E.Smenderovac Thesis

Microbial Community Structure and Function in Coarse Woody Debris and Boreal Forest Soil after Intensified Biomass Harvests

Emily Elizabeth Smenderovac

A thesis submitted in conformity with the requirements for the degree of Masters of Science in Forestry Faculty of Forestry University of Toronto

Microbial Community Structure and Function in Coarse Woody Debris and Boreal Forest Soils under Intensified Biomass Harvests

Emily Smenderovac

Masters of Science in Forestry

Faculty of Forestry University of Toronto

2014 Abstract

Intensified biomass harvesting could prove to be negative for forest ecological health through the impacts this type of forest management could exert on microbial community structure and function in forest soils and in CWD pools. Microbial community functional characteristics as well as community structure (through T-RFLP and pyrotag sequencing of ssu rRNA) were assayed soils in a boreal jack pine forest exposed to a clearcut intensified harvesting gradient. Microbial communities within CWD of various decay stages were also assessed in order to determine habitat specificity of the decomposer communities within them. Soil microbial communities were altered by harvesting, but intensification did not cause further disturbance. Soils in harvested sites were different from fire sites also assayed, meaning that these disturbance types may have different impacts on microbial community structure and functioning. CWD communities within logs had different characteristics in different sites.

Intensification could reduce site specific organisms important in decay initiation.

Acknowledgments

I would like to acknowledge some associates with whom I have shared in the trials and tribulations of this Masters project. Without the excellent teachings of Michael Preston I don’t know if I would have been able to trust my own T-RFLP data. I must admit that the 80’s music thrumming through the lab was uplifting as well. The guidance of Caroline Sadlier through the intimidating process of learning QIIME turned the program equivalent of a ravenous lion into something closer to one of those inbred lions they use for photo opportunities in Florida. To my supervisor, Nathan Basiliko, you may think it was a huge inconvenience to get me to ship up and move to Sudbury when you switched Universities, but I’m glad you did, I have enjoyed my time here. Also, I appreciate how lucky I was to experience the rare opportunity of having both supervisors Nate Basiliko and John Caspersen visit the field with me. I enjoyed hearing about the professor perspective on academia and publishing while we were sampling CWD and soils; all while I was admiring their seemingly supernatural bug tolerance. I would also like to thank Genevieve Noyce for sampling assistance and for remembering where I was the entire time we were in the field. Also, many thanks to Phil Rudz who initially showed me how to identify decay stages. Without Kara Webster, Dave Morris, Rob Fleming, and Paul Hazlett at CFS, I would not have had the chance to work on the Island Lake sites. They are a beautiful experimental set up and I was very lucky to get a chance to work there. Also, I’d like to acknowledge Stephanie Wilson for completing my microbial biomass assays and teaching me how to conduct large scale enzyme assays and SIR at CFS. This research was funded by NSERC-CRD with industrial support from Tembec Inc., and community partners of the Northeast Superior Regional Chief’s Forum and the Northeast Superior Forest Communities.

iii

Table of Contents

Acknowledgments ...... iii

Table of Contents ...... iv

General Introduction ...... 1

Incentives for Intensifying Biomass Removal ...... 2

Forests as Global Carbon Stores ...... 2

Ecological Functions of Coarse Woody Debris ...... 3

Nutritional Contributions to Forest Soils ...... 4

Biodiversity Maintenance ...... 5

Potential Impacts of Harvesting Intensification ...... 6

Study Summary, Thesis Structure, and Co-Authorship Statement ...... 7

Research Objectives and Hypotheses ...... 8

Chapter 1 Impacts of Intensified Harvesting on Microbial Community Structure and Function in Boreal Forest Soils ...... 9

1.1 Introduction ...... 9

1.2 Methods ...... 11

1.2.1 Study Site ...... 11

1.2.2 Sampling ...... 12

1.2.3 Laboratory Methods ...... 12

1.2.4 Statistical Analysis ...... 16

1.3 Results ...... 17

1.3.1 Soil Chemistry and Microbial Biomass ...... 17

1.3.2 T-RFLP based Microbial Richness and Diversity ...... 18

1.3.3 Microbial Community Structure and Functioning ...... 18

1.3.4 Species associated with harvesting treatments ...... 19

1.4 Discussion ...... 20 iv

1.4.1 Harvesting impacts on biomass, moisture and microbial community function .... 21

1.4.2 Microbial community structure doesn’t respond noticeably to intensification of clearcut harvesting on short term time scales ...... 22

1.4.3 Clear cut harvesting as an analog for fire disturbance ...... 25

1.5 Conclusion ...... 26

Chapter 1 Figures ...... 27

Chapter 1 Tables ...... 39

Chapter 2 Coarse Woody Debris Microbial Community Dynamics in Boreal Hardwood and Temperate Mixedwood Forests ...... 42

2.1 Introduction ...... 42

2.2 Methods ...... 44

2.2.1 Study Sites ...... 44

2.2.2 Decay Classification ...... 45

2.2.3 Sampling ...... 46

2.2.4 Laboratory Techniques ...... 46

2.2.5 Statistical Analysis ...... 48

2.3 Results ...... 49

2.3.1 Microbial Richness and Diversity ...... 50

2.3.2 Community Structure ...... 50

2.3.3 Indicator Species ...... 51

2.3.4 Wood Chemistry and Linkages to Community Structure ...... 52

2.4 Discussion ...... 52

2.4.1 Microbial community change through the decay process ...... 52

2.4.2 Site specificity of microbial communities ...... 54

2.4.3 Consistency in the decay process between distinct forest types ...... 57

2.4.4 Interaction of wood and soils through decay stages ...... 58

2.5 Conclusion ...... 59 v

Chapter 2 Figures ...... 60

Chapter 2 Tables ...... 77

General Conclusion ...... 83

References ...... 86

Appendix 1: Extended Methods ...... 104

Laboratory methods ...... 104

Statistical Analysis ...... 107

Appendix 2: Chapter 1 Additional Figures ...... 109

Appendix 3: Chapter 2 Additional Figures ...... 120

vi 1

General Introduction

In a society where answers to most questions can be found literally in ones hand, and travelling across the world in a day is possible, the perception of our world has shifted to one decidedly smaller than generations past. It becomes easy to forget that these luxuries have an energetic cost: energy unfortunately derived from mostly non-renewable resources. In an environment of continual population growth these energy demands rise. The impacts wrought from global climate change and the anticipated future scarcity of oil resources with high environmental costs of extraction have engaged global attention, and encouraged exploration of alternative energy sources to meet these rising energy demands (Antoni et al., 2007; Berch et al., 2011; Dorian et al., 2006). Bioenergy is just one of the potential avenues for future exploitation. Agriculture and forestry are the main sources of the biomass for bioenergy production. The low frequency of harvesting disturbance and the lengthy growth cycle in forestry systems is considered by some to be more sustainable than conventional agricultural operations as a possible source of bioenergy production; agricultural operations are known to advance soil degradation processes and damage watersheds (Hoogwijk, 2003; Stonehouse & Bohl, 1990; Stupak et al., 2007; Tilman et al., 2002; Werhahn-Mees et al., 2011). Forestry practices are also less likely to exacerbate food shortage; many commercial forestry operations occur on lands unsuitable to agriculture (Dorian et al., 2006; Puddister et al., 2011; Stupak et al., 2011). Although there is a long history of bioenergy production in the forestry sector, bioenergy in North American forestry operations is rarely the primary product of interest. Energy is generated onsite from wood residues and black liquor that are otherwise unusable for sawmill and pulping operations (Hoogwijk, 2003). As tree boles are a valuable commodity for saw and pulpwood, it is more likely enhanced bioenergy production in the forestry sector will be supported by feedstocks derived from tree residues that are not typically removed (e.g. tree tops, small branches, stumps). Residues can be used to produce energy directly, through combustion, or can be converted to ethanol and other petroleum substitutes through various chemical and biological mechanisms (Hoogwijk, 2003; Janowiak & Webster, 2010).

Incentives for Intensifying Biomass Removal

The idea of using woody biomass for centralized energy production is not a novel concept. Chemical recovery boilers have been in operation since the 1930’s at pulp mills and various operations utilize bark or sawmill wastes for direct combustion or for the production of wood pellets which can then be converted to thermal energy off-site (Hoogwijk, 2003; Sabourin et al., 1992; Stupak et al., 2007; Tenenbaum, 2005). Also, the potential for wood biomass as an energy source is substantial: wood derived electrical energy in the US is predicted to rise to 495 billion KWH by 2025 (Energy Information Administration, 2007). In Canada, heating during winter is often partially or fully supported through non-centralized biomass combustion. For many households, wood biomass could be a greater constituent of the total energy budget. Currently, forestry supplies 5-6% of Canada’s energy supply, but this is expected to rise (Natural Resources Canada, 2014).

Harvesting solely for the purpose of producing biomass is not economically viable for most forest systems (Smeets & Faaij, 2007). However, the increasing energy demands and the goal of reduced reliance on fossil fuels create incentives for forest management to utilize materials they would otherwise leave on site. In short, there is now increased possibility of profit from intensified harvesting practices. This trend of intensified harvesting has begun in the boreal forests of Canada where some operations are moving to whole-tree from stem-only harvests (Myketa et al., 1998). The effects of these different harvesting methods have not been extensively investigated in the unique boreal ecosystem, and so the impacts upon ecosystem processes must be studied. Though Canadian operations have thus far been restricted to whole- tree harvests, in Europe, intensified biomass removal in softwood and mixed wood forests can be so extreme as to involve stump and root removal and removal of forest floor (i.e. organic rich surface litters and soil) (Lindroos, 2011).

Forests as Global Carbon Stores

The importance of forests to the global biosphere and atmosphere warrants careful study and understanding of the vital ecological processes that occur within them. Greenhouse gas derived global warming has brought attention to the value of forests as global long-term carbon stores and this has been reflected in the literature (Birdsey et al., 2006; Nunery & Keeton, 2010; Smeets & Faaij, 2007). In previous studies of high intensity harvesting systems, a gradient of C

3 sequestration ability has been seen: with lower carbon pools in the higher intensity systems (Nunery & Keeton, 2010). This range of carbon storage may be attributed to the amount of downed woody debris (DWD) allotted to each of these systems. DWD contains both large downed woody debris greater than 10 cm diameter (CWD) and smaller diameter fine woody debris (FWD). Since CWD is highly variable between different forests, the actual contribution to the carbon storage capabilities of a given forest can range from 3%-30% of the above ground carbon storage (Laiho & Prescott, 2004). Intensified harvesting practices can reduce the subsurface carbon storage capabilities of forest systems as well. A recent study by Clemmensen et al., (2013) showed that mycorrhizal fungi are responsible for a large portion of carbon storage in the mineral soils of boreal forests. In intensified systems, these mycorrhizal networks could be damaged by stump removal, or mycorrhizal survivorship might suffer with fewer DWD residues to provide refuges for these organisms during high-stress events such as drought or forest fire (Harmon et al., 2004; Kauserud et al., 2008; Lehto & Zwiazek, 2011). It is important to monitor these systems to understand the implications of intensified biomass removal in a variety of ecosystems. Despite the risks of losing a portion of ecosystem-level C stocks via soil organic matter and DWD, intensified biomass harvesting in forest systems might remain a desirable option for bioenergy production particularly when compared to agricultural options.

