SOIL MICROBIAL DIVERSITY OF A MANAGED FOREST ECOSYSTEM AND
THE POTENTIAL FOR LIGNOCELLULOSE DEGRADATION AND METAL
BIOACCUMULATION BY WHITE ROT FUNGI
by
FRITZ AKUO NTOKO
A DISSERTATION
Submitted in partial fulfillment of the requirements
for the degree of Doctor of Philosophy
in the Department of Plant and Soil Science
in the School of Graduate Studies
Alabama A&M University
Normal, Alabama 35762
May 2013
CERTIFICATE OF APPROVAL
Submitted by FRITZ AKUO NTOKO in partial fulfillment of the requirement for
the degree of DOCTOR OF PHILOSOPHY in PLANT AND SOIL SCIENCE.
Accepted on behalf of the Faculty of the Graduate School by the Dissertation
Committee:
Dr. Veronica Acosta-Martinez
Dr. Leopold Nyochembeng
Dr. Elica Moss
Dr. Zachary Senwo
Major Advisor
Dean of the Graduate School
Date
ii
Copyright by
FRITZ AKUO NTOKO
2013
iii
DEDICATION
This work is dedicated to my late sister (Victorine Ebude Ntoko), my sons (Chrys
and Fritz), and all those who have contributed to my academic success.
iv
SOIL MICROBIAL DIVERSITY OF A MANAGED FOREST ECOSYSTEM AND
THE POTENTIAL FOR LIGNOCELLULOSE DEGRADATION AND METAL
BIOACCUMULATION BY WHITE ROT FUNGI
Fritz Akuo Ntoko, PhD., Alabama A&M University, 2013. 187 pp.
Dissertation Advisor: Dr. Z. N. Senwo, PhD.
ABSTRACT
This study investigated the impact of forest management practices such as prescribed
burning and thinning, on soil microbial community diversity and metabolic function,
using ester-linked fatty acid methyl ester (EL-FAME) analysis and DNA pyrosequencing,
coupled with enzymatic assays. It also explored the potential of white rot fungi in the
forest to degrade plant biomass, as well as bioaccumulate mercury. Results obtained from
EL-FAME analysis suggest that the application of prescribed burning without thinning
resulted in an overall decrease in total microbial population when compared to the
control. However, light thinning with or without prescribed burning resulted in an
increase in total microbial population. The bacterial and fungal species diversities, of the
various treatments were all greater than that of the control, with the highest species
diversities found in the lightly-thinned/9yr burn cycle treatments. The structural and
compositional similarities of bacterial species between the different treatments were
greater than that of the fungi species, as depicted by the Chao-Jaccard-Raw abundance
based similarity indices and shared species. The application of burning without thinning
resulted in the lowest metabolic functions, as observed with the no-thin/3yr burn cycle
treatments. The lightly-thinned 9yr-burn cycle treatments had the highest metabolic
activity based on the geometric mean of enzyme activities (GMea) indices. Evaluation of
v
biodegradation of woody (red oak) and non woody plant biomass (corn and wheat) with
two white rot fungi, Pleurotus floridanus and Perenniporia nanlingensis, suggests that P.
floridanus would be a better candidate to pre-treat wheat and wood, while P. nanlingensis
would be better for pre-treating corn. The concentration of Hg in the various fruiting
bodies ranged from 1.37 to 0.03 µg Hg per gram of fungi tissue, and differed
significantly among the species of the various fungi, with the highest bioaccumulation
recorded with Metschnikowia sp. Gerronema strombodes, Boletus sp., and Amanita
alboverrucosa above the EPA acceptable level of 0.3 ppm.
KEY WORDS: metabolic activity, mercury, white rot fungi, pyrosequencing, fatty acid
analysis.
vi
TABLE OF CONTENTS
CERTIFICATE OF APPROVAL ................................................................................... ii
DEDICATION .............................................................................................................. iv
ABSTRACT ....................................................................................................................v
LIST OF TABLES ........................................................................................................ ix
LIST OF FIGURES ....................................................................................................... xi
ACKNOWLEDGMENTS ........................................................................................... xiii
INTRODUCTION AND REVIEW OF LITERATURE ...................................................1
1.1
Introduction .......................................................................................................1
1.2
Soil microbial diversities and functions in soils .................................................7
1.2.1
Drivers and concept of diversity ............................................................... 10
1.2.2
Ecological importance of microbial biodiversity ....................................... 14
1.3
Measurement of microbial diversity................................................................. 17
1.3.1
Dilution plating and culturing methods ..................................................... 18
1.3.2
Community-level physiological profiles .................................................. 20
1.3.3
Fatty acid analysis .................................................................................... 21
1.3.4
Protein based methods ............................................................................. 26
1.3.5
Nucleic acid analyses for soil ecology studies .......................................... 28
1.3.6
Soil microorganisms ................................................................................. 36
1.4
Forest management and soil ecology ............................................................... 43
1.4.1
1.5
Effects of thinning and fire on soil properties ........................................... 48
Rationale and research objectives .................................................................... 51
METHODOLOGY ........................................................................................................ 56
2.1
Study site description....................................................................................... 56
2.2
Soil sampling and analyses .............................................................................. 60
2.3
Microbial communities sizes and structures ..................................................... 60
vii
2.4
Bacterial and fungal diversity by pyrosequencing techniques ........................... 62
2.5
Metabolic capacity of the forest ecosystem via enzyme activities..................... 65
2.6
Collection and screening and identification of fungi fruiting bodies ................. 66
2.7
Molecular identification of fungi fruiting bodies .............................................. 67
2.8
Characterization of biomass and compositional analysis of biomass ................ 68
2.9
Pretreatment of substrate and estimation of enzyme production ....................... 69
2.10 Analysis of mercury in fungi tissue .................................................................. 70
2.11
Statistical analyses ........................................................................................... 71
RESULTS AND DISCUSSION .................................................................................... 73
3.1
Microbial community size and structure by EL-FAME .................................... 73
3.2
Microbial community diversity, and richness, according to pyrosequencing .... 83
3.2.1
Bacterial community richness and diversity .............................................. 83
3.2.2
Shared species richness and similarity in the bacterial community
structure ................................................................................................... 98
3.2.3
Fungal community richness and diversity ............................................... 101
3.2.5
Shared species richness and similarity in the fungal community
structure ................................................................................................. 117
3.3
Metabolic capacity of the forest ecosystem by enzymatic assays .................... 121
3.4
Additional soil properties............................................................................... 132
3.5
Evaluating biodegradation of plant biomass ............................................ 136
3.6
Bioaccumulation of mercury in fungi tissue ............................................ 154
CONCLUSION ........................................................................................................... 158
REFERENCES ............................................................................................................ 163
APPENDIX ................................................................................................................. 176
viii
LIST OF TABLES
Table
Page
2 1.
Treatment applications at the Bankhead National Forest ............................... 58
3.1.
Principal Component Analysis (PCA) factor loadings and percent
contributions of variables to relationships between soil microbial
community compositions, and enzymatic activities with treatments, at
0 - 10cm soil depth. ...................................................................................... 78
3.2.
Changes in microbial community compositions and size at 0 - 10
depth, relative to the control site. .................................................................. 81
3.3.
Analysis of variance for abundance of microbial groups and biomass
of the different treatments at 0 - 10 cm, and with depth (0 - 10cm and
10 – 20cm). .................................................................................................. 82
3.4.
Analysis of variance for relative abundance of bacterial classes with
different treatments. ..................................................................................... 93
3.5.
Bacterial species diversity indices of various treatments. .............................. 95
3.6.
Pearson’s correlation matrix between bacterial classes and soil
properties. .................................................................................................... 96
3.7.
Pearson’s correlation matrix between bacterial classes and soil
enzymatic activities. ..................................................................................... 97
3.8
Observed and estimated shared bacterial species between treatments. ........... 99
3.9.
Chao-Jaccard-Raw abundance-based similarities between pairs of
treatments, calculated from shared OTUs. .................................................. 100
3.10.
Analysis of variance for relative abundance of fungal phyla with
different treatments. ................................................................................... 109
3.11
Analysis of variance for relative abundance of fungal classes with
respect to thinning, burning, and combined treatments................................ 110
3.12.
Diversity indices of fungal species in the different treatments. .................... 114
3.13.
Pearson’s correlation matrix between fungal classes and soil
properties. .................................................................................................. 115
ix
3.14.
Pearson’s correlation matrix between fungal classes and soil enzymes. ...... 116
3.15.
Observed and estimated shared fungi species between treatments. .............. 119
3.16.
Chao-Jaccard-Raw abundance-based similarities between pairs of
treatments, calculated from shared OTUs. .................................................. 120
3.17.
Analysis of variance for enzyme activities, with the different
treatments at 0-10 cm, and depth (0 - 10 cm and 10 – 20 cm) ..................... 124
3.18.
Correlation matrix showing correlation between soil properties,
enzymatic activities and microbial communities ......................................... 130
3.18.
Cont’d. Correlation matrix showing correlation between soil
properties, enzymatic activities and microbial communities ........................ 131
3.19.
Analysis of variance for soil properties, with the different treatments
and depths. ................................................................................................. 134
3.20.
Percent change in plant biomass composition with pretreatment time. ........ 141
3.21.
Incidence and abundance of fungi collected from treatment sites. ............... 156
x
LIST OF FIGURES
Figure
Page
2.1.
Map of the Bankhead National Forest showing treatment stands. .................. 59
3.1.
Discriminant Analysis (DA) plots showing relationships between soil
microbial indicator fatty acids (EL-FAMEs), with (a) thinning and (b)
burning treatments. ....................................................................................... 75
3.2.
Principal Component Analysis (PCA) plot showing relationships
between soil microbial community compositions, and enzymatic
activities with respect to (a) thinning and (b) burning. .................................. 76
3.3.
Principal Component Analysis (PCA) plot showing relationships
between soil microbial community compositions, and enzymatic
activities with different depths. ..................................................................... 77
3.4.
Relative abundance of major bacterial phyla in the no-burn treatments:
(a) not-thinned, (b) lightly-thinned, and (c) heavily-thinned. ......................... 85
3.5.
Relative abundance of major bacterial phyla in 3yr-burn cycle
treatments: (a) not-thinned, (b) lightly-thinned, and (c) heavilythinned). ....................................................................................................... 86
3.6.
Relative abundance of major bacterial phyla in the 9yr-burn cycle
treatments: (a) not-thinned, (b) lightly-thinned, and (c) heavilythinned. ........................................................................................................ 87
3.7.
Bacterial species richness estimates of different treatments........................... 94
3.8.
Relative abundance of fungal phyla in no-burn treatments: (a) notthinned, (b) lightly-thinned, and (c) heavily-thinned. .................................. 103
3.9.
Relative abundance of fungal phyla in 3yr-burn cycle treatments: (a)
not-thinned, (b) lightly-thinned, and (c) heavily-thinned. ............................ 104
3.10.
Relative abundance of fungal phyla in 9yr-burn cycle treatments: (a)
not-thinned, (b) lightly-thinned, and (c) heavily-thinned. ............................ 105
3.11.
Relative abundance of major fungal classes determined in the
Bankhead National Forest soils. ................................................................. 106
3.12.
Fungi species richness estimates of different treatments. ............................ 113
xi
3.13.
Enzymatic activities involved in C cycling. ................................................ 125
3.14.
Enzyme activities involved in (a) carbon and nitrogen cycling, (b)
phosphorus, and (c) sulphur cycling ........................................................... 126
3.15.
Microbial biomass C and N in various treatments. ...................................... 135
3.16.
Effects of pretreatment on wheat straw. ...................................................... 138
3.17.
Effects of pretreatment on corn stalk. ......................................................... 139
3.18.
Effects of pretreatment on wood (red oak) sawdust. .................................... 140
3.19.
Percent composition of wheat straw pretreated with Pleurotus
floridanus. .................................................................................................. 142
3.20.
Percent composition of corn stalk pretreated with Pleurotus floridanus. ..... 143
3.21.
Percent composition of wood sawdust pretreated with Pleurotus
floridanus. .................................................................................................. 144
3.22.
Percent composition of wheat straw pretreated with Perenniporia
nanlingensis. .............................................................................................. 145
3.23.
Percent composition of corn stalk pretreated with Perenniporia
nanlingensis. .............................................................................................. 146
3.24.
Percent composition of wood sawdust pretreated with Perenniporia
nanlingensis. .............................................................................................. 147
3.25.
Laccase (a) and MnP (b) activities in pretreated wheat straw extract. .......... 150
3.26.
Laccase (a) MnP (b) activities in pretreated corn stalk extract..................... 151
3.27.
Laccase (a) MnP (b) activities in pretreated wood (red oak) sawdust
extract. ....................................................................................................... 152
3.28.
Growth rate of Pleurotus floridanus and Perenniporia nanlingensis
cultured on potato dextrose agar (PDA). ..................................................... 153
3.29.
Bioaccumulation of mercury in white rot fungi. ......................................... 157
xii
ACKNOWLEDGMENTS
First, I am thankful to God. I would like to express my appreciation to Dr.
Zachary Senwo for his advisement and assistance throughout the course of this study. I
am also thankful to members of my advisory committee: Dr. Veronica Acosta-Martinez,
Dr. Leopold Nyochembeng, and Dr. Elica Moss, for their valuable suggestions and
willingness to serve on my graduate committee. My gratitude is also expressed to the
faculty, colleagues, and friends who have been of assistance to me throughout the period
of this study. Funds for this work came from the College of Agricultural, Life and
Natural Sciences.
xiii
CHAPTER 1
INTRODUCTION AND REVIEW OF LITERATURE
1.1
Introduction
Forest soils represent an important biotic reservoir of plants, animals, and
microorganisms diversities and a complex abiotic environment (Eldor, 2007). Below
ground biodiversity is several orders of magnitude higher than above ground biodiversity
(Tate, 2000; Gardi et al., 2009). Soil biota contribute, directly or indirectly, to
biogeochemical nutrient cycling, organic materials decomposition, and soil formations
(Gardi et al., 2009). The various trophic levels impact soil biological processes and
activities differently. Soil animals such as earthworms, mites, and nematodes may
contribute to the decomposition of complex organic matter by breaking larger plant
components into smaller pieces, thereby increasing the surface area, or even directly
decomposing the plant biomass. Although biodiversity is usually discussed in terms of
large organisms, microorganisms are more ubiquitous, abundant and diverse.
Microorganisms numerically dominate terrestrial biodiversity, and play important
biochemical and geochemical roles in the environments they inhabit. It has been
1
estimated that one gram of soil contains as many as 10 10 – 1011 bacteria, 6000 – 50000
bacterial species, and up to 200 m of fungal hyphae (Van der Heijden et al., 2008).
Bacteria and fungi communities play key roles in soil ecosystems because they
constitute a major part of the biomass contributing to various biogeochemical cycles
(Kirk et al., 2004). These soil microorganisms influence soil carbon, nitrogen,
phosphorus and sulfur cycling via their role in processes that influence the decomposition
and mineralization of organic constituents. They also constitute part of the food web that
supports populations of invertebrates and protozoans. The diversity of soil microbial
communities and the abundance of specific antagonistic microbes are important to soils
capacity to suppress soil borne plant diseases via antibiosis, competition or stimulation of
plant host defenses (Pal et al., 2006). Soil bacteria and fungi are also critical in increasing
soil aggregate stability, cation exchange capacity, water holding capacity, water
infiltration and soil porosity (Nannipieri et al., 2003). They represent the unseen majority
in soil and comprise a large portion of the genetic diversity on earth (Van der Heijden et
al., 2008).
The microbial communities residing within forest ecosystems have to deal with
complex interactions between biotic and abiotic factors, and other anthropogenic
disturbances which might affect soil biogeochemical properties, forest microbial activity,
and community structure. Forest management practices (such as prescribed burning,
thinning and combinations of both) may influence soil quality through biomass removal
and the opening of the forest canopy which alters the microclimatic properties of the soil,
as well as influence the quantity and quality of substrate inputs into the soil (Olajuyigbe
2
et al., 2012). The management practices that make use of prescribed burning, and
thinning to maintain a healthy understory species plant community traditionally fail to
consider the implications of such practices on soil microbial communities. Such practices
could directly impact soil biochemical and microbial communities key to global element
cycling patterns. With the increase in thinning and burning for forest restoration and
conservation, there is the need to assess their effects on forest management practices and
ecosystem services (Certini, 2005).
Enzymes respond to soil management changes long before other soil quality
indicator changes are detectable. Soil microbes control and produce most of the enzymes
involved in the breakdown of organic matter and the net changes of soil carbon and
nutrients via decomposition, mineralization, and immobilization processes (Nannipieri et
al., 2003). Soil organic compounds such as cellulose, lignin, and chitin are degraded
enzymatically and assaying the activity of extracellular enzymes can provide insights into
the metabolic requirements of the soil microbial community, as well as information on
nutrient and substrate availability (Rietl and Jackson, 2012). Lignin decomposition is of
particular interest because it is a major constituent of plants and represents an important
means of global C storage. The physiological importance of lignin biodegradation is the
destruction of the matrix it forms, so that the microorganisms can access the carbohydrate
substrates (cellulose, glucose) to obtain energy. Lignin wraps around portions of
carbohydrates, creating a physical barrier to cellulases and β-glucosidases enzymes and
preventing them from accessing cellulose and cellobiose (Achyuthan et al., 2010).
3
To understand structure and function of complex ecosystems, it is essential to
identify primary drivers of microbial diversities and community structures. The
complexities of total microbial communities make it difficult to carry out its study intoto. In the past, the lack of knowledge on the biodiversity and integration of prokaryotes
in ecosystems analyses have been mainly caused by methodological and conceptual
problems (Torsvik and Øvreås, 2006). Microbial ecology has long been conceptually
separated from the ecology of higher organisms. The most important challenge in the
assessment of soil microbial diversities and ecology has been the inability to culture and
characterize most of the organisms that can be observed by microscopic analysis.
Historically, the study of microbial ecology was limited to microbes capable of
being cultured and enumerated. Direct counts, using epifluorescence microscopy, has
shown that 1 gram of soil contains more than 10 billion prokaryotic organisms, whereas
plate counts are often 100 to 1000-fold lower, a phenomenon known as the Great Plate
Count Anomaly (Torsvik and Øvreås, 2006). There have also been limitations in the
determination of microbially mediated reactions because the assays to determine the
overall rate of the entire metabolic processes (such as respiration) or specific enzyme
activities do not allow any identification of the microbial species directly involved in the
measured processes (Nannipieri et al., 2003). However, the methodological limitations
that have hampered microbial diversities, structures and activities can be circumvented by
novel techniques such as protein based methods, fatty acid analyses, or methods based on
the analysis of nucleic acids supplemented with tools from bioinformatics to make broad
diversity estimates based on molecular and/or biochemical measurements.
4
Analytical methods for DNA sequencing have progressed over the last 30 years
allowing for increasingly detailed analysis of microbial communities. Microbes can now
be recognized at the DNA level without cultivation, through molecular techniques which
analyze content based on microbial DNA isolated from environmental samples. Not only
do these methods provide DNA-based information for identifying taxa, they also
facilitate testing of ecological hypotheses, contributing for a better understanding of the
structure and functioning of ecosystems, enabling gaining tremendous insights into their
phylogenetic and functional relationships. In general, molecular microbial studies target
one specific short DNA region and rely on the identification of operational taxonomic
units (OTUs): sequence similarity based surrogates for taxa. The OTUs are the
foundation for estimates of richness, frequency, abundance, and distributions.
Characterization of soil microbial communities is increasingly being used to
determine soils responses to stress and disturbances and in assessing ecosystems
sustainability (Banning et al., 2011). A better understanding of soil microbial ecology lies
in the relationships between microbial diversities and soil functions and requires the use
of more accurate assays to taxonomically and functionally characterize soil microbial
communities. Methodologies such as ester-linked fatty acid methyl ester (EL-FAME)
analysis of fatty acids extracted from soils and the pyrosequencing of DNA extracted
from soil samples, coupled with enzymatic assays provide vital insights into microbial
communities structures, functions and diversities that can aid in our understanding of
below-ground microbial communities dynamics, possibly an indication of soil biotic
recovery and restoration progress in disturbed areas (Schutter and Dick, 2000). Hence,
5
analysis of microbial communities is important to elucidate the role of prescribed burning
and thinning of forest ecosystems and in determining the contributions of microbes to
ecosystem recovery after such actions.
The forest also serves as a reservoir for different fungal genera. Macrofungi that
includes numerous wild-edible species do not normally constitute a large portion of the
human diet; however, interest in the consumption of mushrooms is increasing in many
countries due to awareness of their high content of various essential nutrients, including
trace minerals. Furthermore, in the wild, small mammals also feed on fungi. A review of
the natural history literature of small mammal feeding habits, based on many fortuitous
field observations, some analysis of stomach contents, and a review feeding experiments,
shows that diverse animals feed on similarly diverse fungi (Fogel and Trappe, 1978). The
fact that many species of wild mushrooms possess the ability for their fruiting bodies to
accumulate various toxic metals (Falandysz and Chwir, 1997) should be of concern.
Bioaccumulation of mercury in fish and other aquatic organisms has generated increasing
public concern during the past decade (Beaulieu et al., 2012). The importance of fungi in
toxic metal cycling in terrestrial environments has been neglected in favor of studies on
aquatic systems. This is particularly true for the biogeochemical cycle of mercury
(Fischer et al., 1995). In this respect, the fact that wild mushrooms accumulate large
amounts of both macro- and trace minerals has prompted researchers to analyze them for
their Hg contents (Ayaz et al., 2011), especially since many mushrooms accumulate high
levels of heavy metals such as cadmium, mercury, lead, copper and arsenic that can have
severe toxicological effects on humans, even at very low levels.
6
1.2
Soil microbial diversities and functions in soils
Microorganisms are generally divided into five major taxonomic categories:
algae, bacteria, fungi, protists and viruses (Prescott et al., 1996). In soils, they are closely
associated with soil particles, mainly clay-organic matter complexes. Microbes are often
found as single cells or as micro-colonies embedded in a matrix of polysaccharides. Their
activities and interactions with other microbes and larger organisms and with soil
particles depend largely on conditions at the micro-habitat levels that may differ among
micro-habitats even over very small distances. The micro-habitats for soil
microorganisms include the interior as well as exterior surfaces of soil aggregates with
varying sizes and compositions (Giri et al., 2005).
The driving forces of microbial diversities include the genetic constitutions of the
microbes, the environment in which they are found, and ecological interactions with other
components of the biosphere. The result is an extraordinary richness of microbial
diversities, most of which remains to be explored. Soil microbial diversities can be
regarded as an integral part of soil ecosystems functioning, and may be used as a
biological indicator of soil quality and fertility. To date soil biological diversities studies
are being directed primarily at evaluating bacterial and sometimes fungal community
diversities. Perhaps a justification for using microbial species as indicators of soil
diversities resides in the vast number of different species that exist in a gram of soil and
their primary role in the biological decompositions of organic matter. By virtue of their
short generation time, metabolic flexibility and ability to acquire genomic information
across phylogenetic barriers, nearly every terrestrial environment with conditions to
7
sustain life contains microbes. The biodiversities dominated by microorganisms can
regulate behaviors and functions of the environments they inhabit. Further support for the
use of microbial diversities to assess soil functions is derived from the fact that the
decomposer populations in soils serve as an integrator of all physical, chemical, and
biochemical properties of the soils on all soil biological processes (Tate, 2000). Soil
functions such as organic matter decomposition and element cycling are regulated by
complex interactions between physicochemical factors and the soil micro-biota, and such
interactions create spatial and temporal habitat variability that buttress microbial
diversities (Torsvik and Øvreås, 2006). Although the reciprocal interactions between soil
microbes and soil environments takes place at various levels as microbes function in their
local microenvironments, these interactions can affect soil ecosystems at broader scales.
These broad scale changes in diversities, measured as the presence or absence of certain
groups will clearly influence the ecosystems functioning.
Microbial and biochemical characteristics are used as potential indicators of soil
quality, even if soil quality depends on a complex of physical, chemical and biological
properties. The rationale to use microbial and biochemical characteristics as soil quality
indicators is their central role in nutrient cycling and their sensitivity to change
(Nannipieri et al., 1990). Microbial activity is a term used to indicate the vast range of
activities carried out by microorganisms in soil. Microorganisms produce enzymes to
perform many biochemical processes that include various inorganic and redox reactions.
The production of hydrolytic enzymes is a very important function in the decomposition
and mineralization of nutrients, a process critical to ecosystem functioning. The
8
composition of microbial species assemblage (taxonomic diversity) determines the
community’s potential for enzyme synthesis. The actual rate of enzyme production and
fate are modified by environmental factors as well as ecological interactions. The
spectrum and amount of active enzymes are responsible for the functional capability of
the microbial community irrespective of being active inside or outside the cell. Presence
or absence of a certain function, as well as quantifying the potential of the community to
realize these function, has to be considered in ecological studies. The study of enzymatic
activities in soil samples is a useful tool to assess the functional diversities of soil
microbial communities or soil organic mass turnover (Baldrian, 2009).
Measuring enzyme activities in soils has a long tradition in connection with
evaluating soil fertility and quantifying processes in natural and semi natural ecosystems
with a high turnover of organic compounds, such as in forest and grassland soils. This
approach may permit evaluation of the status of changed ecosystems (e.g., by soil
pollution, soil management, global change) while providing insights into the functional
diversities of the soil microbial communities. In fertile soils, heterotrophic
microorganisms are supplied with detritus from plants and other biomass that is rich in
carbon and nutrients that are required for cell maintenance and growth. Incapable of
directly transporting these large molecules into the cytoplasm, these microorganisms rely
on the activities of a myriad of enzymes that they synthesize and release into the
immediate environment. The extracellular enzymes that are released can depolymerize
organic compounds, thereby generating soluble low-number oligomers and monomers
that are then recognized by cell wall receptors and transported across the outer membrane
9
into the cell (Burns and Wallenstein, 2011). Soil is an inherently hostile environment for
these extracellular enzymes, because as soon as they leave the cells they are exposed to
denaturation, degradation, and inactivation through both biotic and abiotic mechanisms.
This might make the breakdown of organic macromolecules seem like an impossible task
at first glance. It is conceptually wrong to assume a simple relationship between a single
enzyme activity and microbiological activity in soils. The need to measure the activities
of a large number of enzymes and to combine these measured activities in a single index
has been emphasized to provide information on microbial activities in soils (Nannipieri et
al., 2002). However, most of the assays used to determine microbiological activities in
soils present the same problem in measuring potential rather than real activities (Burns,
1982; Nannipieri et al., 1990). Indeed, assays are generally made at optimal pH and
temperature and at saturating concentration of substrate. Furthermore, synthetic rather
than natural substrates are often used, and soils are incubated as slurry (Nannipieri et al.,
1990). Generally microbially-mediated processes are the most sensitive to perturbations
in soils; and for this reason soil’s capacity to recover from perturbations can be assessed
by monitoring microbial activities.
1.2.1 Drivers and concept of diversity
Plants exert a strong influence on the compositions of soil microbial communities
through rhizo-deposition and the decay of litter and roots. The link between plant species
and microbial communities in soil rhizosphere results from co-evolution (Nannipieri et
al., 2003). Although unproven for microorganisms, diversity models predict that, if a
10
fixed pool of potential species and communities are characterized by trade-offs in
competition between species, maximal diversity occurs in relatively unproductive habitat
at intermediate rates of disturbance (‘hump-back’ pattern of diversity). According to the
intermediate disturbance hypothesis, the number of species in a community is greatest
when some intermediate intensity, frequency, scale, or duration of disturbance is present.
Disturbance reduces the density of competitive species having poor dispersal, thereby
creating opportunities for less competitive ones having good dispersal. With intermediate
disturbance, both groups of species coexist; competitively dominant species cannot
monopolize all of the resources and pioneering species can still find sites to colonize
(Graham and Duda, 2011).
Biodiversity science has traditionally focused on comparing species richness
across space, time and environments. Out of necessity, microbial diversity studies usually
examine the richness (i.e. number) of operational taxonomic units (OTUs), where OTUs
are sequence similarity based surrogates for microbial taxa, which can be difficult to
define. In addition to richness, OTUs have been used to characterize the abundance,
range, and distribution of microbes, thereby improving our understanding of natural
ecosystems. OTUs are commonly identified by aligning sequences of the small subunits
of ribosomal RNA from one or more samples and identifying groups of related sequences
using a hierarchical clustering algorithm. This clustering is based upon a measure of
distance between all pairs of sequences, which is typically defined using some variant of
the percent sequence identity. For example, researchers traditionally cluster sequences
that are no more than 3% diverged into the same OTU. This designation has been
11
proposed as being roughly equivalent to a species-level classification, though evidence
suggests that it may result in underestimating the true number of species (Sharpton et al.,
2011).
The term ‘diversity’ can be confusing in that it encompasses two very different
components of community structure; richness and evenness. Richness is simply the
number of unique operational taxonomic units (OTUs) in a given sample, area, or in a
given community. In contrast, evenness describes the distribution of individuals among
the OTUs (the proportional abundances of OTUs) and evenness is maximized when all
OTUs have the same number of individuals (Fierer, 2008). Richness estimation offers an
alternative to rarefaction in comparing richness among incompletely inventoried
communities. Instead of interpolating ‘‘backward’’ to smaller samples as in rarefaction,
richness estimators extrapolate beyond what has been recorded to estimate the unknown
asymptote of a species accumulation curve. Simple (regression-based) or sophisticated
(mixture model) curve-fitting methods of extrapolation can be used, or nonparametric
richness estimators can be computed. The latter depends on the frequencies of the rarest
classes of observed species to estimate the number of species present but not detected by
the samples. The simplest nonparametric estimator, Chao1, augments the number of
species observed (Sobs) by a term that depends only on the observed number of
singletons (a, species each represented by only a single individual) and doubletons (b,
species each represented by exactly two individuals):
12
The simplest measure of species diversity is species richness, but a good case can
be made for giving some weight to evenness as well. For example, the subjective sense of
tree species richness is likely to be greater for a naturalist walking through a forest
composed of 10 species of trees, each equally represented, than a forest of 10 species in
which one species contributes 91% of the individuals and the others each 1% (Colwell,
2009). A range of diversity indices have been used with bacterial communities, in
particular the ubiquitous Shannon index, the evenness indices derived from it, and
Simpson’s dominance index (Hill et al.,
2003). Diversity indices are mathematical
functions that combine richness and evenness in a single measure, although usually not
explicitly. Although there are many others, the most commonly used diversity indices in
ecology are Shannon diversity, Simpson diversity, and Fisher’s a. If species i comprises
proportion pi of the total individuals in a community of S species, the Shannon diversity
is:
∑
and Simpson diversity is:
∑
13
Both Shannon and Simpson diversities increase as richness increases, for a given
pattern of evenness, and increase as evenness increases, for a given richness, but they do
not always rank communities in the same order. Simpson diversity is less sensitive to
richness and more sensitive to evenness than Shannon diversity which in turn, is more
sensitive to evenness than is a simple count of species (richness, S) (Colwell, 2009).
1.2.2 Ecological importance of microbial biodiversity
Soil organisms are major components of all soils. Often their biomass is low
compared with the mineral or humus fraction, but the organisms’ activities are absolutely
crucial for a functioning soil. The soil biota can be regarded as the “biological engine of
the earth” and is implicated in most of the key functions soil provides in terms of
ecosystem services, by driving many fundamental nutrient cycling processes, soil
structural dynamics, soil fertility, degradation of pollutants, regulation of plant
communities and agricultural pests. Microbially driven soil processes play key roles in
mediating global climate change, by acting as C sources and sinks and by generating
greenhouse gases such as nitrogen oxides and methane (Breure, 2004). Microbial
activities comprise vital links in the chain of geochemical events that occur when nutrient
elements are cycled. Many microorganisms carry out unique geochemical processes
critical to the operation of the biosphere and there are no geochemical cycles where they
are not involved (Nardini et al., 2010). The metabolic variety of microbes is enormous,
ranging from photo- and chemosynthesis and to degradation of various anthropogenic
xenobiotic compounds. Microorganisms are the primary organisms responsible for the
14
degradation of a great variety of natural organic compounds, including cellulose, hemicellulose, lignin, and chitin which are the most abundant organic matter on Earth.
