both need to

have good time. Most of all, thanks to my amazing wife, Kelly Meadow, for working

with me through all of the challenges of graduate school and for her constant support.



v

TABLE OF CONTENTS

1. INTRODUCTION .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1

Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Geothermal Soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2
Arbuscular Mycorrhizal Fungi . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
Biological Soil Crusts . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
High-Throughput DNA Sequencing of Soil Microbial Communities. . . . . . 8

Overview of Dissertation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 12

2. LINKING SYMBIONT COMMUNITY STRUCTURES IN A MODEL
ARBUSCULAR MYCORRHIZAL SYSTEM .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15

Contribution of Author and Co-Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
Manuscript Information Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16
Summary. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
Materials and Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24

Plant and Soil Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 24
DNA Extraction, PCR .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25
Phylogeny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 27
Statistical Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 29

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Soil Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
Molecular Identification and Phylogeny . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
Diversity and Community Composition . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 39
Cluster and Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 42

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 44
Acknowledgments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

3. SPATIAL HETEROGENEITY OF EUKARYOTIC MICROBIAL COM-
MUNITIES IN AN UNSTUDIED GEOTHERMAL DIATOMA-
CEOUS BIOLOGICAL SOIL CRUST: YELLOWSTONE NA-
TIONAL PARK, WY, USA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50

Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 50
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 51
Experimental Procedures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57

Sample Collection and DNA Extraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 57
Bar-coded Pyrosequencing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Sequence Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 59
Statistical Analysis. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60



vi

TABLE OF CONTENTS (cont.)

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Sequence Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62
Sample Dissimilarity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
Subsample Clustering . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
Eukaryotic Combined Samples . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
Diatoms. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 71
Spatial Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

4. PROKARYOTIC COMMUNITIES DIFFER ALONG A GEOTHER-
MAL SOIL PHOTIC GRADIENT.. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81

Contribution of Author and Co-Author . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 81
Manuscript Information Page . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
Abstract. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 84
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86

Site Description and Sample Collection . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 86
DNA Extraction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Bar-coded Pyrosequencing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 89
Sequence Processing. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90
Phylogeny and Statistical Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91

Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
Sequence Processing . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
Ordination and Discriminant Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 93
Phylum-Level Comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95

Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104

5. CONCLUSIONS. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 105

LITERATURE CITED .. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 112

APPENDICES . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 129

APPENDIX A: Sample Total Standardization Equations . . . . . . . . . . . . . . . . . . . . . 130
APPENDIX B: Influence of Rarefaction on β-Diversity . . . . . . . . . . . . . . . . . . . . . . . 132
APPENDIX C: Combined 18S and 16S Ordination . . . . . . . . . . . . . . . . . . . . . . . . . . . 140



vii

LIST OF TABLES

Table Page

2.1. Mean Soil Parameter Values . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 34

2.2. Mean Soil Parameter Values (cont.) . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 35

2.3. Operational Taxonomic Unit Metadata . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37

2.4. Operational Taxonomic Unit Metadata (cont.). . . . . . . . . . . . . . . . . . . . . . . . . . . 38

3.1. Chemical characterization of geothermal water. . . . . . . . . . . . . . . . . . . . . . . . . . . 63

3.2. Chemical characterization of geothermal water (cont.) . . . . . . . . . . . . . . . . . . 63

3.3. Results from Analysis of Similarities (ANOSIM). . . . . . . . . . . . . . . . . . . . . . . . . 71

3.4. BLAST Identification of Diatoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 74

3.5. Results from quadratic model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 75

4.1. FSO model results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

4.2. Permutational MANOVA results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98

4.3. Multiple comparisons of three ecologically relevant phyla . . . . . . . . . . . . . . . 100

B.1. Rarefaction comparisons for 16S data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135

C.1. Normalization of combined datasets. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 142

C.2. Normalization of abundance/combined datasets . . . . . . . . . . . . . . . . . . . . . . . . . 145



viii

LIST OF FIGURES

Figure Page

2.1. Paired site framework . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22

2.2. Sampling Effort Assessment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33

2.3. Bayesian Inference Phylogeny. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 36

2.4. Full bipartite network visualization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40

2.5. Diversity comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 41

2.6. Comparison of Clustering Solutions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43

3.1. Source Geothermal Spring . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54

3.2. Diatomaceous geothermal soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 55

3.3. Class-level taxonomic distribution of OTUs after rarefaction . . . . . . . . . . . 65

3.4. Soil temperature measured at 10cm depth in sinter. . . . . . . . . . . . . . . . . . . . . . 66

3.5. Comparison of dissimilarities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67

3.6. Agglomerative hierarchical clustering of subsamples . . . . . . . . . . . . . . . . . . . . . 69

3.7. Silhouette and partition analysis results for subsample data . . . . . . . . . . . . 70

3.8. DB-RDA for all eukaryotes . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72

3.9. DB-RDA for diatoms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 73

3.10. Subsample dissimilarities regressed against distance . . . . . . . . . . . . . . . . . . . . . 75

4.1. Diatomaceous geothermal soils . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87

4.2. Breakdown of the relative abundance of prokaryotic phyla . . . . . . . . . . . . . . 93

4.3. Comparison of dissimilarities . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 95



ix

LIST OF FIGURES (cont.)

4.4. NMDS of samples across soil types and depth. . . . . . . . . . . . . . . . . . . . . . . . . . . . 96

4.5. FSO of soil type, depth, and the combination of both variables . . . . . . . . 97

4.6. Clade-wise phylum comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99

B.1. Rarefied dissimilarity correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136

B.2. Rarefied NMDS comparisons . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137

B.3. Rarefied procrustes errors from NMDS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 138

B.4. Iterated rarefaction dissimilarity correlations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139

C.1. Combined ordination comparisons. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 143

C.2. Combined ordination of 150 most abundant OTUs . . . . . . . . . . . . . . . . . . . . . . 146

C.3. OTU abundance distributions for normalized datasets . . . . . . . . . . . . . . . . . . 147



x

ABSTRACT

Microbial communities in soil are among the most diverse and species-rich of
any habitat, but we know surprisingly little about the factors that structure them.
Geothermal soils present unique and relatively unexplored model systems in which
to address ecological questions using soil microbial communities, since harsh condi-
tions in these soils exert strong filters on most organisms. This work represents two
very different approaches to studying soil ecology in geothermal soils in Yellowstone
National Park: 1) Arbuscular mycorrhizal fungal (AMF) communities living in the
roots of Mimulus gutattus in contrasting plant community types were compared to
assess a link in community structure between plants and their AMF symbionts; and
2) soil microbial communities were surveyed across multiple spatial scales in an un-
studied diatomaceous biological soil crust in alkaline siliceous geothermal soils, using
bar-coded 454 pyrosequencing of 18S and 16S rDNA.

Mycorrhizal communities living in plant roots from contrasting community types
showed a striking difference in taxon richness and diversity that appears to transcend
soil-chemical differences, though robust conclusions are difficult since plant and fungal
communities are structured by some of the same confounding soil conditions. Cluster
and discriminant analyses were employed to compare drivers of AMF community
structure.

Eukaryotic and prokaryotic communities in a diatomaceous biological soil crust
differ significantly from that of an adjacent sinter soil, and along a photic depth
gradient. Along with a description of this unique system, extensive multivariate
community analyses were used to address outstanding questions of soil microbial
community spatial heterogeneity and the methodologies best suited to the unique
assumptions of these datasets. Depending on the intended scope of inference, much
detail can be gained by investigation of microbial communities at the aggregate or
soil particle scale, rather than through composite sampling. Additionally, β-diversity
patterns are apparent with relatively few sequences per sample.



1

CHAPTER 1.

INTRODUCTION

This dissertation explores several aspects of soil microbial communities and their

ecology in Yellowstone National Park (YNP) geothermal soils. These soils present,

with caveats, elegant model systems in which to address outstanding questions in the

field of soil microbial ecology; they are embedded within a mosaic landscape with

highly variable conditions that generally exert strong filters on inhabitants, and thus

an investigator is able to work with organisms living at the limits of environmental

tolerance. In some ways the complex chemical and physical conditions within these

systems confound one another in such a way as to complicate study and analysis, while

on the other hand, life in these soils is so difficult for potential denizens that the noise

and complexity associated with ecological studies in less harsh conditions is cut away

to expose the core necessities of life within an interacting community. The work

presented herein takes advantage of these remarkable soil environments to better un-

derstand ecological interactions, community assembly, and the inherent heterogeneity

of soil microbial communities using a variety of molecular methods aimed at sampling

and studying environmental DNA. Additionally, ecological statistical methods that

were designed for, and have long been used to understand, communities of macroor-

ganisms are increasingly being applied to massive microbial ecology datasets, and



2

this dissertation presents statistical approaches that are useful in exploring microbial

communities.

Background

Geothermal Soils

The research presented in this dissertation was conducted exclusively in the

geothermally-influenced soils of Yellowstone National Park (YNP), WY, USA. Geother-

mal soils present an elegant opportunity to study ecological concepts in a model sys-

tem context, because abiotic factors dictate harsh conditions for soil organisms and

plants in a highly variable mosaic landscape. These soils are often a direct product

of geothermal waters and their aqueous chemical constituents. Conditions unique to

geothermal soils include elevated soil temperature that increases with depth, peri-

odic inundation of chemically diverse waters, belowground influence from steam and

geothermal water, and aqueous saturation of chemical elements that act as soil parent

material and drivers of soil-chemical processes.

Channing et al. (2004) described an alkaline-silceous geothermal soil, in close

proximity and similar to that presented in Chapters 3 & 4 of the present work, that

was composed of taphonized plant and microbial biomass and opaline silica precipitate

from Si-saturated thermal water, and they also found that this biomass taphonization

can occur within the span of a single year (Channing & Edwards 2004). Channing

(2001) suggested similar aqueous taphonization as the primary mechanism responsible



3

for the well-preserved fossils in the Rhynie Chert formation. The high Si content in

thermal water found in these systems results from geothermally-heated groundwater

moving through high-Si igneous substrata. “Sinter” is the name commonly used

to refer to the opaline-siliceous deposits that form when aqueous Si precipitates at

the spring surface, which eventually breaks down into soils, while travertine deposits

and their mineral soils result from calcareous parent materials in combination with

thermal conditions (Rodman et al. 1996). All soils used in the present research, with

the exception of diatomaceous residuum, are primarily composed of sinter material

and are subsequently referred to as such.