Ecological Functions of Coarse Woody Debris

The value of forests extends far past their carbon storage capabilities. Forests remain hotspots of biodiversity in an increasingly urbanized and agriculturally dominated world landscape. Canada is lucky in that we have the largest intact forest in the world: the northern boreal forest. Though this area has of yet remained largely undeveloped, mining industries have begun to encroach on this territory, and if future markets for wood residues increase, it may become plausible for logging operations to move into these areas. Currently however, logging operations are restricted to the southern portion of Canada (Natural Resources Canada, 2014b). Biodiversity has already been impacted in these areas by the introduction of roads, noise, and other types of disturbance introduced by logging operations. In order to decrease the extensiveness of these impacts, different tiers of biodiversity need to be characterized and observed in the face of intensified harvesting. CWD is known to be an important resource in forest systems, contributing habitat and nutrients to the forest floor. In fact, the importance of this resource is so great that accepted sustainable management systems attempt to ensure the

4 maintenance of these ecosystem functions by designating specific levels of woody residues to be left on site after harvest (Stupak et al., 2011; Vanderwel, 2010).

Nutritional Contributions to Forest Soils

Moving past “stem-only” harvesting introduces the issue of reduced nutrient pools from loss of nutrient rich litter inputs such as leaves and needles as well as FWD that has a proportionally large amount of nutrient rich bark. Loss of these important nutrient sources could have potential to reduce future forest regeneration capabilities (Thiffault et al., 2011; Werhahn- Mees et al., 2011). The nutritional contribution of CWD to the landscape consists mostly of carbon and calcium, but also minor amounts of nitrogen and phosphorus (Harmon et al., 2004; Laiho & Prescott, 2004; Spears & Holub, 2003). The flux of nitrogen and phosphorus in CWD is dynamic and greatly influenced by its resident biotic community. Nitrogen and phosphorus concentrations change as CWD decays, and this is likely due to a combination of leaching, fragmentation, and active transport by fungi (Harmon et al., 2004; Laiho & Prescott, 2004). Fungi have many unique adaptations allowing them to persist in environments that should be unsuitable to their survival. Rhizomorphs allow fungi to not only persist, but flourish in some of these environments. These thick masses of hyphae can transport water, and limiting nutrients from surrounding environments to the actively growing portion of the mycelial network, and similarly, mycelia can transport nutrients, albeit over smaller distances (Boddy & Watkinson, 1995; Lehto & Zwiazek, 2011; Marjanovic & Nehls, 2008). The transport of water and extraneous nutrients allow fungi to make CWD suitable as a growth medium and carbon substrate by other organisms. This transformation of woody material to a moist, more nutrient rich environment also allows bacteria to act on wood tissues and further release nutrients from wood cell components (Boddy & Watkinson, 1995; Fransson et al., 2004). Bacterial presence in wood tissues may also allow for another source of nitrogen to enter CWD: nitrogen fixed directly from the atmosphere. It is not known whether nitrogen input through bacterial fixation is a significant source of nitrogen for forests. This activity could possibly be a contributor of nitrogen, as nitrogen-fixing activity has been reported in CWD residues (Brunner & Kimmins, 2003).

Biodiversity Maintenance

Habitat functions of DWD include shelter, mating habitat and food resources. These functions are important to various plant and animal species, dispersed among the various stages of decay and are directly related to the degradation of DWD by microorganisms (Botting & DeLong, 2009; Langor et al., 2008; Maser et al., 1979; McCay & Komoroski, 2004; Mengak & Guynn, 2003). The removal of CWD specifically has been demonstrated to reduce diversity of organisms in some intensely managed forest systems in Europe (Berch et al., 2011). The various stages of decay represent a spectrum of characteristics: early decay features wood residues that are rigid, solid, low in nitrogen and phosphorus and only utilized by some insects and insectivores as food; later stages of decay are higher in moisture, have much more structural instability and contain hollow spaces along with elevated levels of phosphorus and nitrogen compared to early decay, increasing the suitability of the CWD material to support a greater range of ecosystem functions (Fransson et al., 2004; Harmon et al., 2004; Maser et al., 1979). Some of these habitats are amenable to mycorrhizal fungi (Harmon et al., 2004; Juutilainen et al., 2011; Rajala et al., 2011). Whether they are actively using nutrients for the wood, or just find a suitable place through which to form fruiting body structures, the storage of their hyphae in is important. Survival of ectomycorrhizae in CWD after disturbance events may assist in successful tree re-establishment (Kauserud et al., 2008; Lehto & Zwiazek, 2011). The dynamism exhibited by these characteristics, facilitated by microorganisms, changes the state of CWD to such an extent that the microbial community itself reflects the variation exhibited through the decay stages.

Recent technologies have allowed for the advancement of studies concerning microbial communities in the environment. Previous studies of fungi involved with the stepwise processes of wood decomposition (de-lignification by white rot and breakdown of celluloses and hemicelluloses by brown and soft rot fungi) focused mostly on fruiting body prevalence or culture methods, both of which are selective (i.e. do not likely characterize in situ communities accurately) and far from exhaustive (Allmér et al., 2006; Kernaghan & Patriquin, 2011; Nadkarni et al., 2002). Molecular surveys have brought forth an era where it is possible to reveal sequences of thousands of distinct species within a single sample. Most of these surveys have shown that this complexity is vastly underrepresented in previous culture techniques, and that the ecology of CWD is far more complex than previously recorded with known decomposers often

6 only appearing in early decay stages (Kubartová et al., 2012; Ottosson, 2013; Rajala et al., 2011). It is important to understand microbial community structure at each stage of decay within and between forest types that may serve keystone functions. From some of the few studies that have employed new techniques to characterize CWD microbial communities, it has been shown that decay stage was more important than proximity in predicting similarity between decomposer communities among logs (Kubartová et al., 2012). The connection between decay class and community structure introduce the possibility of including biodiversity indices into established landscape level CWD stock prediction models (eg. Vanderwel et al., 2008). If enough community consistency is observed, this may allow for the prediction of disturbance impacts on landscape-level microbial diversity.

Potential Impacts of Harvesting Intensification

Intensification of forest harvesting has been shown to affect microbial community composition. A study by Hartmann et al., (2012) featuring soils from full-tree and stumped harvesting systems established between 1992 and 2000 in conifer forests across British Columbia (Western Canada) detected significant differences between treated and untreated sites. However, the studies were completed a considerable amount of time after harvest, so the effectiveness of microbial community as an indicator of early disturbance effects has not been examined. Also, the inherent differences that could have existed between the plots due to temporal and spatial variation in sampling could have influenced differences witnessed in this study (Hartmann et al., 2012).

Intensified harvesting systems involve disturbances that are distinct from the impacts of more common stem-only biomass removal. Intensified harvesting processes likely lead to greater physical soil disturbance and altered soil properties due to leaching, weathering, compaction and erosion are the main consequences of this increased exposure (Brown, 1979; Fernández et al., 2004; Tan et al., 2005). The more drastic the disturbance, the more the ambient hydrological and nutrient dynamics are altered. Compaction can have a large influence on the hydrological characteristics of forest soils (Hartmann et al., 2012; Tan et al., 2005). Differences across increasing levels of mechanical disturbance have been observed, however, ultimately, the natural heterogeneity of study sites has made these observations difficult to accurately quantify (Hartmann et al., 2012). In general there is a lack of understanding of how intensified biomass

7 removal and related loss of DWD will impact both soil and wood microbial communities, which are drivers of essential ecosystem processes and decay and related ecological succession. This is particularly true in the context of the vast boreal forest of Ontario.

Study Summary, Thesis Structure, and Co-Authorship Statement

This study used a new (2012) harvest trial installation (hereafter “Island Lake”) that included replicated treatments of 4 levels of intensified biomass removal from a jack pine forest in Northern Ontario. This research trial provides a unique opportunity to examine short-term disturbance effects, which may have important consequences in regards to seedling survivorship and establishment of mycorrhizal associations. Inference of longer term impacts of biomass removal that are more likely linked to reduced slash at time of harvest required investigating CWD pools of various decay stage. Microbial communities within CWD and soil in an unharvested nearby old growth jack pine stand were characterized to link association of particular organisms decay stage, and to explore linkages between CWD and underlying soils. This may provide indications of whether important decay organisms might be lost by reducing future CWD pools through intensified harvesting practices. To address questions of whether or not CWD microbial communities are specific by forest type, CWD communities were also characterized in a contrasting sugar maple dominated Great Lake St. Lawrence forest in Central Ontario, and to address if communities are species specific, CWD communities were also characterized in a sugar maple grove of the Carolinian forest of Southern Ontario. Ultimately, this study will help discern the effect of different harvesting intensity and verify the utility of soil microbial communities as an indicator of environmental change. It also contributed fundamental understanding of the diversity and role of microbial communities in wood decomposition. The thesis comprises primarily 2 semi-stand-alone manuscript-style research chapters: the first addresses soil microbial disturbance associated with biomass harvesting while the second focuses on CWD microbial communities. These 2 chapters are framed with this general introduction and a comprehensive conclusion section that revisits interrelated aspects of the 2 chapters that are not covered in each alone as well as suggestions for future related work. Graduate supervisors and research colleagues (Nathan Basiliko, John Caspersen, Kara Webster, Paul Hazlett, and Dave Morris) played important roles in this research and will be co-authors on each chapter as they are submitted for consideration by scientific journals, however the research was carried out and written up primarily by me.

Research Objectives and Hypotheses

This thesis will 1) address the impacts of biomass removal intensities on soil microbial structure and functioning and 2) characterize the microbial communities within CWD across decay stage and forest type to allow predictions of how intensified harvesting will affect future levels of biodiversity and ecosystem functioning.

Project 1 will test the following hypotheses: Increased biomass removal, though reduced slash and increased physical soil disturbance, will reduce soil moisture and organic matter inputs and thus lead to lower microbial biomass; Microbial degradative capabilities will also be reduced and microbial community structure will change with harvesting intensity; the microbial community structure resulting from harvesting impacts is not comparable to natural fire disturbance. Project 2 will investigate the following hypotheses: Community structures will differ based on decay stage of wood with initial stages containing fewer species (likely white and brown rot fungi will dominate) that facilitate greater diversity in fungi and bacteria for later decay stages by increasing and transforming available nutrients; similarities exist between late decay communities from different environments; CWD of similar species should have more similar microbial community compositions than that of different residue types; Microbial community structure in different locations within CWD will be different; CWD of different decay status should have different compositions in soils beneath them due to biological and physical nutrient exchange with CWD above them.