In the carbon cycle, methanogenic microorganisms can influence the climate by
producing methane gas, which is a major greenhouse gas. Microbial respiration
replenishes atmospheric carbon dioxide and reduces significant amounts of atmospheric
oxygen to water and other compounds. In addition to the effects of carbon dioxide and
methane in the atmosphere, nitrifying and denitrifying microorganisms produce nitrous
oxide which photochemically reacts with ozone, contributing to events that admit
increasing concentrations of ultraviolet radiation to the earth’s atmosphere. Some
bacterial species oxidize elemental nitrogen, which ultimately is returned to the
atmosphere by bacteria that reduce the nitrogen in nitrate compounds to the elemental
state. Unique processes of the nitrogen cycle carried out by microorganisms include
nitrogen fixation, oxidation of ammonia and nitrite to nitrate, and nitrate reduction with
formation of dinitrogen and nitrous oxide gases (Nardini et al., 2010). Microbial diversity
is intimately related to soil structure and functions. Soil governs the productivity of plants
and sustainability of agriculture, forestry, and natural ecosystems. Some of the best soils
are formed in grassland pastures, where bacteria are associated with root materials and
are attached to clay particles (Lynch and Poole, 1979). In most cases, these bacteria are
responsible for transforming and cycling carbon, nitrogen, phosphorus, iron, and sulfur in
the soil and for the manner in which aggregates and clumps of soils are formed. In welldrained soils sustained by a healthy bacterial flora, much of the spaces between the soil
aggregates are filled with air. Since oxygen is necessary for plant roots metabolism, this
15
aerated structure of the soil is necessary for soil productivity. Unfortunately, if certain
microbial species become dominant in soils, the system can become anaerobic. Also,
some bacteria excrete gums and cements (Margulis et al., 1989) that can block soil pores.
Another example of the diverse ecological activities of bacteria is their ability to
control insects. Many insects carry a microbial flora on their surface and in their gut.
Populations of microorganisms pathogenic for the insect may develop if the insect is
injured. Bacteria also produce chemical compounds that adversely affect insect growth.
Thus, manipulating microbial populations provides a mechanism by which agricultural
pests can be controlled. Microorganisms are useful in restoring habitats to a functional
ecological state because they are capable of degrading pollutant compounds, purifying
water and soils in the process. The presence of heavy metals in soils is a severe public
health concern and due to their detrimental effects on humans and the environment, their
removal is deemed important to the protection of environmental health. Mushrooms or
macrofungi can act as an effective biosorbent of toxic metals. Many species of wild
mushrooms possess the ability to accumulate in their fruiting bodies various toxic metals
such as cadmium, copper, mercury, lead and zinc (Falandysz and Chwir, 1997; Das,
2005). The ability of fungi to transform a wide variety of hazardous chemicals has
aroused interests in using them in bioremediation. During the past two decades, in
addition to reporting on heavy metals in mushrooms, many studies have also investigated
the contents of both nutritionally significant major and trace minerals in macrofungi in
the northern hemisphere (Ayaz et al., 2011).
16
1.3
Measurement of microbial diversity
Traditionally, the analysis of soil microbial communities has relied on culturing
techniques using a variety of culture media designed to maximize the recovery of diverse
microbial populations. However, only a small fraction (<0.1%) of the soil microbial
community has been accessible with this approach. The vast majority of microbial
communities in nature have not been cultured in the laboratory. The inherent limitations
of culture-based methods have caused soil microbial ecologists to increasingly turn to
culture-independent methods of community analysis. Using culture-independent
methods, the composition of communities can be inferred based on the extraction,
quantification, and identification of molecules from soils that are specific to certain
microorganisms or microbial groups (Hill et al. 2000). Therefore, the primary source of
information for these uncultured but viable organisms is their biomolecules such as
nucleic acids, lipids, highly conserved genes and proteins (Torsvik and Øvreås, 2006).
Methods such as the analysis of phospholipid fatty acids, ester-linked fatty acids and
community-level physiological profiles have been utilized in an attempt to access a
greater proportion of soils’ microbial communities. In recent years, molecular methods
for soils’ microbial community analyses have provided a new understanding of the
phylogenetic diversities of microbial communities in soils. Among the most useful of
these methods are those in which small subunit rRNA genes are amplified from soilextracted nucleic acids. Using these techniques, it is possible to characterize and study
soil microbes that currently cannot be cultured. Microbial rRNA genes can be detected
directly from soil samples and sequenced. These sequences can then be compared with
17
those from other known microorganisms. The use of these techniques provides new ways
to assess soil microbial diversities and ultimately, a more complete understanding of the
potential impacts of environmental processes and human activities on responses of
microorganisms in soils (Hill et al., 2000).
1.3.1 Dilution plating and culturing methods
A typical protocol to culture and isolate a diverse bacterial community from soil
would include: the suspension of a soil sample in sterile phosphate solution; a series of
1:10 dilutions in a sterile buffer or saline; inoculation onto agar-plates with nutrients; and
incubation of the inoculated agar plates for one to several days at an optimum
temperature, usually above 20°C. This will result in the growth of microbial colonies, and
assuming that each colony is generated from a single cell (colony forming unit – CFU),
the number of bacteria per g of soil can be calculated. Due to the fact that microbes have
different nutritional requirements, the choice of nutrient agar has a tremendous impact on
the number and richness of bacterial types can be isolated. This is a limitation, but one
that has been exploited, as many different soil bacteria can be isolated by varying culture
media and incubation conditions, giving rise to different morphological characteristics.
Several improved cultivation procedures and culture media have been devised that mimic
natural environments in terms of nutrients (composition and concentration), oxygen
gradient, pH, etc. to maximize the cultivable fraction of microbial communities (Rastogi
and Sani, 2011a). Based on the different colony types, it is possible to define and
differentiate operational taxonomic units (OTUs), which can potentially be identified
18
down to the species level, if it maintains its ability to grow under laboratory conditions.
For the fact that some bacterial species have more than a single colony type and, on the
other hand, many other species have colony types that are highly similar or the same,
colony morphology is not a reliable indicator.
It is obvious that with a rather unspecific, generalized cultivation approach, only
dramatic changes in the total bacterial community would become detectable. As we know
today from using DNA techniques, only a small number of soil bacteria can be detected
with cultivation. Microorganisms retrieved using common culture methods are rarely
numerically abundant or functionally significant in the environment from which they
were cultured. These cultured microorganisms are considered as the “weeds” of the
microbial world and constitute <1% of all microbial species (Hugenholtz, 2002). For
example, most of the isolates cultured from soil samples belong to one of four phyla (the
“big four”), Proteobacteria, Firmicutes, Bacteroidetes, and Actinobacteria, primarily due
to their ease of cultivation under laboratory conditions. Although, Acidobacteria
constitutes on average 20% of soil bacterial communities, these organisms are difficult to
culture and are represented by few genera (Schloss and Handelsman, 2004). These
findings suggest that molecular techniques that circumvent the need for isolation and
cultivation are highly desirable for in-depth characterization of environmental microbial
communities (Rastogi and Sani, 2011a).
19
1.3.2
Community-level physiological profiles
A culture-dependent methods used to analyze soil microbial communities has
been the community-level physiological profiles (CLPP), which is a rapid, communitylevel approach that assesses patterns of sole carbon source utilization by mixed microbial
samples, has been used increasingly to study microbial community dynamics (Garland,
1997). This technique takes advantage of the traditional methods of bacterial taxonomy in
which bacterial species are identified based on their utilization of different carbon
sources. Community-level physiological profiles have been facilitated by the use of a
commercial taxonomic system, known as the BIOLOG® system, which is currently
available and has been used extensively to analyze soil microbial communities. The
method involves direct inoculation of environmental samples into Biolog microtiter
plates, and uses color formation from reduction of a tetrazolium dye to assess utilization
of 95 separate sole carbon sources during a 2–7 day incubation period. Direct inoculation
of environmental samples into the microplates, and use of the resulting response are used
to describe differences in microbial communities. The responses, or CLPP, involves the
overall rate of color development, the richness and evenness of response among wells (or
diversity), and the pattern, or relative rate of utilization, among wells. (Garland, 1997).
Direct incubation of whole environmental samples in BIOLOG plates, therefore, may
produce patterns of metabolic response suitable for the rapid classification of
heterotrophic microbial communities (Garland and Mills, 1991). This approach has been
effective at distinguishing spatial and temporal changes in microbial communities.
20
1.3.3 Fatty acid analysis
Microbial community characterization by chemotaxonomic fingerprinting
methods based on qualitative and quantitative analysis of biomolecules (other than
nucleic acids and proteins), such as fatty acids has been used without relying on
culturing. Fatty acids are present in a relatively constant proportion of the cell biomass,
and signature fatty acids exist in microbial cells that can differentiate major taxonomic
groups within a community. Studies of microbial responses to, and recovery from, drastic
disturbance, have historically been few, partly due to the difficulties associated with
microbial community analysis. Fatty acid analysis is a reliable method used to analyze
soil microbial communities and is needed to provide restoration ecologists with
information about the status of the microbial community, providing relative biomass
measures for total bacteria and fungi, a potentially useful indicator of ecosystem selfreliance and soil health (Mummey et al., 2002). More than 200 different fatty acids have
been characterized from prokaryotic organisms. FAMEs are named by standard
nomenclature: the total number of carbon atoms, followed by a colon and the number of
double bonds. The position of the first double bond is indicated by ‘‘ω’’ followed by the
number of carbon atoms from the aliphatic end. The suffixes ‘‘c’’ and ‘‘t’’ refer to cis
and trans conformations, respectively. Methyl branching at the iso and anteiso positions
and at the 10th carbon atom from the carboxyl end is designated by the prefixes ‘‘i’’, ‘‘a’’
and ‘‘10 Me’’, respectively. The prefix ‘‘cy’’ denotes cyclopropane fatty acids (Steger et
al., 2003).
21
Fatty acids with different chain structure have been described to be typical of
distinct microbial groups, with indicator fatty acids for fungal populations (16:1ω5c,
18:3ω6c, 18:1ω9c and 18:2ω6c) and bacterial populations including Gram-positive
bacteria (i.e., i15:0, a15:0, i17:0, a17:0), Gram-negative bacteria (i.e., cy17:0, cy19:0,
i13:0 3OH and i17:0 3OH) and actinomycetes (10Me16:0, 10Me17:0 and 10Me18:0)
(Klose et al., 2006; Gardner et al. 2011). The fatty acids are extracted by saponification
followed by derivatization to give the respective fatty acid methyl esters (FAMEs), which
are then analyzed by gas chromatography, often coupled with mass spectroscopy. To
identify the fatty acids, the retention times are compared to those obtained for standard
fatty acid mixtures. Fatty acids are quantified by correlating the peak areas to the peak
area of the internal standard 19:0 (nonadecanoic acid methylester). The emerging pattern
is then compared to a reference FAME database to identify the fatty acids and their
corresponding microbial signatures by multivariate statistical analyses.
Fatty acids can be obtained from soil samples by different extraction protocols,
which in turn, vary in their abilities to access different lipid pools in soil samples.
Phospholipid fatty acid (PLFA) analysis is more laborious than other alternative methods,
and employ direct and mild, alkaline methanolysis of phospholipids, the resulting
extraction of FAMEs from soil samples. Two other methods exist that differ basically in
the intensity of the extraction procedures and in the types of fatty acids converted into
FAMEs. The ester-linked FAME method (EL-FAME), employs mild alkaline reactions
for FAME production, and another method adapted from the commercial protocol used
22
by microbial identification system (MIDI), relies on a sequence of strong alkaline
saponification and acid methylation, done at high temperature, to produce FAMEs.
1.3.3.1
Phospholipid fatty acid (PLFA) analysis
The determination of the phospholipid fatty acid (PLFA) pattern of soil organisms
has become one of the most commonly used methods to study microbial community
structure (Frostegård et al., 2011). Phospholipids are essential membrane components of
all living cells. They are not found in storage products or in dead cells. Viable microbes
have an intact membrane which contains fatty acids as components of its phospholipids.
Rapid changes in microbial communities and structures can be detected by changes in
PLFA patterns. This suggests that PLFA analysis is suitable for detecting rapid changes
in living populations(Zelles, 1999). The PLFA extraction produces fatty acid methyl
esters (FAMEs) after methylation of the phospholipid fraction, that have been separated
from neutral and glycolipids by solid phase extraction in silica columns. Following a
mild, alkaline methanolysis of phospholipids, the resulting FAMEs, dissolved in
isooctane or hexane, are separated and quantified by gas chromatography. PLFA profiles
of soil samples offer rapid and reproducible measurements for characterizing the
numerically dominant portion of soil microbial communities, including non-cultivable
organisms. The advantage of this method is that it specifically describes the structure of
the living microbiota, as phospholipids are rapidly converted into neutral lipids upon
microbial death. PLFA analysis measures fatty acids from intact, polar lipids, mostly
phospholipids. Therefore, in environmental samples, this analysis determines the
23
membrane components mainly of living cells, since phospholipids are rapidly degraded
upon cell death. The ester-linked (EL) extraction method and the commercially available
microbial identification system (MIDI) represent a different approach. Besides the intact,
polar lipids, these methods also measure fatty acids originating in neutral lipids and
glycolipids, including those in dead organic matter.
Steger et al., (2003) analyzed samples taken from a 200-L laboratory compost
reactor, treating organic household wastes, and found that during the initial stages of the
process, the total concentration of fatty acids in compost samples treated with the EL and
MIDI methods was many times higher than with the PLFA method. They attributed this
to the presence of fatty acids from the organic materials in the original wastes. However,
this substantial difference between PLFA and the other two methods was not found later
in composting. Although the PLFA method gave the most detailed information about the
growth and overall succession of the microbial communities, the much simpler MIDI and
EL methods also successfully described the shifts from the initially dominating straight
chain fatty acids to iso- and ante-iso branched, 10 Me branched and cyclopropane fatty
acids in the later stages of the process. A drawback with the PLFA analysis is that it is a
comparatively time consuming procedure, a fact that is of importance with respect to the
demand for rapid analyses (Steger et al., 2003). To monitor changes in the microbial
communities and structures, simpler methods to determine the fatty acid compositions of
the microorganisms have been developed, such as microbial identification system (MIDI)
and the recently developed ester-linked (EL) fatty acid analysis.
24
1.3.3.2
MIDI FAME analysis
This is another method of fatty acid analysis, adapted from the commercial
protocol used by the microbial identification system (MIDI) that extracts lipids directly
from soil samples. This protocol relies on a sequence of strong alkaline saponification
and acid methylation, done at high temperature, to produce FAMEs from the whole cell
content. In addition to the ester-linked fatty acids, the extracts also include FAMEs
derived from free fatty acids as well as from lipids containing amide- and ether-bonded
fatty acids. Some of these extra FAMEs may be derived from microbial components not
included by the other two methods and, for this reason, could potentially provide a more
comprehensive picture of microbial communities. However, this drastic direct method
would also increase the chances of contamination of the FAME profiles by non-microbial
products.
The MIDI procedure is quicker and easier than the PLFA method. MIDI
extraction can be advantageous relative to PLFA when sample size is limiting, as it
requires a smaller amount of soil than PLFA to maintain a reliable microbial community
fingerprint. This method uses a strong extractant and directly extracts FAs from soils
without any previous separation of lipid fractions. It has been suggested that the main
disadvantage of extending this method to investigate microbial communities and
structures in soils is that a portion of the FAs may be derived from non-living organic
matter (Drenovsky et al., 2004).
25
1.3.3.3
Ester-linked FAME method (EL-FAME) analysis
The ester-linked FAME method (EL-FAME) employs the mild alkaline reaction
for FAME production as used in PLFA, which extracts only ester-bonded types of fatty
acids from soil samples, without separating the other lipid fractions from phospholipids.
Bypassing both the extraction and the fractionation of the whole lipids makes EL-FAME
an easier and much less time consuming method, substantially increasing sample output.
However, because the extraction procedure is performed directly in contact with soil
samples, with no previous separation of lipid fractions, FAMEs are formed from esterlinked fatty acids from all lipid molecules rather than just from phospholipids. Among
these lipid sources, the neutral lipids, used as storage by eukaryotes and present in dead
cells, and glycolipids, would be the major potential interferences in the evaluation of
microbial structures. Nonetheless, EL-FAME has been quite similar to PLFA (Drijber et
al., 2000) methods for discriminating soil management and environmental effects for
microbial communities. Bacteria have minimal storage lipids, which likely increases the
similarity between EL-FAME and PLFA methods for representing bacterial FAMEs.
1.3.4
Protein based methods
The cellular protein profiles have been suggested and are being used to classify
prokaryotic organisms. Signature sequences and phylogenies based on different proteins
permit a reconstruction of the basic evolutionary history of prokaryotes involving
minimal assumptions (Gupta, 1998). Since closely related organisms are expected to have
more similar protein profiles than distantly related ones, and given the fact that proteins
26
can be distinguished in terms of their sizes and/or charges, based on these features one
can obtain reproducible patterns or fingerprints, which can be used to distinguish and
classify prokaryotes. Both one dimensional and two dimensional polyacrylamide gel
electrophoresis patterns have been used. However, the two-dimensional techniques based
on electrophoresis in one direction and isoelectric focusing in another direction has made
it possible to distinguish hundreds of proteins in cell extracts. The proteins can be
identified by coupling the above methods with mass spectroscopy, which separates the
charged particles according to mass. The development of novel technology such as
matrix-assisted laser desorption-ionization and time-of-flight (MALDI-TOF) mass
spectrometry allows for the detection and analysis of large numbers of protein molecules
(Lay, 2002).
In MALDI-TOF mass spectrometry, the samples are mixed with an organic
compound that acts as a matrix to facilitate desorption and ionization of compounds in
the sample (usually an aromatic organic acid that donates a proton to the analyte) and
dried onto a metal sample plate. After the plate is placed in a high vacuum source
chamber in the mass spectrometer, a small portion of the sample is vaporized (desorption)
by blasts from a nitrogen laser. The analyte molecules are distributed throughout the
matrix so that they are completely isolated from each other. Some of the energy incident
on the sample plate is absorbed by the matrix, causing rapid vibrational excitation. The
analyte molecules can become ionized by simple protonation by the photo-excited matrix,
leading to the formation of the typically single charged ions. The analyte ions are then
accelerated by an electrostatic field to a common kinetic energy. The ions produced 'fly'
27
up a tube to the mass analyzer and their masses (actually their mass-to-charge ratio) are
determined by their 'time-of-flight'. If all the ions have the same kinetic energy, the ions
with low mass to charge ratio (m/z) travel faster than those with higher m/z values,
therefore, they are separated in the flight tube and the number of ions reaching the
detector at the end of the flight tube is recorded as the intensity of the ions (Liu et al.,
2003). The range of measurements using MALDI is in excess of 100 kDa although
accuracy falls off at this size. The lower practical limits are several hundred daltons as
there is considerable interference in this range of the spectra from matrix and other low
mass contaminants (Stensballe and Jensen, 2001).
1.3.5
Nucleic acid analyses for soil ecology studies
The development of methods to analyze nucleic acid composition in
environmental samples has opened a new dimension to study microbial communities. Of
all the cell component molecules tested to date, nucleic acids have been the most useful
in providing a new understanding of microbial communities and structures. Nucleic acid
based methods were first introduced into soil microbiology in 1980 when the first DNA
extraction protocol was published. Current detection methods target either DNA or RNA
from soils based on the research objectives, although DNA has been the target of most
published nucleic acid based detection studies in soils due to its stability. DNA provides
information on the presence of specific bacteria on the basis of specific target DNA
sequences in their genome.
28
Of the various nucleic acid techniques used to estimate microbial community
compositions and diversities in complex habitats, the most useful is determining the
sequences of 16S ribosomal RNA (rRNA) genes (i.e. encoded by rDNA) in prokaryotes
and 5S or 18S rRNA genes in eukaryotes. These small subunit (SSU) rDNA molecules
are particularly suited because, they are found universally in all three forms of life (the
domains bacteria, archaea, and eucarya), and these molecules are composed both of
highly conserved regions and also of regions with considerable sequences variations. The
phylogenetic information held in the SSU rDNA molecule is further enhanced by its
relatively large size (e.g. 1.5 kb for the 16S rDNA molecule) and the presence of many
secondary structural domains. Also, SSU rDNA can be easily amplified using polymerase
chain reaction (PCR) and rapidly sequenced (Hill et al., 2000).
The subsequent development of PCR and its application to 16S rRNA gene
sequences directly from environmental samples has revolutionized microbial ecology
making it possible to detect the uncultivable bacteria. The 16S rRNA gene has several
characteristics that explain why is so widely used to study bacterial diversity, ubiquitous
distributions among prokaryotes, relatively slow evolution rate, and the coexistence of
highly variable and conserved regions. The variable regions enable a comparison between
very divergent bacteria, while the highly conserved domains serve as templates for
designing specific PCR amplification primers or specific nucleotide probes. Thus, the
diversity of a bacterial community in a natural environment can be investigated without
any culture, solely based on molecular phylogeny (Giovannoni et al., 1990; Santos and
Ochman, 2004). Potentially, most of the sequences present in the environment can be
29
detected by PCR. Consequently, there is a tremendous difference in the estimation of
bacteria diversity based on culture-independent and culture-dependent approaches since
cultivation has inherent selection towards certain bacteria.
Most methods require that target molecules be separated from the soil matrix prior
to analysis. A few techniques, such as fluorescence in situ hybridization (FISH), do not.
Only recently commercial kits became available for DNA extraction from soils; and this
has simplified and miniaturized this crucial step for cultivation-independent analysis
methods. DNA can be extracted either directly from the soil matrix or after prior recovery
of the microbial fraction. The advantage of the direct nucleic acid extraction approach is
that it is less time-consuming and that a much higher DNA yield is achieved; however,
directly extracted DNA often contains considerable amounts of co-extracted substances
such as humic acids, which interfere with the subsequent molecular analysis (Hartmann
et al., 2007). Much of our ability to make sense of results of molecular analyses and to
design more robust methods depends on the continuing development of nucleic acid and
protein sequence databases and associated bioinformatics analytical tools. Nucleic acid
techniques used fall into three basic categories: (i) analysis of nucleic acids in situ, (ii)
direct analysis of extracted DNA/RNA, and (iii) analysis of PCR-amplified segments of
the DNA molecule.
Techniques based upon nucleic acid colony hybridization (colony blotting) have
particular value in rapidly screening bacterial isolates for their identity, such as
identifying specific rhizobia strains occupying root nodules or screening libraries
containing DNA clones obtained from a soil community. Nucleic acid hybridization
30
probes can also be used to detect specific phylogenetic groups of bacteria in appropriately
prepared soil samples. A nucleic acid probe is fluorescently labeled and hybridized to
target sequences contained within microbial cells in situ using the Fluorescent in-situ
hybridization (FISH) technique. The FISH technique employs an oligonucleotide probe
conjugated with a fluorescent molecule (or fluorochrome). The probe is designed to bind
to complementary sequences in the rRNA of the 16S subunit of the ribosomes within
bacterial cells. Because metabolically active cells contain a large number of ribosomes,
the concentration of fluorescently labeled probe is relatively high inside the cells, causing
them to fluoresce under UV light. The key advantage of FISH is the ability to visualize
and identify organisms on a micro-scale in their natural environment. Such techniques
have enormous potential for studying microbial interactions with plants and the ecology
of target microbial populations in soils; however, the binding of fluorescent dyes to
organic matter resulting in nonspecific fluorescence is a common problem in soils with
high organic matter contents, such as peats, or other particles with high surface charge,
such as black carbon. Image analysis software is readily available to detect only those
aspects of an image that meet specified criteria (Eldor, 2007).
DNA-DNA re-association kinetics is another technique that has also been used. If
a DNA molecule is denatured either by heat or a denaturant such as urea and allowed to
re-associate when the temperature is lowered or the denaturant is removed, the
complexity of the genome dictates the rate at which duplex DNA will form. If we
consider a simple molecule that consists of alternating GCs, this molecule will be able to
form a duplex quicker than a molecule that consists of repeating blocks of AGCT. As
31
complexity increases, the time it takes for complementary strands to re-anneal increases.
Experimentally, this is referred to as a Cot curve, where Co is the initial molar
concentration of nucleotides in single-stranded DNA and t is time. This measure reflects
both the total amount of information in the system (richness or number of unique
genomes) and the distribution of that information (evenness or the relative abundance of
each unique genome), thus making it among the more robust methods for estimating
existent diversity in a given sample. Yet, it provides no information on identity or
function of any member of the microbial community (Thies, 2007).
Recently the hybridization technique has gained momentum by the introduction of
DNA microarray technology which allows for the parallel, high throughput detection and
quantification of specific nucleic acid molecules. Nucleic acid microarrays, represent a
further development of conventional membrane-based technology, and represent the
latest advancement in molecular detection technology. As a result of their sheer high
throughput power, DNA microarrays offer enormous advantages of multiplex detections.
Nucleic acid hybridization is the principle on which the technique is based. The main
difference between past protocols and microarrays is that the oligonucleotide probes,
rather than the extracted DNA or RNA targets, are immobilized on a solid surface in a
miniaturized matrix. Thus, thousands of probes can be tested for hybridization with
sample DNA or RNA simultaneously. In contrast to other hybridization techniques, the
sample nucleic acids to be probed are fluorescently labeled, rather than the probes
themselves. After the labeled sample nucleic acids are hybridized to the probes contained
on the microarray, positive signals are detected by use of confocal laser scanning
32
microscopy (CSLM) or other laser microarray scanning device. Over the last few years,
microarrays have been developed, based on bacterial sequences deposited in public
databases including GenBank at the NCBI. Several phylogenetic microarrays based on
16S rRNA gene sequence databases such as the PhyloChip, a high-density array with
500,000 probes in total, identifying 9000 species/taxa. These microarrays are regularly
updated as more information becomes available from the rapidly expanding DNA
databases. They are reported to reveal greater soil bacterial phyla diversity and many
more individual taxa, compared to more conventional cloning/sequencing methods
(Hirsch et al., 2010).
The general principle of most molecular fingerprinting techniques relies on the
electrophoretic separation of a pool of PCR products (based on intrinsic maker genes)
amplified from DNA or RNA (following reverse transcription) directly extracted from
soils. The differences in the sequences of the amplified genes fragment can then be
exploited for separation based on: different melting behavior of the double-stranded PCR
products due to differences in the primary structures of the target gene fragment – using
denaturing gradient gel electrophoresis (DGGE) or temperature gradient gel
electrophoresis (TGGE); different localization of restriction endonuclease digestion sites
along the investigated gene using terminal restriction length polymorphism (T-RFLP),
restriction fragment length polymorphism (RFLP) or amplified ribosomal DNA
restriction analysis (ARDRA); different electrophoretic mobilities of single DNA strands
in non-denaturing gels using single strand conformational polymorphism analysis
(SSCP); and by length polymorphism of entire gene fragments, using length
33
heterogeneity PCR (LH-PCR) or (automated) ribosomal intergenic spacer analysis
(ARISA).
PCR amplification of 16S rRNA genes (16S rDNA) using consensus bacterial
primers and separation of the resultant PCR amplicons either by cloning, by denaturing
gradient gel electrophoresis (DGGE) or temperature gradient gel electrophoresis (TGGE)
constitute the most popular molecular ecology techniques used to describe soil bacterial
ecology to date. Clones or bands on gradient gels can be sequenced and the resultant
information used to infer something about the diversity of the original sample. Over the
last few years we have seen a proliferation of these studies applied to soils as molecular
techniques have been systematically applied to many diverse environments. To date,
perhaps the greatest contribution these studies have made to soil microbiology is a
sequence-based taxonomy. Based on ribosomal sequences, the way in which we view the
bacterial kingdom and evolution has dramatically changed (Macrae, 2000).
The conventional cloning and Sanger sequencing methods are time consuming
and limit the number of samples that can be processed. Methods are now being developed
to automate the diagnostic process which provides the possibility of investigating
microbial functional diversity in many thousands of samples. Currently, massively
parallel high-throughput pyrosequencing methods can process hundreds of thousands of
sequences simultaneously (Hirsch et al., 2010). The fast development and wide
applications of the next-generation sequencing (NGS) technologies provide genomic
sequence information that has greatly improved the quality of microbial studies. The
next-generation sequencing technology, in particular pyrosequencing using the 454 GS
34
FLX Titanium from Roche, has been applied to studies in microbial ecology (AcostaMartínez et al., 2010; Nacke et al., 2011; DeAngelis et al., 2011; Gardner et al., 2011).
Pyrosequencing of 16S rRNA genes (16S pyrotagging or 16S pyrosequencing) has
virtually replaced the Sanger-based 16S rRNA sequencing method (e.g., clone library) for
microbial diversity analysis because it offers several advantages. For example, thousands
of sequences can be obtained by pyrosequencing for a given sample. Additionally, by
using barcoded primers to PCR amplified 16S rRNA genes, microbial communities from
multiple samples can be simultaneously examined and compared. Pyrosequencing also
provides insights into the microbial community structure and diversity at a resolution of
10 – 100 fold higher and at a cost of 10 – 100 fold lower than the clone library approach
(Tamaki et al., 2011).
DNA extracted from soil can be subjected to PCR amplification prior to
sequencing using selective primers that are specific for particular genes, and this will
change the nature of the DNA that is to be sequenced. A limitation of this approach is
that it assumes that all prokaryotes possess 16S rRNA gene sequences that are
homologous to the primers used for the PCR amplification step. This is not necessarily
the case as there are some examples of bacteria and archaea that differ to the consensus
primer sequence for this gene, which are based on known, mostly culturable organisms.
Nevertheless, as the technology improves, and longer fragments can be sequenced in each
run, it will become even more useful as a tool. Current methods for high-throughput
sequencing cannot detect less abundant but ecologically essential groups without preselection, thus negating the benefit of direct and unbiased sampling. However, with the
35
new/improved methods being developed, sequencing will become increasingly efficient
and important in the future and likely to be limited only by the bioinformatic analysis of
sequence data (Hirsch et al., 2010). Other NGS systems are typically represented by
SOLiD/Ion Torrent PGM from Life Sciences, and Genome Analyzer/HiSeq 2000/MiSeq
from Illumina, (Liu et al., 2012).
1.3.6 Soil microorganisms
Soil microorganisms are important for conservation and management of
biological diversity, and play important roles in many ecosystem processes, including
organic matter decomposition, nutrient mineralization and immobilization, and the
development and maintenance of soil structure (Mummey et al., 2010; Scharenbroch et
al., 2012). Microbes in the soil are important to us in maintaining soil fertility /
productivity, cycling of nutrient elements in the biosphere and sources of industrial
products such as enzymes, antibiotics, vitamins, hormones, organic acids etc. At the same
time certain soil microbes are the causal agents of human and plant diseases.
Saprophytic fungi play a major role in the decomposition because they must rely
on dead organic matter as their source of carbon and energy. Some species of bacteria are
also important in the decomposition process, contributing particularly to the
mineralization of nutrients (Lucas et al., 2007). The application of molecular ecological
methods, especially those based on surveys of genes after PCR amplification, has allowed
cultivation-independent investigations of the microbial communities of soils to be made
(Janssen, 2006). Large-scale sequencing technologies allow us to investigate deeper and
36
deeper layers of the microbial communities and are vital in presenting an unbiased view
of phylogenetic composition and functional diversity of environmental microbial
communities.
1.3.6.1
Dominant prokaryotic groups in soil
Forests soils have very high diversity of prokaryotes compared to other habitats
(Leckie, 2005). Molecular analyses of the 16S rRNA genes from soil bacteria are
affiliated with at least 32 phylum-level groups. The contributions that members of
different phyla make to the different soil bacterial communities vary. The dominant phyla
in the libraries are Proteobacteria (with the classes Alpha-, Beta-, and Gammaproteobacteria),
Acidobacteria,
Actinobacteria,
Verrucomicrobia,
Bacteroidetes,
Chloroflexi, Planctomycetes, Gemmatimonadetes, and Firmicutes (Janssen 2006; Youssef
and Elshahed, 2009; Nacke et al., 2011). Members of these nine phyla make up an
average of 92% of soil libraries (normalized for the size of the individual libraries).
Although there are at least 52 bacterial phyla (Rappé and Giovannoni, 2003), and 24 are
recognized by Bergey's Manual, soils seem to be dominated by only a small number of
these. Bacteria from some of these groups (e.g., the Proteobacteria or the
Actinobacteria), can also be recovered from soil samples with classical cultivation
techniques, but others are normally missed.
The
Proteobacteria
encompass
an
enormous
level
of
morphological,
physiological and metabolic diversity, and are of great importance to global carbon,
nitrogen and sulfur cycling. Despite this phylum containing more validly described
37
isolates than any other phylum, the vast majority of Proteobacteria in soils are yet to be
cultivated. Nevertheless, Proteobacteria remains the most abundant soil phylum,
regardless of the utilized approach, which aside from PCR-based clone libraries and
pyrosequencing has included metagenomics, fluorescent in situ hybridization and
microarray analysis (Spain et al., 2009).
The Proteobacteria are a physiologically and morphologically diverse group. The
classes of Proteobacteria such as Alpha-, Beta-, and Gamma Proteobacteria are the most
dominant in soils, while members of the Delta-, Epsilon- and Zeta Proteobacteria are less
prominent in soils. Alpha-proteobacteria includes many bacteria that interact with
eukaryotic hosts. Interactions with plants comprise the colonization of the rhizosphere,
and the establishment of pathogenic or symbiotic relationships. Soil inhabiting Alphaproteobacteria, such as Methylomonas sp., aerobically utilizes methane or other C1compounds as carbon and energy sources. The Beta- and Gamma-proteobacteria include
many relatively fast-growing organisms that can be highly abundant in different soils typical representatives include the genera Burkholderia, Comamonas, Variovorax (all
Betaproteobacteria),
and
Stenotrophomonas,
or
Pseudomonas
(both
Gammaproteobacteria).