Soil-chemical conditions in thermal areas are often highly variable across a rela-

tively fine spatial scale due to source waters that experience different localized subsur-

face chemical concentrations, groundwater temperature, pressure, and residence time

(Morgan 2007). For instance, Burr et al. (2005) reported NH+
4 -N concentrations up

to 800 mg L−1 in a geothermal soil within 2 km of the diatomaceous system reported

in Chapters 3 & 4, even though NH+
4 and NO−3 concentrations in my study system

were below detection limits in the source spring water, and total N levels (as detected

by combustion) closely followed a predictable 10:1 C:N ratio for all soils tested herein;

full chemical results are presented in relevant chapters and appendices.

Relative to the wide attention garnered by geothermal spring systems in microbial

ecology research, little work has been done in the adjacent soils, with a few notable

exceptions. Burr et al. (2005) studied biologically mediated N transformations in an



4

acidic soil, and found potential for most steps of a soil N-cycle. Geothermal heating

events select for a particular subset of the soil microbial community following a shift

of geothermal activities in a forested soil (Norris et al. 2002), including a shift to

a less complex, and more thermophilic community. A similarly discriminant fungal

community was reported by Redman et al. (1999), who were able to culture several

thermophilic and thermotolerant fungal species from an acidic thermal soil. Geobiol-

ogy and phototrophic ecology in endolithic microbial communities from geothermal

lithic deposits have been investigated (Ciniglia et al. 2004; Walker et al. 2005; Norris

& Castenholz 2006), and advances in molecular biology have been made using ther-

mal soil microbial communities (e.g., Botero et al. 2005). Consistent with efforts in

extreme geothermal springs, some novel microorganisms have also been cultured and

described in thermal soils (e.g., Stott et al. 2008). Some work has also been done to

explore the more apparent constituents of thermal soils, plants, and their ecological

strategies (Stout et al. 1997; Stout & Al-Niemi 2002; Tercek & Whitbeck 2004), and

arbuscular mycorrhizal fungal communities colonizing thermal plants have also been

investigated under a variety of soil conditions (Bunn & Zabinski 2003; Bunn et al.

2009; Appoloni et al. 2008). Conditions imposed upon plants and soil organisms

living within these soils, whether temperature, chemistry, or isolation, clearly select

for those specially capable of life in harsh conditions. Microbial communities in this

context often contain organisms whose strengths might lie not in fierce competitive



5

ability, but rather in existing beyond the comfort levels of other prospective organ-

isms. The relatively small spatial scale of extreme soil variability is also a major

factor in the selection of these soils as models for soil ecological studies, and this

dissertation was able to take advantage of exactly that in two different systems.

Arbuscular Mycorrhizal Fungi

A majority of plants in all terrestrial ecosystems engage in a root symbiosis with

arbuscular mycorrhizal fungi (AMF). The fungal component of this symbiosis is a

monophyletic group (Glomeromycota) containing ≈ 300 described taxa (Schüßler

et al. 2001; Rosendahl 2008). The primary symbiotic mechanism entails exchange

of plant photosynthates for soil P, though plants have also been observed to benefit

from increased water and nutrient status, as well as some pathogen resistance (Par-

niske 2008). AMF have been recognized as obligate symbionts, while mycorrhizal host

plants exist on a continuum ranging from facultative to obligate (Klironomos 2003;

Johnson et al. 2006). All members of the Glomeromycota are microscopic and puta-

tively asexual, and they do not produce fruiting bodies. Instead, dispersal depends

mainly on the production of subterraneous, and in some cases endorhizal, multinucle-

ate spores, or on extension of fungal hyphae from colonized roots and root fragments

(Smith & Read 2008). There is currently no formalized species concept for AMF,

and it is unknown whether their high degree of single-spore genetic variation is the

result of hetero- or homokaryosis (Pawlowska & Taylor 2004; Bever & Wang 2005);

this, along with their coenocytic, anastomosing nature, challenges formal definitions



6

of individuals, populations, and communities (Rosendahl 2008). Mycorrhizal fungi

can be especially beneficial for plants living under harsh environmental conditions

(Schechter & Bruns 2008; Bunn et al. 2009; Fitzsimons & Miller 2010; Lekberg et al.

2011), and, especially relevant to the current work, are often found heavily colonizing

thermal-tolerant plants in geothermal soils (Bunn & Zabinski 2003; Appoloni et al.

2008).

Given their paramount role in ecosystem function and plant community ecology,

AMF are a well-studied group of organisms and the AM symbiosis has often been

used in addressing larger ecological questions concerning symbioses and community

ecology (e.g., Dumbrell et al. 2010; Lekberg et al. 2007, 2011). Some studies have

shown specificity and conditional benefit in the AM symbiosis (Klironomos 2003;

Sanders 2003; Helgason et al. 2007; Öpik et al. 2009), though some of these observed

differences can be linked to taxon-level environmental limitations of either plants

or fungi. While most AMF research has revolved around single-species pairings in

greenhouse experiments, mixed plant and fungal community studies reveal that the

AM symbiosis in natural systems likely influences, and is affected by, plant density,

biodiversity, and age structure (van der Heijden et al. 1998; Bever 2002; van der

Heijden et al. 2003; Johnson et al. 2003; Schroeder & Janos 2004; van der Heijden

2004; Hausmann & Hawkes 2009, 2010; van de Voorde et al. 2010), and seasonal

variation and inequality of resources also affect AMF communities (Dumbrell et al.

2011; Collins & Foster 2009).



7

Biological Soil Crusts

Soils in arid ecosystems often exhibit low plant cover due to limited precipitation,

and this results in increased solar radiation at the soil surface. These systems are

often inhabited by a phototrophically-driven community of soil microbes collectively

known as biological soil crusts (BSC) (Belnap & Lange 2003; Belnap 2003; Whitford

2002; Büdel 2005; Rosentreter et al. 2007; Ward 2009). BSCs are variously composed

of cyanobacteria, lichens, mosses, algae, free-living fungi, and other bacteria and ar-

chaea. These assemblages can be conceptually thought of as microbial mat systems,

since their cohesive exudative properties effectively hold surface soil particles together

in a C- and N-rich matrix of microbial biomass, cyanobacterial filaments, exudates,

hyphae, rhizoids, and rhizines. Biological soil crusts are distinct from chemical or

physical soil crusts, such as mineral salt evaporites or desert pavements, in that the

surface is held together by biotic activity rather than chemical or physical properties.

Metabolism of BSC constituents is strongly regulated by pulse-precipitation events,

and most BSC organisms are anhydrobiotic (Belnap 2002, 2003; Cardon et al. 2008).

Organisms associated with BSCs are responsible for a major portion of C and N fixed

in some arid systems where plant activity is limited, and this is largely a function

of cyanobacterial activity at and near the soil surface (Belnap 2002; Hawkes 2003;

Evans & Lange 2003). Cyanobacteria and their exudates give BSC-affected soils most

of their recognizable surface texture, and this helps to stabilize otherwise highly erodi-

ble desert soil (Chaudhary et al. 2009). This visible surface texture, along with soil



8

type, is diagnostic of BSC microbial community composition (Bowker & Belnap 2008;

Rosentreter et al. 2007; Belnap & Lange 2003). Given their roles in nutrient cycling,

soil-water relations and soil stability, it is not surprising that BSCs influence plant nu-

trition and communities (Schwartzman & Volk 1989; Harper & Belnap 2001; Hawkes

2003; Housman et al. 2007). Organisms associated with BSCs are uniquely suited to

life in arid soils, and as such, several novel microorganisms have been cultured from

crusted soils (e.g., Gundlapally & Garcia-Pichel 2005; Bates et al. 2006). Much work

has also gone into characterizing the communities of cyanobacteria associated with

BSCs (Garcia-Pichel et al. 2001; Schlesinger et al. 2003; Gundlapally & Garcia-Pichel

2006; Bhatnagar et al. 2008), as well as other constituents of BSCs and associated

lichen communities (Soule et al. 2009; Bates et al. 2011a, 2012). Biological soil crusts

also present an opportunity to study ecological concepts in microbial communities,

since their constituents are relatively well-studied, and multiple trophic levels and

biogeochemical processes co-occur on a relatively fine scale (Bowker et al. 2009).

High-Throughput DNA Sequencing
of Soil Microbial Communities

Soils hold an unimaginable diversity of microorganisms that vary with environ-

mental conditions at the µm scale (Sylvia et al. 2004; Paul 2007; Madigan et al.

2008; Fierer & Jackson 2006; Fierer et al. 2009a; Fierer & Lennon 2011). These

organisms were classically studied with culture-dependent methods, though it is in-

creasingly clear that our ability to grow soil microbes is still limited; dependence



9

on these techniques historically skewed impressions of microbial biodiversity (Venter

et al. 2004; Martiny et al. 2006). Thus culture-independent molecular characteri-

zation techniques, such as DNA sequencing, and especially those designed to deal

with the heterogeneity of environmental samples, has the potential to greatly im-

prove our understanding of soil microbial communities, processes, and biodiversity

(Fulthorpe et al. 2008; Lemos et al. 2011). Molecular biology techniques have fol-

lowed Moore’s Law (technological output per unit cost increases exponentially over

time; Carlson 2003), and the current cost of sequencing DNA from environmental

samples has fallen far below 1¢/Kb sequenced; this has had a tremendous effect on

the study of soil microbial ecology, since thousands of samples can now be sequenced

together for relatively little cost. Chapter 2 of the current work employs single-colony

direct Sanger-sequencing (Sanger et al. 1977), since I was able to utilize AMF col-

onization partitioning in situ in plant roots with Glomeromycota-specific primers.

Chapters 3 & 4, on the other hand, use 454 bar-coded pyrosequencing (Hutchinson

2007; Hamady et al. 2008; Meyer et al. 2008), which allowed simultaneous sequencing

of prokaryotic and eukaryotic sequences from nearly 100 aggregate-sized soil samples.