Chapter 1 Impacts of Intensified Harvesting on Microbial Community Structure and Function in Boreal Forest Soils 1 1.1 Introduction

Global dependence on non-renewable petroleum derived energy and increasing scarcity of oil resources have driven the exploration of alternative energy sources as potential solutions for global energy needs (Berch et al., 2011; Dorian et al., 2006). The forestry sector in North America already represents one of the largest producers of centralized bio-energy and many companies are looking towards increasing production and marketing beyond internal utilization. The additional demand for wood biomass encourage intensified harvesting practices that could impact increase levels of soil disturbance at time of harvest and future stocks of woody debris in forest ecosystems. Most current silvicultural guidelines in Ontario prescribe stem-only harvesting, but nevertheless, these activities are already taking place in some areas of the boreal forests of Canada (e.g. favouring whole-tree v stem-only harvests) (Myketa et al., 1998). The differences in ecology, climate and geology could cause different responses to intensified biomass harvests in the unique boreal ecosystem compared to studies completed in Europe and Western Canada (Berch et al., 2011; Hartmann et al., 2012).

The advancement of biomass removal past stem-only harvesting can negatively influence biodiversity and soil nutrient pools. Litter such as leaves and small branches and large and downed coarse woody debris (forest floor woody debris >10cm in diameter) are widely acknowledged as an important component of forest ecosystems for habitat and nutritional contributions to the forest system (Boddy & Watkinson, 1995; Brunner & Kimmins, 2003; Fransson et al., 2004; Harmon et al., 2004; Lehto & Zwiazek, 2011; Tedersoo & Kõljalg, 2003; Thiffault et al., 2011; Werhahn-Mees et al., 2011). Depletion of downed fine and coarse woody debris (DWD), could reduce nitrogen, phosphorus, calcium and carbon inputs to soils. The ecosystem services and strong dependence of microbial organisms on their immediate soil environment makes microbial community analysis a powerful indicator tool to detect soil disturbances from harvesting activities. Studies have shown altered chemical characteristics in soils from intensified biomass harvesting practices that could have impacts on the microbial ecology within forest environments (Thiffault et al., 2011). In the literature, there is a lack of

10 consistency regarding the response of microbial communities to increased biomass removal past stem only harvest: techniques used and responses vary between papers. Many studies have reported differences in microbial community structure and function between harvested and unharvested sites, likely associated with the physical disturbances which accompany logging operations (Chatterjee et al., 2008; Hynes & Germida, 2012; Siira-Pietikäinen et al., 2001). Machinery in harvesting processes often cause compaction in soils, which can alter hydrology and nutrient dynamics (Busse & Beattie, 2006; Hartmann et al., 2012; Janowiak & Webster, 2010). Lower resolution technologies (e.g. RISA, PLFA) did not observe community changes from biomass removal past stem-only harvesting, excepting the most extreme scenario of complete organic matter removal (Hannam et al., 2006; Kataja-aho et al., 2011; Mariani et al., 2006). No response of microbial communities to biomass removal intensification in these studies could have been due to the low resolution of techniques applied. A study by Hartmann et al., (2012) using high-throughput sequencing saw distinct clustering of communities from forest soils exposed to different levels of organic material removal. Few studies have investigated the impacts of intensified biomass removals in short term scenarios. This time-frame is important as seedlings are typically planted quickly after harvests occur. Reductions of plant symbionts and microorganisms important to nutrient cycling functions in soils that could occur from intensified biomass removal would have the potential to inhibit seedling establishment (Kernaghan & Patriquin, 2011; Lehto & Zwiazek, 2011; Tedersoo et al., 2008). The detection of community responses to intensified biomass harvesting in higher resolution molecular techniques where more coarse techniques have failed, as well as the lack of work investigating the short term implications of these management decisions, suggest that the impacts of intensified biomass harvesting upon microbial diversity in forest soils requires additional study before generalizations can be applied.

Microbial communities in Ontario boreal forest soils need to be investigated in the short term time frames involved in planting after harvest. This timescale has not been extensively studied due to the age of existing study sites and the previous expense associated with the advanced molecular techniques typically used to study microbial communities. It is anticipated that the increased intensity harvesting will reduce soil moisture and microbial biomass through reduction of slash and increased physical disturbance, microbial degradative capabilities will change with harvesting intensity, microbial community structure will change with increased

11 harvesting intensity and microbial community structure in naturally disturbed sites will not reflect the changes which occur from harvesting disturbance due to the different amount and mechanism of soil organic losses in fire disturbance compared to harvesting disturbance.

1.2 Methods

1.2.1 Study Site

The Island Lake site was located near Chapleau, Ontario in the Ontario Shield Ecozone (Lake Abitibi boreal Ecoregion) of Ontario. Prior to harvesting in 2011 the site consisted of ca. 50 year old jack pine (Pinus banksiana) monoculture planted in the early to mid 1960s. The landscape was relatively flat with minimal slope and so was relatively uniformly distributed across the landscape prior to harvesting. Soils were generally sandy and acidic belonging to the Brunisolic order. There were four applied treatments: Bladed, which consisted of removal of all biomass and LFH layer from the plot; Stumped, where the full tree and stumps were removed from the plot; Full tree, where the entirety of each tree upwards from the stump was removed from the plot; and, tree length where only the bole of each tree was removed and the stumps and upper branches were left on site after harvest. Five replicates of each biomass harvesting intensity were performed on randomly distributed plots within the treatment area and five control plots were placed within an unharvested area bordering the treatment plots. Each plot was 70 m2 and one half of each treatment plot was planted with jack pine and the other black spruce (Picea mariana) and each sub-plot split again and one half treated with glyphosate to suppress broadleaf plant growth. Chapleau receives an annual average of 796mm of precipitation a year and has an average yearly temperature range of -16 to 17°C. A nearby old-growth stand predominately of jack pine that has not burned in ca. 80 y based on ages of most mature trees was also sampled and this was the site where was sampled (local name “Nimitz”). Although the old growth controls were not included in the randomized replicated experimental design, the five replicate sampling locations were spaced up to 1km apart, and thus might still be suitably comparable to the main treatment and control plots to explore harvesting impacts (as opposed to other between- site differences). A nearby fire site consisting of a 35 year old Jack Pine stand that experienced a burn in 2012 was added to the study in 2013. The 5 sampling plots in the fire site were established approximately 1km apart.

1.2.2 Sampling

Soils were sampled in Control, Old Growth, Tree-Length, Full-Tree, Stumped and Bladed sites June 25th-29th, 2012. During June 17th-21st, 2013 sampling was repeated for all Control, Tree- length, Full-tree and Stumped treatments as well as two Bladed treatments and all plots of the fire site. 10 cm mineral Soil cores and LFH grab samples were taken from six points along two cross-sections of each plot (for each herbicide and non-herbicide treatment) on the flats within the site (the areas with minimal physical disturbance). In fire sites, 6 samples were taken along the approximate mid-point of each site. These samples were then pooled, mixed and stored at 4°C at the earliest convenience. Samples were processed in the lab by sieving through a 2 mm sieve to homogenize the samples and remove any large debris. A small portion of soil was frozen without sieving for molecular tests.

1.2.3 Laboratory Methods

1.2.3.1 Substrate Induced Respiration

Substrate induced respiration was performed using the MicroRESP™ system as referenced in Bérard et al. (2014). Sieved soil samples were used to fill 96 well deepwell plates. Deepwell plates were incubated at room temperature for 3 days for LFH samples and 5 days for mineral soils. Substrates were prepared so that applied solutions would result in concentrations of approximately 30 mg per gram soil water, given application of a 25 µL aliquot. Clear 96 well plates were prepared with agar containing Cresol Red, KCl and NaHCO3. The indicator in the agar underwent a detectable colorimetric change upon acidification when exposed to CO2. Indicator plates were secured over the deepwell plates and incubated at room temperature for 6 hours after application of substrate solutions. Substrates utilized were alanine, glucose, arabinose, butyrate, arginine, ketose, citrate, oxalate, cysteine, maltose, fructose, and trehalose. Plate absorbance was read with a spectrophotometer microplate reader at 570 nm.

1.2.3.2 Enzyme Assays

1.2.3.2.1 Hydrolase Assay

Hydrolase assays were completed using a method based on that of Hassett & Zak, (2005). Stock 200 µM solutions were made for MUB β-D-glucopyranoside, MUB β-D-cellobioside, MUB-phosphate, MUB N-acetyl- β-D-glucosaminide and MUB standard. The MUB-standard

13 was diluted to 10 µM. 5 g of sieved soil were measured and blended with 62.5 mL of 0.05 M sodium acetate buffer for 1 minute. The sample was agitated in a petri plate and 200 µL were aliquotted into a black 96 well microplate. Substrates were loaded into the microplate. Plates were incubated in the dark at room temperature for 90 minutes. Fluorescence was measured at an excitation of 360 nm and an emission wavelength of 450 nm.

1.2.3.2.2 Lignase Assay

Lignase activity was determined in a manner similar to Hassett & Zak, (2005). Fresh 0.025 M L-DOPA solution was used as a substrate for lignase activity measurement. 62.5 mL of 0.05 M sodium acetate buffer was blended with 0.5 g of sieved soil. The sample was agitated in a petri plate and 200 µL were aliquotted into a clear 96 well microplate. Substrates were loaded into the microplate in 50 µL volumes. Plates were incubated in the dark at room temperature for 90 minutes. Absorbance was measured in a spectrophotometer microplate reader at 450 nm.

1.2.3.3 Chloroform Fumigation

Sieved sampled were used to analyze microbial biomass generally following Basiliko et al., (2009). Soil moisture was measured by subtraction of dry weight from wet weight. Samples were weighed to 5 g dry weight for LFH and 20 g dry weight for mineral soil. 0.5 M K2SO4 solution was used as a blank. Two sets of samples for each soil sample were made: one set of samples was fumigated with chloroform and the other set was left untreated. The chloroform fumigated samples were placed in a desiccator with a 100 mL beaker containing 50 ml chloroform and boiling chips. A vacuum was applied to the desiccator until the chloroform had boiled for 2 minutes. The samples were then left to fumigate for 24 hours with a garbage bag over the desiccator. Volumes of 50 mL of 0.5M K2SO4 were added to the unfumigated samples and blanks and incubated at room temperature with shaking for 1 hour. After chloroform fumigation, the amount of chloroform remaining in the beaker was measured and samples were evacuated under vacuum for 2 hours. Mineral samples were gravity filtered with 09-801E filters. LFH samples were vacuum filtered and then fine filtered. Samples were stored at 4°C until processing.