The Actinobacteria are Gram-positive bacteria and many of them are considered
typical soil organisms. They are metabolically versatile, and many of them are sporeforming and can thus resist drought periods. The most prominent and abundant members
include Rhodococcus, Frankia (induces molecular nitrogen-fixing root nodules of trees),
and many species of the genus Streptomyces. Streptomyces are typical inhabitants of litter
38
layers and they produce secondary metabolites (i.e., different antibiotics and geosmin, a
compound that can be responsible for the earthy smell of a soil). Members of the
Actinobacteria are among the most important litter decomposers in soils (Kopecky et al.,
2011). Acidobacteria, despite their high abundance in many soils, are also represented by
only a few cultivated genera (i.e., Acidobacterium, Geothrix, and Holophaga).
Acidobacterium is a moderately acidophilic heterotrophic organism. The ubiquity and
abundance of acidobacteria in soils and their ability to withstand polluted and extreme
environments suggest that they serve functions that are important in the environment and
that are potentially quite varied (Ward et al., 2009). Geothrix fermentans is an Fe(III)reducing bacterium that has been isolated from a hydrocarbon-contaminated aquifer;
Holophaga foetida has been isolated from anaerobic mud and can degrade aromatic
compounds (Benckiser and Schnell, 2007).
Isolates of the Planctomycetes have been obtained by cultivation from soils (i.e.,
one species related to the genus Gemmata and one to Isosphaera). Other isolates have
been obtained from wastewater treatment plants or the marine environment. Interestingly,
planctomycetes also include bacteria that are capable of oxidizing ammonium under
anaerobic conditions in the presence of nitrite (“Anammox bacteria”). Whether these or
similar bacteria are also active in anaerobic niches in soils is an open question. The
phylum Verrucomicrobia is only represented by a few cultivated species and it is unclear
how representative they are for this large group. Some isolates from rice field soils are
characterized by anaerobic metabolisms, and some of them, as well as other
Verrucomicrobia, have a very small cell size (“ultramicrobacteria”). Other members are
39
nonmotile
with
cellular
appendices,
like
Verrucomicrobium
spinosum
or
Prosthecobacter. The ecological range within this group seems to be large, as recently
some species of the genus Xiphinematobacter have been characterized as obligatory
endosymbionts in nematodes (Benckiser and Schnell, 2007).
Members of the Bacillus and Clostridium group (Phylum: Firmicutes) can
frequently be detected by both cultivation-based and cultivation-independent analyses of
soil samples, and sometimes their abundance is very high. This group is characterized by
their capacity to form endospores and survive for very long periods of time without the
need for external carbon or energy sources. A common motif of many of these bacteria is
the capacity for rapid growth when relatively simple carbon sources become available.
The substrates can be supplied by roots in the rhizosphere, by digestive processes in the
gut of invertebrates or by plant residues, the latter also indicated by the rapid
development of thermophilic Bacillus species in composting processes. Some members
of the genus Bacillus have developed strategies to multiply in different host organisms
and become pathogens (e.g., B. thuringiensis strains in certain insects or B. anthracis in
mammals).
1.3.6.2
Fungi in soil
Forest soils (including litter, humus and coarse woody debris) host diverse
microbial communities that impact tree health and productivity, and play pivotal roles in
terrestrial carbon sequestration, and biogeochemical cycles. Among these microbial
communities, fungi are undoubtedly major players. Fungi are a diverse component of soil
40
microbial communities, in which they function as decomposers, mycorrhizal mutualists,
and pathogens. Decomposers or saprophytic fungi convert dead organic matter into
fungal biomass (i.e. their own bodies), carbon dioxide and organic acids. They are
essential for the decomposition of hard woody organic matter. By consuming the
nutrients in the organic matter they play an important role in immobilizing and retaining
nutrients in the soils. Fungi are the main organisms responsible for degrading such
biopolymers as lignin, cellulose, hemicellulose, and chitin in forest ecosystems.
Some fungi are mutualists, with mutually beneficial relationships with plants.
They colonize plant roots where they help the plant to obtain nutrients from soils. Their
mass hides roots from pests and pathogens, and provides a greater root area through
which the plant can obtain nutrients. Mycorrhizae are perhaps the best known of the
mutualists. Arbuscular mycorrhizae are the most common form of mycorrhiza, especially
in agricultural plant associations. Pathogenic fungi such as Verticillium, Phytophthora,
Rhizoctonia and Pythium, penetrate the plant and decompose the living tissue, creating a
weakened, nutrient deficient plant. Soils with high biodiversity have been shown to
suppress soil-borne fungal diseases. Suppression mechanisms include the suite of native
organisms out-competing the pathogenic organisms, physically protecting roots and
providing better nutrition to the plant. Studying the ecological interactions of these
organisms is challenging because of the extremely high diversity of soil fungi, the
complexity of the substrate, and the difficulty of direct observations of these
communities. However, fungi communities have proven difficult to study in conventional
biotic surveys (O’Brien et al., 2005).
41
Sequence-based studies have been carried out on fungi from grass leaves, plant
roots, and soils. These methods collectively have the potential to improve our
understanding of fungal biodiversity and facilitate ecological studies of soil communities.
The majority of fungal sequences recovered in a study by O’Brien and colleagues in
2005, on forest soils, using high-throughput DNA sequencing, belonged to the
Ascomycota and Basidiomycota, with a slightly greater proportion of Ascomycota
sequences. The Ascomycota are the largest groups in number of species and span a range
of nutritional modes from parasites and plant pathogens, animals, and other fungi through
mutualists (forming both lichens and some ectomycorrhizas) and saprotrophs.
Ascomycota are many of the most important soil-borne plant pathogens, including wilts
caused by Fusarium and Verticillium and root and stem rots caused by Cochliobolus,
Giberella, Gaeumannomyces, Phymatotrichopsis, and Sclerotinia (Thorn and Lynch,
2007). The basidiomycota include a range of significant pathogens such as rust fungi and
smut fungi, but also symbiotic ectomycorrhizal fungi (Amanita, Boleteus, Cortinarius,
Lactarius and Russula spp). The basidiomycete fungi forming the familiar larger fruiting
structures such as muchrooms, brackets, and puffballs belong to the class
hymenomycetes that include a wide range of saprotrophs that have the ability to degrade
polymers such as lignin in the soil as well as leaf litter and woody debris.
The phylum Glomerulomycota is a newly distinguised phylum that includes the
fungal species forming arbascular mycorrhiza, symbiotic associations with terrestrial
plants as well as the endophytic fungus Geosiphon pyrifomis, forming symbiosis with
cyanobacteria. On the orther hand, the phylum zygomycota is more diverse as it includes
42
genera with different ecologies and morphologies, such as mucor, Rhizopus,
Thermomucor, and Phycomyces. Members of this phylum grow as saprotrophs in soils.
Others species especially in the order Entomorphthorales, are important as insects
parasites. The chytridomycota are unique among true fungi in that they are able to
produce motile, flagellate zoospores. Any of them are saprotrophs that can cause the
decay of aquatic vegetation or degrade organic matter in soils. Rhizophlyctis rosea and
Allomyces macrogynus commonly occur in soils.
1.4
Forest management and soil ecology
Attempts to exclude fire since the early 1900's, combined with drought and
epidemic levels of insects and diseases, had produced extensive forest mortality. The use
of prescribed fire faced strong resistance from policy makers and natural resource
managers through much of the 20th century, but is increasingly recognized as a useful tool
to increase rangeland and forest productivity, biodiversity, and in reducing wildfire risk
and severity (Yoder, 2002). After more than a century of fire exclusion and past timber
management practices that led to increased stand densities and fuel accumulation, current
US policies for federal lands emphasize the use of prescribed fire, either alone or in
combination with mechanical, chemical, biological, or manual techniques to meet fuel
reduction objectives (Moghaddas and Stephens, 2007). Motives for using prescribed fire
includes hazard reduction, silviculture, vegetation management, range improvement,
wildlife habitat improvement, watershed management, pest control, disease control, and
other ecological effects (e.g., addition of soil nutrients, initiates germination of fire43
dependent species, maintains biological diversity). Thinning, prescribed burning, and
combinations of the two above techniques (thinning and prescribed burning), are
common forest management practices (Switzer et al., 2012).
Thinning removes a portion of the trees, usually low quality trees that are
competing with healthier trees for sunlight, water and nutrients, and is aimed at
maintaining or improving the growth rate of the stands as well as redistributing the
potential resources available for the remaining trees (Tian et al., 2010). Thinning reduces
the vertical connectivity between surface and aerial (crown) fuels. Thinning that is
conducted with the primary goal of fuels reduction emphasizes the removal of small
diameter trees. Larger, more fire resistant trees are generally left behind. However, tree
thinning produces large amounts of slash materials which are typically disposed of by
burning (Jiménez Esquilín et al., 2007). Thinning operations transfer nutrients from
aboveground biomass to soil surface and opens the forest canopy so that greater
quantities of water and sunlight reach the forest floor. Green litter from thinned trees left
on site is likely to contain higher concentrations of nutrients that are more readily
decomposed than litter returned to the forest floor after senescence (Girisha et al., 2003).
Conversely, slash (the woody residue left on ground after harvesting) is likely to be more
recalcitrant than litter dominated by foliage, as it contains less nutrients and has much
greater carbon/nitrogen ratios (O’Connell, 1997; Cookson et al., 2008a) Therefore, the
quantity and quality of organic substrates presented to the soil microbial community
within thinned and non-thinned forests may vary widely. As the individual species, which
comprise the microbial community, have different capacities to oxidize different organic
44
substrates (Degens and Harris, 1997), forest thinning may give rise to a soil microbial
community that is distinct from that of no thinned soil. In turn, the subsequent ability of
these microbial communities to oxidize a wide variety of added organic substrates may
also differ.
Prescribed fire is a planned, controlled fire ignited by land managers to
accomplish specific natural resource improvement objectives. Prescribed burning may
reduce the amount of leaves, branches and dead trees accumulated on the forest floor that
could fuel a wildfire, and also promote the growth of new forage and succulent plants,
which are important sources of food for many wildlife species including small mammals
(Converse et al., 2006). Prescribed fire is a powerful management tool that alters the
structure and function of forest ecosystems, but the effects of prescribed fire depend on
its intensity, severity and frequency. At the extremes, fires of high intensity and severity
can have a greater effect on ecosystem structures and functions than clear-cutting or other
intensive management practices. The application of a prescribed fire is driven by the goal
to obtain a desired outcome or future forest conditions. These desired future conditions
have historically been encompassed only in such silvicultural goals as fuel reduction, and
competition reduction to promote commercial species, or site preparations. However,
more recently, the desired future conditions have been expanded to include more
ecologically based goals, such as restoration of fire-dependent ecosystems. In fact, fires
prescribed for silvicultural goals often produce "byproduct" ecological effects that may
be compatible or incompatible with pre-defined short- or long-term silvicultural goals
(Vose, 2000).
45
Thinning followed by prescribed burning, often termed ‘ecosystem restoration
practices’ (Agee and Skinner, 2005), is therefore used to restore and improve wildlife
habitat as well as promote renovation of the dominant vegetation through elimination of
undesired species and transient increase of pH and available nutrients. Prior to burning,
surface fuels may be left or collected into piles, which may affect fire temperatures, with
consequent effects on the underlying soils (Switzer et al., 2012). Moderate frequency and
intensity fires, such as those prescribed in forest management, can improve above and
below-ground ecosystem structures and functions, and help maintain the biodiversity and
ecological balance of forest ecosystem structures, functions and processes. Besides the
reduction or elimination of aboveground biomass, soil physical, chemical and biological
properties are affected to a greater or lesser extent depending on the severity and duration
of fire (Sun et al., 2011; Certini, 2005).
Inherent in the use of forest restoration as an ecological management approach is
the assumption that restoration of historic forest conditions will provide for current forest
ecological values such as wildlife diversity (Converse et al., 2006). Due to the combined
effects of historic and contemporary disturbances and fire exclusion, most pine-hardwood
ecosystems in the southern Appalachians is generally characterized by high overstory
mortality and slow growth rates; inhibited regeneration of overstory species; heavy fuel
loads (i.e., large nutrient and carbon pools) in the forest floor and shrub layer; decreased
herbaceous abundance and diversity; and increased susceptibility to insect infestations
(Vose, 2000). The recognition of the role of fire in maintaining biodiversity and its
usefulness as a forest management tool resulted in the active use of prescribed fire by the
46
Forest Service in the Southern Appalachian Mountains in the 1980’s. Over the past
decade, the Bankhead National Forest, which is part of National Forest System land in
the Southern Appalachian Mountains in Alabama, has experienced Southern Pine Beetle
(SPB) infestations at epidemic levels, primarily in loblolly pine forests that coincided
with drought incidents (Nobles et al., 2009). The epidemic peaked in the summer of 2000
and continued at very high levels through 2001. An estimated 18,600 acres of pine forest
have been killed by this epidemic and has resulted in large acres of standing dead trees
that are a public safety hazard along trails/roads and an increased forest fuel loads that
escalate the risk of damaging wildfires in the future (Gaines and Creed, 2003). Prescribed
burning and thinning of forest understory is now used extensively in the Southern
Appalachian forest regions as management tools to restore degraded forest communities
(Nobles et al., 2009) and to prevent uncontrollable wildfires. According to Klos et al.,
(2009), extensive forest areas across the southeastern United States have experienced
several severe droughts (e.g., 1954 – 1957, 1986 – 1989, and 1998 – 2001) as indicated
by the Palmer drought severity index (PDSI). As the southern region faces these
droughts, the use of prescribed fire and thinning are expected to increase.
Although these forest management strategies may be effective in reducing fire
severity (Prichard et al., 2010), and may successfully restore pre-settlement tree species
composition, size distributions, and spatial patterns, such management interventions also
have the potential to influence long-term forest health and sustainability in less obvious
ways via effects on forest soils. These practices could have consequences on the
compositions and functionalities of soil microbial communities. As the soil environment
47
changes, the relative abundance of fungi and bacteria may change as well, in response to
changes in soil organic carbon, soil pH (Guerrero et al., 2005) and other physical,
chemical and biological properties (Switzer et al., 2012), through significant biomass
removal and changes in microclimatic dynamics after opening forest canopy (Olajuyigbe
et al., 2012). Relative to the effects of fire on plant communities, the effects on soil
microbiota are underrepresented in the restoration literature (Rietl and Jackson, 2012). As
the implementation of burning and thinning for restoration and conservation practices
increases, there is a vital need to assess their effects on the ecosystem as a whole,
including soil quality and function.
1.4.1 Effects of thinning and fire on soil properties
The soil C pool is determined by the balance between C input by fall of litter and
rhizo-deposition on the one hand and the release of C during decomposition on the other.
The turnover of soil organic matter (SOM) depends on the chemical quality of the C
compounds (labile or stable C), site conditions, and soil properties (clay content, soil
moisture, pH, nutrient status). Several of these factors are directly or indirectly influenced
by forest management and with forest soils responding more strongly under other forms
of land use (Jandl et al., 2007). Soil organic matter acts as the primary reservoir for
several nutrients and, can therefore be considered the source for most of the available
phosphorus (P) and sulfur (S), and of the available nitrogen (N). Nutrients stored in OM
are released slowly during mineralization and decomposition, providing an efficient,
steady source of nutrients that keeps leaching losses at low levels. Soil organic matter and
48
humus also provide chemically active cation exchange sites that retain many of the
important cations (e.g. NH4+, K+, Ca++), and therefore play significant roles in cation
exchange capacity of some forest soils. Organic matter serves as a powerful aggregating
agent and, as such, is significant in creating and maintaining a well-aggregated soil. Soil
aggregation improves soil structure that creates macro pore space, and improves soil
aeration. The welfare of soil microorganisms also depends on OM because it provides
both a suitable environment and C compounds that serve as an energy sources for soil
microorganisms. Both of these functions are critical to maintain the nutritional quality
and moisture-holding capacity of forest soils (Harvey et al., 1987).
Thinning interventions often increase the radial growth of the remaining trees at
the expense of the total biomass and are not primarily aimed at maximizing C
sequestration (Sobachkin et al., 2005). Thinning changes the microclimate and as the
soils become warmer and possibly wetter due to reduced evapotranspiration and as the
soil C pool decreases, decomposition of forest floor C is temporarily stimulated.
However, the stand microclimate returns to previous conditions unless the thinning
intervals are short and intensities are high. Apart from the changed microclimate, litterfall is temporarily lowered in heavily thinned stands, and this reduces accumulations on
forest floors while contributing to lower soil C stocks (Jandl et al., 2007). The loss of soil
OM also affects cation exchange capacity, organic chelation, aggregate stability, macropore space, infiltration, and soil microorganisms. On the other hand, the input of thinning
residues to soils may compensate for losses. Fire affects nutrient cycling and the physical,
chemical, and biological properties of forest soils. The temperatures reached during fire
49
influence the chemical changes that dictate post-fire soil pH, moisture, nutrient
concentrations, microbes and rooting survivability. Changes caused by wildfires or
prescribed burning affect soil physical, chemical and biological properties (Mabuhay et
al., 2005). Forest fires usually decrease the total site nutrient pools (the total amount of
nutrients present) through some combination of oxidation, volatilization, ash transport,
leaching, and erosion (for example, N, P, S). For example, a low intensity slash fire
resulted in the following reductions in understory and forest floor fuel nutrient pools: 5475% of N, and 37 – 50% of P, 43 – 66% (Raison et al., 1985). Heavy precipitations
immediately after a burn can lead to a loss of the nutrient-rich ash layer through erosion
and overland flow (Hamman et al., 2008).
One of the impacts of fire exclusion is the reduction of both resistance and
resilience characteristics of forest ecosystems. For example, heavy fuel accumulation
may result in fire intensity and severity levels that exceed the lethal threshold in thickbarked species. Similarly, species directly or indirectly dependent upon fire for
regeneration (e.g., buried seed, serotinous cones, mineral soil conditions) may be lost
from the ecosystem at intervals between burning. While the short-term effects of
restoration treatments and wildfire on soil processes are likely driven by changes in
microclimate and the quantity of organic matter inputs, longer term effects may be more
influenced by changes in the quality of organic matter inputs (Grady and Hart, 2006).
Nitrogen is a very important nutrient because it is most likely to limit tree growth in
forests the ecosystems (Maars et al., 1983). Significant losses of N during a fire could
adversely affect long-term site productivity in many forest ecosystems. In wildfires, loss
50
of plant uptake coupled with large inputs of detritus available for microbial metabolism
may accelerate leaching losses of N, whereas, following low severity prescribed burning,
increased nutrient availability would not lead to greater nutrient loss (Grady and Hart,
2006; Kaye et al., 1999). There is the concern about the possible decrease in the total N
capital of a forest ecosystem as substantial amounts of N may be volatilized during
burning (Covington and Sackett, 1986). Nitrogen contained in unburned forest litter and
soils is released solely by biological processes. The role of sulfur in ecosystem
productivity is not well understood, although its fluctuations in soils appear to parallel
that of inorganic N. Sulfur is considered the second most limiting nutrient in some coastal
forest soils of the Pacific Northwest, particularly when forest stands are fertilized with N
(Barnett, 1989).
1.5
Rationale and research objectives
The forest management practices that make use of burning to maintain a healthy
understory species plant community traditionally fail to consider the implications of such
practices to soil microbial diversities. To the extent that such practices could directly
impact soil biochemical and microbial communities that determine global element
cycling patterns, investigating them is an essential first step towards devising better forest
ecosystem management strategies. There is also a continuous need to understand the C
cycle in order to increase C sequestration and optimize the use of renewable organic C
for energy. The study should elucidate basic biochemical and microbial processes and the
microbial communities that control nutrient cycling dynamics in response to management
51
approaches that affect forest ecosystems. A better understanding of the microbial
structure and functions in response to disturbance due to management of the forest is
fundamental to the nutrient cycling and soil quality of the forest ecosystems, and should
result in better management practices that will enhance long-term forest productivity and
sustainability. There has been paucity of studies on microbial responses to management
practices at the Bankhead National Forest. A rapid, reliable method to analysis soil
microbial diversities is needed to provide restoration ecologists with information about
inherent microbial communities. Fatty acids are present in a relatively constant
proportion in cell biomass, and signature fatty acids exist in microbial cells that can
differentiate major taxonomic groups within a community. The fatty acids are extracted
by saponification followed by derivatization to give the respective fatty acid methyl
esters (FAMEs), which are then analyzed by gas chromatography. The emerging patterns
are then compared to a reference FAME database to identify the fatty acids and their
corresponding microbial signatures by multivariate statistical analyses (Rastogi and Sani,
2011b). Fatty acid methyl ester (FAME) analysis has shown promise in the
characterization of soil microbial communities. FAME is advantageous in that it is not
dependent on the cultivability of microorganisms and provides relative biomass measures
for the abundance of broad taxonomic groups of bacteria, fungi and actinomycetes (a
useful indicator of ecosystem self-reliance and soil health) in soils (Schutter and Dick,
2000). The phospholipid fatty acid (PLFA) methods for fatty acid analysis are time
consuming as several steps are required before the fatty acids are methylated to produce
the FAMEs. The MIDI-FAME and ester-linked fatty acid methyl ester (EL-FAME)
52
methods are simpler methods, without the phospholipids extraction step. However,
previous studies have suggested that the EL method only extract ester-linked fatty acids
(not organic bound fatty acids) because it employs a mild alkaline hydrolysis to lyse cells
and release fatty acids from lipids once the ester bonds are broken (Acosta-Martínez et
al., 2010) compared to the MIDI method where fatty acids extracted may originate not
only from living microorganisms, but possibly fatty acids associated with soil humic
substances and plant roots. EL-FAME analysis coupled with microbial biomass C (MBC)
and N (MBN) estimations, and soil enzyme activities involved in nutrient cycling have
been reported as sensitive ecological sensors of land management effects on soil qualities
and functionalities (Acosta-Martínez et al., 2011).
In this study we evaluated the enzymatic activities responsible for the oxidation of
soil N, P, S and in the degradation of both labile and recalcitrant C forms in soils. These
enzymes include: β-glucosaminidase (βGlm), acid phosphatase (AP), arylsulfatase (AS),
β-glucosidase (βGlc), xylanase (Xyl), laccase (lac), and manganese (II) peroxidase
(MnP). This suite of enzymes should represent the responses of diverse microbial groups
to a wide range of substrate types, as well as an indication of the nutritional status of the
forest ecosystem under study. Enzyme production in soil profiles reflects the availability
and chemical forms of nutrients present (Snajdr et al., 2008). Measures of the fate of the
microbial diversities followed by restoration efforts would serve as an indicator of the
effects of restoration processes to forest ecosystems (Schutter and Dick, 2000). The forest
ecosystems are home to fungi which are important in lignin degradation and carbon
cycling. Current challenges in biofuel production include lignin degradation of plant
53
biomass and the production of ligninolytic enzymes such as laccase and MnP. We
evaluated the responses of soil microbial communities to forest thinning and burning
practices using EL-FAME and pyrosequencing techniques linked to soil metabolic or
functional changes; bioaccumulation of mercury and plant biomass degradation by white
rot fungi collected from the study area.
The specific objectives of this study were:
1. Study the spatial distribution of microbial diversities and structures in response to
forest thinning and burning practices.
Objective 1 was performed on soil samples, collected from different treatment
(thinning, burning and combinations of the two) sites of the Bankhead National Forest
(BNF). The microbial communities were assessed by analysis of fatty acids using the
ester linked fatty acid methyl esters (EL-FAME) method as described by Schutter and
Dick (2000), as well as pyrosequencing of extracted DNA, using the tag-encoded FLXTitanium amplicon pyrosequencing approach. Microbial community structure was
assessed by PCA of EL-FAMEs extracted from soil lipids to separate microbial
communities of disturbed soil from microbial communities of undisturbed soils. It is
hypothesized that the long-term disturbances caused by forest thinning and burning
practices results in shifts in of the associated microbial communities.
2. Evaluate the metabolic capacity of the forest ecosystem via enzymatic activities
of enzymes involved in nutrient cycling.
54
Objective 2 was done by measurements of such soil enzymatic activities as laccase
(lac), and manganese (II) peroxidase (MnP). xylanase (Xyl), β-glucosaminidase (βGlm),
acid phosphatase (AP), arylsulfatase (AS), β-glucosidase (βGlc), as well as such soil
nutrient physiochemical properties as microbial biomass carbon (MBC) and nitrogen
(MBN), total organic carbon (TOC) and N (TON) concentrations, soil moisture and pH.
It is hypothesized that the long-term disturbances caused by forest burning and thinning
practices results in shifts in enzymatic activities.
3. Inventory of the diversity of fungi fruiting bodies via molecular survey of their
phylogenetic profiles, biomass degrading potentials, and heavy metal (Hg)
bioaccumulation.
Objective 3 was by the inventory of fungi diversity via molecular identification of fungi
fruiting bodies collected from the study area (BNF). Their laccase and MnP enzyme
activities during pretreatment of plant biomass degradation were also assessed. Sanger
DNA sequencing was used for the identification and for the first time develop a DNAsequence database framework (based on ribosomal ITS sequences) linked with collection
sites within the Bankhead National Forest. The biomass degradation potential of the fungi
was assessed by assaying ligninnolytic enzyme activities, including mercury
bioaccumulation by the fungi fruiting bodies.
55
CHAPTER 2
METHODOLOGY
2.1
Study site description
This study site was the Bankhead National Forest (BNF) in Northwest Alabama.
The BNF is located on the southern Cumberland Plateau and extends through Lawrence,
Winston, and Franklin counties (34o30’ N, 87o30’ W), covering 73,078 ha. Soils at the
research sites are classified as Typic Hapludults of the Sipsey (fine-loamy, siliceous,
semiactive, thermic Typic Hapludults) series in the USDA-NCRCS preliminary soil map
of Lawrence County (Nobles et al., 2009). The native vegetation at the BNF consists
predominantly of oak and oak-pine woodlands. Predominant pine species include
Virginia (Pinus virginiana Mill.) and loblolly (Pinus teada L.) pines. Predominant oak
species include scarlet (Quercus coccineacata Michx.) black (Quercus velutina Lam.)
and white (Quercus alba L.) oaks. In the 1960s, areas of the forest were replaced with
faster growing loblolly pine to improve economic yields. The Bankhead National forest
has experienced several southern pine beetle infestations in recent years, resulting in
large areas of standing dead trees that present hazards to the public as well as increase the
risk of wildfires (Nobles et al., 2009; Gaines and Creed, 2003). The United States
56
Department of Agriculture (USDA) Forest Service personnel began attempting to convert
affected areas of the forest to native hardwood communities by treating stand dominated
by 15- to 45-yr old loblolly pines with combinations of prescribed thinning and burning
(Gaines and Creed, 2003).
The treatments comprised of three burning patterns (no burn, 3- and 9-yr burn
cycles) and three levels of thinning (no thin, thin to 75ft 2 acre-1 basal area, and to 50ft 2
acre-1 basal area) with a combination of thinning and burning making a total of nine
forest treatments to fit a completely randomized design. At the time of collection of soil
samples, burning in the 3-yr burn cycle had been carried out twice and the 9yr burn cycle
sites had been burnt once (5yrs since the first burn). The reference site that is located at
the Sipsey Wilderness Area had been converted to loblolly pines in the 1960’s, and has
not received any burn or thinning treatment since the conversion. The thinning treatments
were carried out by privately owned companies in August and September of 2005 (Table
2.1). Thinning was implemented to release native hardwood species such as oak by
removing competing loblolly pine species. Cut pine trees were skidded to a landing at the
edge of the treatment area, where tree tops and branches were removed. The bulk of the
thinning slash was accumulated in the landing areas. However, small residual amounts of
slash produced during harvesting and tree removal processes were left in situ. Prescribed
burning was performed by the BNF staff in the winter of 2005 to 2006 (Table 2.1). The
nature of the prescribed fire combined with the winter weather resulted in low intensity
and low severity burn.
57
Table 2 1. Treatment applications at the Bankhead National Forest
Treatment Number
Treatment code
Application
1
T1
Control, No Burn/ No Thin
2
T2
9 Year Burn/ No Thin
3
T3
3 Year Burn/ No Thin
4
T4
No Burn/ Heavy Thin†
5
T5
No Burn /Light Thin*
6
T6
3 Year Burn /Heavy Thin†
7
T7
3 Year Burn/ Light Thin*
8
T8
9 Year Burn/ Heavy Thin†
9
T9
9 Year Burn/ Light Thin*
†
Heavily thinned sites to 50 feet2 acre-1
*
Lightly thinned sites to 75 feet2 acre-1
58
Figure 2.1. Map of the Bankhead National Forest showing treatment stands.
59
2.2
Soil sampling and analyses
Triplicates soil samples from 0-10 and 10-20cm depths were collected in fall of
2011 from the various treatment sites with three levels of thinning (no thin, light thin, and
heavy thin) and three burning patterns (no burn and two burn cycles – 3-yr and 9-yr
cycles) and combinations of thinning and burn cycles. The collected samples were
composited and stored in sealed bags at 4oC. Microbial biomass and EL-FAME analysis
were conducted on field moist soil samples, while the soil pH was determined on airdried samples using 1:2.5 soil: water ratio using an Orion conductivity meter (model160). Total C, N and S were determined using air-dried finely ground samples by dry
combustion using a vario Max CNS analyzer (Elemmemtar Americas Inc., Mt. Laurel,
NJ).
2.3
Microbial communities sizes and structures
Microbial biomass C (MBC) and N (MBN) were determined in 15-g field-moist
soil equivalent using the chloroform-fumigation-extraction method. The soil samples
were fumigated for 24 h in the dark at ambient temperature. The organic C and N were
extracted by adding 75 mL of 0.5M K2SO4 and shaking for 1h. The values from
fumigated and non-fumigated (control) soil samples were quantified using a CN analyzer
(Shimadzu Model TOC-V/CPH-TN, Shimadzu Corporation, Japan). The MBC and MBN
(difference between fumigated and non-fumigated values) were calculated, and the kEC
factor of 0.45 for C and kEN factor of 0.54 for N, (Wu et al., 1990; Jenkinson et al., 1988)
were applied respectively.
60
The soil microbial community structures were determined using Ester-linked
Fatty Acid Methyl Esther (EL-FAME) Analysis as described by Schutter and Dick,
(2000). This method involved a saponification and methylation step of ester-linked fatty
acids, incubation of 3 g of field-moist soil in 15 mL of 0.2M KOH in methanol at 37oC
for 1 h. During this period, the samples were vortexed every 10 min, and 3 mL of 1.0M
acetic acid were used to neutralize the mixture pH at the end of the incubation. In the next
step, FAMEs were partitioned into an organic phase by adding 10 mL of hexane followed
by centrifugation at 480 × g for 10 min. The hexane layer was transferred to a clean glass
test tube and the hexane was evaporated under a stream of N2 gas. In the final step,
FAMEs were dissolved by adding 200 μL of 1:1 methyl-tert-butyl ether and hexane
containing methyl nonadecanoate (19:0) as an internal standard (0.5 mg mL−1). Samples
were vortexed and transferred to a 250-μL glass insert in a 2-mL GC vial. FAME analysis
was conducted in an Agilent 6890 N gas chromatograph with a 25 m×0.32 mm×0.25 μm
(5 % phenyl)-methylpolysiloxane Agilent HP-5 fused silica capillary column (Agilent,
Santa Clara, CA) and flame ionization detector (Hewlett Packard,Palo Alto, CA) with
ultra-high purity hydrogen as the carrier gas. The temperature program ramped from 170
°C to 270 °C at 5 °C min-1 then ramped to 300 °C for 2 min to clear the column.
Fatty acids were identified and quantified by comparison of retention times and
peak areas to components of the MIDI standards using the TSBA6 aerobe program from
MIDI (Microbial ID, Inc., Newark, DE). FAMEs are described by the number of C
atoms, a colon, the number of double bonds, then the position of the first double bond
from the methyl (ω) end of the molecule. Other notations used include methyl (Me), cis
61
(c) and trans (t) isomers, and iso (i) and anteiso (a) branched FAMEs. Selected FAMEs
used as microbial markers according to previous research included Gram-positive
(Gram+) bacteria (i15:0, a15:0, i17:0, a17:0), Gram-negative (Gram−) bacteria (cy17:0,
cy19:0), and actinomycetes (10Me17:0, 10Me18:0). Fungal markers included saprophytic
fungi (18:1ω9c, 18:3ω6c), arbuscular mycorrhizal fungi (AMF) (16:1ω5c) and absolute
amounts of FAMEs (nmolg−1 soil) were calculated according to Zelles, (1999) using the
19:0 internal standard and the values subsequently used to calculate mol percentages.
Bacterial sums were calculated using the Gram+, Gram−, and actinomycetes markers
listed above; fungal sums were calculated using both saprophytic and AMF fungal
markers listed above, and the fungal/bacteria ratio was calculated by dividing the sum of
fungi by the sum bacteria.