The difference between the two methods are vast in terms of effort, time consump-

tion, and the quantity of data generated; the similarity is that both give a relatively

clear, culture-independent identification of microbial taxa present in a sample. Thus

all microbial community data in this dissertation are derived from DNA sequencing.



10

High-throughput DNA sequencing (including massively-parallel 454 pyrosequenc-

ing, mentioned above) has allowed the large-scale study of complex soil microbial

communities not previously feasible, and this has expanded our understanding of the

processes and factors that control microbial community assembly in a variety of soil

environments (Fierer et al. 2007a; Roesch et al. 2007; Ramette et al. 2009; Lemos

et al. 2011; Fierer et al. 2011); the application of ecological theory in microbial re-

search has accelerated as a result (Martiny et al. 2006; Prosser et al. 2007; Fierer et al.

2007b, 2009a; Philippot et al. 2011). Using 16S bar-coded sequences from biomes

across the North American continent, Lauber et al. (2009) showed pH to be a strong

predictor of bacterial community composition, after accounting for biome covariates

such as plants, plant functional types, elevation, and climate, though all of these

factors are inherently interrelated; these findings have been reinforced by additional

work (Rousk et al. 2010a, b). Though some ecological principles can be generalized

to include plants, animals, and microbes (Ramirez et al. 2010; Nemergut et al. 2010),

additional research has illustrated the fundamental ecological differences in biodiver-

sity and biogeographical patterns between macro- and microorganisms (Fulthorpe

et al. 2008; Chu et al. 2010; Fierer et al. 2011). In fact strong evidence points to the

ecological coherence of higher taxonomic groups of bacteria (Philippot et al. 2011).

Network analysis has been used to show that co-ocurrences among bacterial groups

in soils are non-random (Berberán et al. 2011), and Bates et al. (2011b) used archaeal

16S sequences to relate community patterns to soil-chemical conditions in ecosystems



11

across the globe. Most pyro-tagged studies conducted in soils have utilized 16S ribo-

somal sequences; to my knowledge, no study has yet been able to capture the whole

eukaryotic soil microbial community, and part of the reason is the limited ability for

primers within the 18S region to capture all of Eukarya. Methods for detecting groups

within Eukarya have been developed and used in other, primarily aquatic, environ-

ments (Amaral-Zettler et al. 2009; Medinger et al. 2010; Chariton et al. 2010; Behnke

et al. 2011). Fungi have probably been investigated most often compared to other

groups of soil eukaryotes, owing to their important role in soil processes. Fungi have

also been targeted more often because identification of fungal primers that capture

species-level distinctions within specific groups, while difficult for fungi as a whole, is

more feasible than for all eukaryotes (e.g., Taylor et al. 2008; Öpik et al. 2009; Rousk

et al. 2010a, b).

Along with the rapid creation and accumulation of unprecedented volumes of

microbial community nucleotide data, methods for dealing with these data have

also increased rapidly (Gilbert et al. 2011; Lemos et al. 2011). Several bioinfor-

matics pipeline software packages are currently available for use with different types

of metagenomic data (e.g., Caporaso et al. 2010b; Sun et al. 2010; Giardine et al.

2005), and these incorporate many choices for analyzing sequence data. The bar-

coded 454 data presented in Chapters 3 & 4 of this dissertation were processed using

QIIME (Caporaso et al. 2010b). Phylogenetic measures of dissimilarity have become



12

a common way to summarize multivariate community data, and the UniFrac dis-

tance (Lozupone et al. 2006) is an index that allows for statistical comparisons of

communities using clustering and ordination techniques, and provides insight into

community differences that are not only based on community-organism content, but

also the evolutionary interrelatedness of potentially interacting community members.

With choices in every step of analysis, it has become increasingly clear that analyti-

cal methods can have a tremendous effect on results and inference (Schloss 2010; Liu

et al. 2007). Along with the infusion of ecological community analyses into microbial

ecology, concerns over experimental design, replication, and meaningful inference are

being discussed in a way reminiscent of past discussions in macroecology (Hurlbert

1984; Prosser 2010; Lennon 2011; Lemos et al. 2011); these concepts were especially

taken into consideration during the design of experiments presented in Chapters 3 &

4, and it is my hope that this work adds to the ongoing conversation.

Overview of Dissertation

Surprisingly little research has been conducted in geothermal soils, even though

the study of more charismatic thermal features has yielded breakthroughs in the

understanding of life on Earth. The formation of these soils is unique in soil science,

since the primary factor determining the elemental content of particles, the chemical

conditions, and thus biotic inhabitants, is geothermal-influenced water. Arbuscular

mycorrhizal fungi and biological soil crust communities, the two major groups of soil



13

organisms investigated in this dissertation, are both well-studied, though some basic

ecological questions stand out since in situ studies are difficult for both groups, and

little research has gone into their habitation in geothermal soils.

Chapter 2 entails the study of arubuscular mycorrhizal communities inhabiting

the roots of Mimulus guttatus in paired, adjacent contrasting plant community types –

a situation that illustrates the utility of geothermal soils as model systems. Using

28S ribosomal sequences from fungi within plant roots, I assessed a potential link

in symbiont community structures. This study represents a step in our understand-

ing of community assembly of symbiotic communities, and especially for arbuscular

mycorrhizal fungi.

Chapter 3 describes a previously unstudied geothermal diatomaceous biological

soil crust, as well as its eukaryotic microbial component. This system is a product

of a combination of geothermal conditions, and while the formation appears to have

a small geographic extent and perhaps minimal influence on the surrounding biome,

this is a very unique soil situation that challenges conventional definitions of parent

material, soil particles, porosity, and soil development. I happened across this unique

thermal environment while sampling plants for another project (Chapter 2). Every

time I walked through the area, I was intrigued by the texture of the soil surface, the

fibrous cohesion of the soil material, and the visibly active phototrophic community

in the top few centimeters of the epipedon. After brief correspondence with leaders in

the field of biological soil crusts and geothermal microbial ecology, I obtained funding



14

to survey the microbial communities inhabiting the soils. In this study, I was able to

take advantage of differences in soil material and microbial communities across spatial

scales in order to investigate questions regarding microbial community heterogeneity

and metagenomic sampling design.

Chapter 4 expands on the findings presented in Chapter 3, and here I analyze

the prokaryotic component of the diatomaceous biological soil crust community. Ad-

ditional steps were taken in this study to incorporate phylogeny so that evolutionary

history is added to community analysis. I also compare occurrences of ecologically-

important bacterial phyla, cyanobacteria, verrucomicrobia, and planctomycetes along

a photic gradient, and across soil types. Additional work concerning sampling effort

assessment and rarefaction of pyrosequencing datasets is included as an appendix.



15

CHAPTER 2.

LINKING SYMBIONT COMMUNITY STRUCTURES IN A MODEL
ARBUSCULAR MYCORRHIZAL SYSTEM

Contribution of Author and Co-Author

Manuscript in Chapter 2

Author: James F. Meadow

Contributions: Conceived the study, obtained partial funding, collected and analyzed
output data, and wrote the manuscript.

Co-author: Catherine A. Zabinski

Contributions: Conceived the study, obtained partial funding, assisted in collecting
data, and assisted with study design and discussed the results and implications and
edited the manuscript at all stages.



16

Manuscript Information Page

James F. Meadow and Catherine A. Zabinski
Journal Name: New Phytologist
Status of Manuscript:

Prepared for submission to a peer-reviewed journal
Officially submitted to a peer-reviewed journal

XAccepted by a peer-reviewed journal
Published in a peer-reviewed journal

Accepted January 31, 2012



17

Title: Linking symbiont community structures in a model arbus-

cular mycorrhizal system

Short Running Title: Linking symbiont community structures

Key words: species-area; symbiotic; community structure; arbuscular mycorrhizal

fungi; Yellowstone National Park; habitat diversity; host diversity.

Authors: James F. Meadow∗ and Catherine A. Zabinski

Author Affiliations:

Department of Land Resources and Environmental Sciences

Montana State University

334 Leon Johnson Hall

Bozeman, MT 59717

phone: (406) 994-4227

fax: (406) 994-3933

Email: jfmeadow@gmail.com∗; cathyz@montana.edu



18

Summary

• The influence of plant communities on symbiotic arbuscular mycorrhizal fungal

(AMF) communities is difficult to study in situ since both symbionts are strongly

influenced by some of the same soil and environmental conditions, and thus we

have a poor understanding of the potential links in community composition and

structure between host and fungal communities.

• AMF were characterized in colonized roots of thermal soil Mimulus guttatus

in both isolated plants supporting AMF for only a few months of the growing

season, and in plants growing in mixed plant communities composed of annual

and perennial hosts. Cluster and discriminant analysis were used to compare

competing models based on either communities or soil conditions.

• M. guttatus in adjacent contrasting plant community situations harbored dis-

tinct AMF communities with few fungal taxa occurring in both community

types. Isolated plants harbored communities of fewer fungal taxa with lower

diversity than plants in mixed communities. Host community type was more

indicative of AMF community structure than pH.

• Our results support an inherent relationship between host plant and AMF com-

munity structures, though pH-based models were also statistically supported.

Key words: symbiotic; community structure; arbuscular mycorrhizal fungi; Yellow-

stone National Park; habitat diversity; host diversity.



19

Introduction

Understanding the drivers and controls that structure biotic communities is among

the most fundamental goals in ecology (Begon et al. 2006), and a major focus of mi-

crobial ecology in recent years (Fierer & Jackson 2006; Ramette et al. 2009). Soil

microbes, in particular, exist in a complex heterogeneous environment and their pres-

ence is often a result, to varying degrees, of dispersal, environmental tolerance, and

ecological interactions (Martiny et al. 2006; Miransari 2011; Unterseher et al. 2011).

The relative importance of these controls is poorly understood for the majority of soil

organisms, especially microorganisms (Fierer et al. 2009b).