1.2.3.4 Soil Chemistry

Sieved samples were sent to CFS analytical laboratories for total carbon analysis, total nitrogen analysis and extractable ions (P, K, Ca, Mg, Fe, Cu, Mn, Zn, Al, Na, and S) completed by Johanna Curry (Soil And Plant Analytical Lab at the Great Lakes Forestry Centre in Sault Ste. Marie, Ontario). Total carbon and nitrogen were determined using a NCS combustion analyzer (Model Vario EL III, Elementar Americas Inc., Mt. Laurel, NJ, USA). Exchangeable cations were determined in unbuffered 1 M NH4Cl solution (Kalra & Maynard, 1991) obtained from a mechanical vacuum extractor. Solution concentrations were determined with a Varian Vista simultaneous axial inductively coupled argon plasma (ICAP) emission spectrometer. Total elements in LFH horizons were extracted through an HNO3/H2O2 microwave digestion and then determined with a Varian Vista simultaneous axial inductively coupled argon plasma (ICAP) emission spectrometer (Kalra & Maynard, 1991). Carbon functional groups within the organic and mineral layer were determined using the Diffuse Reflectance Infrared Fourier Transformation (FTIR) method on a Varian Scimitar FTS 800 mid-IR FTIR spectrometer with a

60o PermaLine interferometer and ambient DTGS detector at Ontario Forest Research Institute (OFRI) by Ravi Kanapayor. Samples were mixed with IR grade KBr (1:6 ratio- S:KBr) and ground (by hand) to a fine homogenous mixture. This was loaded into the 10mm (diameter) x 2.3 mm (depth) EasiDiff cup. The cup was placed on the Diffuse Reflectance Accessory used for direct analysis of solid samples (Vergnoux et al., 2011).

1.2.3.5 DNA Extraction

DNA was extracted from soil samples using the PowerSoil® DNA Isolation kit (MO BIO Laboratories Inc., Carlsbad, CA). A 5 minute homogenization step was performed using a 16 tube MiniBeadbeater TM (Biospec Products Inc., Bartlesville, OK) as opposed to the MO BIO vortex adapter described in the protocol. Samples were frozen and stored for analysis at a later date.

1.2.3.6 Molecular Analysis

1.2.3.6.1 PCR

PCR reactions were performed in an MWG AG Biotech Primus 96+ Thermocycler. Previously determined optimized primer combinations and cycles from Preston, Smemo,

McLaughlin, & Basiliko, (2012) were utilized for the amplification of bacterial 16S ribosomal DNA, fungal 18S ribosomal DNA and archaeal 16S ribosomal DNA sequences.

1.2.3.6.2 T-RFLP

T-RFLP analysis of bacterial, archaeal and fungal ribosomal gene segments was performed as described by Preston et al., (2012), with a 5’ end 6-carboxyfluorescein (6-FAM) modification on the Eu27f, Ar912r and Fu1536r primers. T-RFLP analysis was completed at the Agriculture and Food Laboratory in Guelph (University of Guelph Agriculture and Food Laboratory, Guelph, ON). T-RFLP data was preprocessed in R using a custom function created using the algorithm described by Ishii, Kadota, & Senoo, (2009) with slight modifications to accommodate the data format supplied by Guelph. The cutoff distance was set to 2bp and the final output was expressed as proportion total peak height per TRF by sample (Kaplan & Kitts, 2003).

1.2.3.6.3 Pyrotag sequencing

Pyrotag sequencing on the Roche 454 platform was completed on samples collected in 2013. All replicates of a given harvesting regime, herbicide use and type of soil were pooled and sent to Mr.DNA (Shallowater, TX, U.S.A.) where pyrotag sequencing was completed as per Dowd et al. (2008). Bacterial 16S rDNA was amplified with 16S universal Eubacterial forward primer (AGRGTTTGATCMTGGCTCAG) was used to amplify fragments from the bacterial 16S rRNA gene and 18S universal Fungal forward primer (TTAGCATGGAATAATRRAATAG) was used to amplify fragments from the Fungal 18S rRNA gene.

454 data was analyzed in the quantitative insights into microbial ecology (QIIME) pipeline (Caporaso et al., 2010). 454 data were quality filtered using QIIME default parameters (quality score = 25, min length=200, max length = 1000). Additional quality filtering and OTU clustering was performed with the Usearch 5.2.236 program, which utilizes the UCHIME algorithm to identify chimera sequences for removal against the gold.fa dataset (Edgar, 2010; Edgar et al., 2011). De novo OTU picking with uclust was used to form the representative OTU dataset (Edgar, 2010). Bacterial taxonomy was assigned using the RDP classification algorithm against the Greengenes 13_5 database 97% confidence rep set (McDonald et al., 2011; Wang et al., 2007). Muscle was used to produce a denovo alignment of all OTU and make phylogenetic

16 trees (Edgar, 2004). Fungal taxonomy was assigned using the RDP classification algorithm against the 97% Silva database for the eukaryotic 18S ribosomal DNA gene (Quast et al., 2013; Wang et al., 2007). Muscle was used to produce a denovo alignment of all OTU and make phylogenetic trees (Edgar, 2004).

Final OTUs represented potential species based on unique rRNA sequences that were discriminated at a level of 97% similarity.

1.2.3.6.4 Gene Identification

Indicator species sequences collected from pyrotag sequencing data were entered into the BLAST genome database and correlated to the closest matching documented sequence (Madden et al., 1996). Closest match for species identification of OTUs was determined using NCBI Blast using the Blastn algorithm with an expect threshold of 10, word size 11, match/mismatch Scores 2,-3, Gapcosts: Existence 5, Extension 2.

1.2.4 Statistical Analysis

1.2.4.1 T-RFLP-based community analysis

Differences between T-RFLP microbial communities in residues were compared using DCA with the vegan package provided by R statistical software. OTU peaks were analyzed in the context of abundance as well as presence/absence in samples (Core Team, 2013; Oksanen et al., 2013). An adonis test using an eigenvalue method was performed to determine whether there were significant differences between groupings (Oksanen et al., 2013). The variance explained by each factor was used to determine which subsets of data should be further investigated. Relationships between community composition and sample physical and chemical characteristics were determined using an eigenvalue based dbRDA where axis were determined using forward selection.

1.2.4.2 Pyrotag sequencing-based phylogenetic and community analyses

454 data were analyzed in QIIME pipeline and R packages phyloseq, indicspecies and vegan (De Caceres & Jansen, 2013; Core Team, 2013; McMurdie & Holmes, 2013; Oksanen et al., 2013). Indicator species were identified using indicator species analysis in the indicspecies

17 package. Only those OTUs that were significantly associated with a treatment or combination treatments (p-value < 0.05) were considered indicator species and retained in the dataset utilized for chart and figure construction. Richness and diversity were calculated in vegan. Relationships between community composition and sample physical and chemical characteristics were determined using an eigenvalue based dbRDA where axis were determined using forward selection.

1.3 Results

There was a striking difference in environmental conditions between 2012 and 2013. The average temperature in 2012 was almost 3°C higher than 2013, with this trend being present throughout the year. There was 283 mm more precipitation in 2013 compared to 2012. The sampling period in 2012 was drier than the 2013 sampling week, having 1.2 mm of precipitation compared to the 12.8 mm of precipitation in the 2013 sampling. The average temperature for 2013 sampling days was 5°C higher than the dates in 2012 (Environment Canada, 2014). It was visually obvious that there was more low stature vegetation/biomass in the harvested sites during the second sampling year compared to the first sampling year. Blueberry was prominent in the harvested and fire sites whereas moss dominated the understory in control and old growth sites. Vegetation was generally less prevalent in herbicide treated sites but did not contain significantly different microbial consortia.

1.3.1 Soil Chemistry and Microbial Biomass

Chemical characteristics of soils sampled in 2012 were different between mineral and LFH soil horizons (Figure 1). When subsets of data by layer were considered, there were important differences in control sites compared to treatments in the LFH layer (Figure 2). In mineral soils, the difference between control and treatment soils was generally not present, except that bladed sites had different amounts of total extractable ions than other treatments (Figure 3). Microbial biomass in control sites and old growth LFH soils was, on average, higher than that of soils in harvest treatment plots, and this difference was significant between old growth sites and all treatments (Figure 4). Microbial biomass was also higher in control mineral soils, though not significantly different from all harvesting treatments (Figure 5).

1.3.2 T-RFLP based Microbial Richness and Diversity

T-RFLP based archaeal richness and diversity from soils sampled in 2012, were not significantly different between treatments, though they appeared to be increasing in mineral soils with disturbance intensification (Figures in Appendix 2, Table 1). These patterns were not present in 2013 and there was again no significant effect of harvesting. Bacterial community richness from soils sampled in 2012 was only significantly different between old growth mineral and bladed mineral sites (Figures in Appendix 2, Table 1). There were no within year significant differences between richness or diversity for bacteria in samples collected 2013, or for fungi in samples collected either 2012 or 2013.

1.3.3 Microbial Community Structure and Functioning

The impacts of intensified biomass removal harvesting, though visually quite extreme in the field, did not appear to have an effect on community composition in soils sampled in 2012 (Figures in Appendix 2). There was a general harvesting impact on community structure in year 2, 23 months after treatments were implemented where harvested LFH soils were different from control and fire sites (Figure 6).

Archaeal, bacterial and fungal communities as characterized with T-RFLP analyses, significantly differed based on sampling year (Table 2). Overall T-RFLP community composition was also significantly different between soil horizons in 2012 and 2013 (Figure 7, Table 2). The significant differences in 2012 mineral soils were generally between the old growth forest site and the Island Lake harvest treatment sites (Figure in Appendix 2, Table 2). Communities in the 2012 LFH control and old growth soils appeared to be different from harvested sites, which could have driven the significant differences between the clusters (Figure in Appendix 2, Table 2). The metabolic profiles in mineral horizons of soils sampled in 2012 were different between control samples and all harvest treatments (regardless of biomass removal intensity), and control sites were clustered more closely than other treatments indicating greater heterogeneity in microbial functioning after disturbance (Figure in Appendix 2) Strangely, microbial functioning in old growth and bladed sites seemed to be more similar to each other than other treatments (Figure in Appendix 2). Controls had more variability in LFH horizons compared to mineral soils perhaps due to the higher potential for moisture and nutrient fluctuations in LFH horizon because of increased contact with mosses and external environment

19 compared to mineral horizon soil (Figure 8, Figure in Appendix 2). Differences between harvested and unharvested sites were more apparent in LFH soils, where old growth and control soils had more variation and ordinated differently than harvest treatments (Figure 8). Though community differences were significant between 2013 mineral samples these differences were not strongly represented with ordination (Figure 9, Table 2). In 2013 LFH soils however, significant differences in community structure were observed; with control and fire sites clustering separately from tree-length and stumped sites (Figure 6, Table 2). Herbicide application did not significantly alter microbial community structure within mineral or LFH soils (Table 2).

Pyrotag sequencing did not reveal strong, universal differences in communities with harvesting intensity. However, some differences between harvested treatments and the fire and control sites were apparent in the fungal communities, and between fire and bladed treatments from other samples in bacterial communities (Figure 10, Figure 11). Distributions of unique taxa were also investigated across treatments and controls. Fire and bladed sites appeared to have quite a few indicator species that were not present in other treatments (Figure 12). However, fungal communities were similar amongst the various treatments, excepting control, fire, and bladed samples in which unique OTU distributions appeared to be present (Figure 13).