2.4
Bacterial and fungal diversity by pyrosequencing techniques
In this study we used a new bacterial tag-encoded FLX-Titanium amplicon
pyrosequencing (bTEFAP) approach based upon similar methodology principles
described by Acosta-Martínez et al., (2008) and Gardner et al., (2011), utilizing the
titanium reagent procedures and a one-step PCR labeling reaction, a mixture of Hot Start
and Hot Start high fidelity taq polymerases, and amplicons. A similar approach was used
for fungal population. DNA was extracted from approximately 0.5 g of field moist soils
(oven dried basis) using the Fast DNA Spin Kit for soil (QBIOgene, Carlsbad, CA, USA)
and following the manufacturer’s instructions. The DNA extracted was quantified using
Nanodrop ND-1000 spectrophotometer (Nanodrop Technologies, Wilmington, DE,
62
USA). The integrity of the DNA extracted from the soils was confirmed by running DNA
extracts on 0.8% agarose gel with 0.5X TBE buffer (45 mM Tris-borate, 1 mM EDTA,
pH 8.0). The DNA samples for PCR reaction (50 μL) were diluted to a 100 ng/μL
concentration.
The
16S
universal
bacterial
primers
28F/519R
(5’-
GAGTTTGATCNTGGCTCAG/5’-GTNTTACGNCGGCKGCTG) were used to amplify
the ~600 bp region of 16S rRNA genes. For fungi, the fungal primer pair ITS1F/ITS4R
(5’-CTTGGTCATTTAGAGGAAGTAA / 5’-TCCTCCGCTTATTGATATGC) was used
to amplify the ITS region (Pant and Adholeya, 2007; Prewitt et al., 2008). HotStarTaqPlus Master Mix Kit (Qiagen, Valencia, CA, USA) was used for PCR under the
following conditions: 94 °C for 3 minutes followed by 32 cycles of 94 °C for 30 seconds;
60 °C for 40 seconds and 72°C for 1 minute; and a final elongation step at 72°C for 5
minutes. A secondary PCR (6 cycles rather than 32) was performed for FLX Amplicon
sequencing under the same condition using designed special fusion primers with different
tag sequences as: LinkerA-Tags-28F and LinkerB-519R as well as LinkerA-Tags-ITS1F
and LinkerB-ITS4R. After secondary PCR, all amplicon products from different samples
were mixed in equal volumes, and purified using Agencourt Ampure beads (Agencourt
Bioscience Corporation, MA, USA).
Pyrosequencing was used to characterize primary predominant bacterial
populations. In preparation for FLX sequencing (Roche, Nutley, NJ, USA), the size and
DNA concentration fragments were accurately measured using DNA chips under a BioRad Experion Automated Electrophoresis Station (Bio-Rad Laboratories, CA, USA) and
a TBS-380 Fluorometer (Turner Biosystems, CA, USA). A 9.6 × 106 sample of double-
63
stranded DNA molecules/μL with a size of 625 bp were combined with 9.6 million DNA
capture beads, and then amplified by emulsion PCR. After bead recovery and bead
enrichment, the bead-attached DNAs were denatured with NaOH, and sequencing
primers were annealed. A two-region 454 sequencing run was performed on a 70 × 75 GS
PicoTiterPlate (PTP) by using a Genome Sequencer FLX System (Roche, Nutley, NJ,
USA). All FLX related procedures were performed following Genome Sequencer FLX
System manufacturers instructions (Roche, Nutley, NJ, USA). Quality trimmed
sequences obtained from the FLX sequencing run were processed using a custom scripted
bioinformatics pipeline. In short, each sequence was trimmed back to utilize only high
quality sequence information, tags were extracted from the FLX generated multi- FASTA
file, while being parsed into individual sample specific files based upon the tag sequence.
Tags which did not have 100% homology to the original sample tag designation were not
considered as they might be suspect in quality. Sequences which were less than 200 bp
after quality trimming were not considered. Samples were then depleted of definite
chimeras using B2C2 software, described by Research and Testing Laboratory (Lubbock,
TX, USA; www.researchandtesting.com/B2C2.html). The resulting sequences were then
evaluated using BLASTn against a custom database derived from the RDP-II database
and GenBank (http://ncbi.nlm.nih.gov). The sequences contained within the curated 16S
database were those considered of high quality based upon RDP-II standards and which
had complete taxonomic information within their annotations. Following best-hit
processing, a secondary post-processing algorithm was utilized to combine genus and
other taxonomic designations generating data with relative abundance of each taxonomic
64
entity within the given sample, and phylogenetic assignments were based upon NCBI
taxonomic designations.
2.5
Metabolic capacity of the forest ecosystem via enzyme activities
Enzyme activities important for C (β-glucosidase), C and N (β-glucosaminidase),
P (acid phosphatase) and S (arylsulfatase) cycling were evaluated using 1 g of soil with
their appropriate substrate and incubated for 1 h (37 °C) at their optimal pH as described
previously (Tabatabai, 1994; Parham and Deng, 2000). Additionally, enzymes involved
in lignocellulose degradation (laccase, manganese peroxidase, and xylanase), were also
evaluated.
Laccase
activity
was
assayed
using
ABTS
(2,2-azino-bis(3-
ethylbenzthiazoline-6-sulfonic acid)). Reactions was carried out in 3mL curvets
containing 0.6 ml of 200 mM sodium phosphate/100 mM citric acid buffer at pH 5.0, 0.2
mL of water, 1.0 mL of soil homogenate and 0.2 mL of 1.0 mM substrate and was
monitored at 420nm. Manganese peroxidase was measured based on the oxidation of
phenol red (De Souza-Cruz et., 2004). Five mL of reaction mixture contained 1.0 mL
sodium succinate buffer (50 mM, pH 4.5), 1.0 mL sodium lactate (50 mM, pH 5.0), 0.4
mL manganese sulphate (0.1 mM), 0.7 mL phenol red (0.1 mM), 0.4 mL H2O2 (50 μM),
gelatin 1 mg mL−1 and 0.5 mL of soil homogenate. The reaction was initiated by adding
H2O2 and conducted at 30°C. One mL of reaction mixture was added to 40 μL of 5N
NaOH solution, whose absorbance was measured at 610 nm. Xylanase activity was
measured using a modified method by Biely et al., (1988). The reaction mixture which
contained 0.5 mL of soil homogenate and 0.5 mL of 0.1mg/mL RBB-xylan was
65
incubated at 40°C for 10 min in a water bath shaker. The reaction was then terminated by
adding 1 mL of 96% ethanol and the tubes were equilibrated at room temperature for 10
minutes, followed by vigorous mixing and centrifugation at 1500 g for 10 minutes to
clarify the solution. The absorbance of the reaction mixture was measured at 590 nm
using a spectrophotometer and the enzyme activities determined by reference to a RBB
standard curve.
2.6
Collection and screening and identification of fungi fruiting bodies
Several fungi fruiting bodies were sampled at the forest study areas. Suspected
white rot fungi that will cause great decay to plants was collected from the forest. Pure
cultures and isolates was obtained by placing small fragments about 1 mm diameter of
fruit body aseptically or decayed wood under the fruit body on Potato dextrose agar
(PDA) plates as well as yeast malt extract (YMG) broth containing appropriate antibiotics
(50mg L-1 streptomycin) to prevent bacteria growth. Mycelia were repeatedly transferred
onto new plates until the cultures are pure. Stock cultures of fungi were maintained on
PDA slants at 4°C. Qualitative screening for fungi capable of degrading lignocelluloses
was carried out by screening for laccase activity based on the observation of green color
in the colorless ABTS agar or by the formation of reddish brown color in media
supplemented with guaiacol.
66
2.7
Molecular identification of fungi fruiting bodies
Fungal tissue or fresh mycelia taken broth cultures were used for DNA extraction
and molecular identification. The fungal tissue or mycelia were ground under liquid N2
using a sterile pestle and mortar (Pant and Adholeya, 2007) and DNA was extracted
using the E.Z.N.A.TM High Performance (HP) DNA kit obtained from Omega bio-tek,
GA. This procedure relies on the well-established properties of the cationic detergent,
cetyltrimethyl ammonium bromide (CTAB), in conjunction with the selective DNA
binding of Omega Bio-tek's HiBind® matrix. Samples were homogenized and lysed in a
high salt buffer containing CTAB and extracted with chloroform to remove
polysaccharides and other components that interfere with many DNA isolation and
downstream applications. After adjusting the binding conditions, DNA was further
purified using HiBind ® DNA spin columns. Standard PCR protocol was carried using
ITS1-F (CTT GGT CAT TTA GAG GAA GTA A) and ITS4-B (TCC TCC GCT TAT
TGA TAT GC), primers in a 25 ul volume reaction. The universal primers were used
together as controls for amplification (Pant and Adholeya, (2007). Amplified products
were purified with ExoSAP (USB Corporation, Cleveland, OH USA). Sequencing of the
PCR products was done using the Sanger method, and was carried out on an automated
multicapillary DNA sequencer, ABI Prism 3130xl Genetic analyzer (Applied
Biosystems, Foster city, CA, USA) using the Big Dye Terminator v.3.1 (Pant and
Adholeya, 2007). The obtained sequences were compared with nucleotide sequences in
Genbank (http://www.ncbi.nlm.nih.gov) and the most similar organism sequences (>95%
similarity) to the query sequences was used to determine the likely identity.
67
2.8
Characterization of biomass and compositional analysis of biomass
Biomass samples (wheat straw, corn stalk, and red oak sawdust) were prepared
for compositional analysis using the National Renewable Energy Laboratory (NREL)
laboratory analytical procedure (LAP) method A (Hames et al., 2008). In this method, the
biomass material was spread out on a surface and allowed to air-dry before milling. The
air-dried biomass was milled to pass through the 2 mm screen in the bottom of the mill.
Determination of lignin, cellulose, and hemicelluloses contents of plant biomass was
carried out using the ANKOM analyzer. The ANKOM protocol for fiber analysis (neutral
detergent fiber, (NDF); acid detergent fiber, (ADF); and acid determined lignin, (ADL))
was used to determine the composition of the biomass, in terms of the amount of
hemicellulose, cellulose, and lignin. Each of the extractions was done in the order of
NDF, ADF and then ADL. During NDF extraction, the fraction that was washed off
contains soluble cell contents like carbohydrates, lipids, pectin, starch, soluble proteins,
and none-protein nitrogen, while the fraction left in the bag contain hemicelluloses
proteins bound to cell walls, cellulose, lignin, and recalcitrant materials. During ADF
determinations, hemicelluloses and bound proteins are washed off, leaving behind
cellulose, lignin, and recalcitrant materials. Also, during ADL determinations, cellulose is
washed off leaving only lignin and recalcitrant materials. The recalcitrant materials that
consist of minerals are determined by ashing. The hemicellulose content was estimated as
the difference between NDF and ADF, and the cellulose as the difference between ADF
and ADL while the lignin was estimated by the differences between ADL and ash
content.
68
2.9
Pretreatment of substrate and estimation of enzyme production
The fungi were tested for their ability to degrade biomass in terms of loss in
weight and lignin (Arora et al., 2001). Two and half grams of dried substrate was placed
in 250 mL conical flasks and moistened with 10 mL water and sterilized at 121°C for 15
min. Each flask was inoculated with a 10 mm diameter fungal disc obtained from 7–10
days grown cultures on potato dextrose agar (PDA) plates and incubated at 27°C.
Duplicate set of flasks were assessed before and after 10, 20, 30 and 40 days of
incubation. Substrates were extracted with sodium acetate buffer, supplemented with
Tween 60 according to Souza-Cruz et al., (2004). The filtrate was centrifuged and used
for laccasse and MnP assays, whereas residual plant biomass collected on the filter paper
was dried at 90°C to a constant weight. Loss in weight was calculated from the
differences between the control and the inoculated flasks. The lignin was estimated using
the ANKOM protocol for fiber analysis.. Percent lignin loss was based on the difference
in amount of lignin in the control and the inoculated substrate.
The enzymes were extracted with 50 mM sodium acetate buffer (pH 5.5)
supplemented with Tween 60 (0.1 g/L) (Souza-Cruz et al., (2004). The entire content of
each Erlenmeyer flask were extracted with 25 mL of extracting solution (for 2.5g
substrate). Extractions were performed at 120 rpm for 4 h at 4oC. The crude extracts were
recovered by centrifugation at 8500g at 4°C and filtration of the supernatant through
0.45µ glass filter. The filtrates were used for laccase and MnP enzyme assays. Laccase
activity was assayed using ABTS (2,2-azino-bis(3-ethylbenzthiazoline-6-sulfonic acid)).
Reactions was carried out in 3mL curvets containing 0.6 ml of 200 mM sodium
69
phosphate/100 mM citric acid buffer at pH 5.0, 0.2 mL of water, 1.0 mL of soil
homogenate and 0.2 mL of 1.0 mM substrate and was monitored at 420nm. Manganese
peroxidase was measured based on the oxidation of phenol red (De Souza-Cruz et.,
2004). Five mL of reaction mixture contained 1.0 mL sodium succinate buffer (50 mM,
pH 4.5), 1.0 mL sodium lactate (50 mM, pH 5.0), 0.4 mL manganese sulphate (0.1 mM),
0.7 mL phenol red (0.1 mM), 0.4 mL H2O2 (50 μM), gelatin 1 mg mL−1 and 0.5 mL of
soil homogenate. The reaction was initiated by adding H2O2 and conducted at 30°C. One
mL of reaction mixture was added to 40 μL of 5N NaOH solution, whose absorbance was
measured at 610 nm.
2.10
Analysis of mercury in fungi tissue
Bioaccumulation of mercury in fungi tissue was analyzed as described by
Tazisong et al., (2012). Fungi tissues were analyzed for Hg using direct mercury analyzer
(DMA-80) with a detection limit of 0.005 ng Hg. A 0.05 - 0.1g of fungi tissue was loaded
in quartz boats and introduced into DMA-80 system with thermal combustion (drying
120 s at 250 oC, thermal decomposition 120 s at 650 oC). The amalgamator in the DMA80 selectively traps Hg after the system is flushed with oxygen for 60 s to remove any
remaining gases or decomposition products. At the end of the elapsed flush time, the
amalgamator is rapidly heated (12 s), releasing Hg vapor. Absorbance is measured at
253.7 nm as a function of Hg concentration.
A two-range calibration (0-30 and 30-500 ng of Hg) of the instrument was
performed before Hg analysis, because the instrument operates on two measuring cells.
70
After initial calibration, a blank (no sample boats and empty sample boats) run was
performed to free the system from Hg residual and contamination. All sample boats used
for analysis were run through the system to get rid of any residual Hg on the boats. A 1
ppm HgCl2 solution was used as a reference material and analyzed at the beginning and
end of each set of samples (typically 5) to verify that the instrument remains calibrated
during the course of the study. A certified reference material (plant tissue) from the
National Institute of Standard and Technology was used for quality assurance and control
when residual Hg fraction was analyzed. A blank (i.e., an empty sample boat) was also
analyzed at the end of each sample set to prevent Hg from being carried over between
samples and each extraction step. All reference materials fell within 95 to 105% recovery
of the expected values.
2.11
Statistical analyses
To evaluate the effect of the various burning and thinning treatments and
combinations on the FAME microbial indicators, analysis of variance (ANOVA) and
mean comparisons (using Tukey’s test) was performed using the SPSS statistical
software Version 17.0. Chicago: SPSS Inc. Principal Component Analysis (PCA) was
used to assess similarity in microbial communities as well as enzymatic catalytic
activities and soil properties across all treatment samples, using the XLSTAT Statistical
Software (Addinsoft, New York, NY, USA). Pearson’s correlation analysis was used to
examine the possible influence of some soil physiochemical properties on microbial
communities (according to FAME concentrations), and soil enzymatic activities. The
71
geometric mean of all enzymatic activities was calculated and used to compare soil
metabolic activity. The statistical software, EstimateS (Version 8.20, R. K. Colwell,
http://purl.oclc.org/estimates), was used to compute microbial community richness, as
revealed by the observed species richness (Sobs), Chao1 richness estimators, and the
abundance-based coverage estimator (ACE), the species diversity by Shannon index, the
shared community richness as depicted by the shared species observed (SSobs) and the
Chao shared estimates (CSE), and the similarity in the community structure was depicted
by the Chao-Jaccard-Raw Abundance-based index.. Differences between enzymatic
activities and weight losses of fungal-treated samples were evaluated with analysis of
variance (ANOVA). Differences in Hg bioaccumulation by the fungal samples was
evaluated with analysis of variance (ANOVA) and Tukey’s pairwise comparisons of
means at a level of significance of 0.05 using the SPSS version 17.0 statistical software.
72
CHAPTER 3
RESULTS AND DISCUSSION
3.1
Microbial community size and structure by EL-FAME
The fatty acid analyses using discriminant analysis (DA) based on all FAME
biomarkers revealed that the different levels of thinning and burning treatments were not
distinctly differentiated by the their FAME profiles as evidenced by cluster overlap (Fig.
3.1). The biomarkers included fatty acids indicative of Gram positive bacteria (i15:0,
a15:0, i17:0, a17:0), Gram negative bacteria (cy 17:0, cy 19:0ω8c), actinomycetes (10Me17:0, 10-Me18:0), fungi (16:1ω5c, 18:3ω6c (6,9,12), 18:1ω9c), and protozoa
(20:4ω6,9,12,15c). With respect to thinning, the first factor explained 57.12% and the
second factor explained 42.88% of variability, while for burning, the first factor
explained 68.48% and the second factor explained 31.52% of variability (Fig. 3.1). Less
heterogeneity was observed in the 3yr burn cycle than the no-burn and 9yr burning cycles
as evidenced by the sizes of the confidence ellipses. This suggests that the more frequent
burning reduces the heterogeneity of the microbial population. A more frequent burning
gives less time for the recovery of some microbial population, as the more vulnerable
populations hardly recover before they are subjected to another burning treatment. The
73
FAME profile of the 9yr burn cycle treatments seems to be more related to the profile of
the no-burn treatments (Fig. 3.1). This is understandable since the microbial community
in these sites had a longer recovery time (5 years, at time of sample collection). On the
other hand, there was a higher level of heterogeneity in the FAME profiles with respect to
different levels of thinning. Among these profiles, the no-thinned treatments showed the
lowest heterogeneity, suggesting that thinning promoted an overall divergence of the
microbial population.
The distribution of microbial community with respect to burning and thinning
treatments (Fig. 3.2), as well as depth (Fig. 3.3), as illustrated by a PCA biplot showed
that F1 and F2 accounted for 54.62% and 61.97% of total variability, respectively. The
results showed that within the 0 - 10 cm, F1 contributed about 35.24%. The factor
loadings and relative contributions (Table 3.1) showed that actinomycetes, followed by
gram positive bacteria, fungi, protozoa, and gram negative bacteria (in that order) were
the top influential variable in determining the differences among the thinning and burning
treatments, contributing more than 5% to F1. This demonstrates the relative importance
of these variables in detecting the changes that occur in the microbial community
structures and functions in relation to burning cycles and level of thinning. There was a
higher variation in microbial communities and an obvious separation between depth 1 (0
- 10 cm) and depth 2 (10 - 20cm) as seen in Fig. 3.3. The overall reduction in microbial
population in the lower soil depths can be ascribed to fewer amounts of minerals, low
oxygen content and increased carbon-dioxide concentration, increased compaction, and
low organic matter content.
74
(a)
4
thin-heavy
thin-light
thin-no
Centroids
3
2
F2 (42.88 %)
1
thin-no
thin-light
0
-4
-3
-2
-1
0
-1
1
2
3
4
5
thin-heavy
-2
-3
-4
F1 (57.12 %)
(b)
5
burn-3-yr
burn-9-yr
burn-no
Centroids
4
3
F2 (31.52 %)
2
1
burn-3-yr
burn-no
0
-5
-4
-3
-2
-1
0
1
-1
2
3
4
burn-9-yr
-2
-3
-4
F1 (68.48 %)
Figure 3.1. Discriminant Analysis (DA) plots showing relationships between soil
microbial indicator fatty acids (EL-FAMEs), with (a) thinning and (b) burning
treatments.
(a) Thinning (thin-no = no thinning; thin-light = light thinning; thin-heavy = heavy
thinning), (b) Burning (burn-no = no burn; burn-3-yr = 3-yr burn cycle; burn-9-yr = 9-yr
burn cycle) treatments.
75
(a)
5
β-Gluc
F2 (19.38 %)
β-Glm
thin-no
thin-no
thin-heavy
thin-light
Centroids
AP
Xyl
Prz
GramFungi
thin-light Gram+
Actinos
0
AS
thin-heavy
MnP
Lac
-5
-8
-3
2
7
F1 (35.24 %)
(b)
5
β-Gluc
F2 (19.38 %)
β-Glm
burn-no
burn-no
burn-9-yr
burn-3-yr
Centroids
AP
Xyl
Prz
Gram-
0
burn-9-yr
AS
Fungi
Gram+
Actinos
burn-3-yr
MnP
Lac
-5
-8
-3
F1 (35.24 %)
2
7
Figure 3.2. Principal Component Analysis (PCA) plots showing relationships between
soil microbial community compositions, and enzymatic activities with respect to (a)
thinning and (b) burning.
(a) Thinning (thin-no = no thinning; thin-light = light thinning; thin-heavy = heavy
thinning), (b) Burning (burn-no = no burn; burn-3-yr = 3-yr burn cycle; burn-9-yr = 9-yr
burn cycle) treatments. Actinos = Actinomycetes; Gram+ = Gram positive bacteria; Prz =
Protozoa; Gram- = Gram negative bacteria; AP = Acid phosphatase; AS = Aryl sulfatase;
β-Gluc = β-glusosidase; MnP = Manganese peroxidase; Xyl = Xylanase; Lac = Laccase;
β-Glm = β-glutaminase.
76
4
Depth-1
Depth-2
Centroids
2
F2 (21.81 %)
Fungi
Gram+
Actinos
Depth-2
Prz Gram-
0
β-Gluc
MnP
Lac
ASAP Depth-1
β-Glm
Xyl
-2
-4
-4
-2
0
2
F1 (40.16 %)
4
6
Figure 3.3. Principal Component Analysis (PCA) plot showing relationships between soil
microbial community compositions, and enzymatic activities with different depths.
Actinos = Actinomycetes; Gram+ = Gram positive bacteria; Prz = Protozoa; Gram- =
Gram negative bacteria; AP = Acid phosphatase; AS = Aryl sulfatase; β-Gluc = βglusosidase; MnP = Manganese peroxidase; Xyl = Xylanase; Lac = Laccase; β-Glm = βglutaminase.
77
Table 3.1. Principal Component Analysis (PCA) factor loadings and percent
contributions of variables to relationships between soil microbial community
compositions, and enzymatic activities with treatments, at 0 - 10cm soil depth.
Factor loadings
F1
0.941
0.939
0.892
0.735
0.707
0.461
0.294
0.293
0.289
0.253
0.236
0.19
Contribution of the variables (%):
F2
-0.121
-0.114
-0.068
0.127
0.022
0.411
-0.146
0.779
-0.713
0.247
-0.737
0.604
F1
20.942
20.869
18.813
12.791
11.831
5.021
2.04
2.027
1.971
1.518
1.32
0.856
F2
0.632
0.558
0.199
0.698
0.021
7.277
0.912
26.13
21.895
2.624
23.367
15.687
Actinos
Gram+
Fungi
Prz
GramAP
AS
β-Gluc
MnP
Xyl
Lac
β-Glm
Actinos = Actinomycetes; Gram+ = Gram positive bacteria; Prz = Protozoa; Gram- =
Gram negative bacteria; AP = Acid phosphatase; AS = Aryl sulfatase; β-Gluc = βglusosidase; MnP = Manganese peroxidase; Xyl = Xylanase; Lac = Laccase; β-Glm = βglutaminase.
78
Comparison of fatty acid abundance among the various treatments by their
relative percentage differences with respect to the control showed that, the difference in
abundance of all the microbial groups at the 0 - 10 cm depth ranged from -100% (mostly
in the ‘burn only’ treatments) to 66.5% (for Gram positive bacteria in 3-yr burn/lightthinned treatments). However, the differences were higher with some FAMEs, and were
up by 120.9% with 17:0i in 3-yr burn/ light-thinned treatments (Table 3.2). In the
treatments that had light-thinning only, the abundance of the major microbial groups as
well as total FAME were higher, while these were lower in the treatments that were only
heavily thinned. This same trend was observed with the thinned treatments under the 9-yr
burn cycle, except for fungi, which showed a decrease, although the decrease was more
in the heavily thinned/ 9yr-burn cycle treatments. The fungi/bacterial ratio was generally
higher for heavily-thinned treatments than the lightly-thinned treatments. On the other
hand, in the lightly-thinned and heavily-thinned treatments under 3-yr burn cycle, both
showed positive differences in the abundance of the major microbial groups, but these
differences in abundance was more positive in the light thinned/3-yr burn cycle
treatments than the heavily-thinned/3-yr burn cycle treatments. The treatments that
consisted of burning with no thinning, showed negative percentage differences (except
the Gram negative bacteria) from the control (Table 3.2).
Based on analysis of variance of the microbial community (FAME data), there
were no significant differences in variations of the microbial community among the
burning treatments, but the major microbial groups (Gram positive bacteria, Gram
negative bacteria, actinomycetes, fungi, and protozoa) showed significant differences
79
among the thinning treatments (Table 3.3). There were no significant differences amongst
the combinations of thinning and burning treatments effects. The abundance of microbial
groups was generally higher in the 0 - 10 cm depth than the 10 - 20 cm depth. This was
significant for Gram negative bacteria. At all levels of burning, within the 0 - 10 cm soil
depth, the sum of fungal fatty acid indicators were highest with light thinning (Table 3.3).
Similar pattern was observed for total bacteria as well as protozoa. In the burn only
treatments (0 - 10 cm), the fatty acids indicative of Gram positive, actinomycetes, and
fungi were below detectable limits.
There was a shift in the abundance of the microbial community which increased
in the lightly-thinned treatments and decreased in the heavily-thinned treatments
compared to the no-burn/no-thinned (control) treatment. The negative effect due to heavy
thinning on the microbial abundance may be related to the moisture content of these sites.
Heavy thinning may result in increased loss of soil moisture. Although microbial growth
and activity is usually limited by C availability, Kelliher et al., (2004) showed that soil
microbial activity is limited more by water than C or N in the ponderosa pine forest of
Oregon (Grayston and Rennenberg, 2006). The results in this study did not show any
significant changes in the microbial population as a result of the combination of burning
and thinning treatments effects. However, the populations of the different microbial
groups were higher in cases of combinations of burning with light thinning than with
heavy thinning. This can be related to the fire intensity, since more combustible material
may result from the heavy thinning.
80
Table 3.2. Changes in microbial community compositions and size at 0 - 10 depth,
relative to the control site.
control
no burn
3yr burn
light
thin
No
thin
heavy
thin
9yr burn
light
thin
heavy
thin
No
thin
light
thin
heavy
thin
µmols g-1
soil
195.1
25.1
-60.5
-30.2
34.6
12.2
-65.0
37.3
-10.7
Gram+
68.44
37.7
-55.2
-100
15:0 i
34.6
44.7
-58.2
-100
66.5
49.5
22.7
5.3
-85.6
-100
24.7
14.5
-15.1
-22.4
15:0 a
14.0
32.3
-44.1
-100
70.3
57.9
-100
48.2
-12.2
17:0 i
10.2
38.8
-55.0
-100
39.3
-47.2
37.6
15.8
17:0 a
9.8
19.5
-60.3
-100
120.
9
64.2
16.6
-53.8
13.8
-25.6
Gram-
106.8
14.6
16.0
-20.5
-64.9
-73.0
27.4
17.7
12.2
2.3
0.6
-16.7
-45.4
-61.3
46.1
25.1
-4.2
-25.2
19:0 cyclo w8c
92.2
21.8
-63.6
28.9
13.8
3.4
-42.9
49.5
-0.9
Actinomycetes
10-Me 17:0
19.8
6.4
31.3
17.8
-55.8
-63.9
-100
-100
44.9
31.4
38.3
-5.6
-100
-100
32.9
9.2
-30.6
-40.2
10-Me 18:0
13.4
37.6
-51.9
-100
51.3
59.1
-100
44.1
-26.1
182.5
20.0
18.8
19.0
-55.2
-54.3
-100
-100
27.6
74.2
27.1
30.4
-100
-100
-11.3
36.9
-42.7
-17.5
30.0
-64.1
-43.0
-100
-100
14.8
-100
-100
-100
132.6
37.5
-58.0
-100
49.5
29.4
-100
1.5
-33.5
6.19
6.2
3.4
3.4
-72.1
-72.1
-14.5
-14.5
2.7
2.7
-17.9
-17.9
-35.9
-35.9
33.1
33.1
-13.4
-13.4
0.9
-5.3
12.8
-100
-5.3
12.8
-100
-36.2
-36.2
-81.2
14.1
-26.0
FAMEs
Bacteria (B)
17:0 cy
Fungi (F)
16:1 w5c
18:3 w6c
(6,9,12)
18:1 w9c
Protozoa
20:4
w6,9,12,15c
F/B
% difference
Community
size
µmols g-1
Total FAME
microbial
biomass
carbon
nitrogen
383.8
% difference
21.8
-58.2
-63.2
mg/g soil
30.8
18.8
% difference
448.1
3.7
5.3
1.8
-30.2
-25.4
-3.3
-42.4
-33.2
68.0
-2.9
0.3
5.6
-28.1
-17.6
-2.0
-52.0
-35.7
Values in bold represent percent increase, while the negative values represent percent decrease in
abundance relative to the reference site.
81
Table 3.3. Analysis of variance for abundance of microbial groups and biomass of the
different treatments at 0 - 10 cm, and with depth (0 - 10cm and 10 – 20cm).
Burn
thin
burn*thin
Depth
F
Pr > F
F
Pr > F
F
Pr > F
F
Pr > F
MBC
5.655
0.012
3.443
0.054
1.277
0.316
15.269
0.000
MBN
3.580
0.049
3.622
0.048
0.859
0.507
13.436
0.001
Gram+
0.288
0.753
5.572
0.013
1.503
0.243
0.833
0.366
Gram-
1.508
0.248
3.987
0.037
2.697
0.064
9.685
0.003
Actinos
0.423
0.661
5.062
0.018
1.620
0.213
1.161
0.286
Fungi
1.192
0.326
3.856
0.040
1.787
0.175
0.317
0.576
Prz
0.531
0.597
3.713
0.045
1.550
0.230
1.066
0.307
Bacteria
0.474
0.630
5.180
0.017
1.611
0.215
4.611
0.036
Total
FAME
0.647
0.535
4.555
0.025
1.678
0.199
1.779
0.188
F:B
2.286
0.130
4.128
0.033
2.829
0.055
3.654
0.061
Values in bold are different from 0 at significance level of α = 0.05. MBC = microbial
biomass carbon; MBN = microbial biomass nitrogen; Actinos = Actinomycetes; Gram+ =
Gram positive bacteria; Prz = Protozoa; Gram- = Gram negative bacteria; F:B =
fungi:bacteria ratio.
82
3.2
Microbial community diversity, and richness, according to pyrosequencing
The EL-FAME approach to assess microbial community structure cannot be used
to evaluate the microbial species diversity, because it is limited to larger groups of
microbes. To further characterize the microbial community, pyrosequencing was used.
The pyrosequencing technique permits the determination of microbial populations to the
species level, and therefore enabled the assessment of the species diversities in the
microbial communities.
3.2.1 Bacterial community richness and diversity
Phylogenetic analyses grouped the soil associated bacterial sequences (141,260
sequences) to 24 bacterial phyla. Proteobacteria, Acidobacteria, and Actinobacteria
averaged just over 90% of the relative abundance of the total bacterial populations in all
the treatments. The relative abundances of the phyla are represented in Figs. 3.4, 3.5, and
3.6. The phylum distribution showed that the most dominant phylum was Proteobacteria,
accounting for 59.4% in no-burn/light-thin treatments to 66.4% in 9yr-burn/light-thin
treatments. Other dominant phyla noted were Acidobacteria, and Actinobacteria, which
accounted for 20 to 28.5% and 2.6 to 5.7% in abundance respectively. Bacteroidetes (0.5
to 4.9%) Firmicutes, (0.6 to 3.9%) and Verrucomicrobia, (0.7 to 1.97%) averaged 3.8,
2.9, 1.4, and 1.3% respectively across all treatments. The members of rare phyla (<1% of
all classified sequences), in reference to abundance only, included Chloroflexi,
Planctomycetes,
Nitrospirae,
Deinococcus-Thermus,
Gemmatimonadetes,
Elusimicrobia,
TM7,
Cyanobacteria,
83
OP10,
Deferribacteres,
Fusobacteria,
WS3,
Lentisphaerae,
Spirochaetes,
Aquificae,
Fibrobacteres,
OD1,
and
Thermodesulfobacteria. Some phyla were present in ≤ 2 treatment type: Fusobacteria,
Spirochaetes, Fibrobacteres, OD1, and Thermodesulfobacteria. Analysis of variance
(ANOVA) showed significant effects of thinning treatments when considering some
bacterial groups. Specific differences were observed between the treatments with
different levels of thinning for the phylum Acidobacteria (P < 0.05), while
Actinobacteria showed significant distinctions (P< 0.01) with respect to thinning
treatments.