Arbuscular mycorrhizal fungi (AMF) form a root symbiosis with a majority of

vascular plants in all terrestrial soil systems, and influence processes from the scale of

individual microbial interactions to ecosystems (Smith & Read 2008). The relation-

ship between the two symbionts is generally regarded as mutualistic and primarily

entails the exchange of plant photosynthates for immobile soil nutrients, especially

phosphorus. Plants might receive additional benefits in improved water relations,

pathogen resistance, and improved survival when establishing in existing mycorrhizal

networks (Ruiz-Luzano 2003; Newsham et al. 1995; van der Heijden 2004), and plant

growth and community composition are affected by mycorrhiza and AMF community

structure (van der Heijden et al. 1998, 2003). A small number of AMF taxa (<300

described) appear to engage in mycorrhiza rather non-selectively with the majority of

plant taxa; this taxonomically lopsided relationship might be one reason why AMF

communities have previously been assumed to be somewhat less influenced by plant



20

community composition. Two-way controls on communities have been shown using

feedback experiments (e.g., Bever 2002), and host plant identity, as well as biome,

have been cited as important in affecting AMF community composition and diversity

(Mummey et al. 2005; Hausmann & Hawkes 2010; van de Voorde et al. 2010; David-

son et al. 2011). Recent advances in the molecular technology required to study AMF

communities in situ have shown that AMF diversity and community structure can

change with differences in soil conditions such as pH, texture, and nutrient levels, and

that fungal communities are sometimes controlled more by environmental character-

istics than by dispersal limitations (Redecker 2006; Öpik et al. 2006; Lekberg et al.

2011). Meta-analysis of molecular AMF studies has revealed potential generalists

and specialists within the Glomeromycota (Öpik et al. 2010), and Kivlin et al. (2011)

reported that plant community type, soil temperature and moisture were all associ-

ated with changes in AMF community composition. The relative importance of host

versus soil environment, however, is difficult to study in natural systems since plant

and fungal communities both respond to variation in soil conditions; the result is that

we have an incomplete understanding of the relative influences of soil conditions and

host communities as as drivers of AMF community structure and composition.

Geothermal soils in Yellowstone National Park (YNP) are a unique example in

soil formation in that these soils are largely a product of geothermal influences, in-

cluding steam and elevated temperatures, periodic inundation by chemically diverse

geothermal waters, and rapid taphonomy of plant and microbial biomass by mineral-

saturated waters; mineral precipitates are the primary parent materials for many of



21

these soils (Channing & Edwards 2004; Rodman et al. 1996). These complex soil for-

mation factors result in a mosaic of highly-variable soil conditions across a relatively

small spatial scale. The heterogenous and depauperate nature of biotic communities

in these thermal systems presents an elegant study system for addressing ecological

questions without some of the confounding influences of distance, atmospheric dif-

ferences and dispersal barriers. Plants living in these soils often exist in conditions

that are far beyond the tolerance limits of most vascular plants (Bunn & Zabinski

2003), resulting in a subset assemblage of thermal-tolerant plants within the larger

context of the Greater Yellowstone Ecosystem. One of these thermal-tolerant plants,

a facultative-thermal, annual forb, is Mimulus guttatus DC., and when M. guttatus

grows in geothermal soils it is often heavily colonized by AMF (Bunn & Zabinski

2003). Mimulus guttatus appears to have a wide tolerance for geothermal soil condi-

tions and shows up in two very different plant community situations in geothermal

soils: as a patch within an existing plant community consisting of grasses and other

forbs, including annual and perennial plants (subsequently referred to as ‘communal’

sites; Fig. 1); and as individual, isolated plants emerging in essentially bare soil with

few or no other plants (subsequently referred to as ‘isolated’ sites; Fig. 2.1). Addi-

tionally, these disparate plant communities often occur within 10m of one another,

offering a unique opportunity to analyze the influence of plant community differences

and edaphic conditions on the soil-microbial community. Vectors for AMF dispersal

have previously been identified in YNP thermal soils by Lekberg et al. (2011), indicat-

ing that dispersal limitations between paired sites are not likely to play a major role



22

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Figure 2.1. Paired site framework. All paired sites consisted of a communal (left) and
an isolated (right) component. Hypothesized AMF community differences are based
on the discrepancy in carbon supply (central panels) between these two host commu-
nity situations, where communal sites might foster fungal networks throughout the
growing season with some winter support, while isolated plant mycorrhizal support
consists of C from a single plant during a short growing season window.

in structuring these communities. The reason for the difference in plant community

types is not known.

From the perspective of the obligate mycorrhizal fungal community interacting

with M. guttatus, these plant community types present two very different symbiont life

history situations (Fig. 2.1). In the case of AMF colonizing hosts in mixed communi-

ties, host availability comes during the majority of the year from different host-plant



23

species, and with some element of winter root support in the case of sites that remain

unfrozen. For those in isolated sites, all host support comes in a relatively short

several month window, the duration of the M. guttatus life cycle, and symbiosis then

shuts down for the remaining majority of the year. Given interspecific differences

in sporulation and spore tolerance to harsh environmental conditions (Klironomos

et al. 2001), the AMF communities in these two situations might differ either due

to taxon-level variation in dispersal limitations (e.g., insufficient sporulation rates for

some taxa to adequately disperse into isolated soils before M. guttatus germination)

or in spore tolerance to storage during the months between host life cycles. AMF

taxa in both host community types experience some degree of harsh, geothermal soil

conditions, though mixed plant host communities are present throughout most of the

year and for the entire regular growing season, and this host community structure

allows for year-round mycorrhizal networks rather than obligatory spore production

and storage. The severe filter exerted by isolated plants existing for only two months

in otherwise bare soil would, on the other hand, likely favor those AMF taxa that

are either prodigious spore producers that are dispersing into isolated sites at some

point before M. guttatus germinates, or are especially tolerant of long-term storage

as spores in geothermal soil conditions. Either way, the communities associated with

these isolated sites are potentially a subset of AMF taxa occurring in communal

plants, or, alternatively, a unique set of AMF that are selected for by conditions im-

posed by an isolated host, or a combination of the two. A substantive difference in

AMF communities between host community types, if sufficiently independent of soil

characteristics, would indicate an inherent link in symbiont community structures



24

associated with taxon richness and some predilection among AMF taxa in terms of

association with ephemeral or perennial plant community types.

In the present study, we assess the relative roles of pH and host community struc-

ture as controls on AMF community composition in this model system in geothermal

soils in Yellowstone National Park by contrasting AMF communities in the roots of

M. guttatus living in either communal or isolated plant community types. We hy-

pothesize that: a) isolated M. guttatus will harbor depauperate AMF communities in

comparison with communal M. guttatus, and that b) fungal communities associated

with isolated plants will be distinct from those associated with communal plants.

Materials and Methods

Plant and Soil Collection

Five paired sites were included in this study from Imperial Meadow (M1, M2,

and M3; centered around 44◦33′04′′ N, 110◦51′05′′ W) and the Rabbit Creek (R1 and

R2; 44◦30′55′′ N, 110◦49′16′′ W) drainage in Lower and Midway Geyser Basins, re-

spectively, in Yellowstone National Park (WY, USA). Sites were selected based on

the presence of appropriate adjacent paired patches of M. guttatus. Isolated sites

consisted of either sparse or clumped M. gutattus plants with no other plant taxa

growing within 1m. All of these plants were small relative to optimal growth condi-

tions (≤ 10cm tall; Dorn 2001), and though mycorrhizal networks could conceivably

extend beyond 1m, the distance to surrounding plants helps to reduce this possibil-

ity. Between 6 and 20 plants were sampled individually from each patch (5 pairs



25

of patches), and sample number was determined by the patch size to avoid sampling

more than 20% of the individual plants from each patch. Plants sampled from a given

patch were all with 10m of one another; though M. gutattus density was comparable

between isolated and communal sites, total plant density was inevitably higher in

communal sites. Soil temperature was measured in the rooting zone of each plant,

and whole plants were extracted with 5–10g of rhizosphere soil, transported to the

laboratory within 8 hours, and frozen (-80◦C) until being processed.

For processing, individual plants were allowed to thaw at room temperature (≈

23◦C) for 30 minutes before roots were carefully separated from soil. Soil was oven

dried overnight (60◦C) and analyzed for pH (Hendershot et al. 2008, 2:1 water ex-

tract), total C and N (LEKO combustion; Yeomans & Bremner 1991), and texture

(micro-pipette extraction; Miller & Miller 1987). Due to the heavy influence from

adjacent geothermal spring features, pH is considered to be a driving variable in

these soils (Rodman et al. 1996), and is preferentially used during analysis in this

study. The use of pH as a primary variable in ecological analyses of soil microbial

communities is also well established in non-geothermal systems (e.g., Rousk et al.

2010a).

DNA Extraction, PCR

AM fungal colonization rates were estimated for plants from each site (Koske &

Gemma 1989), and washed roots were cut into small segments, with size (≈ 0.5 cm)

based on colonization rates in that more than half of all segments at a given length



26

were colonized at least once. Eight root segments were randomly picked per plant for

molecular analysis.

We extracted DNA from root pieces by first denaturing plant and fungal tissues in

80 µl TBE buffer (95◦C for 2 minutes) after which root pieces were manually crushed

using sterile micro-pestles. A Chelex 100 suspension (20 µl) was added and samples

were vortexed and placed on ice for 2 minutes before a second denaturation, a final

vortexing, and a final icing. Samples were then centrifuged (8000 rpm for 5 min) to

pellet cell contents, and supernatant was drawn and diluted 50x for use as template

in nested PCR.

The first PCR was conducted with eukaryote specific primers (NDL22-0061)

to target the variable D2 region of the large ribosomal subunit, and the second

PCR utilized the Glomeromycota-specific FLR3–FLR4 primer pair (van Tuinen et al.

1998; Gollotte et al. 2004). Even though this primer combination can amplify non-

Glomeromycotan sequences, and some AMF groups might be missed (Mummey & Ril-

lig 2007), we detected taxa from most major AMF groups and no non-Glomeromycetes.