1.3.4 Species associated with harvesting treatments

OTUs that were found to be significantly associated to one or more treatments using indicator species analysis were regarded as indicator species. Most bacterial indicator species were of very low abundance, less than 0.5% of total community composition (Figure 14, Figure 15). Fungal indicator species were of similarly low abundance (Figure 16, Figure 17). Though all bacterial indicator species were of low abundance, several Bradyrhizobiaceae and Hyphomicrobiaceae indicator species were associated with LFH soils in tree-length and fire treatments (Figure 14). In LFH soils, the highest abundance of many indicator species were in fire sites (Figure 15, Table 3). Many fungal indicator species in mineral soils had higher abundance with bladed, stumped and control treatments as opposed to bacterial associations with tree-length, full-tree and stumped treatments (Figure 14, Figure 16). Fungal indicator species in LFH soils varied greatly in abundance, with several OTUs being indicative of control plot conditions, and several responding to the fire disturbance (Figure 17). The Fungal OTUs 38,

582, 1127 and 1004 were identified as belonging to known ectomycorrhizal genera: Hygrophorus, Russula, and Piloderma (Table 4). Many of OTUs that had larger differences in abundance, were more heterotrophic organisms such as Basidioascus, Infundilicybe, Mycogloeae, Pseudoomphalina. Hygrophorus and Piloderma were also higher abundance in control site LFH soils. Also, in mineral soils, a chytrid fungus had elevated abundance within bladed sites (Figure 16). Some ascomycete species were elevated in fire sites. The phylogeny of both fungal and bacterial indicator species was not effectively delineated, most likely because erroneous otu identification due to database insufficiencies prevented effective sequence alignment (Jeraldo et al., 2011).

1.4 Discussion

Microbial communities often respond quickly to disturbance and can be used as indicators of ecosystem functioning and response to stressors (Blazewicz et al., 2013; Evans & Wallenstein, 2011, 2014; Osborn & Smith, 2009). However, as seen in this study, sometimes community structure changes take longer than anticipated. The lack of community structure differences between LFH soils of intensified harvesting treatments may have been because components of woody debris were not yet being incorporated in sufficiently different amounts or microbial communities had not had sufficient time to respond to different levels of inputs. Alternatively, the microbial communities may have just been resilient of resistant to the gradient of disturbance provided (Allison & Martiny, 2008; Shade et al., 2012; Werner et al., 2011). Leachates from CWD change as wood decays, which occurs over a period of years (Kruys et al., 2002; Rudz, 2013; Spears & Lajtha, 2013; Spears & Holub, 2003). Metabolic responses may have been more responsive to short term changes as metabolic adaptations occur on a faster time scale in response to smaller differences than required for large scale changes to community structure (Allison & Martiny, 2008; Shade et al., 2012). It may be that the between harvesting treatment differences could emerge in future years as has been seen in other studies, or they could remain undetectable which has also been observed in intensified systems, albeit with functional assays or low resolution molecular techniques (Busse & Beattie, 2006; Hartmann et al., 2012; Kataja-aho et al., 2011; Mariani et al., 2006; Tan et al., 2005). Though short term differences were not as apparent; soil differences could emerge as CWD leachates change underlying soil chemistry as the leachates from remaining residues change with decay (Spears & Holub, 2003).

1.4.1 Harvesting impacts on biomass, moisture and microbial community function

The differences in microbial metabolic activity seen in LFH soil horizons between control sites and harvest treatments might have been driven by concomitant changes in soil chemical properties also due to harvesting (Figure 2, Figure 8). The lower enzymatic and respiration activities in harvested LFH horizons could be ultimately explained by the higher biomass of the controls compared to harvested sites (Frankenberger & Dick, 1983; Li et al., 2004; Liu et al., 2008). In contrast to the mineral horizon, the difference between control and harvested LFH horizon soils corresponded to lowered enzyme activities rather than the ability to utilize specific substrates; enzyme quantity was changing with harvesting rather than the suite of enzymes being utilized (Figure 8). The reduction of active vegetative material after harvesting disturbance could have contributed to this, as root exudates in the rhizosphere are known to modulate microbial activities, and promote growth of microorganisms (Garbaye, 1991; van Hees et al., 2006; Kuzyakov, 2002; Orr et al., 2011). The harvested sites were equivalently barren of vegetation when enzyme analyses were completed, so perhaps the lack of vegetation in comparison to control plots was the main influence on microbial metabolic functioning. The difference between control and harvested mineral horizons was mostly based on respiration of sugars and amino acids. These nutrients would be more abundant in the rhizosphere environment that was reduced in harvested sites (Huang et al., 2014; Kuzyakov, 2002).The increased exposure of plots to wind may have also been contributing to the change in microbial functioning, increased oxygen availability in soils could change nutrition strategies of microorganisms present in soils if fewer anaerobic microenvironments were present in soils (Fukuda, 1955; Kimball & Lemon, 1971; Picek et al., 2000; Wang et al., 2013). The physical disturbance associated with harvesting may have exacerbated these impacts as well; existing research has shown that increased physical disturbance of soils created noticeable effects on microbial community functioning (Chatterjee et al., 2008; Mariani et al., 2006; Schnurr-Pütz et al., 2006; Tan et al., 2005). It is quite surprising then, that stumped soils which experienced additional upheaval and mixing of soils were not distinct from less intensive full-tree and tree- length harvesting (Figure 2, Figure 8). Kataja-aho et al., (2011) though working in a different boreal system, also did not see stumping impacts. In this case, it seems that even the visually distinct plots that result from the intensification of clear cut harvesting do not have sufficient plot-level variation to produce functionally distinct microbial consortium. Despite this, a clear

22 difference between the functional characteristics of unharvested and harvested sites was observed. It may be that the already biomass intensive clear cut management systems is less impacted by further biomass extractions in comparison to less extreme harvesting regimes such as selection and shelterwood systems.

1.4.2 Microbial community structure doesn’t respond noticeably to intensification of clearcut harvesting on short term time scales

Considering that others have found significant chemical differences in soils between different levels of intensified harvests, it was odd that there weren’t differences between microbial communities in the Island Lake sites (Thiffault et al., 2011). In general there were some differences in microbial community structure between all harvest treatments and the control (and sometimes old growth) sites, but not clear differences among harvest intensity in LFH horizons (Figure 6, Figure 9). Community structure shifts from harvesting have been documented in other systems (Chatterjee et al., 2008; Hynes & Germida, 2012; Mummey et al., 2010; Siira-Pietikäinen et al., 2001). This could indicate that a broader environmental change (e.g. warmer temperatures), lack of living vegetation, or both across all harvest treatments was more important than subtler differences among biomass removal intensity treatments. Communities generally respond to vegetation differences, as some bacterial and fungal species participate in specific mutualistic associations with plants (Huang et al., 2014; Kernaghan & Patriquin, 2011; Morton et al., 2004). Also, general heterotrophic soil microorganisms can be influenced by the root exudates that accompany live vegetation (Huang et al., 2014; Kuzyakov, 2002). Microbial communities respond to the physical disturbances introduced during harvesting as well (Busse & Beattie, 2006; Mariani et al., 2006; Schnurr-Pütz et al., 2006; Tan et al., 2005). Changes in microbial communities with harvest intensity may occur in an extended timeframe, as has been seen in other systems with system intensification (Busse & Beattie, 2006; Hartmann et al., 2012). There is some indication that the lag in response could have been because of drier, hotter conditions that occurred during 2012 (i.e. a regional climate anomaly overrode treatment effects), sampling as there was an increase in community richness and diversity for all kingdoms assayed between 2012 and 2013, though this was not consistent across all treatments (Figures in Appendix 2). Both Brockett et al., (2012) and Evans & Wallenstein (2014) described influences of soil moisture and climate on microbial community characteristics, which could have exacerbated temporal differences between 2012 and 2013 samples. It did appear that soil

23 chemical concentrations were driving some of these inter-annual differences in community structure; for example with corresponding differences in both soil chemistry and community structure in the year of 2012 (Figure 1, Figure 7). Why these differences were constrained to the LFH horizon is debatable; this could have been due to underlying chemical differences, as were seen in 2012 soil analyses. The 2012 soil analyses showed dissimilarity of mineral soil chemistry was due in part to polysaccharide concentrations: perhaps leachates from LFH layers had not changed considerably within the year between sampling (Figure 3). It was strange that this study has not found large differences in microbial community structures between harvesting treatments, considering that these effects have been witnessed in recent molecular studies (Hartmann et al., 2012). Intensified harvesting impacts have been reported in different forest systems, but not consistently and within large, dispersed plots in which natural heterogeneity could have influenced results, and without a complete gradient of removal treatments (Hannam et al., 2006; Mariani et al., 2006).The lack of difference was likely not because of the relatively low resolution of T-RFLP analyses compared to the high-throughput techniques employed in Hartmann et al., (2012), as treatment effects were not clear in pyrotag sequencing data either (Figure 10, Figure 11). This lack of response could also just be a characteristic of the boreal environment: the colder climate generally causes ecological processes to occur on longer timescales, and other boreal studies have seen a similar lack of difference (Kataja-aho et al., 2011; Mariani et al., 2006; Smith et al., 2008). When OTUs responsible for site differences were examined, there appeared to be slight differences in microbial communities from which OTUs were found, which could presumably become more apparent over time (Figure 12, Figure 13). It may be that the plot design has accounted for natural variation within plots in the Island Lake sites, where other studies may have issues related to spatial variations corresponding with treatment variations (Hannam et al., 2006; Smith et al., 2008). It is more likely that the time between sampling and treatment was insufficient for effects to emerge, as they have in studies completed in longer time-frames (Hartmann et al., 2012). The relatively short time since slash input could have impacted residue leachate emissions into soils, as leachates from wood have been reported as lower in early decay stages compared to later decay stages (Hafner et al., 2005; Rudz, 2013). The short term effects investigated in this study may explain the lack of result, which may be of greater interest to the forestry industry in relation to the impact of intensified harvesting on important plant symbionts. The abundance of indicator species were low, and possible fungal symbiont taxa were not responding differentially between harvesting treatments,

24 though all harvesting treatments had lower abundances of these species than controls (Figure 16, Table 4). This low abundance of indicator species and lack of difference in the abundance of Hygrophorus and Piloderma between harvesting treatments may mean that from the perspective of establishment of plant-microbial associations in soils, the impact of intensified biomass harvests may not be much different from business-as usual clear cut operations in the short-term.

Though bacterial indicator species in mineral soils were all in very low abundance, it did not mean that these organisms lacked ecological importance (Bachy & Worden, 2014). OTU 1914 may be one of these organisms, as documented Methylocystis species have been found to subsist on oxidative metabolism of methane and methanol (Table 3; Dedysh et al., 2007). That this OTU appeared to be of higher abundance within mineral soils of tree-length and full-tree systems compared to control and fire sites could mean less methane was being oxidized, or that more methane was available in these harvesting treatments (Figure 14). The loss of the trees on the harvested sites could mean less consistency of environmental characteristics within the treated sites, and increased possibility for the formation of transiently waterlogged microsites in which anaerobic methanogenesis could occur (Benstead & King, 1997; Mer & Roger, 2001).The fact that two water associated fungal species, a chytrid fungus and a Tetrachaetum (OTUs 57 and 293) were found in their highest abundances within mineral soils of Bladed treatments may be due to the same site characteristic (Figure 16, Figure 17). Alternatively Wang et al. (2013) noted that wind speed (and thus soil ventilation with O2 and atmospheric CH4) is a key constraint on upland forest soil CH4 oxidation. In the open, harvested sites with a larger wind fetch, soils were likely exposed to more ventilation. Surface temperature in exposed soils was likely much higher as well, due to decreased shading from lack of trees. The responses of many of the bacterial indicator species were inconsistent between harvesting treatments, and did not follow a pattern associated with disturbance intensity, the ecological significance of this is questionable, as there is limited information on the metabolic activity of bacterial species, and information relating to genera is often non-specific and sometimes not applicable to all species within that genera (Blazewicz et al., 2013; Hirsch et al., 2010; Madsen, 1998; Philippot et al., 2010).