The phylum Proteobacteria is the largest and most varied group of cultivated
bacteria, with great morphologic and metabolic diversities. Because of such
characteristics, it occurs in several different environments, accounting for its
predominance in forest soils (Pereira et al., 2006). As a versatile aerobic chemoorganotrophic gram-positive bacteria, actinobacteria have been recovered from a wide
variety of environments, and together with fungi, they are the most important producers
of extracellular enzymes vital for nutrient cycling in soils (Niva et al., 2006).
84
(a)
Chloroflexi
0.42%
Planctomycetes
0.36%
Nitrospirae
0.36%
Verrucomicrobia
0.70%
Firmicutes
0.94%
TM7
0.16%
OP10
0.14%
Deferribacteres
0.14%
Deinococcus-Thermus
0.06%
Elusimicrobia
0.07%
Cyanobacteria
0.05%
Bacteroidetes
3.24%
Actinobacteria
4.77%
Acidobacteria
28.27%
(b)
Nitrospirae
0.20%
Planctomycetes
0.24%
Chloroflexi
0.53%
Verrucomicrobia
1.94%
Firmicutes
0.70%
Gemmatimonadetes
0.29%
Fusobacteria
0.00%
Lentisphaerae
0.00%
Proteobacteria
60.06%
TM7
0.26%
OP10
0.13%
Deferribacteres
0.07%
Deinococcus-Thermus
0.08%
Elusimicrobia
0.04%
Fusobacteria
0.00%
Cyanobacteria
0.03%
Lentisphaerae
0.04%
Fibrobacteres
0.00%
Actinobacteria
3.69%
Acidobacteria
28.50%
(c)
Nitrospirae
0.34%
Planctomycetes
0.47%
TM7
0.20%
OP10
0.16%
Proteobacteria
59.38%
Deferribacteres
0.06%
Deinococcus-Thermus
0.04%
Chloroflexi
0.25%
Verrucomicrobia
1.57%
Elusimicrobia
0.11%
Cyanobacteria
0.02%
Fusobacteria
0.17%
Lentisphaerae
0.00%
Spirochaetes
0.05%
OD1
0.00%
Firmicutes
3.91%
Actinobacteria
2.70%
Acidobacteria
20.12%
Proteobacteria
66.04%
Figure 3.4. Relative abundance of major bacterial phyla in the no-burn treatments: (a)
not-thinned, (b) lightly-thinned, and (c) heavily-thinned.
85
(a)
Planctomycetes
0.32%
Verrucomicrobia
0.78%
Bacteroidetes
0.45%
Chloroflexi
1.04%
Gemmatimonadetes
0.32%
TM7
0.13%
Nitrospirae
0.62%
OP10
0.29%
Deferribacteres
0.08%
Deinococcus-Thermus
0.06%
Cyanobacteria
0.00%
Elusimicrobia
0.05%
Fusobacteria
0.00%
Thermodesulfobacteria
0.02%
Firmicutes
1.53%
Actinobacteria
5.66%
Acidobacteria
27.59%
(b)
Planctomycetes
0.39%
Chloroflexi
0.47%
Verrucomicrobia
1.49%
Bacteroidetes
2.46%
Actinobacteria
3.16%
Nitrospirae
0.40%
TM7
0.17%
Proteobacteria
61.04%
OP10
0.08%
Firmicutes
1.24%
Lentisphaerae
0.00%
Acidobacteria
26.89%
(c)
Deinococcus-Thermus
0.03%
Cyanobacteria
Elusimicrobia
0.01%
0.01%
WS3
Fusobacteria
0.01%
0.00%
OD1
0.00%
Deferribacteres
0.08%
Nitrospirae
0.24%
Planctomycetes
0.48%
Chloroflexi
0.62%
Verrucomicrobia
1.08%
Firmicutes
1.48%
Bacteroidetes
1.56%
Actinobacteria
3.95%
OP10
0.15%
Proteobacteria
62.84%
Deferribacteres
0.15%
Deinococcus-Thermus
0.08%
Elusimicrobia
0.00%
Cyanobacteria
Fusobacteria
0.08%
0.00%
Lentisphaerae
0.00%
Aquificae
0.00%
Acidobacteria
24.41%
Proteobacteria
65.13%
Figure 3.5. Relative abundance of major bacterial phyla in 3yr-burn cycle treatments: (a)
not-thinned, (b) lightly-thinned, and (c) heavily-thinned).
86
(a)
Planctomycetes
0.51%
Chloroflexi
0.35%
Verrucomicrobia
1.39%
Firmicutes
0.58%
Nitrospirae
0.42%
OP10
0.06%
TM7
0.34%
Bacteroidetes
2.38%
Deferribacteres
0.01%
Deinococcus-Thermus
0.00%
Elusimicrobia
0.03%
Fusobacteria
0.00%
Fibrobacteres
0.00%
Actinobacteria
3.41%
Acidobacteria
24.02%
Proteobacteria
66.34%
(b)
Planctomycetes
0.48%
Chloroflexi
0.49%
Nitrospirae
0.14%
Verrucomicrobia
1.52%
Firmicutes
1.44%
Bacteroidetes
3.91%
Actinobacteria
2.56%
OP10
0.08%
Deferribacteres
0.08%
Deinococcus-Thermus
0.07%
Elusimicrobia
0.00%
Fusobacteria
0.00%
Cyanobacteria
0.04%
Lentisphaerae
0.00%
Fibrobacteres
0.00%
Acidobacteria
22.22%
Proteobacteria
66.44%
(c)
Chloroflexi
0.62%
Planctomycetes
0.38%
Nitrospirae
0.22% Gemmatimonadetes
0.51%
OP10
0.23%
Deferribacteres
0.03%
Deinococcus-Thermus
0.10%
Verrucomicrobia
1.33%
Firmicutes
1.13%
Elusimicrobia
0.00%
Cyanobacteria
0.01%
Fusobacteria
0.01%
Lentisphaerae
0.05%
Spirochaetes
0.00%
Bacteroidetes
4.95%
Actinobacteria
4.03%
Acidobacteria
21.66%
Proteobacteria
64.50%
Figure 3.6. Relative abundance of major bacterial phyla in the 9yr-burn cycle treatments:
(a) not-thinned, (b) lightly-thinned, and (c) heavily-thinned.
87
At the class levels, β-proteobacteria, α-proteobacteria, Acidobacteria (class), γproteobacteria, Holophagae, Actinobacteria (class), Sphingobacteria, and Clostridia
were the dominant bacterial classes found that averaged > 1% in relative abundance
across the treatments. Of the classes of Proteobacteria, β-proteobacteria appeared to be
the most dominant among them, followed by α-proteobacteria. These two showed higher
relative abundance compared to that of any other taxa at the class level. Based on
ANOVA, burning had significant effects on the relative abundance of β-proteobacteria (p
< 0.05), thinning had significant effects on the relative abundance of Actinobacteria
(class) (p < 0.01), while thinning as well as a combination of thinning and burning had
significant effects on the relative abundance of Acidobacteria (class) (p < 0.05). Thinning
as well as burning had significant effects on the relative abundance of Holophagae, but
this was not the case with a combination of thinning and burning treatments. Βetaproteobacteria was significantly affected by burning. Smith et al., (2008) found βproteobacteria to be highly characteristic of the burned treatments. The most dominant
genus of the β-proteobacteria was nitrosovibrio, accounting for an average relative
abundance of 27.3%, which also happens to be the most abundant genus across all
treatments. Nitrosovibrio are chemoautotrophic ammonia-oxidizing bacteria (AOB),
which are biocatalyst in the first step of the nitrification process during which ammonia is
oxidized to nitrate, and in turn transformed to nitrate by nitrite-oxidizing bacteria.
Ammonium (NH4+) and nitrate (NO3-) are the inorganic forms of nitrogen that originate
during burning. Ammonium is a direct product of combustion, while nitrate results from
the nitrification of ammonium after fire (Certini, 2005).
88
Members of the Actinobacteria are among the most important litter decomposers
in soils (Kopecky et al., 2011). This may explain why their relative abundance was
significantly influenced by thinning (Table 3.4). They are metabolically versatile, and
many of them are spore-forming and can thus resist drought periods. Mycobacterium was
the most abundant genus of the Actinobacteria (class) in this study, followed by
Serinicoccus with about 1.5% and 0.8% average relative abundance of all classified
sequences in forest soils respectively across all treatments. Mycobacteria are free living
saprophytes and well adapted to a variety of different environments including soils. They
are able to utilize a large variety of hydrocarbons including branched chain, unsaturated,
aromatic and cyclic hydrocarbons that are present in various forms in pristine boreal
environments and contaminated sites (Niva et al., 2006). Nacke et al., (2011b), found that
mycobacterium was the most abundant genus across all soil samples, representing 3.7%
of all classified sequences in forest soils and 5.7% in grassland soils. Serinicoccus has
mostly been isolated from marine sediments (Xiao et al., 2011).
Acidobacteria (class) and Holophagae had average relative abundances of 17.4%
and 6.1% across all treatments. They are members of the phylum Acidobacteria, which
are moderately acidophilic heterotrophic organisms. Their ability to withstand polluted
and extreme environments (Ward et al., 2009) suggest that they serve functions that are
important in the environment such as the degradation of complex carbon containing
substrates. Holophaga foetida (a member of the class Holophagae), has been isolated and
described as a novel, homoacetogenic bacterium capable of degrading methoxylated
aromatic compounds (Eichorst et al., 2007). Homoacetogenic bacteria are strict anaerobes
89
capable of autotrophic growth on H2/CO2 or CO, and of heterotrophic growth on a wide
range of sugars, alcohols, methoxylated aromatic compounds and one carbon compounds,
yielding acetate as their sole metabolic end-product (Ryan et., 2008).
Bacterial community richness as revealed by the observed species richness
(Sobs), Chao1 richness estimators, and the abundance-based coverage estimator (ACE)
showed that the control (no burn/ no thin) had the lowest species richness compared to
the other treatments (Fig. 3.7). There was a very significant variation (p < 0.001) in the
bacterial species richness as a result of the management practices. The species richness
for all the treatments were significantly higher than the control, as demonstrated by the
Sobs, ACE and Chao1 values, except in the case of the no-thinned 9yr-burn cycle
treatment (T2) whose ACE value was not significantly higher. Analysis of variance also
demonstrated that there seem to be no significant difference in species richness between
the lightly-thinned and heavily-thinned treatments with the same burning regimen, as
shown in appendix 4 (T5 vs T4, T7 vs T6, T9 vs T8). At the thinning levels, the
abundance-based coverage estimates (ACE) showed no significant difference in species
richness attributable to 3yr-burn cycle, although it was significant with the Chao1
estimates. With no-thin and lightly thinned treatments, the abundance-based coverage
estimates (ACE) showed no significant differences in species richness attributable to 9yrburn cycle. The observed species richness as well as the abundance-based coverage
estimates (ACE) values showed no significant differences between the 3yr and the 9yr
burn cycles under the same thinning regimen, although it was significant according to the
Chao1 estimates.
90
The microbial diversities were demonstrated by the Shannon index which ranged
from 2.85 to 3.03. Results showed that the mean Shannon indices of the different
management practices were all greater than that of the control (no burn/ no thin) (Table
3.5). The value of Shannon diversity is usually found to fall between 1.5 and 3.5 and only
rarely it surpasses 4.5. However, the Shannon indices amongst the treatments were not
significantly different. The diversity index is a quantitative measure that reflects how
many different types of species (or OTUs) there are in a dataset, and simultaneously takes
into account how evenly the basic entities (such as individuals) are distributed among
those types. The value of a diversity index increases both when the number of types
increases and when evenness increases. For a given number of types, the value of a
diversity index is maximized when all types are equally abundant.
Correlation analysis was used to identify the relationships between the relative
abundance of bacterial groups and soil properties (Table 3.6). Among the different
bacterial classes with relative abundances of >1%, Acidobacteria (class) showed
significant correlations with most soil properties. Acidobacteria (class) showed positively
significant correlations with %C, C/N, MC, and negative but significant correlations with
pH. Soil moisture content had the most significant correlations with the bacterial classes
with relative abundance of >1%. There were significant correlations between moisture
content and bacteria of the following classes: Acidobacteria (class), Holophagae,
Actinobacteria (class), Sphingobacteria, and Clostridia. The correlations were positive
for Acidobacteria (class), and Sphingobacteria, and negative for the others.
Carbon/Nitrogen ratio had negative but significant correlations with Alphaproteobacteria
91
and Sphingobacteria, while the significant correlation with Acidobacteria (class) was
positive. Acidobacteria (class) and Actinobacteria (class) showed significant correlations
with enzymatic activities. Acid phosphatase and β-glucosidase activities were
significantly and positively correlated with Acidobacteria (class). Acidobacteria (class)
also showed positive correlations with the activities of xylanase and β-glutaminase,
although these correlations were not significant. The Actinobacteria (class) showed
significant correlations (negative) with acid phosphatase and arylsulfatase activities
(Table 3.7).
92
Table 3.4. Analysis of variance for relative abundance of bacterial classes with different
treatments.
Betaproteobacteria
Alphaproteobacteria
Acidobacteria (class)
Gammaproteobacteria
Holophagae
Actinobacteria (class)
Sphingobacteria
Clostridia
Verrucomicrobiae
Deltaproteobacteria
Bacilli
Opitutae
Planctomycetacia
Nitrospira (class)
Flavobacteria
Gemmatimonadetes (class)
Cytophagia
TM7 (class)
Caldilineae
Ktedonobacteria
OP10 (class)
Bacteroidia
Chloroflexi (class)
Deferribacteres (class)
Deinococci
Spartobacteria
Anaerolineae
Elusimicrobia (class)
Epsilonproteobacteria
Fusobacteria (class)
Cyanobacteria (class)
Solibacteres
WS3 (class)
Lentisphaerae (class)
Spirochaetia
Gloeobacteria
Aquificae (class)
Oscillatoriales
Fibrobacteres (class)
OD1 (class)
Thermodesulfobacteria (class)
Erysipelotrichi
thin
F
1.210
0.879
5.389
0.536
3.513
7.603
1.234
2.231
0.729
3.630
0.860
1.081
0.256
2.013
0.138
5.441
0.325
1.778
0.275
1.721
2.179
0.876
1.890
0.445
0.805
1.304
0.142
0.782
0.469
0.982
1.816
0.108
0.045
0.242
0.730
0.629
0.972
0.491
0.921
0.921
0.921
0.921
Pr > F
0.323
0.433
0.015
0.595
0.053
0.004
0.316
0.138
0.497
0.049
0.441
0.361
0.777
0.164
0.872
0.015
0.727
0.199
0.763
0.209
0.144
0.435
0.181
0.648
0.463
0.297
0.869
0.473
0.633
0.395
0.193
0.898
0.956
0.788
0.496
0.545
0.399
0.620
0.417
0.417
0.417
0.417
burn
F
5.009
0.797
0.355
1.330
3.793
0.763
2.264
2.350
0.155
3.570
0.868
0.422
0.368
1.033
1.127
1.086
0.887
0.997
0.717
0.122
0.838
0.778
2.226
1.140
0.060
0.635
0.886
1.848
1.244
0.844
0.767
1.081
1.472
0.236
0.798
0.606
0.978
0.470
0.850
0.850
0.850
0.921
Pr > F
0.019
0.467
0.706
0.291
0.043
0.481
0.134
0.126
0.857
0.051
0.438
0.662
0.698
0.377
0.347
0.360
0.430
0.390
0.502
0.886
0.450
0.475
0.138
0.343
0.942
0.542
0.431
0.188
0.313
0.447
0.480
0.362
0.257
0.792
0.466
0.557
0.396
0.633
0.445
0.445
0.445
0.417
Values in bold are different from 0 at significance level of α = 0.05.
93
thin*burn
F
Pr > F
0.373
0.825
0.788
0.549
4.279
0.014
0.798
0.543
1.696
0.197
0.660
0.628
0.878
0.497
3.519
0.029
0.148
0.962
2.856
0.056
0.813
0.534
0.138
0.966
0.895
0.489
0.408
0.800
0.343
0.845
3.735
0.023
0.138
0.966
0.109
0.978
0.720
0.590
0.270
0.893
0.499
0.737
0.823
0.529
0.772
0.558
0.836
0.521
1.206
0.344
0.286
0.883
0.549
0.702
0.160
0.956
1.293
0.312
0.821
0.529
0.884
0.494
0.607
0.663
0.148
0.961
1.635
0.211
0.957
0.456
1.056
0.408
0.363
0.832
1.086
0.395
0.902
0.485
0.902
0.485
0.902
0.485
0.934
0.468
500
Nunber of OTUs observed/predicted
no thin
light thinning
heavy thinning
400
300
200
100
0
Sobs
ACE
no burn
Chao1
Sobs
ACE
3yr burn
Chao1
Sobs
ACE
Chao1
9yr burn
Treatment
Figure 3.7. Bacterial species richness estimates of different treatments.
Richness is expressed by the number of observed unique OTUs. Sobs (Mao Tau) is the
number of species in pooled samples, given the empirical data. Richness is also estimated
by the abundance-based coverage estimator (ACE), which is a nonparametric richness
estimator based on abundance distribution (>10) and rare (≤ 10) OTUs, and the richness
predictor Chao1which is a nonparametric richness estimator based on singletons and
doubletons distributions.
94
Table 3.5. Bacterial species diversity indices of various treatments.
Treatments
Shannon index of Bacterial
species
Control, No Burn/No Thin
2.85(0.33)
No Burn/ Light Thin
2.99(0.14)
No Burn/ Heavy Thin
2.98(0.17)
3yr Burn/No Thin
2.95(0.21)
3yr Burn/ Light Thin
3(0.09)
3yr Burn/ Heavy Thin
3(0.11)
9yr Burn/No Thin
2.92(0.23)
9yr Burn/ Light Thin
3.03(0)
9yr Burn/ Heavy Thin
3.01(0.07)
Values in parentheses are standard error (SE)
95
Table 3.6. Pearson’s correlation matrix between bacterial classes and soil properties.
%C
%N
%S
C/N
MC
pH
Betaproteobacteria
-0.214
-0.143
0.051
-0.083
0.192
-0.036
-0.259 -0.203
Alphaproteobacteria
-0.338
-0.203
0.020
-0.404
-0.011
0.197
-0.243 -0.213
Acidobacteria (class)
0.437
0.314
0.198
0.424
0.582
-0.471
0.063
0.067
Gammaproteobacteria
0.144
0.091
-0.029
0.140
-0.260
0.104
0.132
0.086
Holophagae
-0.081
-0.168
-0.179
0.173
-0.445
-0.339
0.297
0.343
Actinobacteria (class)
-0.171
-0.216
-0.141
-0.028
-0.453
-0.103
0.110
0.128
Sphingobacteria
-0.204
-0.035
-0.043
-0.546
0.502
0.252
-0.072 -0.083
Clostridia
-0.348
-0.288
-0.011
-0.291
-0.541
0.317
-0.281 -0.302
Values in bold are different from 0 at significance level of α = 0.05.
96
MBC
MBN
Table 3.7. Pearson’s correlation matrix between bacterial classes and soil enzymatic
activities.
β -Gluc
β -Glm
AP
AS
0.116
-0.130
-0.052
0.145
0.270
0.386
0.106
-0.051
0.072
-0.254
0.036
Acidobacteria (class)
-0.362 -0.292
0.230
0.409
0.131
0.463 -0.082
Gammaproteobacteria
-0.016 -0.087 -0.182
-0.021
-0.017
-0.051 -0.117
Holophagae
-0.306 -0.264
0.131
-0.231
-0.134
-0.130 -0.071
Actinobacteria (class)
-0.366 -0.136 -0.070
-0.041
-0.007
-0.491 -0.435
Lac
MnP
Xyl
Betaproteobacteria
0.076
0.145
Alphaproteobacteria
0.323
Sphingobacteria
0.307
0.261 -0.083
0.179
0.125
0.013
0.257
Clostridia
0.288
0.272 -0.034
-0.380
-0.201
-0.211
0.069
Values in bold are different from 0 at significance level of α = 0.05. AP = Acid
phosphatase; AS = Aryl sulfatase; β-Gluc = β-glusosidase; MnP = Manganese
peroxidase; Xyl = Xylanase; Lac = Laccase; β-Glm = β-glutaminase.
97
3.2.2 Shared species richness and similarity in the bacterial community structure
The shared community richness was depicted by the shared species observed
(SSobs) and the Chao shared estimates (CSE) (Table 3.8). According to the SSobs values,
B9T50 shared the highest number of species (116) with the control site, followed by T9,
T7, T2, T5, T3, T6, T4, with 92, 91, 86, 82, 78, 74, and 68, shared species respectively.
The estimated shared bacterial species richness with the control site (T1), ranked as
follows: T8, T9, T2, T3, T7, T5, T4, and T6, with 140, 117, 105,105, 103, 98, 93, and 87
shared species respectively.
The similarity in the community structure was depicted by the Chao-Jaccard-Raw
Abundance-based index. Chao's Abundance-based Jaccard index is based on the
probability that two randomly chosen individuals, one from each of two samples (sites),
belong to species shared by both samples (but not necessarily to the same shared species).
These estimators take into account the contributions to the true values made by species
actually present at both sites, but not detected in one or both samples. This approach has
been shown to reduce substantially the negative bias that undermines the usefulness of
traditional similarity indexes, especially with incomplete sampling of rich communities.
The different bacterial communities were compared using Chao’s Jaccard abundancebased similarity index. An average of 78 (range 52 – 116) OTUs were found to be shared
between any two treatments (Table 3.9). Similarity between communities was typically
high, averaging 0.88 (range 0.76 – 0.94) with the raw index. A value of 1 indicates that
all species are shared between the two specimens examined.
98
Table 3.8 Observed and estimated shared bacterial species between treatments.
No-burn
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
98
93
105
103
87
105
117
140
87
90
98
72
77
90
97
92
78
70
61
77
95
90
106
75
95
112
98
88
101
130
87
89
114
90
100
Light-thin
82
Heavy-thin
68
58
3yr-burn
No-thin
9yr-burn
No-thin
78
65
63
Light-thin
91
82
62
74
Heavy-thin
74
64
56
70
82
9yr-burn
No-burn
No
thin
3yr-burn
No-thin
86
68
52
64
79
68
Light-thin
92
77
60
72
90
77
78
Heavy-thin
116
86
77
91
114
96
90
131
111
Values to the left and below the diagonal (italicized) are the shared species observed
(SSobs), those above and to the right of the diagonal (in bold) are Chao Shared Estimates
(CSE).
99
Table 3.9. Chao-Jaccard-Raw abundance-based similarities between pairs of treatments,
calculated from shared OTUs.
No-burn
No-thin
Light-thin
3yr-burn
Heavy-thin
No-thin
9yr-burn
No-burn
No
thin
No-thin
3yr-burn
9yr-burn
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
0.922
0.874
0.912
0.934
0.876
0.923
0.849
0.903
0.914
0.924
0.939
0.906
0.928
0.839
0.88
0.908
0.88
0.878
0.869
0.759
0.844
0.93
0.913
0.914
0.794
0.881
0.916
0.928
0.904
0.926
0.902
0.789
0.876
0.868
0.882
Light-thin
Heavy-thin
Light-thin
0.892
Heavy-thin
Chao's Abundance-based Jaccard index is based on the probability that two randomly
chosen individuals, one from each of two samples (sites). Both belong to species shared
by both samples (but not necessarily to the same shared species). The estimators for this
index take into account the contributions to the true value of this probability made by
species actually present at both sites, but not detected in one or both samples. This
approach has been shown to reduce substantially the negative bias that undermines the
usefulness of traditional similarity indexes, especially with incomplete sampling of rich
communities.
100
3.2.3 Fungal community richness and diversity
A total of 122,655 soil associated fungi sequences were grouped by phylogenetic
analyses into 6 fungal phyla and 1 unclassified group. The most abundant OTUs were
Basidiomycota followed by Ascomycota and unclassified fungi, averaging 52.58, 26.01,
and 21.14% respectively. Ascomycota and Basidiomycota have been reported by several
authors (Lim et al., 2010; Orgiazzi et al., 2012; Wubet et al., 2012) to be the dominant
phyla in soils. Most known white rot fungi are basidiomycetes, although a few
ascomycete genera within the Xylariaceae are also capable of white rot decay. In this
study, the species belonging to the family Xylariaceae had an average relative abundance
of 2.4%. The genus Rhodoveronaea, belonging to a member of this family, was the 15th
most abundant genus. The members of rare phyla (<1% of all classified sequences), in
reference to abundance only, included Glomeromycota, Zygomycota, Chytridiomycota,
and Neocallimastigomycota had less than 1% relative abundances. The relative
abundance of the phyla in the treatments are represented in Figs. 3.8, 3.9, and 3.10. The
phylum distribution within the different treatments showed that the majority of the fungal
sequences recovered in this study belonged to the basidiomycota and Ascomycota (except
for 9-yr burn/ light thin treatments). Basidiomycota had the highest relative abundance,
but for no burn /light thin and 3-yr burn /heavy thin treatments, where ascomycota was
higher. The relative abundance was highest in the 3-yr burn/no-thin (T3), followed
closely by the 3- yr burn/light-thin (T7). The 3-yr burn/no-thin (T3) and the 3-yr
burn/light thin were the only treatments that had higher relative abundance of
basidiomycota than the control plot (no burn/ no thin).
101
Analysis of variance (ANOVA) showed significant effects of thinning treatments
on the ascomycota group (P < 0.05) while burning had significant effects on
glomeromycota (P < 0.01) as shown in Table 3.10. The Tukey test for pairwise
comparison of means showed specific differences between the treatments with different
levels of thinning for the phylum ascomycota (P < 0.05), with the relative abundance of
the phylum ascomycota in the heavily-thinned significantly higher than the no-thin
treatments. The relative abundance of glomeromycota was significantly higher in the noburn treatments than the 3yr and 9yr-burn cycle treatments.
102
(a)
Chytridiomycota
0.00%
Zygomycota
0.15%
Neocallimastigomycota
0.00%
unclassified Fungi
15.44%
Ascomycota
24.83%
Basidiomycota
59.39%
Glomeromycota
0.19%
(b)
Zygomycota
0.00%
Chytridiomycota
0.00%
Basidiomycota
33.93%
Neocallimastigomycota
0.00%
unclassified Fungi
19.61%
Ascomycota
46.09%
Glomeromycota
0.37%
(c)
Chytridiomycota
0.01%
Zygomycota
0.25%
Neocallimastigomycota
0.01%
unclassified
Fungi
13.05%
Ascomycota
31.02%
Basidiomycota
55.53%
Glomeromycota
0.13%
Figure 3.8. Relative abundance of fungal phyla in no-burn treatments: (a) not-thinned, (b)
lightly-thinned, and (c) heavily-thinned.
103
(a)
Zygomycota
0.30%
Chytridiomycota
0.01%
Neocallimastigomycota
0.00%
unclassified Fungi
9.32%
Ascomycota
16.34%
Basidiomycota
74.00%
(b)
Glomeromycota
0.04%
Neocallimastigomycota
0.00%
Chytridiomycota
Zygomycota
0.02%
0.02%
unclassified Fungi
21.25%
Ascomycota
10.29%
Basidiomycota
68.33%
Glomeromycota
0.09%
(c)
Zygomycota
0.15%
Chytridiomycota
0.02%
Neocallimastigomycota
0.00%
unclassified Fungi
18.10%
Basidiomycota
39.43%
Ascomycota
42.19%
Glomeromycota
0.11%
Figure 3.9. Relative abundance of fungal phyla in 3yr-burn cycle treatments:. (a) notthinned, (b) lightly-thinned, and (c) heavily-thinned.
104
(a)
Neocallimastigomycota
0.00%
Zygomycota
0.02%
Chytridiomycota
0.00%
unclassified Fungi
31.16%
Basidiomycota
56.20%
Ascomycota
12.51%
Glomeromycota
0.11%
(b)
Chytridiomycota
0.00%
Zygomycota
0.00%
Basidiomycota
40.13%
Neocallimastigomycota
0.00%
unclassified Fungi
43.93%
Ascomycota
15.86%
Glomeromycota
0.09%
(c)
Chytridiomycota
0.09%
Neocallimastigomycota
0.00%
Zygomycota
0.25%
unclassified Fungi
18.39%
Basidiomycota
46.28%
Ascomycota
34.98%
Glomeromycota
0.01%
Figure 3.10. Relative abundance of fungal phyla in 9yr-burn cycle treatments:. (a) notthinned, (b) lightly-thinned, and (c) heavily-thinned.
105
100
Neocallimastigomycetes
Chytridiomycetes
90
Monoblepharidomycetes
Zygomycota (class)
Glomeromycetes
80
Unclassified Fungi (class)
Arthoniomycetes
Schizosaccharomycetes
70
Lecanoromycetes
Geoglossomycetes
60
Ascomycota (class)
Saccharomycetes
50
Pezizomycetes
Dothideomycetes
Ascoomycota
Relative abundance (%)
Orbiliomycetes
Eurotiomycetes
40
Leotiomycetes
Sordariomycetes
Agaricostilbomycetes
30
Pucciniomycetes
Ustilaginomycetes
20
Dacrymycetes
Microbotryomycetes
10
Cystobasidiomycetes
Basidiomycota
Exobasidiomycetes
Tremellomycetes
Agaricomycetes
0
No
thin
Light Heavy
thin thin
No-burn
No Light
thin thin
3yr-burn
Heavy No
thin
thin
Light Heavy
thin
thin
9yr-burn
Treatment
Figure 3.11. Relative abundance of major fungal classes determined in the Bankhead
National Forest soils.
106
The OTUs present in the samples were grouped into 26 fungi classes plus 1
unclassified fungi class. Phylogenetic analysis showed that there were 9 classes
belonging
to
the
phylum
Cystobasidiomycetes,
basidiomycota:
Microbotryomycetes,
Pucciniomycetes,
Exobasidiomycetes,
Ascomycota
12
had
Dothideomycetes,
classes:
and
Agaricomycetes,
Dacrymycetes,
Ustilaginomycetes,
Agaricostilbomycetes.
Sordariomycetes,
Pezizomycetes,
Tremellomycetes,
Leotiomycetes,
Saccharomycetes,
The
phylum
Eurotiomycetes,
Ascomycota
(class),
Geoglossomycetes, Lecanoromycetes, Orbiliomycetes, Schizosaccharomycetes, and
Arthoniomycetes.
The
Chytridiomycota
was
represented
by
2
classes,
Monoblepharidomycetes, and Chytridiomycetes while the phyla Glomeromycota,
Zygomycota, and
Neocallimastigomycota were represented by 1 class each;
Glomeromycetes, Zygomycota (class), and Neocallimastigomycetes, respectively. At the
class level, Agaricomycetes belonging to phylum basidiomycota was the dominant fungal
class, averaging 51% of the relative abundance across the treatments. The unclassified
fungi had an average relative abundance of 19.3% while Sordariomycetes had an average
of 10.7%. Other abundant classes to note are Leotiomycetes, Eurotiomycetes,
Tremellomycetes, Dothideomycetes, Pezizomycetes, Saccharomycetes, and Ascomycota
(class) with relative abundance of 5.15, 4.06, 2.93, 1.57, 1.51, 1.48, and 1.03
respectively. All of the other classes had relative abundances that were less than 1% (Fig.
3.11).
Analysis of variance did not show any significant effects due to treatments on the
relative abundance of the most dominant classes. However, rare classes such as
107
Exobasidiomycetes were significantly affected by thinning (p < 0.001), burning (p <
0.001) and a combination of both (p < 0.001) as shown in Table 3.10. The class
Exobasidiomycetes in this study was represented by Malassezia restricta. Malassezia
restricta has been found associated with the nematodes of the genus Malenchus sp. in
forest soils found in Europe (Renker et al., 2003). Other rare classes such as
Glomeromycetes and Microbotryomycetes were affected by burning and thinning
respectively.
108
Table 3.10. Analysis of variance for relative abundance of fungal phyla with different
treatments.
thin
F
Pr > F
burn
F
Pr > F
thin*burn
F
Pr > F
Unclassified Fungi (phylum)
1.845
0.197
2.729
0.102
0.732
0.586
Ascomycota
4.604
0.031
2.731
0.102
3.079
0.055
Glomeromycota
2.874
0.093
8.376
0.005
2.027
0.150
Basidiomycota
1.624
0.235
1.179
0.338
1.531
0.251
Chytridiomycota
1.727
0.216
0.582
0.573
0.980
0.452
Zygomycota
1.625
0.235
0.138
0.873
0.409
0.799
Neocallimastigomycota
0.568
0.580
0.591
0.568
0.635
0.647
Values in bold are different from 0 at significance level of α = 0.05.