All PCR was performed using GoTaq Green Master Mix (Promega, Madison, WI,

USA), and with the following thermocycling conditions for PCR-1: 1 minute at 95◦C;

30 cycles of 1 minute at 95◦C, 1 minute at 53◦C, and 1 minute at 72◦C adding 4 sec-

onds to the elongation step for each cycle; followed by a final elongation of 5 minutes

at 72◦C. The PCR-2 program was the same but with an annealing temperature of

56◦C and only 25 cycles without the stepped elongation times. All PCR was per-

formed with 50µl volume, including 2µl 50x diluted DNA extract or 50x diluted



27

PCR-1 product, for PCR-1 and PCR-2, respectively. This sequencing approach, in-

cluding small-root-segment PCR coupled with direct sequencing, was used to avoid

cloning and to attempt to capture single colonizations. The trade-off associated with

direct sequencing is that more root segments can be analyzed with less effort and cost

compared to cloning and sequencing, though mixed sequences are lost. Using this ap-

proach, approximately half of all root segments are expected to result in positive PCR

products. Additionally, the majority of these positive products are expected to re-

turn singular AMF sequences. The first indication of multiple AMF sequences in the

same PCR product is the appearance of multiple bands on agarose gel, though some

combinations of AMF taxa produce segments that are nearly equal length. Thus

PCR products were visualized using electrophoresis on agarose gel, and products

showing single bands were cleaned using the QIAquick PCR Purification Kit (Qia-

gen, Chatsworth, CA, USA) and submitted for direct sequencing at the Idaho State

University Molecular Research Core Facility (www.isu.edu/bios/MRCF). Some PCR

products, even those with apparently singular bands on agarose gel, were composed

of multiple sequences, and these were filtered out upon sequence trace visualization.

Phylogeny

All sequence traces were manually screened using FinchTV version 1.4.0 for Mac

(www.geospiza.com), and sequences showing signs of contamination, indicating mixed

colonizations, were discarded. All screened sequences were queried using BLAST

(Altschul et al. 1990). Operational taxonomic units (OTUs) were designated by clus-

tering at 97.5% sequence similarity based on a neighbor joining tree; representative



28

sequences from each taxon cluster were used in final alignment and phylogenetic recon-

struction. Though 97% similarity effectively separated most OTUs, a bimodal clade

within Glomus A was resolved at 97.5%, and this had identical results for all other

OTUs. Since our rDNA dataset was relatively small, and since known difficulties in

assigning AMF OTUs based on sequence similarities occur with all candidate primer

sets, we felt that manual clustering and assessment of an effective similarity cutoff

was a more conservative approach to OTU selection than the use of automated clus-

tering algorithms and a hard similarity cutoff. This also preserves potentially novel

OTUs, since AMF in YNP thermal have not been extensively studied. Close Gen-

Bank sequences, as well as INVAM and BEG isolates, were included with each OTU

representative to better distinguish clades. Sequences were aligned with ClustalW

in Mega 5.0 for Mac (www.megasoftware.net), and maximum likelihood model tests

were performed using PhyML (PhyML 3.2 for Linux; Guidon et al. 2010; Paradis et al.

2004) in R (version 2.11.1 for Linux; R Development Core Team 2010) with the ape

package (Paradis et al. 2004). Bayesian inference phylogenetic trees were produced

using BEAST (http://beast.bio.ed.ac.uk) for Linux and phylogenetic tree images were

produced in R using ape. Trees were run under a general time-relaxed model with a

5-parameter Γ site distribution and with some sites assumed as invariant (G+Γ+I )

based on PhyML results (AIC = 15928). Trees were run for 107 iterations, saving

every thousandth result, and compiling after a 20% ‘burn in.’ Tentative OTU names

were assigned based on newly proposed AMF phylogeny (Schüßler & Walker 2010;

Krüger et al. 2011). Partial rRNA sequences from these samples were deposited in



29

GenBank under accession numbers JN836499, JN836501–9, and JN836511–28 (Table

3; Supplemental table in accepted manuscript).

Statistical Analysis

Relative abundance data were calculated for each fungal taxon in each site by

combining presence in plants and applying a sample total standardization (Equations

1 & 2 in Appendix A); sample total standardized data were used for all analyses other

than initial rarefaction. All statistical analyses were done in R. Network visualizations

were performed using the bipartite package (Dormann et al. 2008), and individual

sites were summarized with α-diversity metrics (richness, Shannon-Wiener diversity,

and Shannon-Wiener evenness). Tests of these indices were performed as Kruskal-

Wallis rank-sum tests. We tested for a bias in sampling effort by comparing the

total number of sequences per site, the number of plants that returned clean AMF

sequences per site, and the average numbers of sequences per plant for each site,

all with two-sample t-tests. We also created species accumulation curves for each

community type using the ‘rarefaction’ method implemented in the vegan package

(Oksanen et al. 2011).

Raup-Crick probabilistic dissimilarity values were computed for multivariate anal-

yses as instituted in the the vegan package (Raup & Crick 1979; Chase et al. 2011).

Agglomerative hierarchical clustering was conducted using the flexible-β approach (β

= -0.25) to cluster sites by community similarity. Silhouette and heat plots were

created with the optpart package (Roberts 2010c) to assess goodness-of-clustering



30

for each solution. Analysis of similarities (ANOSIM), to assess significance of cluster-

ing solutions, was performed with the vegan package. Four clustering solutions were

tested with agglomerative hierarchical clustering, and these correspond to Fig. 2.6:

a) community type; b) bimodal pH, with M2c in the high-pH cluster; c) balanced

ranked pH, exchanging M2c and M1i; and d) unbalanced ranked pH, including M3i

in the low pH cluster. The latter three clustering solutions (b, c, & d) represent three

different views of 2-cluster solutions based on pH rather than community type.

Results

Soil Analysis

All soil physical and chemical characteristics measured, with the exception of soil

temperature, were predictably correlated with pH (Pearsons correlation coefficient r

= 0.72, 0.64 & 0.57; for pH compared to total N, total C, and clay, respectively)

and none were distributed as evenly as pH (Tables 2.1 & 2.2). While temperature

has previously been explored as a primary driver of plant and fungal communities

in YNP thermal soils and has been noted to fluctuate during growing seasons (Bunn

& Zabinski 2003; Bunn et al. 2009), soils used in this study had a relatively narrow

range of average temperature, from 24.2◦C to 29.8◦C. This narrow range, and the

fact that we collected soil temperature data at only a single time point, did not allow

the complete investigation of temperature as a primary gradient of interest in driving

AMF community patterns in this study. There was also no difference in average soil

temperature between the two community types (t = -0.62, P=value = 0.56; from a



31

two-sample t-test). Isolated plant communities were all found growing in high-pH

soils (mean = 8.97) with low percentages of total C and N (mean = 1.17% & 0.16%,

respectively), while plants growing in communal sites were generally in more neutral

to slightly acidic soils with a single notable exception. Though pH is quite different

between plant community types (P = 0.015; Table 2.1), one communal site, M2c, has

a high mean pH (pH = 9.0) that is more consistent with isolated sites in this study,

and its other measured soil characteristics follow suit.

Molecular Identification and Phylogeny

DNA was extracted from 1,128 root segments for PCR, and, by design, approx-

imately half of all root segments showed positive PCR products on agarose gel, of

which 478 showed singular bands and were submitted for sequencing. After manu-

ally quality-filtering for mixed sequences and putative chimeras, 228 sequences were

used for final analysis. These were individually searched using BLAST and assigned

tentative taxonomic identifiers based on nearest BLAST result or known INVAM or

BEG isolate. Pairwise alignments and phylogenetic construction with sequences from

known isolates resulted in twenty-eight OTUs in eight genera. Posterior support for

every genera-level clade is 1.0, while similarly high support is consistent throughout

most major phylogenetic groupings (Fig. 2.3). We also found no difference between

isolated and communal sites in the number of plants returning AMF sequences (t =

-1.23, P-value = 0.26; from a two-sample t-test), but communal plants did harbor

more OTUs per plant (1.69 & 1.27 OTUs per communal and isolated plant, respec-

tively; t = -2.38, P = 0.056; from a two-sample t-test of average OTUs per plant



32

in each site). Isolated plants, returned an average of 0.52 more clean sequences per

plant than communal plants (mean sequences per plant = 2.63 & 2.11, for isolated

and communal plants, respectively; t = 1.96, P-value = 0.09; from a two-sample t-

test), and an average of 14 more clean sequences were found per site in isolated sites

as a result (29.8 and 15.8 sequences per site for isolated and communal sites, respec-

tively; t = 2.04, P = 0.09; from a two-sample t-test). These tests together indicate an

approximately even sampling effort across community types in terms of the number

of plants sampled, but the large difference in the number of sequences returned per

site is perhaps an indication of the larger number of mixed colonizations in commu-

nal plants that were discarded (Fig. 2.2a & b). More sequences per plant and per

site could potentially bias richness and diversity toward isolated plants, but this was

certainly not the case in the present study, as communal plants were far more OTU

rich and diverse. It is also likely, given the number of singletons detected in com-

munal plants, that this sequencing approach resulted in an underestimate of OTU

richness in communal plants, as indicated by the steep terminus of the communal

species accumulation curve (Fig. 2.2c).



33

1

2

3

4

5

6

7

8

S
e
q
u
e
n
c
e
s
 p

e
r 

P
la

n
t

isolated communal

(a)

M1i M2i M3i R1i R2i M1c M2c M3c R1c R2c

1

2

3

4

5

A
M

F
 O

T
U

s
 P

e
r 

P
la

n
t

(b)

Plants per Site
Sequences per Site

n=8 n=14 n=5 n=11 n=18 n=5 n=4 n=9 n=8 n=13

N=17 N=42 N=13 N=33 N=44 N=9 N=10 N=19 N=21 N=20

0 10 20 30 40 50

0

5

10

15

20

25

Plants

A
M

F
 O

T
U

s

communal

plants

isolated

plants

(c)

Figure 2.2. Sampling Effort Assessment. a) Boxes show the distribution of sequences
returned by individual plants within each site; and b) the distribution of OTUs de-
tected per plant (n = number of plants per site; N = number of clean sequences per
site). We found no difference between isolated and communal sites in the number of
plants returning AMF sequences (t = -1.23, P-value = 0.26; from a two-sample t-test
of n), but communal plants did reveal more OTUs per plant (1.69 & 1.27 OTUs per
communal and isolated plant, respectively; t = -2.38, P = 0.056; from a two-sample
t-test of average OTUs per plant in each site). Isolated plants returned an average
of 0.52 more clean sequences per plant than communal plants (mean sequences per
plant = 2.63 & 2.11, for isolated and communal plants, respectively; t = 1.955, P-
value = 0.088; from a two-sample t-test), and an average of 14 more clean sequences
were found per site in isolated sites as a result (29.8 and 15.8 sequences per site for
isolated and communal sites, respectively; t = 2.04, P = 0.09; from a two-sample
t-test on N ). c) Species accumulation curves for isolated (white confidence interval)
and communal (gray confidence interval) plants show that the sampling effort likely
captured only a subset of AMF OTUs from communal sites, but that most isolated
AMF OTUs were detected.