The low abundance of fungal indicator species emphasized the lack of large community differences observed with both T-RFLP and pyrotag sequencing based analyses. Other studies have also seen negligible effects of additional biomass removal as compared to stem-only harvesting on soil fungi, though the methods used were less discriminant than the ones applied in

25 this study and included fungi as a portion of overall changes in microbial community (Chatterjee et al., 2008; Kataja-aho et al., 2011). This is contrary to the findings of Hartmann et al. (2012) where fungal community structure was more impacted by harvesting practices than bacterial community structure. Unlike bacterial indicator species, it did seem that taxonomy was related to the responsiveness of these organisms to particular treatments. Basidiomycetes appear to be have higher abundance in controls versus Ascomycetes which had higher abundances in disturbed sites, though their abundances in all harvested and fire treatments were inconsistent. It is possible that the Basidiomycete OTU’s were representing organisms associated with living plants. This was particularly true of OTU 1086, Hygrocybe, a symbiont of moss (Cantrell & Lodge, 2001). The Ascomycete species may have been adapted to disturbances and were responding to specific, stressed niches in which they were more competitive. That these changes were all occurring in the rare biosphere may be due to sensitive organisms in the rare biosphere responding to small changes more quickly than larger community components (Bachy & Worden, 2014). OTUs likely representing ectomycorrhizae were more abundant in control sites (Figure 16). This was most likely because of the distinct absence of plant life in harvested sites, and thus inhospitable environment for these organisms in harvested soils.

1.4.3 Clear cut harvesting as an analog for fire disturbance

The different community compositions in LFH horizons between fire sites and harvesting treatments could have been from communities adapting to the transformed and reduced amount of organic material by fire exposure (Figure 6). That the abundance of some Hyphomicrobiaceae and Bradyrhizobiaceae species was higher in fire sites compared to control and harvest plots suggests that there could be some plant symbionts involved in fire succession that are not stimulated by harvesting. Perhaps because of the differences between the organic losses as well as forms of organic matter in fire sites compared to harvested ones. Generally, it does not seem as though fire characteristics were reflected in the biota of harvested environments, as there were several OTUs that were only detected in fire samples (Figure 14). There could be specific interactions occurring between these unidentified OTUs and fire adapted plant species (Hart et al., 2005). Also, the generally heterotrophic nature of most of the fire related organisms may provide nutrient recycling functions through microbial degradation of remaining fire-transformed carbon and nutrient sources that would be otherwise unavailable for biological use (González- Pérez et al., 2004; Knicker, 2007; Mataix-Solera et al., 2009). The OTUs present were likely as

26 yet unidentified fire adapted species utilizing the small amount of organic material remaining in LFH layers after the fire event, or species that were taking advantage of the rhizosphere environment of the vegetation present in the fire sites (Denison & Kiers, 2011; Huang et al., 2014; Mataix-Solera et al., 2009). From the perspective of soil microbiological characteristics, clear cut harvesting did not produce a similar ecological impact as fire disturbance; though the similar levels forest fragmentation introduced by these two disturbance types has been used as justification for clear cut harvesting management systems (Bergeron et al., 1999; Gluck & Rempel, 1996; Suffling et al., 1988). There may be important ecological implications related to differences in microbial characteristics, as these organisms could be important in successional plant dynamics and plant diversity (Bever et al., 2010; Van Der Heijden et al., 2008; Reynolds et al., 2003).

1.5 Conclusion

Though microbiological properties of harvested and unharvested systems differ, intensifying the harvesting process did not seem to further cause additional disruptions to microbial community structure and function, despite the obvious physical disruptions that occur in intensified harvesting processes. There were some indications that changes will emerge in time, as patterns existed between controls and treatments and between sampling years. Also, some microbial OTUs, even if relatively rare, were responding to treatment differences. Perhaps these small changes will not impact plant community establishment in the short term, as there was possibility for functional redundancy in the system, which could be present in the largely unaffected community composition. It was also apparent that microbial community structures resulting from clear cut treatments were distinctly different from those of natural fire disturbance. The activity and ecology of fire adapted microbes need to be more thoroughly investigated in order to effectively determine the ecological implications of these differences. Nevertheless, many forests have clear cut management regimes, and although changes within microbial communities with intensified biomass removal do not appear to be a short-term phenomenon, long term effects may still occur and could have serious impacts on long-term soil function and productivity should intensification be implemented. To begin to address this potential issue, in the next chapter I investigate microbial communities across a longer-term decay spectrum of CWD.

Chapter 1 Figures

Figure 1: PCA of all 2012 soil samples based on total ion chemistry as well as FTIR estimations of soil organic functional groups.

Figure 2: PCA of 2012 LFH soil samples based on total ion chemistry as well as FTIR estimations of soil organic functional groups.

Figure 3: PCA of 2012 mineral soil samples based on total ion chemistry as well as FTIR estimations of soil organic functional groups.

Figure 4: Average microbial biomass of Chapleau LFH soils sampled 2012 as determined using chloroform fumigation. Points labeled with the same letter are not significantly different (p < 0.05).

Figure 5: Average microbial biomass of Chapleau mineral soils sampled 2012 as determined using chloroform fumigation. Points labeled with the same letter are not significantly different (p < 0.05).

Figure 6: Weighted DCA of TRFLP community structure based on bacterial 16S, fungal 18S and archaeal 16S genes for DNA isolated from organic soil samples collected in 2013 in Island Lake biomass harvesting trial sites.

Figure 7: Weighted DCA of TRFLP community structure based on bacterial 16S, fungal 18S and archaeal 16S genes. Points represent mineral and organic soil types of samples from 2012 sampling of soils from Island Lake biomass harvesting trials. Confidence ellipses of 95% were placed around soil type clusters.

Figure 8: PCA of SIR and hydrolase activity based metabolic profiles from Island Lake Biomass trial LFH soils sampled in 2012.

Figure 9: Weighted DCA of TRFLP community structure based on bacterial 16S, fungal 18S and archaeal 16S genes for DNA isolated from mineral soil samples collected in 2013 in Island Lake biomass harvesting trial sites.

1.0 Tree−length 0.5 Full−TreeFull−Tree Stumped Tree−length Stumped Tree−length Stumped

DCA2 ControlStumped 0.0 Control Fire Tree−length Fire − 0.5

Bladed M

− 1.0 O

−1.0 −0.5 0.0 0.5 1.0 1.5 2.0 DCA1

Figure 10: DCA of sample community based on bacterial 16S pyrotag sequencing community.

1.0 Full−Tree 0.5 Tree−length Full−Tree Full−Tree Full−TreeBladed Tree−Stumpedlength Tree−length Tree−length Stumped Control 0.0 Stumped Stumped Control DCA2 − 0.5 − 1.0

Fire M O − 1.5 −1.5 −1.0 −0.5 0.0 0.5 1.0 1.5 2.0 DCA1

Figure 11: DCA of sample community based on fungal 18S pyrotag sequencing community.

Figure 12: Detrended Correspondence Analysis of Bacterial pyrotag sequencing of 2013 Chapleau soil samples. Only OTUs present in the described treatment are shown, which are coloured based on presence (black points) and absence (grey X’s) in different harvesting regimes.

Figure 13: Detrended Correspondence Analysis of Fungal pyrotag sequencing of 2013 Chapleau soil samples. Only OTUs present in a given treatment are shown, which are

Figure 14: Average relative abundance of Bacterial OTU’s from mineral soils sampled from Island Lake and Fire control sites in 2013. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

Figure 15: Average relative abundance of Bacterial OTU’s from organic soil sampled from Island Lake and Fire control sites in 2013. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

Figure 16: Average relative abundance of Fungal OTU’s from mineral soils sampled from Island Lake and Fire control sites in 2013. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

Figure 17: Average relative abundance of Fungal OTU’s from organic soils sampled from Island Lake and Fire control sites in 2013. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

Chapter 1 Tables

Table 1: Significance of harvesting treatment effects on diversity and richness based on anova testing. Significance was designated as p < 0.05. Kingdom Year Test Chao Richness Shannon Diversity Archaea 2012 p > 0.05 p > 0.05 Archaea 2013 p > 0.05 p > 0.05 Bacteria 2012 p = 0.02 p > 0.05 Bacteria 2013 p > 0.05 p > 0.05 Fungi 2012 p > 0.05 p > 0.05 Fungi 2013 p > 0.05 p > 0.05

Table 2: Adonis testing results of bacterial, archaeal and fungal TRFLP based community data. Data is displayed within cells as the test p-value over the F2 value for a particular grouping category. Significant results are bolded. Dataset Category tested Year Soil Horizon Harvesting Herbicide Plot treatment application Full dataset 0.001/0.132 0.001/0.065 0.001/0.061 0.788/0.003 0.381/0.004 2012 only NA 0.001/0.050 0.010/0.087 0.844/0.006 0.522/0.009 2013 only NA 0.001/0.184 0.001/0.144 0.705/0.006 0.212/0.012 2012 LFH NA NA 0.021/0.160 0.899/0.014 0.351/0.026 2012 Mineral NA NA 0.008/0.170 0.677/0.014 0.708/0.013 2013 LFH NA NA 0.001/0.321 0.133/0.029 0.082/0.034 2013 Mineral NA NA 0.014/0.174 0.169/0.030 0.156/0.031

Table 3: NCBI BLAST identities of Bacterial indicator species determined from pyrotag sequencing of Chapleau soils sampled in 2013. Matches below 90% identity were not included. OTU BLAST Identification Percent e_value identity 96 Edaphobacter aggregans 93.36 0 100 Nitrobacter vulgaris 94.95 2E-164 142 Rhodomicrobium vannielii 96.21 0 145 Candidatus Solibacter usitatus 93.44 0 172 Granulicella mallensis MP5ACTX8 96.57 0 213 Gemmata obscuriglobus 90.69 7E-177 249 Variovorax paradoxus 98.25 6E-165 257 Derxia gummosa 90.42 3E-156 309 Caulobacter mirabilis 96.21 0