109
Table 3.11 Analysis of variance for relative abundance of fungal classes with respect to
thinning, burning, and combined treatments.
thin
F
burn
Pr > F
F
thin*burn
Pr > F
F
Pr > F
Agaricomycetes
1.18
0.34
1.34
0.30
1.73
0.20
unclassified Fungi (class)
1.85
0.20
2.73
0.10
0.73
0.59
Sordariomycetes
1.94
0.18
0.99
0.40
1.40
0.29
Leotiomycetes
0.39
0.68
2.76
0.10
0.13
0.97
Eurotiomycetes
0.75
0.49
0.23
0.80
1.29
0.33
Tremellomycetes
2.56
0.12
1.20
0.33
2.04
0.15
Dothideomycetes
1.92
0.19
0.97
0.40
0.63
0.65
Pezizomycetes
2.85
0.09
1.11
0.36
1.37
0.30
Saccharomycetes
0.31
0.74
1.80
0.20
0.88
0.50
Ascomycota (class)
0.18
0.84
1.68
0.22
1.69
0.21
Geoglossomycetes
3.21
0.07
1.35
0.29
1.60
0.23
Lecanoromycetes
1.47
0.27
0.03
0.97
0.80
0.55
Zygomycota (class)
1.62
0.23
0.14
0.87
0.41
0.80
Glomeromycetes
2.87
0.09
8.38
0.005
2.03
0.15
Cystobasidiomycetes
4.50
0.03
1.90
0.19
1.39
0.29
Microbotryomycetes
9.22
0.003
0.84
0.45
1.34
0.31
Orbiliomycetes
0.68
0.53
0.34
0.72
0.19
0.94
Dacrymycetes
0.57
0.58
0.62
0.55
0.66
0.63
Monoblepharidomycetes
1.56
0.25
0.77
0.48
1.08
0.41
Ustilaginomycetes
0.30
0.75
1.75
0.21
0.73
0.59
Chytridiomycetes
1.18
0.34
0.10
0.91
0.90
0.49
Pucciniomycetes
3.10
0.08
0.89
0.44
0.94
0.47
Schizosaccharomycetes
1.03
0.38
1.29
0.31
1.32
0.31
50.67
< 0.0001
46.10
< 0.0001
48.13
< 0.0001
Agaricostilbomycetes
0.57
0.58
0.59
0.57
0.63
0.65
Arthoniomycetes
0.57
0.58
0.62
0.55
0.66
0.63
Neocallimastigomycetes
0.57
0.58
0.59
0.57
0.63
0.65
Exobasidiomycetes
Values in bold are different from 0 at significance level of α = 0.05.
110
Fungi community richness was expounded by the observed species richness
(Sobs), Chao1 richness estimators, and the abundance-based coverage estimator (ACE)
showed that the control (no burn/ no thin) had the lowest species richness compared to
the others (Fig. 3.12). There were highly significant variations (p < 0.001) in the fungi
species richness as a result of the management practices. The species richness for all the
treatments were significantly higher than the control, as demonstrated by the Sobs, ACE
and Chao1 values, except in the case of the 3yr-cycle burn/no thin and the 9yr-cycle
burn/no-thin treatments, whose ACE values were not significantly higher. Tukey’s
multiple comparison of the mean of the fungal species richness indicator, based on the
abundance-based coverage estimates (ACE), demonstrated that there seem to be no
significant difference in species richness between the lightly-thinned and heavily-thinned
treatments with the same burning regimen, as shown in appendix 5 (T5 vs T4, T7 vs T6,
T9 vs T8) and also observed with the bacterial species richness. At the no-thin, lightly
and heavily-thinned treatments, the abundance-based coverage estimates (ACE) showed
no significant differences in fungal species richness attributable to 3yr-burn cycle, as well
as the 9yr-burn cycle, although it was significant with the Chao1 estimates. Multiple
comparisons of the means of Chao1 indexes showed significant differences in fungal
species richness between all except between 3yr burn/lightly-thinned and 9yr
burn/heavily thin, which was also shown to be non-significant according to Sobs and
ACE.
The community diversity of fungal species was demonstrated by the Shannon
index which ranged from 3.31 to 4.15 (Table 3.12). This is a higher range than that of the
111
bacterial species (2.85 to 3.03). Results show that the mean Shannon indices of the
different management practices were all greater than that of the control, as was seen with
the bacterial species diversity. Correlation analysis was used to identify the relationships
between the relative abundances of fungal groups and soil properties (Table 3.13).
Among the different fungal classes with relative abundance of >1%, Leotiomycetes and
Tremellomycetes had significant correlations with C/N and %C respectively. There were
significant correlations between β-glucosidase activities and the fungi classes:
Tremellomycetes, Dothideomycetes, and Ascomycota (class) (Table 3.14).
112
500
Nunber of OTUs observed/predicted
no thin
light thinning
heavy thinning
400
300
200
100
0
Sobs
ACE
no burn
Chao1
Sobs
ACE
3yr burn
Chao1
Sobs
ACE
Chao1
9yr burn
Treatment
Figure 3.12. Fungi species richness estimates of different treatments.
Richness is expressed by the number of observed unique OTUs. Sobs (Mao Tau) is the
number of species in pooled samples, given the empirical data. Richness is also estimated
by the abundance-based coverage estimator (ACE), which is a nonparametric richness
estimator based on abundance distribution (>10) and rare (≤ 10) OTUs, and the richness
predictor Chao1which is a nonparametric richness estimator based on singletons and
doubletons distributions.
113
Table 3.12. Diversity indices of fungal species in the different treatments.
Treatments
Shannon index of
Fungi species
Control, No Burn/No Thin
3.31(0.76)
No Burn/ Light Thin
3.84(0.18)
No Burn/ Heavy Thin
3.7(0.24)
3yr Burn/No Thin
3.47(0.38)
3yr Burn/ Light Thin
4.01(0.12)
3yr Burn/ Heavy Thin
3.96(0.12)
9yr Burn/No Thin
3.31(0.41)
9yr Burn/ Light Thin
4.15(0)
9yr Burn/ Heavy Thin
4.09(0.07)
Values in parentheses are standard error (SE).
114
Table 3.13. Pearson’s correlation matrix between fungal classes and soil properties.
Variables
%C
%N
%S
C/N
MC
pH
MBC
MBN
-0.228
-0.303
-0.252
0.102
-0.278
-0.228
0.252
0.319
Fungi (class)
0.006
0.070
0.324
0.050
0.106
-0.074
-0.368
-0.412
Sordariomycetes
0.017
-0.068
-0.126
-0.070
0.073
0.137
-0.185
-0.142
Leotiomycetes
0.409
0.396
0.119
-0.553
0.353
-0.279
0.250
0.214
Eurotiomycetes
0.110
0.115
0.033
0.020
-0.032
-0.276
0.163
0.235
Tremellomycetes
0.589
0.414
0.194
0.133
0.203
-0.219
-0.067
0.031
Dothideomycetes
-0.276
-0.362
-0.311
-0.068
-0.300
0.314
0.022
0.017
Pezizomycetes
-0.128
-0.222
-0.096
-0.077
-0.309
0.416
0.014
0.064
Saccharomycetes
0.292
0.188
-0.018
-0.038
0.216
0.013
0.157
0.211
Ascomycota (class)
0.271
0.237
0.081
0.149
0.280
-0.199
-0.077
-0.043
Agaricomycetes
Values in bold are different from 0 at significant level α = 0.05.
115
Table 3.14. Pearson’s correlation matrix between fungal classes and soil enzymes.
β -Gluc
β -Glm
0.103
-0.300
0.195
0.084
0.295
0.242
Leotiomycetes
0.083
Eurotiomycetes
Variables
Lac
MnP
Xyl
AP
AS
-0.241
-0.314
-0.246
-0.101
-0.206
Fungi (class)
0.192
0.139
0.279
0.160
0.186
Sordariomycetes
-0.068
-0.069
0.165
-0.152
-0.195
-0.231
0.215
0.358
0.213
0.305
0.019
-0.141
0.137
-0.042
-0.104
-0.185
0.033
-0.178
Tremellomycetes
-0.317
-0.181
0.113
0.440
0.219
0.138
-0.247
Dothideomycetes
0.292
-0.003
-0.246
-0.485
-0.132
-0.246
-0.162
Pezizomycetes
0.157
-0.070
-0.060
-0.338
-0.236
-0.331
-0.126
Saccharomycetes
Ascomycota
(class)
0.036
-0.260
0.130
0.110
0.276
-0.218
-0.182
-0.090
0.048
0.073
0.454
0.336
0.057
-0.100
Agaricomycetes
Values in bold are different from 0 at significant level α = 0.05. AP = Acid phosphatase;
AS = Aryl sulfatase; β-Gluc = β-glusosidase; MnP = Manganese peroxidase; Xyl =
Xylanase; Lac = Laccase; β-Glm = β-glutaminase.
116
3.2.5 Shared species richness and similarity in the fungal community structure
The shared fungi species community richness was depicted by the shared species
observed (SSobs) and the Chao shared estimates (CSE) (Table 3.13). The number of
shared fungi species observed ranged from 0 to 58. The SSobs values show that the
numbers of shared fungi species was highest between the control site and the 3yr burn
/heavily thinned treatments, with 58 observed shared species. This ranking was true with
the Chao shared estimates, with 71 estimated shared fungi species. The lightly-thinned
(T5), 9yr cycle burn (T2), as well as the 9yr cycle burn/heavily thinned (T8) treatments
had no observed shared fungi species with the rest of the treatments. This was also true
for the estimated shared species (Chao shared estimates).
We analyzed the similarities in the fungi species community structure using the
Chao-Jaccard-Raw Abundance-based index. The Chao's Abundance-based Jaccard index
is based on the probability that two randomly chosen individuals, one from each of two
samples (sites), both belong to species shared by both samples (but not necessarily to the
same shared species). The estimators for this index take into account the contribution to
the true value of this probability made by species actually present at both sites, but not
detected in one or both samples. This approach has been shown to reduce substantially
the negative bias that undermines the usefulness of traditional similarity indexes,
especially with the incomplete sampling of rich communities.
The Chao’s Jaccard abundance-based similarity indexes were typically very low,
ranging from 0 to 0.59 (Table 3.14). The Chao’s Jaccard abundance-based similarity
indexes between the fungi communities of the lightly-thinned(T5), 9-yr cycle burn (T2),
117
as well as the 9-yr cycle burn/heavily-thinned treatments had a value of 1 (Table 3.14)
although, these treatments do not share any species with the other treatments. The 3yr
burn/ light thin treatment, which had the second highest shared number of fungal species
with the control, had the highest similarity index (0.59) followed by the no burn/ heavy
thin treatment (0.46), which had the third highest shared fungi species with the control.
118
Table 3.15. Observed and estimated shared fungi species between treatments.
No-burn
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
0
60
49
62
71
0
43
0
0
0
0
0
0
0
0
43
50
53
0
36
0
42
63
0
35
0
62
0
38
0
0
33
0
0
0
Light-thin
0
Heavy-thin
55
0
3yr-burn
No-thin
9yr-burn
No-thin
43
0
40
Light-thin
57
0
45
36
Heavy-thin
58
0
50
46
54
9yr-burn
No-burn
No
thin
3yr-burn
No-thin
0
0
0
0
0
0
Light-thin
40
0
34
30
33
30
0
Heavy-thin
0
0
0
0
0
0
0
0
0
Values to the left and below the diagonal (italicized) are the shared species observed
(SSobs). Those above and to the right of the diagonal (in bold) are Chao Shared
Estimates (CSE).
119
Table 3.14. Chao-Jaccard-Raw abundance-based similarities between pairs of treatments,
calculated from shared OTUs.
No-burn
No-thin
Light-thin
3yr-burn
Heavy-thin
No-thin
9yr-burn
No-burn
No
thin
No-thin
3yr-burn
9yr-burn
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
No
thin
Light
thin
Heavy
thin
0
0.467
0.538
0.59
0.281
0
0.42
0
0
0
0
0
1
0
1
0.254
0.253
0.361
0
0.283
0
0.336
0.29
0
0.22
0
0.36
0
0.191
0
0
0.187
0
0
1
Light-thin
Heavy-thin
Light-thin
0
Heavy-thin
Chao's abundance-based Jaccard index is based on the probability that two randomly
chosen individuals, one from each of two samples (sites), both belong to species shared
by both samples (but not necessarily to the same shared species). The estimators for this
index take into account the contribution to the true value of this probability made by
species actually present at both sites, but not detected in one or both samples. This
approach has been shown to reduce substantially the negative bias that undermines the
usefulness of traditional similarity indexes, especially with incomplete sampling of rich
communities.
120
3.3
Metabolic capacity of the forest ecosystem by enzymatic assays
Principal component analysis (PCA) at sampling depth of 0-10 cm showed a
closer association of ligninolytic enzymes (laccase and manganese peroxidase) and to a
lesser extent, arylsulfatase with the 3yr- and 9yr-burn cycles treatments as well as the
lightly-thinned treatments. A close association was also seen between β-glucosidase, βglucosaminidase, acid phosphatase and xylanase with the no-burn treatments (Fig 3.2). At
the soil sampling depth of 0 - 10cm, the enzyme activities of laccase (p < 0.05), MnP (p <
0.001), β-glucosidase (p <0. 001), and arylsulfatase (p < 0.01) varied significantly with
thinning but not with burning, with the effects of the interactions between burning and
thinning being significant with β-glucosidase (p < 0.05), and arylsulfatase (p < 0.05)
according to ANOVA (Table 3.17).
There was an upward trend of laccase activity, with increasing thinning in
treatments with same degree of prescribed burning cycle as follows; no thinning < light
thinning < heavy thinning (Fig. 3.13). The same trend was also observed for treatments at
same level of thinning with increasing intervals of burning, except for the no-thinning
treatments with 3-yr and 9-yr burning cycles. Laccase was found to have a positive
significant correlation with thinning (Table 3.18). Activities of MnP, which is also
involved in lignin degradation showed similar trends as laccase with respect to thinning.
Pearson’s correlations showed that MnP activity had positive and significant correlations
with burning and thinning; however, at the 10 - 20 cm depth, the trends were similar for
both enzymes activities. Xylanase activity was not significantly affected by burning and
thinning, and showed no obvious trends although the activities in the no-burn treatments
121
seem to be slightly higher than the treatments with 3-yr and 9-yr burning cycles (Fig.
3.13). The activities in most cases were higher with increase in thinning although this
correlation was not significant (Table 3.18).
The trend in activities of β-glucosidase was different from that shown by the
ligninolytic enzymes activities (laccase and MnP). Tukey’s mean comparison of the
interactions between burning and thinning treatments suggests that the treatment with no
burning or thinning (control) was significantly higher than most of the other treatment
combinations (Fig. 3.13). Βeta-glucosaminidase, which plays an important role in both
soil C and N cycling varied significantly with burning, and showed similar trends to the
β-glucosidase activities (Fig. 3.14). The same trend was also observed for acid
phosphatase and arylsulfatase in the treatments with no burning (Fig. 3.14). However, for
the treatments with 3-yr and 9-yr burn cycles there is a marked increase in the activities
due to light thinning, for these two enzyme activities, although the arylsulfatase activities
with 9-yr burn cycle treatments is significantly higher than the no-burn treatments (Fig.
3.14). Acid phosphatase activity was not significantly affected by burning of thinning
(Table 3.17). Acid phosphatase and β-glucosaminidase activities were significantly and
positively correlated with β-glucosidase activities, while laccase and MnP activities were
significantly and positively correlated (Table 3.18).
The geometric mean of assayed enzyme activities (GMea) of all eight enzymes
assayed, at the 0-10 cm depth was calculated and used as an index for soil quality (Table
3.17). The GMea showed significant variation with thinning and burning, as well as the
combination of these treatments. With respect to thinning, GMea values had the
122
following trend; lightly thinned > heavily thinned > not thinned, with lightly thinned
being significantly higher than the others. With respect to the burning cycle, the GMea
trend was follows; 9yr-burn cycle > no-burn > 3yr-burn cycle. The GMea value for the
9yr burn cycle was significantly higher than that of the 3yr burn cycle. However neither
of them was significantly different than the no burn treatment.
123
Table 3.17. Analysis of variance for enzyme activities, with the different treatments at 0 10 cm, and depth (0 - 10 cm and 10 – 20 cm)
burn
thin
burn*thin
Depth
F
Pr >
F
F
Pr > F
F
Pr > F
F
Pr > F
Lac
0.777
0.475
5.865
0.011
0.370
0.827
32.887
< 0.0001
MnP
5.873
0.011
17.544 <
0.0001
1.768
0.179
27.279
< 0.0001
Xyl
1.521
0.245
0.413
0.668
0.698
0.603
256.425 < 0.0001
BGluc
14.054
0.001
9.349
0.002
3.476
0.029
2.016
0.162
BGlm
4.719
0.023
1.422
0.267
0.657
0.629
75.905
< 0.0001
AP
1.175
0.331
3.294
0.060
0.449
0.772
107.709 < 0.0001
AS
2.596
0.102
7.704
0.004
3.191
0.038
54.757
< 0.0001
Gmea
6.689
0.007
8.580
0.002
3.042
0.044
-
-
Values in bold are different from 0 at significance level of α = 0.05. Acid phosphatase;
AS = Aryl sulfatase; β-Gluc = β-glusosidase; MnP = Manganese peroxidase; Xyl =
Xylanase; Lac = Laccase; β-Glm = β-glucosaminidase.
124
no thinning
light thinning
heavy thinning
125
a
c
30
a
Laccase (ppm h -1)
a
a
a
a
A
75
a
50
A
a
A
a
A
A
A
A
Manganese peroxidase (ppm h-1)
abc
100
bc
25
abc
20
abc
B
15
AB
ab
10
a
A
AB a
25
AB
a
5
A
A
0
10-20
0-10
No Burn
0-10
10-20
3-yr Burn
0-10
10-20
0-10
No Burn
9-yr Burn
A
A
AA
10-20
0
0-10
10-20
0-10
3-yr Burn
10-20
9-yr Burn
0.006
125
a
a
xylanase (ppm h -1)
100
a
-Glucosidase (ppm h -1)
a
a
a
0.004
a
a
a
0.002
A
A
A
A
10-20
No Burn
ab
ab
A
50
0-10
10-20
3-yr Burn
A
A
a
AA
A
a
a
a
AA
a
a
25
A
A
0.000
0-10
75
A
A
A
A
b
0-10
0
10-20
0-10
10-20
No Burn
9-yr Burn
0-10
10-20
3-yr Burn
0-10
10-20
9-yr Burn
Figure 3.13. Enzymatic activities involved in C cycling.
Same letters implies means are not significantly different, different lettered bars implies
significant difference; lower case letters applies to 0 - 10 cm depth, while upper case
letters applies to 10 - 20 cm depth.
125
(a)
no thinning
light thinning
ab
75
heavy thinning
-Glucosaminidase (ppm h -1)
b
ab
a
50
a
a
a
a
a
A
A
25
A
A A
A
A A
A
0
0-10
10-20
0-10
No Burn
0-10
10-20
3-yr Burn
10-20
9-yr Burn
(b)
(c)
400
b
600
350
a
a
400
a
a
a
300
a
a
200
A
100
A
A
250
No Burn
0-10
10-20
3-yr Burn
a
150
a
A
10-20
0-10
10-20
No Burn
9-yr Burn
A
AA
50
A
0-10
a
A
0
10-20
a
a a
A
A
0
0-10
a
200
100
A
AA
ab
300
a
a
Arylsulfatase (ppm h -1)
Acid Phosphatase (ppm h -1)
500
0-10
A
10-20
3-yr Burn
A
A
A
0-10
10-20
9-yr Burn
Figure 3.14. Enzyme activities involved in (a) carbon and nitrogen cycling, (b)
phosphorus and (c) sulphur cycling
Same letters implies means are not significantly different, different lettered bars implies
significant difference; lower case letters applies to 0 - 10 cm depth, while upper case
letters applies to 10 - 20 cm depth.
126
Forest thinning has been shown to impact litter, organic matter and nutrient
content of surface soils and would directly influence microbial substrate utilization
(Cookson et al.,
2008b) and demonstrated to increase laccase activities (Giai and
Boerner, 2007). In this study, both thinning treatments exhibited greater laccase and MnP
activities than the no-thinned treatments. The activities of these enzymes in the thinned
treatments were significantly greater than those in the non-thinned treatments. The cooccurrence and correlation of these two enzymes signifies the presence of basidiomycetes
fungi that are the only known producers of MnP (Hofrichter, 2002; Valášková et al.,
2007; and Šnajdr et al., 2008). The strong correlation between laccase and MnP can thus
be ascribed in a greater part to the basidiomycetous fungi which are also able to produce
MnP. However, this study did not show significant correlation between these enzymes
and the fungi community. Lucas et al., (2007), in a study on soil microbial communities
and extracellular enzyme activity in New Jersey Pinelands, found that changes to the
microbial community did not necessarily have any effect on extracellular enzyme
activity. Laccase and MnP activities were highest in the 9yr-burn cycles, and least in the
no-burn cycle, although the effect was significant in the case of MnP (the 9yr-burn cycle
significantly higher than no burn). These enzymes are involved in the degradation of
lignin and other recalcitrant compounds, and their increase is consistent with the increase
in low quality soil organic matter attributable to the deposition of large amounts of
woody debris in thinning treatments. On the other hand, burning increased the utilization
of the relatively poor organic material as a result of direct combustion of the more labile
organic matter fractions. In this study the combination of burning and thinning had no
127
significant impact on the laccase and MnP activities. Xylanase, which is associated with
the hydrolysis of hemicellulose, also demonstrated greater enzymatic activities in the
plots with thinning treatments than the no-thinned treatments, although these differences
were not significant. Burning had no significant effect on xylanase, neither did the
combination of thinning and burning.
Βeta-glucosidase showed the opposite trend in response to thinning and burning,
compared to the ligninolytic enzymes. The activity was significantly affected by burning,
thinning, and a combination of both. The activity was significantly lower in the heavilythinned treatments than the lightly- and no-thinned treatments. It was significantly lower
for the 3yr-burn cycle treatments than the 9yr-burn cycle and no-burn treatments. Βetaglucosidase is involved in the hydrolysis of the β-1,4 glucosidic bonds in cellulose. It
recognizes the presence of cellulose and participates in degradation cellulose into
glucose. The significantly lower activity in the heavily-thinned treatments corresponds to
an increase in the deposition of woody materials with restricted access to cellulose, while
the significantly lower activities in the 3yr-burn cycle treatments corresponds to the
shorter recovery time for the microbial communities after prescribed burning. Βetaglucosaminidase activities showed similar trends as those of β-glucosidase although there
was no significant difference due to the amount of thinning. These two enzymes were
also found to be significantly correlated. Acid phosphatase and arylsulfatase showed
significant variations with respect to the level of thinning, with heavily-thinned
treatments being significantly lower. There were no significant differences in these
128
activities as a result of burning cycle, level of thinning, and combinations of thinning
levels and burning cycles.
With the results obtained in this study, we were able to use the soil geometric
mean enzyme activity (GMea) index to discriminate between burning and thinning
practices. The calculation of this index was based on soil enzymatic activities involved in
key degradation processes of importance to nutrient cycling in forest ecosystems. In this
study the GMea index for the treatments with light thinning had a significantly higher
value than the heavily-thinned and the no-thinned treatments (ANOVA), signifying
higher microbial metabolic functions. On the other hand, the significantly lower
metabolic function was observed for the 3yr-burn cycle which is also a shorter recovery
time for the soil microbial community after burning. With the combination of treatments,
the treatments with the 3-yr burn cycle and no thinning had the lowest metabolic
functions while the 9yr-burn cycle with lightly-thinned treatments had the highest
metabolic activity according to mean comparison by the GMea indices. The GMea
showed positive correlations with microbial communities, although this was only
significant with the gram negative bacteria and the actinomycetes.
129
Table 3.18. Correlation matrix showing correlation between soil properties, enzymatic
activities and microbial communities.
Variables
thin
%C
-0.418
%C
%N
%S
C/N
MC
pH
MBC
MBN
%N
-0.447
0.917
%S
-0.282
0.546
0.736
C/N
-0.363
0.392
0.187
0.110
MC
-0.246
0.417
0.459
0.222
-0.049
pH
0.580
-0.570
-0.462
-0.112
-0.376
-0.247
MBC
-0.300
0.269
0.140
-0.238
0.199
-0.113
-0.375
MBN
-0.289
0.207
0.071
-0.300
0.197
-0.133
-0.355
0.960
Lac
0.500
-0.350
-0.139
0.099
-0.564
0.033
0.477
-0.501
-0.479
MnP
0.687
-0.501
-0.424
-0.071
-0.356
-0.007
0.527
-0.684
-0.643
Xyl
0.178
0.223
0.197
0.099
-0.078
0.201
-0.290
0.090
0.062
β -Gluc
-0.484
0.659
0.678
0.510
0.250
0.607
-0.454
0.199
0.151
β -Glm
-0.282
0.220
0.316
0.205
0.133
0.281
-0.314
0.259
0.143
AP
-0.258
0.635
0.640
0.599
0.103
0.543
-0.324
-0.108
-0.189
AS
-0.131
0.023
0.215
0.296
-0.109
0.324
0.174
-0.337
-0.351
Gmea
0.147
0.099
0.327
0.525
-0.311
0.532
0.137
-0.471
-0.531
Gram+
0.244
0.126
0.175
0.397
0.160
0.293
0.131
-0.332
-0.308
Gram-
-0.156
0.048
0.054
0.332
0.364
-0.002
-0.157
-0.219
-0.222
Actinos
0.263
0.138
0.204
0.478
0.151
0.214
0.165
-0.322
-0.307
Fungi
0.257
0.115
0.172
0.349
0.149
0.192
0.196
-0.219
-0.195
Prz
-0.175
0.049
0.094
0.327
0.296
0.152
-0.098
-0.179
-0.190
Total FAME
0.193
0.118
0.169
0.408
0.211
0.198
0.123
-0.279
-0.260
burn
0.000
-0.274
-0.143
0.109
-0.064
0.072
0.053
-0.466
-0.442
Values in bold are different from 0 with a significance level alpha=0.05. Actinos = Actinomycetes; Gram+ = Gram
positive bacteria; Prz = Protozoa; Gram- = Gram negative bacteria; AP = Acid phosphatase; AS = Aryl sulfatase; βGluc = β-glusosidase; MnP = Manganese peroxidase; Xyl = Xylanase; Lac = Laccase; β-Glm = β-glutaminase.
130
Table 3.18. Cont’d. Correlation matrix showing correlation between soil properties,
enzymatic activities and microbial communities.
MnP
Xyl
β Gluc
β Glm
Variables
Lac
AP
AS
Gmea
Gram+
Gram-
Actinos
Fungi
MnP
0.577
Xyl
-0.048
0.080
β -Gluc
-0.363
-0.295
0.215
β -Glm
-0.159
-0.320
0.214
0.494
AP
-0.106
-0.073
0.315
0.515
0.076
AS
0.283
0.090
-0.176
0.050
-0.082
0.335
Gmea
0.540
0.494
0.287
0.339
0.211
0.535
0.585
Gram+
0.297
0.344
0.269
0.164
0.148
0.365
0.138
0.463
Gram-
-0.031
0.107
-0.071
0.125
-0.011
0.221
0.230
0.149
0.514
Actinos
0.312
0.296
0.297
0.142
0.132
0.322
0.148
0.438
0.968
0.552
Fungi
0.201
0.201
0.256
0.122
0.187
0.238
0.137
0.306
0.939
0.484
0.953
Prz
-0.026
0.100
-0.137
0.314
0.152
0.240
0.263
0.240
0.542
0.871
0.538
0.500
Total
FAME
0.208
0.246
0.220
0.149
0.150
0.298
0.172
0.357
0.961
0.656
0.973
0.972
burn
0.168
0.401
-0.208
-0.130
-0.044
-0.031
0.318
0.307
-0.117
0.083
-0.143
-0.253
Values in bold are different from 0 with a significance level alpha=0.05. Actinos = Actinomycetes; Gram+ = Gram
positive bacteria; Prz = Protozoa; Gram- = Gram negative bacteria; AP = Acid phosphatase; AS = Aryl sulfatase; βGluc = β-glusosidase; MnP = Manganese peroxidase; Xyl = Xylanase; Lac = Laccase; β-Glm = β-glutaminase
131
3.4
Additional soil properties
Selected soil chemical and physical properties were investigated in this study in
order to evaluate the effects of forest management practices on these properties, and their
influence on microbial diversities, structures and functions. Most soil properties were
generally higher at 0 – 10 cm compared to the 10 – 20 cm. At 0 - 10cm depth, the noburn and lightly-thinned treatments had the highest percent total C, N, and S, as well as
C: N ratio. The results indicated that thinning had a significant negative correlation with
total C (%C) and total N (%N), and a significantly positive correlation with pH, while
burning showed significantly negative correlations with MBC and MBN. Total C, N, and
S were all significant and positively correlated (Table 3.18). There were significant
positive correlations with β-glucosidase and acid phosphatase but negative correlations
with manganese peroxidase, while total S had significant and positive correlations with
Gram positive bacteria and actinomycetes. Soil pH was significantly positively correlated
with laccase and manganese peroxidase, and negatively with β-glucosidase as well as
total C and N. Vesterdal et al., (1995), found that the accumulation of nutrients in the
forest floor decreased with increasing thinning intensity. Litter-fall is temporarily
lowered in heavily thinned stands, and this reduces forest floor accumulation and
contributes to lower soil C stocks (Jandl et al., 2007) and may rely on the input of
thinning residues into soils to compensate for losses. Furthermore, forest fires usually
decrease the total site nutrient pool (the total amount of nutrients present) through some
combination of oxidation, volatilization, ash transport, leaching, and erosion (for
example, N, P, and S).The C/N ratios did not seem to be significantly affected by
132
thinning intensity to the same extent as the amounts of accumulated carbon and nitrogen.
A tendency towards more favorable conditions for mineralization, i.e. higher pH and
lower C/N ratio, was found in the heavily-thinned-only treatments. The effect of thinning
in a long-term perspective may depend on both frequency and intensity of the individual
thinning operations. The thinning operations which have been repeatedly performed in an
investigated plot may thus be expected to have created different conditions organic matter
mineralization over many years (Vesterdal et al., 1995).
133
Table 3.19. Analysis of variance for soil properties, with the different treatments and
depths.
burn
thin
burn*thin
Depth
F
Pr > F
F
Pr >
F
F
Pr > F
F
Pr > F
%C
4.982
0.019
3.639
0.047
1.028
0.420
90.685
<
0.0001
%N
4.040
0.036
3.986
0.037
0.494
0.741
138.884 <
0.0001
%S
0.657
0.530
1.343
0.286
0.829
0.524
48.141
<
0.0001
C/N
0.070
0.932
1.891
0.180
1.122
0.377
4.477
0.039
MC
7.182
0.005
10.369 0.001
3.333
0.033
3.782
0.057
pH
1.020
0.380
6.826
1.697
0.195
4.772
0.033
0.006
Values in bold are different from 0 at significance level of α = 0.05.
134
no thinning
light thinning
100
600
heavy thinning
500
B
a a a
a
a
80
a
a
a
400
MBN (mg/g soil)
MBC (mg/g soil)
a
a
a
300
a
a
a
AB
a
60
AB
a
AB
a
a
40
A
200
A
A
20
100
B AB ABC
C
BC
A
C
AB
A
AB
0
0-10
10-20
No Burn
0-10
10-20
3-yr Burn
0-10
A
A
0
0-10
10-20
9-yr Burn
10-20
No Burn
0-10
10-20
3-yr Burn
0-10
10-20
9-yr Burn
Figure 3.15. Microbial biomass C and N in various treatments.
Same letters implies the means are not significantly different, different lettered bars
implies significant relationships.
135
3.5
Evaluation of biodegradation of plant biomass
The loss in weight and change in component composition of wheat straw, corn
stalk and sawdust (red oak wood) after biological pretreatment with Pleurotus floridanus
and Perenniporia nanlingensis were evaluated. Both fungi caused the delignification of
all three biomass, with a more or less steady decrease in plant biomass and the decrease
of the lignin component of the biomass from day 0 to day 40 of pretreatment. Results
show that the weight loss in wheat straw biomass was similar for both P. floridanus and
P. nanlingensis (Fig. 3.16), while the weight loss in corn stalk was greater on day 20 to
40, when pretreated with P. floridanus than with P. nanlingensis (with the greatest
difference at day 40) (Fig. 3.17). However, there was little delignification of wood, with
relatively small weight loss in the pretreated wood dust with both fungi (Fig. 3.18). Both
white rot fungi caused higher percent loss in lignin (relative to initial amount) than other
component losses in both wheat straw and corn stalk (Table 3.20). The loss of lignin was
greatest on day-40 when wheat straw pretreated with P. floridanus with about 75%
decrease, and lowest on day-10 and when wood was pretreated with P. nanlingensis,
from initial amount (Table 3.20).