34

T
ab

le
2.

1.
M

ea
n

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oi

l
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ar
am

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V
al

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(±
S
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)
fr

om
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o
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C
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eq

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s

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p

(◦
C

)
p
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∗

%
N
∗∗

%
C
∗∗

M
1

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ed

8
17

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.7

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(±

0.
49

)
8.

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(±

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01

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(±

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(±
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(±
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)

0.
89

(±
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02
)

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3

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(±

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)
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(±

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24

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(±

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01

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(±

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)
R

1
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11

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25

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1

(±
0.

51
)

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(±
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(±
0.

01
)

1.
54

(±
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06
)

R
2

is
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ed

18
44

24
.2

2
(±

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55

)
8.

99
(±

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07

)
0.

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(±

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)
1.

22
(±

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)
M

1
co

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5
9

28
.4

4
(±

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80

)
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(±

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22

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0.

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(±

0.
08

)
3.

41
(±

1.
26

)
M

2
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4
10

26
.2

5
(±

1.
03

)
9.

00
(±

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12

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0.

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(±

0.
02

)
1.

64
(±

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19

)
M

3
co

m
m

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9
19

25
.2

2
(±

0.
36

)
6.

36
(±

0.
15

)
0.

29
(±

0.
02

)
2.

63
(±

0.
18

)
R

1
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8
21

27
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8
(±

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19

)
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(±

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27

)
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(±

0.
06

)
7.

01
(±

0.
93

)
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13
20

26
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7
(±

0.
68

)
6.

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(±

0.
22

)
0.

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(±

0.
02

)
4.

95
(±

0.
26

)
*H

en
d
er

sh
ot

et
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.
(2

00
8)

**
Y

eo
m

an
s

&
B

re
m

n
er

(1
99

1)



35

Table 2.2. Mean Soil Parameter Values (cont.) Values represent averages of mea-
surements (± SE) from rooting-zone soil under individual plants within communal or
isolated sites.

Community
Site Type % Sand† % Silt† % Clay†

M1 isolated 84.38 (±0.84) 14.50 (±0.76) 1.12 (±0.23)
M2 isolated 89.50 (±0.54) 10.50 (±0.54) 0.07 (±0.07)
M3 isolated 74.60 (±1.78) 22.60 (±1.36) 2.60 (±0.68)
R1 isolated 76.45 (±1.40) 22.82 (±1.20) 0.73 (±0.27)
R2 isolated 78.72 (±0.63) 20.22 (±0.57) 1.22 (±0.10)
M1 communal 81.57 (±1.67) 16.86 (±1.32) 1.57 (±0.37)
M2 communal 84.00 (±2.35) 15.75 (±2.53) 0.50 (±0.29)
M3 communal 65.55 (±0.87) 31.45 (±1.05) 3.18 (±0.31)
R1 communal 58.00 (±2.04) 39.12 (±1.92) 2.88 (±0.52)
R2 communal 53.67 (±1.92) 36.20 (±1.32) 10.53 (±0.95)

†Miller & Miller (1987)



36

Rhizophagus 9 (JN836513)
Uncultured (DQ468755)
Uncultured (AJ854593)
Rhizophagus 11 (JN836509)
Rhizophagus 10 (JN836526)
Uncultured (AJ854633)
Uncultured (AJ854628)
Rhizophagus 8 (JN836517)
Uncultured (AJ854592)
Rhizophagus 7 (JN836518)
Glomus mosseae (AJ854592)
Rhizophagus 1 (JN836506)
Glomus intraradices (EU234488)
Glomus intraradices (HM625897)
Rhizophagus 6 (JN836504)
Uncultured (AJ854606)
Rhizophagus 5 (JN836525)
Rhizophagus 4 (JN836505)
Glomus intraradices (HM625894)
Uncultured (AJ854594)
Rhizophagus 3 (JN836524)
Uncultured (AM040415)
Rhizophagus 2 (JN836507)
Uncultured (DQ468717)
Uncultured (GQ149208)
Rhizophagus 12
Uncultured (AJ459374)
Glomus 1 (JN836515)
Glomus 2 (JN836511)
Uncultured (DQ468807)
Glomus 4 (JN836514)
Glomus 5 (JN836527)
Uncultured (DQ468787)
Glomus 7

Funneliformis 4 (JN836503)
Glomus mosseae (GQ330792)
Glomus mosseae (FJ461845)
Glomus mosseae (FJ461844)
Glomus constrictum (FJ461827)
Funneliformis 3 (JN836520)
Uncultured (EU380107)
Funneliformis 2 (JN836512)
Uncultured (AB206243)
Funneliformis 1

Claroideoglomus 1 (JN836519)
Glomus claroideum (AM040317)
Glomus etunicatum (FJ461833)
Claroideoglomus 2 (JN836528)
Scutellospora heterogama (FJ461877)
Scutellospora pellucida (FJ461879)
Scutellospora fulgida (FJ461870)
Scutellospora verrucosa (FJ461881)
Scutellospora 2 (JN836522)
Uncultured (AB547173)
Scutellospora 1 (JN836516)
Diversispora spurca (FJ461849)
Diversispora 1 (JN836523)
Glomus versiforme (FJ461852)
Acaulospora laevis (FJ461802)
Acaulospora 4 (JN836501)
Acaulospora kentinensis (FJ461808)
Acaulospora delicata (FJ461790)
Acaulospora 6
Acaulospora delicata (FJ461791)
Acaulospora longula (AM039980)
Acaulospora 2
Acaulospora morrowiae (FJ461795)
Acaulospora 1 (JN836502)
Acaulospora scrobiculata (FM876791)
Acaulospora 3 (JN836499)
Acaulospora paulinae (FJ461796)
Acaulospora 5 (JN836508)
Ambispora leptoticha (FJ461886)
Ambispora germannii (FJ461885)
Archaeospora trappei (FJ461887 
Archaeospora 1 (JN836502)
Entrophospora schenckii (FJ461809)

Rhizophagus

Glomus

Funneliformis

Claroideoglomus

Scutellospora

Diversispora

Acaulospora

Ambispora

Archaeospora

1

0.99

0.98

0.58

1

0.63

1

1

1

1

1

0.97

1

1

0.86
1

1

Figure 2.3. Bayesian Inference Phylogeny. AMF taxa used in these analyses are
bold and with points at branch tips indicating plant community affiliation; black
were found only in communal sites, white were found only in isolated sites, and gray
were found in both community types. Clade bars indicate genera sensu Schüßler
and Walker (2010) and Krüger et al. (2011). Names assigned during this study fol-
low this convention, while isolates used for alignment retain names given in NCBI
database. Node support values are posterior probabilities resulting from 107 iter-
ations, saving every thousandth result, and compiling after a 20% ‘burn in’. An
ascomycete (Mortierella polycephala; NCBI accession number AF113464) was used
as an out-group and is not shown.



37

T
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le
2.

3.
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T
ax

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71
75

33
98

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ca

u
lo

sp
or

a
p
au

li
n
ae

(F
J
46

17
96

)
96

J
N

83
64

99
A

ca
u
lo

sp
or

a
4

A
J
45

93
57

*
86

A
ca

u
lo

sp
or

a
d
el

ic
at

a
(F

J
46

17
90

)*
87

J
N

83
65

01
A

rc
h
ae

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p

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1
F

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46

18
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98
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n
te

ro
p
h
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(F
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)
98

J
N

83
65

02
F

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n
n
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G

Q
33

07
92

99
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m
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33
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99
J
N

83
65

03
R

h
iz

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h
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6
H

M
62

58
97

99
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lo
m

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s

in
tr

ar
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(H
M

62
58

97
)

99
J
N

83
65

04
R

h
iz

op
h
ag

u
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4
H

M
62

58
94

95
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lo
m

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in
tr

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(H
M

62
58

94
)

95
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83
65

05
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83
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R

h
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99

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m
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in
tr

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91

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83
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A

ca
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ca

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rn

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R

69
23

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N

83
65

08
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h
iz

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11
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66
79

99
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m

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in
tr

ar
ad

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es

(F
J
23

55
69

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98

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N

83
65

09
G

lo
m

u
s

2
D

Q
46

88
07

97
G

lo
m

u
s

in
tr

ar
ad

ic
es

(J
F

43
92

12
)*

82
J
N

83
65

11
F

u
n
n
el

if
or

m
is

2
E

U
38

01
07

97
G

lo
m

u
s

ag
gr

eg
at

u
m

(F
J
46

18
12

)
93

J
N

83
65

12
R

h
iz

op
h
ag

u
s

9
J
F

71
75

62
10

0
G

lo
m

u
s

in
tr

ar
ad

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es

(J
F

43
91

69
)

99
J
N

83
65

13
*B

L
A

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T

se
q
u
en

ce
q
u
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co

ve
ra

ge
<

97
%



38

T
ab

le
2.

4.
O

p
er

at
io

n
al

T
ax

on
om

ic
U

n
it

M
et

ad
at

a
(c

on
t.

)
R

es
u
lt

s
fr

om
B

L
A

S
T

se
ar

ch
es

fo
r

ea
ch

of
th

e
ta

x
on

cl
u
st

er
s

u
se

d
as

O
T

U
s

in
th

is
st

u
d
y.