335 Granulicella tundricola 94.41 0 388 Acidobacterium capsulatum ATCC 51196 90.64 1E-166 392 Candidatus Koribacter versatilis Ellin346 91.44 1E-173 393 Acidocella aminolytica 91.94 8E-132 418 Methylibium petroleiphilum 95.61 0 481 Afipia birgiae 34635 96.02 0 570 Bauldia consociata 94.44 0.00002 573 Bauldia consociata 96.02 4E-110 614 Candidatus Koribacter versatilis Ellin347 96.43 0 618 Candidatus Solibacter usitatus 93.36 0 675 Filomicrobium insigne 91.49 4E-167 687 Bradyrhizobium pachyrhizi 92.26 0 706 Mycobacterium fallax 97.87 5E-84 712 Burkholderia phytofirmans 93.87 3E-175 722 Moraxella nonliquefaciens 90.76 0 773 Pseudomonas carboxydohydrogena 95.07 0 795 Candidatus Koribacter versatilis Ellin348 90.97 1E-180 909 Phaeospirillum tilakii 94.04 5E-96 949 Rhodoplanes roseus 90.59 7E-177 962 Acidisphaera rubrifaciens 93.89 0 975 Rhodoplanes elegans strain AS132 91.96 0 1052 Acidobacterium capsulatum ATCC 51196 90.12 9E-87 1074 Acidobacterium capsulatum ATCC 51196 91.43 0 1154 Curtobacterium citreum 95.87 0 1196 Arthrobacter alkaliphilus 96.25 1E-180 1232 Conexibacter woesei 93.25 0 1274 Rhodopseudomonas rhenobacensis 94.41 0 1344 Moorella humiferra 96.3 0.15 1345 Burkholderia fungorum 98.46 0 1356 Zavarzinella formosa 96.67 0.004 1378 Phenylobacterium lituiforme 96.01 0 1498 Candidatus Koribacter versatilis Ellin349 91.42 0 1529 Acidiphilium multivorum 91.64 3E-150 1548 Candidatus Koribacter versatilis Ellin350 91.2 0 1629 Actinomadura alba 92.89 4E-154 1816 Asticcacaulis benevestitus 93.14 3E-67 1896 Pseudomonas carboxydohydrogena 95.66 5E-166 1897 Methylocapsa acidiphila strain B3 93.95 0 1914 Methylocystis echinoides 93.56 0 2208 Mycobacterium shimoidei 95.91 1E-174 2264 Reyranella soli 95.32 0 2276 Gluconacetobacter liquefaciens 92.09 4E-180 2313 Acidobacterium capsulatum ATCC 51196 95.28 0

2524 Bradyrhizobium canariense 93.36 0 2546 Bradyrhizobium liaoningense 99.64 1E-135 2698 Piscinibacter aquaticus 92.02 2E-146

Table 4: OTUs identified using NCBI BLAST of Fungal indicator species determined from pyrotag sequencing of Chapleau soils sampled in 2013. Matches below 90% identity were not included. OTU BLAST Identification Percent e_value identity 38 Piloderma fallax 99.5 0 75 Mycogloea sp. TUB FO40962 99.26 0 92 Basidioascus sp. SN-2013 95.78 0 126 Pseudoomphalina pachyphylla 98.73 0 130 Psilocybe cyanescens 98.76 0 155 Tulostoma macrocephala 94.8 8E-176 213 Woodruffides metabolicus 94.91 6E-171 288 Lecythophora sp. 6-14c 99.5 0 293 Tetrachaetum elegans 98.52 0 406 Cryptococcus terricola 96.48 2E-177 407 Calyptrozyma arxii 98.74 7E-151 427 Symbiotaphrina buchneri 98.61 3E-174 470 Entorrhiza fineranae 96.53 0 482 Gelatinomyces siamensis 98.15 0 582 Hygrophorus flavodiscus 97.02 3E-149 630 Hygrophorus flavodiscus 98.04 0 709 Bimuria novae-zelandiae 95.66 2E-163 914 Leucospiridium sp.AY30 99.5 0 960 Hygrophorus pustulatus 98.56 4E-129 991 Galzania incrustans 99.75 0 1004 Russula exalbicans 98.4 2E-176 1029 Tetrachaetum elegans 99.25 0 1060 Tetracladium sp. 3146-i2 100 3E-104 1086 Hygrocybe aff. Neofirma 98.71 0 1105 Infundibulicybe gibba 100 0 1127 Hygrophorus flavodiscus 93.44 1E-160 1159 Penicillium decumbens 96.96 1E-96 1164 Geastrum cf. triplex C KH-2011 96.6 2E-101

Chapter 2 Coarse Woody Debris Microbial Community Dynamics in Boreal Hardwood and Temperate Mixedwood Forests 2 2.1 Introduction

Global dependence on non-renewable energy sources is driving research into bioenergy (Berch et al., 2011; Dorian et al., 2006). Whether through combustion of raw biomass and processing byproducts or from biomass derived ethanol created in fermentation systems, there is potential for the forestry industry to provide some of the biomass derived energy that may replace non-renewable sources (Hoogwijk, 2003; Janowiak & Webster, 2010; Ladanai & Vinterbäck, 2009; Stupak et al., 2007). Some modification of harvesting practices in Canada has already occurred (e.g. favouring whole-tree v stem-only harvests and there is potential for some of the higher intensity systems employed in Europe, such as stumping and blading, to move into Canada should the process become profitable (Berch et al., 2011; Lindroos, 2011; Rämö et al., 2009; Tenenbaum, 2005). The impacts of reduced woody debris pools must be studied in boreal and north temperate forest ecosystems where these harvesting systems might be employed.

Different stages of decayed downed coarse woody debris (CWD: woody debris on the forest floor with a diameter greater than 10 cm) are recognized as such important components of the forest ecosystem that sustainable management systems aim to maintain CWD input levels to ensure appropriate levels of each decay class will be available in the future (Stupak et al., 2011; Vanderwel, 2010). CWD serves as habitat for many plants, vertebrate animals and invertebrates (Botting & DeLong, 2009; Harmon et al., 2004; Jönsson et al., 2008; Langor et al., 2008; Maser et al., 1979; McCay & Komoroski, 2004; Mengak & Guynn, 2003; Riffell et al., 2011). The function of CWD as reservoirs of fungal diversity can allow survival of important microbial groups such as ectomycorrhizae through disturbances such as fire and drought; this can then enhance seedling establishment (Boddy & Heilmann-clausen, 2008; Harmon et al., 2004; Kauserud et al., 2008; Lehto & Zwiazek, 2011; Stenlid et al., 2008). The characteristics that provide such varied environments through the decay process are related to the microbial community that utilizes, and transforms, CWD residues (Harmon et al., 2004; Laiho & Prescott, 2004; Maser et al., 1979).

Molecular surveys are beginning to illustrate that the diversity of wood decomposers is vast (e.g. with thousands of unique fungal taxa involved in decomposition of a single wood species) and their ecology is complex, with the “known” decomposers appearing primarily in early decay stages only (Jönsson et al., 2008; Kubartová et al., 2012; Prewitt et al., 2014). Many of these saprobic and necrotrophic fungi have host specificity to particular species of wood, which tend to contain unique microbial communities (Freschet et al., 2012; Prewitt et al., 2014). It seems as though the source of these organisms may not be from adjacent wood as decay stage, rather than proximity, has been shown to be a better predictor of similar decomposer communities among logs (Kubartová et al., 2012). This may mean that organisms are being sourced from surrounding soils or from atmospheric deposition of cells. If such was the case however, harvesting history of a site should not impact microbial communities within the CWD pool. Kebli et al., (2012) found different decay communities within the CWD pools of harvested and unharvested stands that were otherwise comparable. Additionally, CWD decay by specific organisms have been studied by microscopy and has shown that the type of rots produced by specific fungal species vary based on which types of wood they inhabit (Schwarze, 2007). Different community compositions could facilitate decay through different mechanisms in unique environments. Contradictory findings between studies of CWD and unknowns pertaining to microbial communities within them require investigation. Alterations to microbial communities in CWD could impact decay rates and resilience of decay communities in the future. Since CWD stocks can be modeled at large spatial and temporal scales under different disturbance regimes (Vanderwel et al., 2008), a finding of consistent microbial diversity across decay class, for similar wood types in different environments, would allow prediction of harvesting or other disturbance impacts on microbial biodiversity.

The different decay stages of wood may all be required in order to establish a healthy landscape scale microbial community. To address questions related to how CWD microbial communities vary across forest region and tree species, 3 study sites were used in Ontario: an old growth boreal jack pine forest in Northeastern Ontario, an uneven-aged sugar maple dominated forest in the Great Lakes St. Lawrence forest region of Central Ontario, and a sugar maple grove in a remnant Carolinian forest in Southern Ontario. In Haliburton and Chapleau microbial community structure was characterized in inner and outer wood samples on logs and in underlying soils to assess potential microbial linkages between soils and CWD. It is expected

44 that community structure should change as decay advances as the composition of the wood is different at the various stages across a particular landscape (Rudz, 2013). Fungi and bacteria may have a larger diversity and presence in later decay stages where the moisture and accumulated nutrients from hyphal sequestration might prove to be more amenable to growth. As decay proceeds, similarities between remnant materials, which initially contained distinct community structures between different environment and residue types, should emerge. The gradual loss of bark, and penetration of fungi into wood residues should also reduce within log variation due to spatial heterogeneity as decay advances. Knowledge of the microbial composition at each stage of decay may provide insight into whether different organisms are consistently present at each stage of decay both within and between forest types and if redundancy or unique organisms exist within the CWD pools present.

2.2 Methods

2.2.1 Study Sites

2.2.1.1 Chapleau

The Nimitz site where jack pine CWD was sampled is located near Chapleau, Ontario in the Ontario Shield Ecozone (Lake Abitibi boreal Ecoregion) of Ontario (Crins et al 2009). The site consisted of ca. 80 year old jack pine (Pinus banksiana) of fire origin and natural regeneration. The landscape was relatively flat with minimal slope and so CWD was relatively uniformly distributed across the landscape. Soils are generally sand or sandy loam textured and acidic belonging to the Brunisolic order. Chapleau receives an annual average of 796 mm of precipitation a year and has an average yearly temperature range of -16 to 17°C.

2.2.1.2 Haliburton Forest

To compare CWD microbial communities across different forest types and regions, a portion of the research was conducted in the Haliburton Forest and Wildlife Reserve, a 33 ha private woodlot in the Great Lakes-St Lawrence Ecoregion of Ontario, Canada (45[degrees]13'N, 78[degrees]35'W). Forests in the region are dominated by sugar maple (Acer saccharum Marsh.) with secondary components of American beech (Fagus grandifolia Ehrh.), yellow birch (Betula alleghaniensis Britt.), and eastern hemlock (Tsuga canadensis (L.) Carriere). The landscape is typical of the Precambrian Shield, with rolling terrain and rock outcrops. Soils are generally

45 quite shallow and moderately acidic Dystric Brunisols. Haliburton Forest has been managed under single-tree selection since the 1970s and prior to that was selectively harvested (high- graded) for eastern white pine (Pinus strobus L.) and yellow birch (Vanderwel, 2010). Haliburton receives an average annual precipitation of 1064 mm and has an average yearly temperature range of -9.8 to 18.3°C.