On day-40 of pretreatment of corn stalk with both fungi isolates, P. nanlingensis
degraded lignin better than P. floridanus, while P. floridanus degraded lignin better than
P. nanlingensis on day-40 of pretreatment of wheat straw and wood, with both fungi
isolates, as demonstrated by the percent loss of lignin in Table 3.20. As with lignin, both
white rot fungi also caused progressive loss in hemicellulose in corn and wheat straw
substrates. The results also show increase in measurable (available) cellulose until day-30
136
and a decrease on day 40, for pretreated wheat straw, for with both P. floridanus, and P.
nanlingensis. There was a decrease in cellulose content in the pretreated corn stalk from
day-20 to day-40 for both fungi, indicating higher cellulose loss during the biological
pretreatment compared to wheat straw. The suitable fungi for the pretreatment of plant
biomass would be one that degrades the most lignin, while consuming the least cellulose,
since the ultimate goal of the pretreatment is to make cellulose more available, from
which microbes can then obtain and ferment glucose to ethanol for example, in the case
of biofuel production. Therefore, in the pretreatment of wheat and wood, P. floridanus
would be suitable, while P. nanlingensis would be more suitable for pretreatment of corn.
137
Pleurotus floridanus
80
Perenniporia nanlingensis
75
Percent weight loss of wheat straw (%)
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
0
10
20
30
Pretreatment time (days)
Figure 3.16. Effects of pretreatment on wheat straw.
138
40
50
Pleurotus floridanus
80
Perenniporia nanlingensis
75
Percent weight loss of corn stalk (%)
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
0
10
20
30
Pretreatment time (days)
Figure 3.17. Effects of pretreatment on corn stalk.
139
40
50
80
Perenniporia nanlingensis
Pleurotus floridanus
Percent weight loss of plant (wood) biomass (%)
75
70
65
60
55
50
45
40
35
30
25
20
15
10
5
0
0
10
20
30
Pretreatment time (days)
Figure 3.18. Effects of pretreatment on wood (red oak) sawdust.
140
40
50
Table 3.20. Percent change in plant biomass composition with pretreatment time.
Perenniporia nanlingensis
Pleurotus floridanus
Corn
pretreatment time 10
(days)
9.80
Hemicellulose
20
30
40
10
20
30
40
15.78
27.70
54.61
23.60
24.98
26.10
31.76
Cellulose
-2.43
3.71
0.52
24.68
-4.87
4.52
12.74
46.74
Lignin
31.19
35.04
61.38
68.51
38.92
36.70
53.82
49.21
Wheat
pretreatment time 10
(days)
6.89
Hemicellulose
20
30
40
10
20
30
40
21.62
29.33
42.78
19.37
30.56
42.30
46.35
Cellulose
-3.35
-7.39
-12.46
6.71
-11.26
-15.98
-27.04
-7.79
Lignin
32.87
32.61
60.02
69.02
19.30
25.89
43.88
75.03
Wood
pretreatment time 10
(days)
10.76
Hemicellulose
20
30
40
10
20
30
40
5.33
13.15
4.66
4.52
9.02
3.05
3.64
Cellulose
1.87
3.89
0.51
7.18
1.95
-2.78
-5.47
-1.92
Lignin
1.39
5.90
11.44
12.41
3.51
7.65
13.94
23.09
Negative values indicate percent decrease, while positive values indicate percent
increase, compared to the initial quantity.
141
70
Hemicellulose
Percent composition of wheat straw (%)
60
Cellulose
Lignin
50
40
30
20
10
0
0
10
20
30
Pretreatment time (Days)
40
Figure 3.19. Percent composition of wheat straw pretreated with Pleurotus floridanus.
142
70
Hemicellulose
60
Cellulose
Percent composition of corn stalk (%)
Lignin
50
40
30
20
10
0
0
10
20
30
40
Pretreatment time (Days)
Figure 3.20. Percent composition of corn stalk pretreated with Pleurotus floridanus.
143
Percent composition of wood sawdust (%)
70
Hemicellulose
Cellulose
Lignin
60
50
40
30
20
10
0
0
10
20
30
Pretreatment time (Days)
40
Figure 3.21. Percent composition of wood sawdust pretreated with Pleurotus floridanus.
144
70
Hemicellulose
Cellulose
Lignin
Percent composition wheat straw (%)
60
50
40
30
20
10
0
0
10
20
30
Pretreatment time (Days)
40
Figure 3.22. Percent composition of wheat straw pretreated with Perenniporia
nanlingensis.
145
Percent composition of corn stalk (%)
70
60
Hemicellulose
Cellulose
Lignin
50
40
30
20
10
0
0
10
20
30
40
Pretreatment time (Days)
Figure 3.23. Percent composition of corn stalk pretreated with Perenniporia nanlingensis.
146
Percent composition of wood sawdust (%)
70
60
Hemicellulose
Cellulose
Lignin
50
40
30
20
10
0
10
20
30
Pretreatment time (Days)
40
Figure 3.24. Percent composition of wood sawdust pretreated with Perenniporia
nanlingensis.
147
Lignin removal is an important technical issue for paper manufacturing and a key
challenge for the conversion of lignocellulosic feedstocks into liquid transportation fuels
such as ethanol (Gutiérrez et al., 2012). To further evaluate pretreatments of corn, wheat
and woody plant material, we assessed the ligninolytic activities of fungi used in the
pretreatment. Enzyme production during incubation of four different fungi is presented in
Figs 3.25 - 3.27. Both fungi produced laccase and manganese peroxide (MnP); however,
LiP was not detected. Laccase and MnP are considered to be the most common
ligninolytic enzymes within white-rot fungi (Dinis et al., 2009). Manganese peroxidase
catalyzes the oxidation of (complexed) Mn2+to Mn3+, which in turn oxidizes lignin.
Laccase oxidizes phenolic units in lignin to phenoxy radicals, which is the same process
as that brought about by the chelated Mn(III) produced by MnP. (Akhtar et al., 1997) .
Laccase activity of P. floridanus was seen to be highest on day-10 for all
substrates. This was the same for P. nanlingensis, except when the substrate was wood, in
which case the laccase activity was highest on day-30. Although the laccase activity of P.
floridanus was higher than that of P. nanlingensis on day-10, P. nanlingensis showed
higher activity for the rest of the pretreatment times for wheat straw, and on days- 30 and
40, for wood (Figs 3.25 -3.27). P. floridanus demonstrated a faster growth rate when both
fungi were cultured on potato dextrose agar (PDA) as demonstrated by the diameter
measurements of the colonies (Fig. 3.28). However, the laccase activity of P.
nanlingensis was greater than that of P. floridanus for the entire pretreatment time for
corn stalk.
148
On the other hand, the peak activities of MnP were markedly lower than the peak
laccase activities. Their peak periods did not coincide with a peak in laccase, except in
the case of P. floridanus at day 10 of the pretreatment for corn. The peak MnP activities
were higher for P. floridanus than P. nanlingensis. The activities of MnP for P.
floridanus peaked on day 30 during pretreatment of wheat and wood, and at day 10 for
corn, while the activities of MnP for P. nanlingensis peaked on day 20 during
pretreatment of wheat and wood, and at day 10 for corn. Both fungi had highest activities
during the pretreatment of wheat. Double peaks of enzyme activities were observed with
the pretreatment of corn stalk, which is in line with previous studies on ligninolytic
enzymes (Arora and Gill, 2000). Reappearance of enzyme activity during later stages of
biomass degradation might be attributed to fungal autolysis resulting in the release of cell
membrane bound or intracellular enzymes in the medium (Arora et al., 2002).
149
(a)
0.5
Pleurotus floridanus
Perenniporia nanlingensis
Laccase (Uml -1)
0.4
0.3
0.2
0.1
0.0
0
10
20
30
40
50
Pretreatment time (days)
(b)
0.10
Pleurotus floridanus
Perenniporia nanlingensis
MnP activity (Uml -1)
0.08
0.06
0.04
0.02
0.00
0
10
20
30
40
50
Pretreatment time (days)
Figure 3.25. Laccase (a) and MnP (b) activities in pretreated wheat straw extract.
150
(a)
1.5
Pleurotus floridanus
Perenniporia nanlingensis
Laccase (Uml -1)
1.0
0.5
0.0
0
10
20
30
40
50
Pretreatment time (days)
(b)
0.015
MnP activity (Uml -1)
Pleurotus floridanus
Perenniporia nanlingensis
0.010
0.005
0.000
0
10
20
30
40
50
Pretreatment time (days)
Figure 3.26. Laccase (a) MnP (b) activities in pretreated corn stalk extract.
151
(a)
0.06
Pleurotus floridanus
Perenniporia nanlingensis
Laccase (Uml -1)
0.04
0.02
0.00
0
10
20
30
40
50
Pretreatment time (days)
(b)
0.008
Pleurotus floridanus
Perenniporia nanlingensis
MnP activity (Uml -1)
0.006
0.004
0.002
0.000
0
10
20
30
40
50
Pretreatment time (days)
Figure 3.27. Laccase (a) MnP (b) activities in pretreated wood (red oak) sawdust extract.
152
8
Pleurotus floridanus
Perenniporia nanlingensis
Colony diameter on PDA (cm)
6
4
2
6
D
ay
5
D
ay
4
ay
D
D
ay
3
0
Cultivation time (days)
Figure 3.28. Growth rate of Pleurotus floridanus and Perenniporia nanlingensis cultured
on potato dextrose agar (PDA).
153
3.6
Bioaccumulation of mercury in fungi tissue
Mercury (Hg) can be released to the atmosphere by natural (e.g. volcanic
eruptions, forest fires, evaporation from soils and water) and anthropogenic processes
(e.g. fossil fuel combustion, gold mining, ore roasting and processing). Most studies
regarding Hg accumulation in mushrooms have been conducted in polluted sites;
however, investigations in non-contaminated sites, in particular forest soils, are rare
(Rieder et al., 2011). According to Melgar et al., (2009), mercury contents of many
species of wild mushrooms could be an order of magnitude higher than that of typical
plant foods such as fruits, vegetables and grains. High accumulating ability in several
species has promoted their being screened as bio-accumulators. The fungi collected and
identified from the BNF are shown in Table 3.21. The 31 species identified belonged to
25 genera, 4 classes, 8 orders, 18 families, and 22 genera. All other species were found at
most of the four forest sites.
The bioaccumulation of mercury in fungal tissues ranged from 1.37 to 0.03 µg Hg
per gram of fungi tissue. The highest accumulation was seen with Metschnikowia sp.,
while the lowest was observed with Trametes versicolor. The concentration of Hg in the
different fruiting bodies differed significantly among the species of different genera. The
highest accumulation was seen with Metschnikowia sp, which happens to belong to the
phylum ascomycota, was significantly greater (p < 0.05) than all the other fungi. Three
other species also had relatively high levels of mercury, with the species Gerronema
strombodes, Boletus sp., and Amanita alboverrucosa. Lower Hg concentration in fruiting
bodies of wood decomposing mushrooms could be the result of the low level of Hg in the
154
wood and the limited amount of substrate compared to saprophytic species colonizing
soils (Rieder et al., 2011). The mechanism by which some heavy metals are accumulated
is somewhat obscure. There is speculation that mercury transport is likely to be affected
by sulfhydryl group content in a protein carrier in mushroom tissues (Kalac and Svoboda,
2000). The age of the fruiting bodies could also affect the level of Hg in the fungi tissues.
According to Kalac and Svoboda, (2000), metal levels in fruiting bodies of wild growing
mushrooms are considerably affected by the age of mycelium and by the interval between
the fructifications .
155
Table 3.21. Incidence and abundance of fungi collected from treatment sites.
Fungi
No Thin
No Burn
Light Thin Heavy Thin No Thin
3yr Burn
Light Thin Heavy Thin No Thin
Pholiota sp.
Amanita alboverrucosa
Boletus magnificus
Boletus sp.
Capronia munkii
Daedalea dickinsii
Fomitopsis cajanderi
Gerronema strombodes
Gloeophyllum sepiarium
Gloeophyllum trabeum
Macrolepiota sp.
Merulius incarnatus
Metschnikowia sp.
Mycorrhaphium adustum
Perenniporia nanlingensis
Phlebia radiata
Pleurotus floridanus
Pleurotus sp.
Russula cf. flavisiccans
Schizophyllum commune
Stereum hirsutum
Termitomyces sp.
Trametes cubensis
Trametes lactinea
Trametes pavonia
Trametes versicolor
Trametopsis cervina
Tremellaceae clone
Trichaptum biforme
Tylopilus sp.
Tyromyces chioneus
The different colors indicate the number of fungi fruiting bodies collected.
= 3
= 2
= 1
156
= 0
9yr Burn
Light Thin Heavy Thin
Bioaccumulation of Mercury (g g-1 fungal tissue)
Pholiota sp.
Merulius incarnatus
T rametes versicolor
Gloeophyllum sepiarium
Fomitopsis cajanderi
Gerronema strombodes
T yromyces chioneus
Metschnikowia sp.
Macrolepiota sp.
T rametopsis cervina
Pleurotus floridanus
T ylopilus sp.
T remella cladoniae
Russula cf. flavisiccans
Daedalea dickinsii
Amanita alboverrucosa
Boletus
Stereum hirsutum
Capronia munkii
Mycorrhaphium adustum
T richaptum biforme
Phlebia radiata
a
1.4
1.2
1.0
b
0.8
bc
c
0.6
0.4
d
de
ef
0.2
fg
h
gh
h
h gh
fgh
h
gh
gh
0.0
Fungi
Figure 3.29. Bioaccumulation of mercury in fungi.
157
gh
fgh
h gh
gh
CHAPTER 3
CONCLUSION
Ecological restoration strategies such as thinning and prescribed burning are
increasingly implemented to reverse undesirable changes in the Bankhead National
Forest (BNF). Thus, understanding the belowground flora responses to thinning and
burning is fundamental to better comprehend how the ecology of this forest ecosystem is
affected by these treatments. In this study, the impact of mechanical thinning and
prescribed burning on soil microbial community structures, community compositions,
species diversity, and microbial community functions, were investigated using esterlinked fatty acid methyl esters (EL-FAME), and pyrosequencing techniques as well as
enzymatic assays. The potential of white rot fungi in the pretreatment of plant biomass
(such as corn, wheat, and wood) was also assessed, as well as the bioaccumulation of
mercury in fungi fruiting bodies sampled from the BNF.
Five years after the application of restoration treatments, the effects of the
treatments has been mixed, with treatment-specific shifts in microbial communities. The
relative abundance of the population of the major microbial groups (Gram positive
bacteria, Gram negative bacteria, actinomycetes, fungi, and protozoa) of the treatments,
158
as depicted by fatty acid analysis, did not differ significantly as a result of prescribed
burning, but showed significant differences due to thinning treatments. Application of
prescribed burning without thinning resulted in an overall decrease in total microbial
population when compared to the control. However, light thinning with or without
prescribed burning resulted in an increase in total microbial population, which was
highest with 3yr-burn cycle. Analysis of the FAME profiles suggests that the more
frequent burning reduced the heterogeneity of the microbial communities, thereby an
overall increase in uniformity of the spatial distribution of the microbial communities.
The spatial distribution of the FAME profiles showed higher levels of heterogeneity in
the different levels of thinning compared to the no-thinned treatments. Analysis of
pyrosequencing data showed that Proteobacteria was the predominant phylum of the
bacterial population, while Basidiomycota and Ascomycota were the predominant fungi
phyla. Acidobacteria and Actinobacteria respectively accounted for 20 to 28.5% and 2.6
to 5.7% of abundance of bacteria phyla, and were significantly affected by thinning levels
in the management treatments. Actinobacteria was most significantly affected by the
level thinning (p < 0.01), and also demonstrated the highest loading factor in the PCA
analysis of the EL-FAME data, suggesting that Actinobacteria can be seen as a sensitive
indicator in response to forest thinning. The diversities of fungal species within the
treatments, as demonstrated by the Shannon index, were higher than those of the bacterial
species. However, the bacterial and fungal species diversities, of the different treatments
were all greater than that of the control, with the highest species diversities in the lightlythinned/9yr-burn cycle treatments. The structural and compositional similarities of
159
bacterial species between the different treatments was greater than that of the fungi
species, as depicted by the Chao-Jaccard-Raw abundance based similarity indices and
shared species observed and estimated.
Thinning was shown to have a positive effect on soil ligninolytic enzymatic
activities, which is vital in the breakdown of litter, as both lightly- and heavily-thinned
treatments exhibited greater ligninolytic enzymatic (laccase and MnP) activities than the
no-thinned treatments. The application of burning-only resulted in the lowest metabolic
functions, as seen with the no-thin/3yr-burn cycle treatments. On the other hand,
intermediate disturbance, as seen in the lightly-thinned 9yr-burn cycle treatments had the
highest metabolic activity according to mean comparisons of the geometric mean of
enzyme activities (GMea) indices.
The microbial communities under lightly-thinned treatments had higher
diversities relative to the no-thinned and heavily-thinned treatments with the same
burning cycle, further suggesting that in a community with moderate disturbances, new
individuals and groups could be introduced in a manner that promotes competitions and
diversities of the communities, thus establishing more stable communities. The lightlythinned 9yr burn treatments seem to best promote the establishment of a diverse and
stable microbial community as well as high metabolic capacity of the soil. However, the
microbial species diversity and metabolic capacity of lightly-thinned 3yr-burn treatments
were similar but slightly lower than the later. Considering that this study was conducted
five years into the restoration treatments, it may be premature to recommend the lightlythinned/9yr-burn treatments. However, when the other goals such as the prevention of
160
wild forest fires are considered, the 3yr burning/light thinning may just be the better
option.
The forest also serves as a reservoir for different fungi which are important in the
degradation of lignocellulose biomass, and can be used in the pretreatment of plant
biomass for biofuel production. The macro-fungi can also serve as indicators of toxic
metal pollution since many mushrooms accumulate high levels of heavy metals such as
cadmium, mercury, lead, copper, and arsenic that can have severe toxicological effects on
humans, even at very low levels. Furthermore, the bioaccumulation of mercury in
mushrooms is a public health concern, as they serve as food to some forest animals such
as deer and rodents. Fungi fruiting bodies collected from the study area were identified,
and evaluated for plant biomass degrading potentials as well as bioaccumulation of
mercury. The white rot fungi, Pleurotus floridanus and Perenniporia nanlingensis
collected from the BNF, were evaluated for their plant biomass degrading potentials of
woody (red oak wood sawdust) and non woody plant biomass (corn and wheat)
biodegradation with Pleurotus floridanus and Perenniporia nanlingensis, showed both
fungi caused the delignification of all three materials, with a more or less steady decrease
in plant biomass and decrease in the lignin component of the biomass from day 0 to day
40 of pretreatment. In the pretreatment of wheat and wood, P. floridanus would be a
suitable biodegrader, while P. nanlingensis would be suitable biodegrader for
pretreatment of corn. The bioaccumulation of mercury in the tissues of the fungi collected
ranged from 1.37 to 0.03 µg Hg per gram of fungi tissue. The highest accumulation was
seen with Metschnikowia sp., while the lowest was observed with Trametes versicolor.
161
The concentration of Hg in the different fruiting bodies differed significantly among the
species of different genera. Mercury bioaccumulation by Metschnikowia sp, which
happens to belong to the phylum ascomycota, was significantly greater (p < 0.05) than all
the other fungi. Gerronema strombodes, Boletus sp., and Amanita alboverrucosa also had
relatively high levels of mercury bioaccumulation, above the EPA screening value of 0.3
part per million (ppm).
162
REFERENCES
Achyuthan, K. E., Achyuthan, A. M., Adams, P. D., Dirk, S. M., Harper, J. C., Simmons,
B. A., and Singh, A. K. (2010). Supramolecular Self-Assembled Chaos:
Polyphenolic Lignin’s Barrier to Cost-Effective Lignocellulosic Biofuels, 8641–
8688.
Acosta-Martínez, V., Dowd, S. E., Bell, C. W., Lascano, R., Booker, J. D., Zobeck, T.
M., and Upchurch, D. R. (2010). Microbial community composition as Affected by
Dryland Cropping Systems and Tillage in a Semiarid Sandy Soil. Diversity, 2(6),
910–931.
Acosta-Martínez, V., Lascano, R., Calderón, F., Booker, J. D., Zobeck, T. M., and
Upchurch, D. R. (2011). Dryland cropping systems influence the microbial biomass
and enzyme activities in a semiarid sandy soil. Biology and Fertility of Soils, 47(6),
655–667.
Agee, J. K., and Skinner, C. N. (2005). Basic principles of forest fuel reduction
treatments. Forest Ecology and Management, 211(1-2), 83–96.
Akhtar, M., Blanchette, R. A., and Kirk, T. K. (n.d.). Fungal Delignification and
Biomechanical Pulping of wood. Advances in Biochemical Engineering and
Biotechnology, 57, 159–195.
Arora, D. S., Chander, M., and Gill, P. K. (2002). Involvement of lignin peroxidase,
manganese peroxidase and laccase in degradation and selective ligninolysis of wheat
straw. International Biodeterioration & Biodegradation, 50(2), 115–120.
Ayaz, F. A., Torun, H., Colak, A., Sesli, E., Millson, M., and Glew, R. H. (2011). Macroand Microelement Contents of Fruiting Bodies of Wild-Edible Mushrooms Growing
in the East Black Sea Region of Turkey. Food and Nutrition Sciences, 02(02), 53–
59.
Baldrian, P. (2009). Microbial enzyme-catalyzed processes in soils and their analysis.
Plant Soil Environ, 2009(9), 370–378.
163
Banning, N. C., Gleeson, D. B., Grigg, A. H., Grant, C. D., Andersen, G. L., Brodie, E.
L., and Murphy, D. V. (2011). Soil microbial community successional patterns
during forest ecosystem restoration. Applied and environmental microbiology,
77(17), 6158–64.
Beaulieu, K. M., Button, D. T., Eikenberry, B. C. S., Riva-Murray, K., Chasar, L. C.,
Bradley, P. M., and Burns, D. A. (2012). Mercury Bioaccumulation Studies in the
National Water-Quality Assessment Program — Biological Data From New York
and South Carolina , 2005 – 2009. U.S. Geological Survey Data Series 705, 13 p.
Benckiser, G., and Schnell, S. (2007). Biodiversity in Agricultural Production Systems.
(G. Benckiser & S. Schnell, Eds.) (p. 461). Boca Raton, FL: Taylor & Francis
Group, LLC.
Breure, A. M. (2004). I International Conference: Soil and Compost Eco-Biology
September 15th – 17th. In Soil Biodiversity : Measurements , Indicators , Threats
And Soil Functions (pp. 83–96). León - Spain.
Burns, R. G., and Wallenstein, M. D. (2011). Ecology of extracellular enzyme activities
and organic matter degradation in soil: a complex community-driven process. In R.
P. Dick (Ed.), Methods of Soil Enzymology (SSSA Book.). Madison, WI: Soil
Science Society of America, Inc.
Certini, G. (2005). Effects of fire on properties of forest soils: a review. Oecologia,
143(1), 1–10.
Colwell, R. K. (2009). Biodiversity: Concepts, Patterns, and Measurement. The Princeton
Guide to Ecology, 257–63.
Converse, S. J., Block, W. M., and White, G. C. (2006). Small mammal population and
habitat responses to forest thinning and prescribed fire. Forest Ecology and
Management, 228(1-3), 263–273.
Cookson, W. R., O’Donnell, a J., Grant, C. D., Grierson, P. F., and Murphy, D. V.
(2008a). Impact of ecosystem management on microbial community level
physiological profiles of postmining forest rehabilitation. Microbial ecology, 55(2),
321–32.
Cookson, W. R., O’Donnell, a J., Grant, C. D., Grierson, P. F., and Murphy, D. V.
(2008b). Impact of ecosystem management on microbial community level
physiological profiles of postmining forest rehabilitation. Microbial ecology, 55(2),
321–32.
164
Covington, W. W., and Sackett, S. S. (1986). Effect of Periodic Burning on Soil Nitrogen
Concentrations in Ponderosa Pine. Soil Sci. Soc. Am. J., 50, 452–457.
Das, N. (2005). Heavy metals biosorption by mushrooms. Natural Product Radiance,
4(6), 454–459.
De Souza-Cruz, P. B., Freer, J., Siika-Aho, M., and Ferraz, A. (2004). Extraction and
determination of enzymes produced by Ceriporiopsis subvermispora during
biopulping of Pinus taeda wood chips. Enzyme and Microbial Technology, 34(3-4),
228–234.
DeAngelis, K. M., Allgaier, M., Chavarria, Y., Fortney, J. L., Hugenholtz, P., Simmons,
B., Sublette, K.,Silver, W. L., Hazen, T. C. (2011). Characterization of trapped
lignin-degrading microbes in tropical forest soil. PloS one, 6(4), e19306.
Degens, B. P., and Harris, J. a. (1997). Development of a physiological approach to
measuring the catabolic diversity of soil microbial communities. Soil Biology and
Biochemistry, 29(9-10), 1309–1320.
Dinis, M. J., Bezerra, R. M. F., Nunes, F., Dias, A. a, Guedes, C. V, Ferreira, L. M. M.,
Cone, J. W.,Marques, G. S. M., Barros, Ana R. N., Rodrigues, M. a M. (2009).
Modification of wheat straw lignin by solid state fermentation with white-rot fungi.
Bioresource technology, 100(20), 4829–35.
Drenovsky, R. E., Elliott, G. N., Graham, K. J., and Scow, K. M. (2004). Comparison of
phospholipid fatty acid (PLFA) and total soil fatty acid methyl esters (TSFAME) for
characterizing soil microbial communities. Soil Biology and Biochemistry, 36(11),
1793–1800.
Drijber, R. A., Doran, J. W., and Parkhurst, A. M. (2000). Changes in soil microbial
community structure with tillage under long-term wheat-fallow management.
Eichorst, S. a, Breznak, J. a, and Schmidt, T. M. (2007). Isolation and characterization of
soil bacteria that define Terriglobus gen. nov., in the phylum Acidobacteria. Applied
and environmental microbiology, 73(8), 2708–17.
Eldor, P. A. (Ed.). (2007). Soil Microbiology, Ecology, and Biochemistry. (Third Edit.).
Academic Press is an imprint of Elsevier 30 Corporate Drive, Suite 400, Burlington,
MA 01803, USA Linacre House, Jordan Hill, Oxford OX2 8DP, UK: Academic
Press is an imprint of Elsevier.
Falandysz, J., and Chwir, A. (1997). The concentrations and bioconcentration factors of
mercury in mushrooms from the Mierzeja Wiślana sand-bar, Northern Poland. The
Science of the Total Environment, 203, 221–228.
165
Fierer, N. (2008). Microbial biogeography : patterns in microbial diversity across space
and time. In K. Zengler (Ed.), Accessing Uncultivated Microorganisms: From the
Environment to Organisms and Genomes and Back (pp. 95–115). Washington DC:
ASM Press.
Fischer, R. G., Rapsomanikis, S., Andreae, M. O., and Baldi, F. (1995). Bioaccumulation
of methylmercury and transformation of inorganic mercury by macrofungi.
Environmental science & technology, 29(4), 993–9.
Fogel, R., and Trappe, J. M. (1978). Fungus Consumption (mycophagy) by Small
Animals. Northwest Science, 52(1), 1–30.
Frostegård, Å., Tunlid, A., and Bååth, E. (2011). Use and misuse of PLFA measurements
in soils. Soil Biology and Biochemistry, 43(8), 1621–1625.
Gaines, G., and Creed, J. (2003). Final Environmental Impact Statement Forest Health
and Restoration Project (p. 369).
Gardi, C., Montanarella, L., Arrouays, D., Bispo, a., Lemanceau, P., Jolivet, C., …
Menta, C. (2009). Soil biodiversity monitoring in Europe: ongoing activities and
challenges. European Journal of Soil Science, 60(5), 807–819.
Gardner, T., Acosta-Martinez, V., Senwo, Z., and Dowd, S. E. (2011). Soil Rhizosphere
Microbial Communities and Enzyme Activities under Organic Farming in Alabama.
Diversity, 3(3), 308–328.
Garland, J. L. (1997). Analysis and interpretation of community-level physiological
profiles in microbial ecology. FEMS microbiology ecology, 24, 289–300.
Garland, J. L., and Mills, A. L. (1991). Classification and Characterization of
Heterotrophic Microbial Communities on the Basis of Patterns of Community-Level
Sole-Carbon-Source Utilization. Appl. Environ. Microbiol., 57(8), 2351–2359.
Giai, C., and Boerner, R. E. J. (2007). Effects of ecological restoration on microbial
activity , microbial functional diversity , and soil organic matter in mixed-oak
forests of southern Ohio , USA, 35, 281–290.
Giovannoni, S. J., TB, Britschgi, T. B., Moyer, C. L., and Field, K. G. (1990). genetic
diversity.pdf. Nature, 345(6270), 60–63.
Giri, B., Giang, P. H., Kumari, R., Prasad, R., and Varma, A. (2005). Microbial Diversity
in Soils. In F. Buscot & A. Varma (Eds.), Microorganisms in Soils: Roles in Genesis
and Functions (Vol. 3, p. 426). New York: Springer.
166
Girisha, G. K., Condron, L. M., Clinton, P. W., and Davis, M. R. (2003). Decomposition
and nutrient dynamics of green and freshly fallen radiata pine (Pinus radiata)
needles. Forest Ecology and Management, 179(1-3), 169–181.
Grady, K. C., and Hart, S. C. (2006). Influences of thinning, prescribed burning, and
wildfire on soil processes and properties in southwestern ponderosa pine forests: A
retrospective study. Forest Ecology and Management, 234(1-3), 123–135.
Graham, J. H., and Duda, J. J. (2011). The Humpbacked Species Richness-Curve: A
Contingent Rule for Community Ecology. International Journal of Ecology,
2011(Figure 1), 1–15.
Grayston, S. J., and Rennenberg, H. (2006). Assessing effects of forest management on
microbial community structure in a central European beech forest. Canadian
Journal of Forest Research, 2604, 2595–2604.
Guerrero, C., Mataix-Solera, J., Gómez, I., García-Orenes, F., and Jordán, M. M. (2005).
Microbial recolonization and chemical changes in a soil heated at different
temperatures. International Journal of Wildland Fire.
Gupta, R. S. (1998). Phylogenies and Signature Sequences : A Reappraisal of
Evolutionary Relationships among Archaebacteria , Eubacteria , and Eukaryotes.
Microbiology and Molecular Biology Reviews, 62(4), 1435–1491.
Gutiérrez, A., Rencoret, J., Cadena, E. M., Rico, A., Barth, D., Del Río, J. C., and
Martínez, A. T. (2012). Demonstration of laccase-based removal of lignin from
wood and non-wood plant feedstocks. Bioresource technology, 119, 114–22.
Hamman, S. T., Burke, I. C., and Knapp, E. E. (2008). Soil nutrients and microbial
activity after early and late season prescribed burns in a Sierra Nevada mixed
conifer forest. Forest Ecology and Management, 256(3), 367–374.
Hartmann, A., Smalla, K., and Sørensen, J. (2007). Microbial Diversity in the
Rhizosphere: Highly Resolving Molecular Methodology to Study Plant-Beneficial
Rhizosphere Bacteria. In G. Benckiser and S. Schnell (Eds.), Biodiversity in
Agricultural Production Systems (p. 460). New York: Taylor & Francis Group,
LLC.
Harvey, A. E., Jurgensen, M. F., Larsen, M. J., and Graham, R. (1987). Decaying organic
materials and soil quality in the Inland Northwest: a management opportunity. In
Gen. Tech. Rep. INT-225 (p. 15). Ogden, UT: U.S. Department of Agriculture,
Forest Service, Intermountain Research Station.
167
Hill, G. T., Mitkowski, N. A., Aldrich-Wolfe, L., Emele, L. R., Jurkonie, D. D., Ficke,
A., Maldonado-Ramirez, S., Lynch, S.T., Nelson, E. B. (2000). Methods for
assessing the composition and diversity of soil microbial communities. Applied Soil
Ecology, 15(1), 25–36.
Hill, T. C. J., Walsh, K. A., Harris, J. A., and Moffett, B. F. (2003). Using ecological
diversity measures with bacterial communities. FEMS microbiology ecology, 43(1),
1–11.
Hirsch, P. R., Mauchline, T. H., and Clark, I. M. (2010). Culture-independent molecular
techniques for soil microbial ecology. Soil Biology and Biochemistry, 42(6), 878–
887.
Hofrichter, M. (2002). Review : lignin conversion by manganese peroxidase ( MnP ),
Enzyme and Microbial Technology 30, 454–466.
Hugenholtz, P. (2002). Exploring prokaryotic diversity in the genomic era. Genome
Biology, 3(2), 1-8.
Jandl, R., Lindner, M., Vesterdal, L., Bauwens, B., Baritz, R., Hagedorn, F., Johnson, D.
W., Minkkinen, K., Byrne, K. a. (2007). How strongly can forest management
influence soil carbon sequestration? Geoderma, 137(3-4), 253–268.
Janssen, P. H. (2006). Identifying the Dominant Soil Bacterial Taxa in Libraries of 16S
rRNA and 16S rRNA Genes, Applied and Environmental Microbiology 72(3),
1719–1728.