S
h
ow

n
ar

e
th

e
m

os
t

si
m

il
ar

re
su

lt
fr

om
B

L
A

S
T

se
ar

ch
w

it
h

si
m

il
ar

it
y,

as
w

el
l

as
m

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t

si
m

il
ar

k
n
ow

n
is

ol
at

e
(i

so
la

te
d

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lt

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re

s
w

it
h

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ll

ep
it

h
et

s)
an

d
th

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r

si
m

il
ar

it
y.

A
cc

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si

on
n
u
m

b
er

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as

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gn

ed
to

se
q
u
en

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fr
om

th
is

st
u
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e

in
O

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ac
ce

ss
io

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m

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(J

N
83

64
99

,
J
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01
–9

,
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.

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en

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(A

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io
n

(%
)

(%
)

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lo

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54
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90

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N

83
65

16
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h
iz

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8
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37
99

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tr

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F

43
91

96
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95
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N

83
65

17
R

h
iz

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h
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7
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M
99

23
81

99
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lo
m

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(A
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76
99

68
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99
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83
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18
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la
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85
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ar

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83
65

19
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(J
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43
91

67
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N

83
65

20
A

ca
u
lo

sp
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1

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94

A
ca

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sp
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gu

la
(A

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38

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94

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N

83
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cu

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ll
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p
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54
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97

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87

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36

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83
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22
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er

si
fo

rm
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N
54

76
35

96
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lo
m

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s

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rs

if
or

m
e

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N

54
76

35
)

96
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N

83
65

23
R

h
iz

op
h
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3
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F
55

45
62

99
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lo
m

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s

in
tr

ar
ad

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es

(H
M

62
58

96
)

98
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N

83
65

24
R

h
iz

op
h
ag

u
s

5
E

U
38

00
34

99
G

lo
m

u
s

in
tr

ar
ad

ic
es

(H
M

62
58

92
)

97
J
N

83
65

25
R

h
iz

op
h
ag

u
s

10
A

J
85

46
28

97
G

lo
m

u
s

in
tr

ar
ad

ic
es

(F
J
23

55
69

)
96

J
N

83
65

26
G

lo
m

u
s

5
J
F

71
74

67
96

G
lo

m
u
s

co
n
st

ri
ct

u
m

(J
F

43
91

80
)

91
J
N

83
65

27
C

la
ro

id
eo

gl
om

u
s

2
F

N
64

31
46

98
G

lo
m

u
s

cl
ar

oi
d
eo

gl
om

u
s

(A
Y

54
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)*

83
J
N

83
65

28
*B

L
A

S
T

se
q
u
en

ce
q
u
er

y
co

ve
ra

ge
<

97
%



39

Diversity and Community Composition

Of the AMF taxa found, eighteen taxa were found only in communal sites and

seven only in isolated sites, while just three taxa were found in both site types, includ-

ing Rhizophagus intraradices and Funneliformis mosseae, (synonymous with Glomus

intraradices and Glomus mosseae, respectively; Schüßler & Walker 2010; Krüger et al.

2011). Bipartite network visualization of the dataset (Fig. 2.4) reveals a striking pat-

tern of diversity differences between communal and isolated sites, regardless of their

spatial proximity in the study area. Sites and fungal OTUs (boxes along the top

and bottom, respectively, of Fig. 2.4) are ordered using a CCA-based χ2 algorithm to

reduce the number of interaction cross-overs, and is thus an indication of underlying

relationships. Isolated sites appear to be dominated by only a single or two fungal

OTUs (represented by the number and relative width of connections in Fig. 2.4),

while communal sites show little sign of domination by any one OTU but rather a

more even and diverse fungal community. Kruskal-Wallis rank-sum tests of taxon

richness, Shannon-Wiener diversity, and Shannon-Wiener evenness all conclusively

illustrate these differences (Fig. 2.5; Supplemental figure in accepted manuscript).

Taxon richness in communal sites is twice as high as that in isolated sites, with 3.8

more fungal taxa in communal sites (χ2 = 7.45, P = 0.006), and Shannon-Wiener

diversity and evenness are 1.9 and 1.2 times higher, respectively, in communal than in

isolated sites (χ2 = 6.82, P = 0.009 for diversity & χ2 = 5.77, P = 0.016 for evenness).

It is notable here that the single uncharacteristically high-pH communal site (M2c)

has diversity indices that are all consistent with other communal sites rather than

with comparably high-pH isolated sites (Fig. 2.4).



40

A
c
a

1
 (

J
N

8
3

6
5

0
2

)

S
c
u

1
 (

J
N

8
3

6
5

1
6

)
S

c
u

2
 (

J
N

8
3

6
5

2
2

)
R

h
i7

 (
J
N

8
3

6
5

1
8

)
A

c
a

4
 (

J
N

8
3

6
5

0
1

)
F

u
n

2
 (

J
N

8
3

6
5

1
2

)

R
h

i4
 (

J
N

8
3

6
5

0
5

)

R
h

i1
 (

J
N

8
3

6
5

0
6

)

G
lo

4
 (

J
N

8
3

6
5

1
4

)

F
u

n
4

 (
J
N

8
3

6
5

0
3

)

C
la

2
 (

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N

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3

6
5

2
8

)
R

h
i5

 (
J
N

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3

6
5

2
5

)
R

h
i3

 (
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N

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3

6
5

2
4

)

G
lo

2
 (

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N

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3

6
5

1
1

)

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h

i1
0

 (
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5

2
6

)
C

la
1

 (
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3

6
5

1
9

)

R
h

i1
1

 (
J
N

8
3

6
5

0
9

)

A
rc

1
 (

J
N

8
3

6
5

0
2

)
G

lo
5

 (
J
N

8
3

6
5

2
7

)
D

iv
1

 (
J
N

8
3

6
5

2
3

)

R
h

i2
 (

J
N

8
3

6
5

0
7

)

R
h

i9
 (

J
N

8
3

6
5

1
3

)

R
h

i8
 (

J
N

8
3

6
5

1
7

)

R
h

i6
 (

J
N

8
3

6
5

0
4

)

A
c
a

5
 (

J
N

8
3

6
5

0
8

)
A

c
a

3
 (

J
N

8
3

6
4

9
9

)
G

lo
1

 (
J
N

8
3

6
5

1
5

)
F

u
n

3
 (

J
N

8
3

6
5

2
0

)

M2i R1i M3i R2i M1i R1c R2c M2c M1c M3c

Isolated Both Communal

Figure 2.4. Full bipartite network visualization of all sites in the study. Boxes above
are sites composed of all plants sampled from that site. Boxes below are individual
AMF OTUs. Interaction width is proportional to relative abundance of fungal OTUs
found at each site after sample total standardization (Supplemental equations 1 & 2).
White sites were isolated, black sites were communal. White OTUs were found only
in isolated sites, black were found only in communal sites, and gray were found in
both site types. The order of sites and OTUs in the graphic is a result of a CCA-based
χ2 algorithm that minimizes the number of interaction cross-overs.



41

3456789

R
ic

h
n

e
s
s

R

●●

●

p
=
0
.0
0
6

C
o
m
m
u
n
a
l

Is
o
la
te
d

0
.8

1
.0

1
.2

1
.4

1
.6

1
.8

2
.0

S
h

a
n

n
o

n
 D

iv
e
rs

it
y

H
I

p
=
0
.0
0
9

C
o
m
m
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n
a
l

Is
o
la
te
d

0
.6

0
.7

0
.8

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.9

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.0

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h

a
n

n
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 E

v
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n

n
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p
=
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1
6

C
o
m
m
u
n
a
l

Is
o
la
te
d

F
ig

u
re

2.
5.

D
iv

er
si

ty
co

m
p
ar

is
on

s.
T

ax
on

ri
ch

n
es

s,
S
h
an

n
on

-W
ie

n
er

d
iv

er
si

ty
,

an
d

S
h
an

n
on

-W
ie

n
er

ev
en

n
es

s
of

si
te

s
se

p
ar

at
ed

b
y

p
la

n
t

co
m

m
u
n
it

y
ty

p
e.

P
-v

al
u
es

ar
e

fr
om

K
ru

sk
al

-W
al

li
s

te
st

s.



42

Cluster and Discriminant Analysis

Two different clustering approaches were employed for comparison: one relying on

community dissimilarity data using the Raup-Crick probabilistic metric, and another

on environmental data. The first strategy resulted in clustering consistent with plant

community type (Fig. 2.6a), but when pH was used instead, clusters were created

with plant community types mixed in two-cluster solutions. The community-type-

based clustering solution was tested against three other possible combinations of pH-

based two-cluster solutions with analysis of similarities (ANOSIM), and while all four

models are supported to some extent, the community-type solution showed the lowest

P-value (P = 0.009), though the R-statistic is perhaps more telling (Fig. 2.6). The

R-statistic from ANOSIM is based on the difference of mean ranks between groups

and within groups, and ranges from -1 to 1, with 0 indicating completely random

grouping and positive values indicating systematic grouping (Legendre & Legendre

1998). The amount of variation explained by the clustering solutions is highest for the

community-based solution, though all pH-based solutions return positive R-statistics.



43

M1i

M2i

M3i

R1i

R2i

M1c

M2c

M3c

R1c

R2c

a

2

1

M2c

M1i

M2i

M3i

R1i

R2i

M1c

M3c

R1c

R2c

b

2

1

M2c

M2i

M3i

R1i

R2i

M1i

M1c

M3c

R1c

R2c

c

2

1

M2c

M2i

R1i

R2i

M3i

M1i

M1c

M3c

R1c

R2c

d

2

1

0.0 0.2 0.4 0.6 0.8 1.0

Silhouette

-0.2 0.2 0.6 1.0

-0.4 0.0 0.4 0.8

Silhouette width si

-0.2 0.2 0.4 0.6 0.8 1.0

Plot-to-Set Similarity

S
e
t

1 2

1

2

S
e
t

1 2

1

2

S
e
t

1 2

1

2

S
e
t

1 2

1

2

ANOSIM

R = 0.728

P = 0.009

R = 0.556

P = 0.015

R = 0.324

P = 0.034

R = 0.298

P = 0.057

Figure 2.6. Comparison of Clustering Solutions. Trees represent clustering solutions
based on: a) plant community type; b) bimodal pH; c) balanced ranked pH; and d)
unbalanced ranked pH. Clade bars on trees represent community type homogeneity
within each cluster; green are communal sites, orange are isolated sites, and blue
include both community types. Silhouette plots (second column) represent within-
group goodness-of-clustering from Raup-Crick dissimilarity. ‘Set-to-Set’ heat plots
indicate goodness-of-clustering across clusters, with white representing the highest
degree of similarity and red representing the highest degree of dissimilarity. P-values
are from Analysis of Similarities (999 permutations); R-statistic represents variance
of dissimilarity explained by the clustering solution, with possible values from -1 to
1, where 0 equals random assignment and 1 equals systematic grouping.



44

Discussion

Symbiotic organisms are limited not only by their own environmental tolerances,

but also by the environmental tolerances of their potential symbionts, and this is

especially true of plants and their obligately biotrophic AMF colonizers. Given the

complexities of these relationships, the relative importance of either factor on the

assembly of symbiotic communities is poorly understood. Our objective in this study

was to elucidate drivers of AMF community composition and structure to assess a

structural link between symbiont communities, and thus to assign relative influence

to these drivers. Because the traits of plant and fungal communities tend to be driven

by some of the same soil conditions, disentanglement of the relative influences from

soil and ecological interactions becomes intractable. Thus studies attempting to eluci-

date drivers of AMF community composition often focus on either of the two drivers:

edaphic factors irrespective of plant community compositional properties (Lekberg

et al. 2011; Schechter & Bruns 2008), and across an edaphic gradient where plant

communities are constant (Wu et al. 2007); or conversely as a factor of plant com-

munity compositional (van de Voorde et al. 2010) and structural differences (Börstler

et al. 2006) across relatively constant edaphic conditions. Missing from this litera-

ture is an in situ comparison of the relative influences of soil and host community

structure on AMF communities. The current study was an attempt to address this

problem; the differences we observed in AMF communities were better predicted by

host community type than by soil pH. Isolated plant communities used in this study



45

were all found in high-pH soils, while only a single communal site (M2c) fit this de-

scription, and all other communal sites were found in near-neutral pH; thus these two

predictors are, unfortunately, nearly completely confounded. Although limited to a

single site, and therefore insufficient for robust conclusions, the overlap between pH

and plant community types in the high pH communal site provides an opportunity

to compare the relative influences of soil and plant community structure on AMF

community composition.

We hypothesized that AMF communities existing in the roots of communal M.

guttatus would be distinct from isolated communities such that a more rich and

diverse AMF community would be found compared to the relatively depauperate

AMF community living in isolated plant roots. The most striking example of such

a difference in AMF community composition and structure can be seen in Fig. 2.4.

While taxon richness is clearly a factor in distinguishing community types, dominance

by one or two fungal OTUs in isolated sites is perhaps more salient for identifying

fungal community structural differences between isolated and communal sites. This

characteristically even distribution was also observed in the lone communal site with

more harsh soil characteristics (M2c), an indication that effects of host vegetation

type on fungal communities are more important than the influence of pH. Though the

overlap in pH between isolated and communal sites was limited to a single example,

this site sheds light on a larger pattern of AMF community assembly; these findings

are consistent with van de Voorde et al. (2010), who showed experimentally that plant

community assembly history can have a major influence on symbiotic communities

living in the roots of those plant communities.



46

Additionally, we found surprisingly little overlap between fungal communities in

these contrasting host situations. In fact, only three of the twenty-eight fungal taxa

were found in both plant community types, while seven taxa were found only in iso-

lated sites and eighteen were found only in communal sites, even though paired sites

are adjacent and assumed to have few barriers to AMF dispersal. Our findings can

likely be attributed, at least in part, to island effects since AMF are not neutral in

their dispersal abilities, though this sheds light on the autecological differences be-

tween AMF taxa, especially given the low degree of OTU overlap between community

types (3 OTUs), relative to OTUs found multiple times in only one community type

(10 OTUs). One of the three fungal taxa found in both community types, Rhizoph-

agus intraradices (Rhi 1), was found primarily in isolated sites and made only rare

appearances in communal sites, indicating that this taxon is either well suited to high-

pH soil conditions or most successful in short-lived plant communities where dispersal

and aggressive colonization are favored. Given that this taxon is found world-wide

in highly variable soil conditions (Rosendahl et al. 2009; Öpik et al. 2010), the latter

seems more apt, and the fact that Rhizophagus intraradices was differentially dom-

inant in the two community types might indicate a more conserved ‘niche’ for this

taxon than is often assumed. The other two of the three shared taxa show no par-

ticular affinity for either community type, and one of these, Funneliformis mosseae

(Fun 4), was among the most commonly detected fungal taxa in this study. This

seems to contradict the idea that isolated AMF communities would be a subset of the

surrounding communal assemblages, but rather lends credence to the hypothesis that

AMF taxa that show up in isolated plants are particularly suited to these conditions,



47

seemingly more so than to life in communal sites where competition among other

colonizing AMF might play a bigger role, though some common taxa might be able

to establish in either situation.

Since this study was designed around detecting differences in AMF community

composition as a function of host community type, a major hurdle was the separation

of soil characteristics, which likely play a major role in structuring these thermal

plant communities, from the effects of the plant community structure itself. Host

community type was only reasonably broken into two clusters, and thus we presented

a comparison of the community-based clustering solution with three different options

for clustering based on ranked pH. While all three pH-based clustering solutions

resulted in positive R-values, indicating that communities were clustered by pH better

than expected by random assignment, even the most significant pH-based clustering

solutions (Fig. 2.6b; R = 0.556; P = 0.015) accounted for substantially less of the

variation explained by the community-based solution (R = 0.728; P = 0.009), which

was the most indicative of fungal community clustering. Clearly these results cannot

be interpreted to infer that pH has no effect on AMF communities since all four

models do show some degree of support. In fact, ANOSIM tests of all three pH-

based clustering solutions resulted in P-values that were low; the R-statistic provides

a better interpretation of the goodness-of-clustering, and both measures (R-statistic

and the resulting P-value) indicate that host vegetation type plays a role in structuring

AMF communities, and in this case, was more closely associated with variation in

AMF communities than was pH.



48

In addition to the modest soil condition overlap, the scope of our study was also

limited by our use of only a single plant species at a given point in time, which was

inherent in our study design; our choice of M. guttatus was based on its ephemeral

life history strategy and its appearance in completely isolated thermal soils near

simultaneously emerging communal patches. Given that this study only explored

the mycorrhizal community associated with a single plant species, more information

will certainly be gained as more plant taxa are investigated for similar community

structure links. Use of a single plant species helped to reduce the possibility that

the differences in fungal community composition were simply due to potential M.

guttatus specificity issues. Since geothermal soils can be subject to seasonal and

diurnal variation in environmental conditions, it is important to note that our study

represents only a snapshot of conditions encountered during the flowering period of

M. guttatus. Differences in AMF community structure and composition are likely

attributable to some combination of three factors discussed thus far: 1) long term

spore-storage in harsh soil conditions limiting the establishment of some AMF taxa

in isolated plant roots; 2) short isolated growing season limiting the accumulation

of AMF taxa in isolated plant roots; or 3) host specificity issues associated with

surrounding plant taxa in communal sites. This study was not designed to disentangle

these three potential contributions to fungal community characteristics, but rather to

detect a community-structure link between host and fungal communities.

By comparing the relative strengths of associations between AMF community and

either soil-chemical characteristics or plant community traits, we found host commu-

nity structure was more predictive of the observed differences in AMF community



49

structure than was pH, and that AMF communities in isolated plant patches were

less species-rich, less even, and less diverse. While this is consistent with our first

hypothesis, a competing pH-based model was also supported, and a more robust con-

clusion will rely on more overlap in soil conditions and more host species than just

one.

This study adds to the growing body of evidence that plant communities exert

some control over their associated AMF communities, and that AMF taxa are not

neutral in their appearance in ecological communities. Our results also illustrate a

potential link in symbiont community structure that might help soil ecologists under-

stand AMF communities and their assembly patterns in both natural and managed

systems.

Acknowledgments

We are grateful to the Yellowstone National Park permit personnel for help with

research permits, to Ylva Lekberg for help with molecular methods, and to Rosie

Wallander for help with soil analysis. We also thank three anonymous referees for

their valuable comments. JM was supported with a GK-12 Graduate Fellowship

from the National Science Foundation and the Big Sky Institute, and with additional

support from USDA NRICG to CZ.



50

CHAPTER 3.

SPATIAL HETEROGENEITY OF EUKARYOTIC MICROBIAL COMMUNITIES

IN AN UNSTUDIED GEOTHERMAL DIATOMACEOUS BIOLOGICAL SOIL

CRUST: YELLOWSTONE NATIONAL PARK, WY, USA

Abstract

Knowledge of microbial communities and their inherent heterogeneity has dramat-

ically increased with the widespread use of high-throughput sequencing technologies,

and we are learning more about the ecological processes that structure microbial

communities across a wide range of environments, as well as the relative scales of

importance for describing bacterial communities in natural systems. Little work has

been done to assess fine-scale eukaryotic microbial heterogeneity in soils. Here, we

present findings from a bar-coded amplicon (18S rRNA) survey of the eukaryotic mi-

crobial communities in a previously unstudied geothermal diatomaceous biological soil

crust in Yellowstone National Park, WY, USA, in which we explicitly compare micro-

bial community heterogeneity at the aggregate-scale within soil cores. Multivariate

analysis of community composition showed that while subsamples from within the

same soil core clustered together, community differences between aggregates in the

same core was unexpectedly high. This study describes an unstudied soil microbial

environment and also adds to our growing understanding of microbial heterogeneity,

and the scales relevant to the study of soil microbial communities.



51

Introduction

Soils hold an immense diversity of prokaryotic and eukaryotic microorganisms;

these microbial communities affect processes from the molecular to the ecosystem

scale (Sylvia et al. 2004). The substantial physiochemical heterogeneity of the soil

habitat contributes to their biodiversity (Fierer & Lennon 2011), and soils are among

the most taxon-rich of any microbial habitat (Madigan et al. 2008). Attempts at

understanding the drivers of microbial community composition have met with mixed