Jiménez Esquilín, A. E., Stromberger, M. E., Massman, W. J., Frank, J. M., and
Shepperd, W. D. (2007). Microbial community structure and activity in a Colorado
Rocky Mountain forest soil scarred by slash pile burning. Soil Biology and
Biochemistry, 39(5), 1111–1120.
Kalac, P., and Svoboda, L. (2000). A review of trace element concentrations in edible
mushrooms. Food Chemistry, 69, 273–281.
Kelliher, F. ., Ross, D. ., Law, B. ., Baldocchi, D. ., and Rodda, N. . (2004). Limitations
to carbon mineralization in litter and mineral soil of young and old ponderosa pine
forests. Forest Ecology and Management, 191(1-3), 201–213.
Kirk, J. L., Beaudette, L. a, Hart, M., Moutoglis, P., Klironomos, J. N., Lee, H., and
Trevors, J. T. (2004). Methods of studying soil microbial diversity. Journal of
microbiological methods, 58(2), 169–88.
168
Klos, R. J., Wang, G. G., Bauerle, W. L., and Rieck, J. R. (2009). Drought impact on
forest growth and mortality in the southeast USA: an analysis using Forest Health
and Monitoring data. Ecological applications : a publication of the Ecological
Society of America, 19(3), 699–708.
Klose, S., Acosta-Martínez, V., and Ajwa, H. A. (2006). Microbial community
composition and enzyme activities in a sandy loam soil after fumigation with methyl
bromide or alternative biocides. Soil Biology and Biochemistry, 38(6), 1243–1254.
Kopecky, J., Kyselkova, M., Omelka, M., Cermak, L., Novotna, J., Grundmann, G. L., …
Sagova-Mareckova, M. (2011). Actinobacterial community dominated by a distinct
clade in acidic soil of a waterlogged deciduous forest. FEMS Microbiology Ecology,
78(2), 386–394.
Lay, J. O. (2002). MALDI-TOF mass spectrometry of bacteria. Mass spectrometry
reviews, 20(4), 172–94.
Leckie, S. E. (2005). Methods of microbial community profiling and their application to
forest soils. Forest Ecology and Management, 220(1-3), 88–106.
Lim, Y. W., Kim, B. K., Kim, C., Jung, H. S., Kim, B.-S., Lee, J.-H., and Chun, J.
(2010). Assessment of soil fungal communities using pyrosequencing. Journal of
microbiology (Seoul, Korea), 48(3), 284–9.
Liu, L., Li, Y., Li, S., Hu, N., He, Y., Pong, R., Lin, D.,Lu, L., Law, M. (2012).
Comparison of next-generation sequencing systems. Journal of biomedicine and
biotechnology, 2012, 251-364.
Liu, Q., Liao, X., Hartemink, A., and Carin, L. (2003). Identification of differentially
expressed proteins using MALDI-TOF mass spectra. The Thrity-Seventh Asilomar
Conference on Signals, Systems & Computers, 1323–1327.
Lucas, R. W., Casper, B. B., Jackson, J. K., and Balser, T. C. (2007). Soil microbial
communities and extracellular enzyme activity in the New Jersey Pinelands. Soil
Biology and Biochemistry, 39(10), 2508–2519.
Macrae, A. (2000). The use of 16s rDNA methods in soil microbial ecology. Brazilian
Journal of Microbiology, 31(2), 77–82.
Melgar, M. J., Alonso, J., and García, M. A. (2009). Mercury in edible mushrooms and
underlying soil: bioconcentration factors and toxicological risk. The Science of the
total environment, 407(20), 5328–34.
169
Moghaddas, E. E. Y., and Stephens, S. L. (2007). Thinning, burning, and thin-burn fuel
treatment effects on soil properties in a Sierra Nevada mixed-conifer forest. Forest
Ecology and Management, 250(3), 156–166.
Mummey, D. L., Clarke, J. T., Cole, C. A., O’Connor, B. G., Gannon, J. E., and Ramsey,
P. W. (2010). Spatial analysis reveals differences in soil microbial community
interactions between adjacent coniferous forest and clearcut ecosystems. Soil
Biology and Biochemistry, 42(7), 1138–1147.
Mummey, D. L., Stahl, P. D., and Buyer, J. S. (2002). Microbial biomarkers as an
indicator of ecosystem recovery following surface mine reclamation. Applied Soil
Ecology, 21, 251–259.
Nacke, H., Thürmer, A., Wollherr, A., Will, C., Hodac, L., Herold, N., Schöning, I.,
Schrumpf, M., Daniel, R. (2011b). Pyrosequencing-based assessment of bacterial
community structure along different management types in German forest and
grassland soils. PloS one, 6(2), e17000.
Nannipieri, P., Ascher, J., Ceccherini, M. T., Landi, L., Pietramellara, G., and Renella, G.
(2003). Microbial diversity and soil functions. European Journal of Soil Science,
54(December), 655–670.
Nardini, E., Kisand, V., and Lettieri, T. (2010). Microbial Biodiversity and Molecular
Approach (p. 49). Luxembourg: Office for Official Publications of the European
Union.
Niva, M., Hernesmaa, A., Haahtela, K., and Salkinoja-salonen, M. (2006).
Actinobacterial communities of boreal forest soil and lake water are rich in
mycobacteria. Boreal Environment Research, 11(February), 45–53.
Nobles, M. M., Dillon, W., and Mbila, M. (2009). Initial Response of Soil Nutrient Pools
to Prescribed Burning and Thinning in a Managed Forest Ecosystem of Northern
Alabama. Soil Science Society of America journal, 73(1), 285–292.
Olajuyigbe, S., Tobin, B., Saunders, M., and Nieuwenhuis, M. (2012). Forest thinning
and soil respiration in a Sitka spruce forest in Ireland. Agricultural and Forest
Meteorology, 157, 86–95.
Orgiazzi, A., Lumini, E., Nilsson, R. H., Girlanda, M., Vizzini, A., Bonfante, P., and
Bianciotto, V. (2012). Unravelling soil fungal communities from different
Mediterranean land-use backgrounds. PloS one, 7(4), e34847.
170
O’Brien, H. E., Parrent, J. L., Jackson, J. A., Moncalvo, J., and Vilgalys, R. (2005).
Fungal Community Analysis by Large-Scale Sequencing of Environmental Samples.
Applied and Environmental Microbiology, 71(9), 5544–5550.
O’Connell, A. M. (1997). Decomposition of Slash Residues in Thinned Regrowth
Eucalpt Forest in Western Australia. Journal of Applied Ecology, 34(1), 111–122.
Pal, K. K., Scholar, V., and Gardener, B. M. (2006). Biological Control of Plant
Pathogens. The Plant Health Instructor, 1–25. doi:10.1094/PHI-A-2006-111702.Biological
Pereira, R. M., Silveira, É. L. Da, Scaquitto, D. C., Pedrinho, E. A. N., Val-Moraes, S. P.,
Wickert, E., Carareto-Alves, Lúcia M., Lemos, E. G. D. M. (2006). Molecular
characterization of bacterial populations of different soils. Brazilian Journal of
Microbiology, 37(4), 439–447.
Prichard, S. J., Peterson, D. L., and Jacobson, K. (2010). Fuel treatments reduce the
severity of wildfire effects in dry mixed conifer forest, Washington, USA. Canadian
Journal of Forest Research, 40(8), 1615–1626.
Raison, R. J., Khanna, P. K., and Woods, P. V. (1985). Mechanisms of element transfer
to the atmosphere during vegetation fires. Canadian Journal of Forest Research,
15(1), 132–140.
Rappé, M. S., and Giovannoni, S. J. (2003). The uncultured microbial majority. Annual
Review of Microbiology, 57(1), 369–394.
Rastogi, G., and Sani, R. K. (2011a). Molecular Techniques to Assess Microbial
Community Structure, Function, and Dynamics in the Environment. In I. Ahmad, F.
Ahmad, and J. Pichtel (Eds.), Microbes and Microbial Technology (pp. 29–58). New
York, NY: Springer New York.
Rastogi, G., and Sani, R. K. (2011b). Molecular Techniques to Assess Microbial
Community Structure, Function, and Dynamics in the Environment. In I. Ahmad, F.
Ahmad, and J. Pichtel (Eds.), Microbes and Microbial Technology: Agricultural and
Environmental Applications (p. 516). New York, NY: Springer New York.
Renker, C., Alphei, J., and Buscot, F. (2003). Soil nematodes associated with the
mammal pathogenic fungal genus Malassezia (Basidiomycota: Ustilaginomycetes)
in Central European forests. Biology and fertility of soils, 37(1), 70–72.
Rieder, S. R., Brunner, I., Horvat, M., Jacobs, A., and Frey, B. (2011). Accumulation of
mercury and methylmercury by mushrooms and earthworms from forest soils.
Environmental pollution, 159(10), 2861–9.
171
Rietl, A. J., and Jackson, C. R. (2012). Effects of the ecological restoration practices of
prescribed burning and mechanical thinning on soil microbial enzyme activities and
leaf litter decomposition. Soil Biology and Biochemistry, 50, 47–57.
Ryan, P., Forbes, C., and Colleran, E. (2008, January). Investigation of the diversity of
homoacetogenic bacteria in mesophilic and thermophilic anaerobic sludges using the
formyltetrahydrofolate synthetase gene. Water Science and Technology : a Journal
of the International Association on Water Pollution Research.
Santos, S. R., and Ochman, H. (2004). Identification and phylogenetic sorting of bacterial
lineages with universally conserved genes and proteins. Environmental
Microbiology, 6(7), 754–759.
Scharenbroch, B. C., Nix, B., Jacobs, K. A., and Bowles, M. L. (2012). Two decades of
low-severity prescribed fire increases soil nutrient availability in a Midwestern,
USA oak (Quercus) forest. Geoderma, 183-184, 80–91.
Schloss, P. D., and Handelsman, J. (2004). Status of the Microbial Census. Microbiol.
Mol. Biol. Rev, 68(4), 686–691.
Schrumpf, M., Daniel, R. (2011a). Pyrosequencing-based assessment of bacterial
community structure along different management types in German forest and
grassland soils. PloS one, 6(2), e17000.
Schutter, M. E., and Dick, R. P. (2000). Comparison of Fatty Acid Methyl Ester (FAME)
Methods for Characterizing Microbial Communities. Methods, 1668(11590), 1659–
1668.
Sharpton, T. J., Riesenfeld, S. J., Kembel, S. W., Ladau, J., O’Dwyer, J. P., Green, J. L.,
Eisen, J. A., Pollard, K. S. (2011). PhylOTU: a high-throughput procedure quantifies
microbial community diversity and resolves novel taxa from metagenomic data.
PLoS computational biology, 7(1), e1001061.
Smith, N. R., Kishchuk, B. E., and Mohn, W. W. (2008). Effects of wildfire and harvest
disturbances on forest soil bacterial communities. Applied and environmental
microbiology, 74(1), 216–24.
Snajdr, J., Valášková, V., Merhautová, V., Herinková, J., Cajthaml, T., and Baldrian, P.
(2008). Spatial variability of enzyme activities and microbial biomass in the upper
layers of Quercus petraea forest soil. Soil Biology and Biochemistry, 40(9), 2068–
2075.
172
Sobachkin, R., Sobachkin, D., and Buzykin, A. (2005). The Influence of Stand Density
on Growth of Three Conifer Species. In D. Binkley and O. Menyailo (Eds.), (Vol.
55, pp. 247–255). Springer Netherlands.
Spain, A. M., Krumholz, L. R., and Elshahed, M. S. (2009). Abundance, composition,
diversity and novelty of soil Proteobacteria. The ISME journal, 3(8), 992–1000.
Steger, K., Jarvis, Å., Smårs, S., and Sundh, I. (2003). Comparison of signature lipid
methods to determine microbial community structure in compost. Journal of
Microbiological Methods, 55(2), 371–382.
Stensballe, A., and Jensen, O. N. (2001). Simplified sample preparation method for
protein identification by matrix-assisted laser desorption/ionization mass
spectrometry: in-gel digestion on the probe surface. Proteomics., 1(8), 955–966.
Sun, Y., Wu, J., Shao, Y., Zhou, L., Mai, B., Lin, Y., and Fu, S. (2011). Responses of soil
microbial communities to prescribed burning in two paired vegetation sites in
southern China. Ecological Research, 26(3), 669–677.
Switzer, J. M., Hope, G. D., Grayston, S. J., and Prescott, C. E. (2012). Changes in soil
chemical and biological properties after thinning and prescribed fire for ecosystem
restoration in a Rocky Mountain Douglas-fir forest. Forest Ecology and
Management, 275, 1–13.
Tabatabai, M. A. (1994). Soil enzymes. In Methods of Soil Analysis: Microbiological and
Biochemical Properties; Weaver, R.W., Angle, J.S., Botttomley, P.S., Eds.; SSSA
Book Series No. 5, Soil Science Society of America: Madison, WI, USA,; pp. 775–
833.
Tamaki, H., Wright, C. L., Li, X., Lin, Q., Hwang, C., Wang, S., Thimmapuram, J.,
Kamagata, Y., Liu, W.-T. (2011). Analysis of 16S rRNA amplicon sequencing
options on the Roche/454 next-generation titanium sequencing platform. PloS one,
6(9), e25263.
Tazisong, I. A., Senwo, Z. N., and Williams, M. I. (2012). Mercury speciation and effects
on soil microbial activities. Journal of Environmental Science and Health, Part A,
47(6), 854–862.
Thies, J. E. (2007). Molecular methods for studying soil ecology. In E. A. Paul (Ed.), Soil
Microbiology, Ecology, and Biochemistry, Third Edition., p. 534. Burlington, MA:
Elsevier Inc.
173
Thorn, R. G., and Lynch, M. D. J. (2007). Fungi and eukaryotic algae. In A. P. Eldor
(Ed.), Soil Microbiology, Ecology, and Biochemistry, Third Edition., p. 534.
Burlington, MA , USA: Elsevier Inc.
Tian, D. L., Peng, Y. Y., Yan, W. D., Fang, X., Kang, W. X., Wang, G. J., and Chen, X.
Y. (2010). Effects of Thinning and Litter Fall Removal on Fine Root Production and
Soil Organic Carbon Content in Masson Pine Plantations. Pedosphere, 20(4), 486–
493.
Valášková, V., Šnajdr, J., Bittner, B., Cajthaml, T., Merhautová, V., Hofrichter, M., and
Baldrian, P. (2007). Production of lignocellulose-degrading enzymes and
degradation of leaf litter by saprotrophic basidiomycetes isolated from a Quercus
petraea forest. Soil Biology and Biochemistry, 39(10), 2651–2660.
Van der Heijden, M. G. a, Bardgett, R. D., and Van Straalen, N. M. (2008). The unseen
majority: soil microbes as drivers of plant diversity and productivity in terrestrial
ecosystems. Ecology letters, 11(3), 296–310.
Vesterdal, L., Dalsgaard, M., Felby, C., Raulund-rasmussen, K., and Jgrgensen, B. B.
(1995). Effects of thinning and soil properties on accumulation of carbon , nitrogen
and phosphorus in the forest floor of Norway spruce stands, 77.
Vose, J. M. (2000). Perspectives on using prescribed fire to achieve desired ecosystem
conditions. In W. K. Moser and F. C. Moser (Eds.), Fire and Forest Ecology:
Innovative Silviculture and Vegetation Management (pp. 12–17). Tall Timbers Fire
Ecology Conference Proceedings, No. 21.Tall Timbers Research Station,
Tallahassee, FL.
Ward, N. L., Challacombe, J. F., Janssen, P. H., Henrissat, B., Coutinho, P. M., Wu, M.,
Xie, G., Haft, D. H., Sait, M., Badger, J., Barabote, R. D., Bradley, B., Brettin,
Thomas S., Brinkac, L. M., Bruce, D., Creasy, T., Daugherty, S. C., Davidsen, T.
M., DeBoy, R. T., Detter, J C., Dodson, R. J., Durkin, A. S., Ganapathy, A., GwinnGiglio, M., Han, C. S., Khouri, H., Kiss, H., Kothari, S. P., Madupu, R., Nelson, K.
E., Nelson, W. C., Paulsen, I., Penn, K., Ren, Q., Rosovitz, M. J., Selengut, J.D.,
Shrivastava, S., Sullivan, S. A., Tapia, R., Thompson, L. S., Watkins, K. L., Yang,
Q., Yu, C., Zafar, N., Zhou, L., Kuske, C. R. (2009). Three genomes from the
phylum Acidobacteria provide insight into the lifestyles of these microorganisms in
soils. Applied and environmental microbiology, 75(7), 2046–56.
Wubet, T., Christ, S., Schöning, I., Boch, S., Gawlich, M., Schnabel, B., Fischer, M.,
Buscot, F. (2012). Differences in Soil Fungal Communities between European
Beech (Fagus sylvatica L.) Dominated Forests Are Related to Soil and Understory
Vegetation. PloS one, 7(10), e47500.
174
Xiao, J., Luo, Y., Xie, S., and Xu, J. (2011). Serinicoccus profundi sp. nov., an
actinomycete isolated from deep-sea sediment, and emended description of the
genus Serinicoccus. International journal of systematic and evolutionary
microbiology, 61(Pt 1), 16–9.
Yoder, J. (2002). Prescribed Fire : Liability , Regulation , and Endogenous Risk. In
American Agricultural Economics Association Meetings (p. 23). Long beach, CA.
Youssef, N. H., and Elshahed, M. S. (2009). Diversity rankings among bacterial lineages
in soil. The ISME journal, 3(3), 305–13.
Zelles, L. (1999). Fatty acid patterns of phospholipids and lipopolysaccharides in the
characterisation of microbial communities in soil: a review. Biology and Fertility of
Soils, 29(2), 111–129.
175
APPENDIX
Table A.1.1. Enzyme activities in no-burn treatments.
Depth
No Burn
No thin
Enzyme activity
Laccase
MnP
Xylanase
β-Glucosidase
β-Glucosaminidase
Acid Phosphatase
Arylsulfatase
GMea
GMea
light thin
heavy thin
-1
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
32.69±5.7
4.78±2.39
7.43±1.04
0±0
0.004±0.001
0.001±0
101.38±15.1
8.68±2.14
56.8±6.1
15.4±1.63
397.88±50.1
124.89±4.6
148.54±11.1
54.32±9.14
16.15
-
ppm h
66.31±14.5
25.83±21.05
8.54±2.38
0±0
0.004±0.001
0±0
63.26±9.9
55.36±5.79
54.56±19.2
14.36±2.9
372.8±155.5
101.69±7.49
143.6± 10.7
33.67±11.76
15.55
-
Values represent mean enzyme activity± standard error.
176
71.22±19.6
23.08±11.55
16.77±1.62
4.37±2.46
0.005±0.001
0.001±0.001
41.12±2.9
47.21±9.31
42.53±6.5
20.46±6.38
311.23±41.7
107.29±9.84
124.29±16.4
36.99±4.95
16.32
-
Table A.1.2. Enzyme activities in 3yr-burn cycle treatments.
Depth
no thin
Enzyme activity
Laccase
MnP
Xylanase
β-Glucosidase
β-Glucosaminidase
Acid Phosphatase
Arylsulfatase
GMea
GMea
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
50.69±11.36
49.69±25.08
5.90±0.43
0±0
0.003±0.0002
0.001±0
28.93±8.2
39.92±15.51
28.58±5.3
12.832.05
265.17±14.2
71.85±4.37
152.31±14.1
37.92±9.39
11.51
-
3yr Burn
light thin
ppm h-1
73.28±7.24
27.53±2.19
17.85±4.68
3.73±3.73
0.004±0.001
0±0
40.72±1.4
42.4±16.82
37.95±2.4
9.6±2.42
389.69±24.2
84.10±6.68
201.49±90.8
45.24±8.95
17.37
-
Values represent mean enzyme activity± standard error.
177
heavy thin
81.48±9.94
40.341±19
19.76±6.88
7.44±3.94
0.004±0.0002
0.001±0
26.61±8.3
44.83±14.99
28.42±5.10
8.81±2.91
195.76±34.5
88.30±10.46
126.14±9.2
33.76± 7.10
13.37
-
Table A.1.3. Enzyme activities in 9yr-burn cycle treatments.
Depth
9yr Burn
light thin
No thin
Enzyme activity
Laccase
MnP
Xylanase
β-Glucosidase
β-Glucosaminidase
Acid Phosphatase
Arylsulfatase
GMea
GMea
heavy thin
-1
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
44.21±15.6
3.15±1.71
7.34±1.36
0±0
0.004±0.001
0.001±0.001
64.67±10.6
58.28±13.52
55.86±5.2
13.27±4.32
326.41±28.6
104.7±26.6
146.75±45.1
58.91±21.5
15.02
-
ppm h
91.93±21.4
23.91±12.76
23.09±1.58
8.73±0.37
0.003±0.0002
0.001±0
55.47±6.8
37.38±21.67
41.11±5.7
20.32±8.62
407.99±47.5
93.38±11.01
352.04±20.4
38.19±12.12
21.65
-
Values represent mean enzyme activity± standard error.
178
71.97±10.8
21.97±6.25
27.99±1.35
14.81±1.34
0.0037±0.0002
0.001±0
38.26±3.7
39.12±17.55
38.57±3.6
4.94±2.19
269.79±43.6
59.82±2.32
114.41±12.7
21.26±2.11
16.39
-
Table A.2.1. Abundance of soil microbial community in no-burn treatments.
Depth
Microbes
Gram+
GramActinomycetes
Fungi
Protozoa
Total
Total Bacteria(B)
Total Fungi (F)
F:B ratio
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
No Burn
No thin
light thin
heavy thin
68.44
50.80
106.83
60.25
19.80
12.60
182.50
119.35
6.19
1.45
383.76
244.44
195.07
123.65
182.50
119.35
0.94
0.97
µmols g-1
94.22
54.66
123.89
50.74
25.99
15.63
216.85
129.10
6.40
1.59
467.36
251.73
244.11
121.03
216.85
129.10
0.89
1.07
30.67
43.24
37.52
99.10
8.76
9.24
81.82
149.46
1.73
4.73
160.51
305.78
76.96
151.59
81.82
149.46
1.06
0.99
‘-‘ = below detectable limit; na = not applicable.
179
Table A.2.2. Abundance of soil microbial community in 3yr-burn cycle treatments.
Depth
3yr burn
No thin
Microbes
Gram+
GramActinomycetes
Fungi
Protozoa
Total
Total Bacteria(B)
Total Fungi (F)
F:B ratio
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
33.75
136.07
110.70
8.15
78.26
5.29
5.42
141.36
236.28
136.07
152.60
78.26
0.51
‘-‘ = below detectable limit; na = not applicable.
180
light thin
heavy thin
µmols g-1
113.92
64.46
119.89
74.81
28.70
17.09
232.91
108.99
6.36
3.62
501.77
268.97
262.51
156.36
232.91
108.99
0.89
0.70
83.97
9.87
107.48
7.50
27.38
231.94
17.60
5.08
455.86
34.97
218.84
17.37
231.94
17.60
1.06
1.01
Table A.2.3. Abundance of soil microbial community in 9yr-burn cycle treatments.
Depth
Microbes
Gram+
GramActinomycetes
Fungi
Protozoa
Total
Total Bacteria(B)
Total Fungi (F)
F:B ratio
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
9yr burn
No thin
light thin
heavy thin
9.88
59.63
58.31
59.88
15.39
112.09
3.97
2.02
72.15
249.02
68.19
134.90
112.09
0.83
µmols g-1
85.36
54.77
156.13
53.30
26.31
13.46
161.92
197.94
8.24
13.40
437.95
332.87
267.79
121.52
161.92
197.94
0.60
1.63
58.09
69.89
102.29
91.69
13.74
23.63
104.57
145.19
5.36
2.99
284.05
333.39
174.12
185.21
104.57
145.19
0.60
0.78
‘-‘ = below detectable limit; na = not applicable.
181
Table A.3.1. Abundance of FAME indicators in no-burn treatments.
Depth
FAMEs
15:0 i
15:0 a
17:0 i
17:0 a
17:0 cy
19:0 cyclo w8c
10-Me 17:0
10-Me 18:0
16:1 w5c
18:3 w6c (6,9,12)
18:1 w9c
20:4 w6,9,12,15c
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
No burn
No thin
light thin
heavy thin
34.55
19.86
13.96
11.73
10.15
10.49
9.78
8.73
14.63
9.77
92.20
50.48
6.40
2.34
13.41
10.25
19.95
11.60
30.00
132.56
107.76
6.19
1.45
µmols g-1
49.98
22.63
18.47
12.98
14.09
10.60
11.69
8.45
11.63
7.37
112.27
43.37
7.54
3.53
18.45
12.10
23.75
14.62
10.77
182.33
114.48
6.40
1.59
14.43
16.37
7.80
9.36
4.57
9.62
3.88
7.90
3.95
10.85
33.57
88.25
2.31
2.17
6.45
7.07
9.12
17.40
17.09
55.61
132.06
1.73
4.73
‘-‘ = below detectable limit; na = not applicable.
182
Table A.3.2. Abundance of FAME indicators in 3yr-burn treatments.
Depth
FAMEs
15:0 i
15:0 a
17:0 i
17:0 a
17:0 cy
19:0 cyclo w8c
10-Me 17:0
10-Me 18:0
16:1 w5c
18:3 w6c (6,9,12)
18:1 w9c
20:4 w6,9,12,15c
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
0-10
10-20
3yr burn
No thin
light thin
heavy thin
17.14
6.47
5.85
4.28
17.22
11.21
118.86
99.49
1.95
6.20
7.05
71.21
5.29
5.42
µmols g-1
51.66
33.94
23.78
12.63
22.42
10.08
16.06
7.81
14.97
7.01
104.92
67.80
8.41
5.16
20.29
11.93
34.75
13.72
198.16
95.28
6.36
3.62
36.38
4.45
22.04
3.12
14.14
1.52
11.40
0.78
12.18
0.72
95.30
6.78
6.04
21.34
26.02
3.41
34.45
171.47
14.19
5.08
-
‘-‘ = below detectable limit; na = not applicable
183
Table A.3.3. Abundance of FAME indicators in 9yr-burn treatments.
Depth
9yr burn
No thin
light thin
heavy thin
25.92
15.58
5.36
10.63
4.52
7.51
5.66
5.96
52.65
53.92
3.03
12.37
14.49
97.60
3.97
2.02
µmols g-1
39.57
23.27
20.69
12.57
13.97
10.65
11.13
8.28
18.30
8.89
137.83
44.42
6.99
3.39
19.32
10.06
27.31
15.13
134.61
182.81
8.24
13.40
26.80
29.37
12.26
15.93
11.75
13.53
7.28
11.05
10.94
11.74
91.34
79.95
3.83
5.03
9.91
18.60
16.46
17.74
88.11
127.45
5.36
2.99
FAMEs
15:0 i
15:0 a
17:0 i
17:0 a
17:0 cy
19:0 cyclo w8c
10-Me 17:0
10-Me 18:0
16:1 w5c
18:3 w6c (6,9,12)
18:1 w9c
20:4 w6,9,12,15c
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
0 - 10
10 - 20
‘-‘ = below detectable limit; na = not applicable.
184
Table A.4: Tukey's multiple comparison test of bacteria species richness and abundance
estimators between treatments.
Tukey's Multiple Comparison
T1 vs T5
T1 vs T4
T1 vs T3
T1 vs T7
T1 vs T6
T1 vs T2
T1 vs T9
T1 vs T8
T5 vs T4
T5 vs T3
T5 vs T7
T5 vs T6
T5 vs T2
T5 vs T9
T5 vs T8
T4 vs T3
T4 vs T7
T4 vs T6
T4 vs T2
T4 vs T9
T4 vs T8
T3 vs T7
T3 vs T6
T3 vs T2
T3 vs T9
T3 vs T8
T7 vs T6
T7 vs T2
T7 vs T9
T7 vs T8
T6 vs T2
T6 vs T8
T6 vs T8
T2 vs T9
T2 vs T8
T9 vs T8
Sobs
***
***
***
***
***
**
***
***
ns
**
*
ns
***
***
***
ns
***
**
***
***
***
***
***
ns
***
***
ns
***
ns
ns
***
**
ns
***
***
ns
ACE
***
***
**
***
***
ns
***
***
ns
ns
ns
ns
*
ns
ns
ns
ns
ns
ns
**
*
**
*
ns
***
**
ns
***
ns
ns
**
ns
ns
***
***
ns
Chao1
***
***
***
***
***
***
***
***
ns
***
**
ns
***
***
***
*
***
***
***
***
***
***
***
**
***
***
ns
***
**
ns
***
***
**
***
***
ns
Means are significantly different at P < 0.05(*), P < 0.01(**), P < 0.001(***). Ns = not significant.
185
Table A.5: Tukey's multiple comparison test of fungi species richness and abundance
estimators between treatments.
Tukey's Multiple Comparison Test
T1 vs T5
T1 vs T4
T1 vs T3
T1 vs T7
T1 vs T6
T1 vs T2
T1 vs T9
T1 vs T8
T5 vs T4
T5 vs T3
T5 vs T7
T5 vs T6
T5 vs T2
T5 vs T9
T5 vs T8
T4 vs T3
T4 vs T7
T4 vs T6
T4 vs T2
T4 vs T9
T4 vs T8
T3 vs T7
T3 vs T6
T3 vs T2
T3 vs T9
T3 vs T8
T7 vs T6
T7 vs T2
T7 vs T9
T7 vs T8
T6 vs T2
T6 vs T8
T6 vs T8
T2 vs T9
T2 vs T8
T9 vs T8
sobs
***
***
***
***
***
**
***
***
*
***
***
ns
***
***
***
*
***
***
***
***
***
***
***
**
***
***
ns
***
***
ns
***
***
ns
***
***
***
ACE
***
**
ns
***
***
ns
***
***
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
ns
**
ns
**
ns
ns
***
**
ns
**
ns
ns
*
ns
ns
***
**
ns
Chao1
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
***
ns
***
***
***
***
***
***
Means are significantly different at P < 0.05(*), P < 0.01(**), P < 0.001(***). Ns = not significant
186
Table A.6: Classification of Fungi collected from the BNF.
Fungi
Pholiota sp.
Amanita alboverrucosa
Boletus
Boletus magnificus
Boletus sp.
Capronia munkii
Daedalea dickinsii
Fomitopsis cajanderi
Gerronema strombodes
Gloeophyllum sepiarium
Gloeophyllum trabeum
Macrolepiota sp.
Merulius incarnatus
Metschnikowia sp.
Mycorrhaphium adustum
Perenniporia nanlingensis
Phlebia radiata
Pleurotus floridanus
Pleurotus sp.
Russula cf. flavisiccans
Schizophyllum commune
Stereum hirsutum
Termitomyces sp.
Trametes cubensis
Trametes lactinea
Trametes pavonia
Trametes versicolor
Trametopsis cervina
Tremella cladoniae
Trichaptum biforme
Tylopilus sp.
Tyromyces chioneus
Phylum
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Ascomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Ascomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Basidiomycota
Class
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Eurotiomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Saccharomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
Tremellomycetes
Agaricomycetes
Agaricomycetes
Agaricomycetes
187
Order
Agaricales
Agaricales
Boletales
Boletales
Boletales
Chaetothyriales
Polyporales
Polyporales
Agaricales
Gloeophyllales
Gloeophyllales
Agaricales
Agaricales
Saccharomycetales
Polyporales
Polyporales
Polyporales
Agaricales
Agaricales
Russulales
Agaricales
Russulales
Agaricales
Polyporales
Polyporales
Polyporales
Polyporales
Polyporales
Tremellales
Polyporales
Boletales
Polyporales
Family
Strophariaceae
Amanitaceae
Boletaceae
Boletaceae
Boletaceae
Herpotrichiellaceae
Fomitopsidaceae
Fomitopsidaceae
Hygrophoraceae
Gloeophyllaceae
Gloeophyllaceae
Agaricaceae
Cyphellaceae
Metschnikowiaceae
Meruliaceae
Polyporaceae
Meruliaceae
Pleurotaceae
Pleurotaceae
Russulaceae
Schizophyllaceae
Stereaceae
Lyophyllaceae
Polyporaceae
Polyporaceae
Polyporaceae
Polyporaceae
Polyporaceae
Tremellaceae
Polyporaceae
Boletaceae
Polyporaceae
VITA
Fritz Akuo Ntoko was born in the sea-side town of Limbe (Victoria) in
Cameroon. He spent a major portion of his academic career in Buea where he pursued a
bilingual (English and French) secondary education and obtained his secondary school
certificates (Brevet d’études du premier cycle - BEPC and GCE ordinary level). He
obtained his GCE advanced level certificate after completing high school in Limbe. He
later obtained his first degree in biochemistry and a second degree in microbiology from
the University of Buea – Cameroon. He later obtained a Master of Science Degree in Soil
Science in 2004 from Alabama Agricultural and Mechanical University. In 2008, he
pursued the Doctor of Philosophy Degree in Soil Science.
188