2.2.1.3 University of Toronto at Mississauga Forest

Additional samples of one CWD decay stage matching the same species (sugar maple) as Haliburton Forest were taken from Forest on the University of Toronto at Mississauga campus. The University of Toronto at Mississauga is located in a forested region of Mississauga Ontario bordered by the Credit River. Forests on the campus are dominated by sugar maple (Acer saccharum) and also contain Basswood (Tilia americana), White Ash (Fraxinus Americana) and White Elm (Ulmus laevis). The underlying soils are primarily loam textured Grey-Brown Luvisols with calcareous parent material (B and C horizons are pH cicumneutral). Mississauga receives an average annual precipitation of 792 mm and has an average yearly temperature range of -6.3 to 20.8°C.

2.2.2 Decay Classification

Defining different stages of decay works fairly well as a proxy for time. This approach has been taken in previous studies (Rajala et al., 2012; Rudz, 2013). This study assigned CWD to decay stages one through five, using characteristics described by Maser et al., (1979). The classes move from decay class one, which is relatively freshly fallen wood, to decay class five, which is extensively decayed to the point where minimal force can cause structural catastrophe. The mid decay classes represent stages between these two extremes. There has been modeling completed for the temporal decay of sugar maple residues in Haliburton, and so these modeled timescales were used to infer a timescale with which to associate community change (Rudz, 2013). As decay dynamics are highly variable in different environments due to differences in moisture, temperature and wood species, the Chapleau data was not associated with discrete timescales.

2.2.3 Sampling

CWD samples were taken from the outer and inner surfaces and soil samples were taken from below the CWD. When sampling, outer samples were pried from the CWD by hand or a hand drill with a ¾ spade bit was used to drill the surface approximately 4 mm of wood or bark tissue. The inner wood sample was taken after the surface sample was removed and the shavings from a drill depth of approximately 2 cm depth or deeper were collected. Samples from decay classes of 4 and 5 were all collected by hand, as the CWD was not structurally compatible with drilling. 10 replicates of each decay class were collected from randomly distributed Acer saccharum samples across Haliburton Forest (though due to loss of identifying characteristics in later decay stages the identification of these wood species could be incorrect), and from Pinus banksiana residues at Nimitz. Samples of decay class three were taken from Acer saccharum CWD residues on the University of Toronto at Mississauga campus in which the inner and outer wood sample was pooled.

2.2.4 Laboratory Techniques

2.2.4.1 DNA Extraction

DNA was extracted from CWD samples with the PowerPlant® DNA Isolation kit (MO BIO Laboratories Inc., Carlsbad, CA). There were deviations from the protocol. Sample incubation at 65°C was performed in “heating block”. Homogenization was performed using a 5 minute cycle on a 16 tube MiniBeadbeater TM (Biospec Products Inc., Bartlesville, OK). Samples were stored frozen for analysis at a later date.

2.2.4.2 Molecular Analysis

2.2.4.2.1 PCR

PCR reactions were performed in an MWG AG Biotech Primus 96+ Thermocycler. Previously determined optimized primer combinations and cycles from (Preston et al., 2012) were utilized for the amplification of bacterial 16S ribosomal DNA, fungal 18S ribosomal DNA and archaeal 16S ribosomal DNA sequences.

2.2.4.2.2 T-RFLP

Bacterial and fungal T-RFLP was performed as described by Preston et al., (2012). T- RFLP analysis was completed at the Agriculture and Food Laboratory in Guelph University of Guelph Agriculture and Food Laboratory, Guelph, ON). T-RFLP data was preprocessed in R using a custom function created using the algorithm described by Ishii, Kadota, & Senoo, (2009) with slight modifications to accommodate the data format supplied by Guelph. The cutoff distance was set to 2bp and the final output was expressed as proportion total peak height per TRF by sample (Kaplan & Kitts, 2003).

2.2.4.2.3 Pyrotag sequencing

Pyrotag sequencing was completed on samples collected in 2013. All replicates of a given harvesting regime, herbicide use and type of soil were pooled and sent to Mr.DNA where pyrotag sequencing was completed as per Dowd et al. (2008). Bacterial 16S rDNA was amplified with 16S universal Eubacterial forward primer (AGRGTTTGATCMTGGCTCAG) was used to amplify fragments from the bacterial 16S rRNA gene and 18S universal Fungal forward primer (TTAGCATGGAATAATRRAATAG) was used to amplify fragments from the Fungal 18S rRNA gene.

454 data were analyzed in the quantitative insights into microbial ecology (QIIME) pipeline (Caporaso et al., 2010). 454 data was quality filtered QIIME default parameters (quality score = 25, min length=200, max length = 1000). Additional quality filtering and OTU clustering was performed with the Usearch 5.2.236 program, which utilizes the UCHIME algorithm to identify chimera sequences for removal against the gold.fa dataset (Edgar, 2010; Edgar et al., 2011). De novo OTU picking with uclust was used to form the representative OTU dataset (Edgar, 2010). Bacterial taxonomy was assigned using the RDP classification algorithm against the Greengenes 13_5 database 97% confidence rep set (McDonald et al., 2011; Wang et al., 2007). Muscle was used to produce a denovo alignment of all OTU sequences and produce a phylogenetic tree (Edgar, 2004). Fungal taxonomy was assigned using the RDP classification algorithm against the 97% Silva database for the eukaryotic 18S ribosomal DNA gene (Quast et al., 2013; Wang et al., 2007). Muscle was used to produce a denovo alignment of all OTU sequences and produce a phylogenetic tree (Edgar, 2004).

Final OTUs represented potential species based on unique rRNA sequences that were discriminated at a level of 97% similarity.

2.2.4.2.4 Phylogenetic Identification

Indicator species sequences collected from pyrotag sequencing experiments were entered into the BLAST genome database and correlated to the closest matching documented sequence (Madden et al., 1996). Closest match for species identification was determined using NCBI Blast using the Blastn algorithm with an expect threshold of 10, word size 11, match/mismatch Scores 2,-3, Gapcosts: Existence 5, Extension 2.

2.2.4.3 Wood Chemistry

Dried and ground Chapleau wood samples were sent to CFS analytical laboratories for total carbon analysis, total nitrogen analysis and extractable ions (P, K, Ca, Mg, Fe, Cu, Mn, Zn, Al, Na, and S) completed by Johanna Curry (Soil And Plant Analytical Lab at the Great Lakes Forestry Centre in Sault Ste. Marie, Ontario). Total carbon and nitrogen were determined using a NCS combustion analyzer (Model Vario EL III, Elementar Americas Inc., Mt. Laurel, NJ, USA). Exchangeable cations were determined in unbuffered 1 M NH4Cl solution (Kalra & Maynard, 1991) obtained from a mechanical vacuum extractor. Solution concentrations were determined with a Varian Vista simultaneous axial inductively coupled argon plasma (ICAP) emission spectrometer. Total elements in LFH horizons were extracted through an HNO3/H2O2 microwave digestion and then determined with a Varian Vista simultaneous axial inductively coupled argon plasma (ICAP) emission spectrometer (Kalra & Maynard, 1991). Total carbon, total nitrogen and extractable ions (P, K, Ca, Mg) for Haliburton residues were estimated using data from Rudz (2013) as the sampling protocol for CWD utilized, as well as the forest surveyed were the same.

2.2.5 Statistical Analysis

2.2.5.1 T-RFLP community analysis

Differences between T-RFLP microbial communities in CWD residues were compared using DCA with the vegan package provided by R statistical software. OTU peaks were analyzed for presence/absence in samples as those species that could be detected by T-RFLP were assumed to be a large component of the total microbial community (R Core Team, 2013; Oksanen et al., 2013). An Adonis test using an eigenvalue method was performed to determine

49 whether there were significant differences between groupings (Oksanen et al., 2013). The variance explained by each factor was used to determine which subsets of data should be further investigated. Relationships between community composition and sample physical and chemical characteristics were determined using an eigenvalue based dbRDA where axis were determined using forward selection.

2.2.5.2 Pyrotag sequencing analysis

454 data were analyzed in QIIME pipeline and R packages phyloseq, indicspecies and vegan (Caceres & Jansen, 2013; R Core Team, 2013; McMurdie & Holmes, 2013; Oksanen et al., 2013) Indicator species were identified using the Indval analysis in the indicspecies package. Only those OTUs that were significantly associated with a treatment or combination treatments (p-value < 0.05) were considered indicator species and retained in the dataset utilized for chart and figure construction. Richness and diversity were calculated in vegan. Relationships between community composition and sample physical and chemical characteristics were determined using an eigen value based dbRDA where axis were determined using forward selection.

2.3 Results

The three environments from which CWD were sampled were very different systems which was apparent upon visual inspection. Chapleau residues had extensive moss cover, whereas moss cover on Haliburton residues were limited to upper portions of log material and samples from Mississauga did not feature mosses on the surfaces. Also, in Chapleau systems, all CWD in decay classes 4 and 5 had extensive moss cover, while moss was not present on all late decay class CWD residues in Haliburton. One of the most easily recognized differences between the CWD in Haliburton and Mississauga compared to the residues in Chapleau was the increased presence of fungal fruiting bodies on residues. On the logs sampled in this study, 22 incidences of fruiting bodies growing on CWD were documented in Haliburton, compared to only 2 incidences in Chapleau and 2 incidences in Mississauga residues (though there may have been more if all decay classes were sampled). Wood chemistry was different between sites with Chapleau residues having higher carbon content and lower N, P, K, Ca and Mg in comparison to Haliburton residues (Table 5). However, late decay wood (decay class 4 and 5) in both sites showed higher levels of N, P, K, Ca and Mg in comparison to early decay wood (decay class 1 and 2). Late decay concentrations of Ca and Mg were quite a bit higher in Haliburton wood

50 compared to Chapleau wood, while early concentrations were closer. There could have been some bias in the Chapleau measurements due to mass loss that were not present in the Haliburton data (Rudz, 2013)

2.3.1 Microbial Richness and Diversity

With the existing number of replicates, diversity and richness were not found to be significantly different (p < 0.05) between decay classes for bacterial or fungal TRFLP data. There was no consistency to any patterns of diversity and richness reduction or increase through decay derived from T-RFLP data.

2.3.2 Community Structure

2.3.2.1 TRFLP-based communities

Eigen-value based Adonis tests of the TRFLP-based community structure of the entirety of the data set indicated significant differences between the different sampling locations on each log as well as between the different sites (Table 6, Table 7). These were visually apparent when investigated using detrended correspondence analysis and used to guide data subsetting in order to investigate more subtle community differences that could have been masked by overall trends (Figure 18). Interestingly, the community composition in Mississauga residues didn’t appear to be more similar to Haliburton residues compared to Chapleau communities (Figure 18). The site differences were present in both weighted (TRFs were analyzed using abundance and presence/absence) and unweighted (TRFs were analyzed using presence/absence only) datasets. Haliburton CWD did not have the significant difference between inner and outer wood communities that was present in Chapleau samples (Table 6, Table 7). The sites were also different in that patterns in Chapleau CWD communities were more apparent in weighted datasets, whereas patterns in Haliburton communities were more apparent in unweighted datasets. Adonis tests revealed significant differences between decay classes in Chapleau outer wood surface communities, Haliburton wood communities and both Haliburton and Chapleau soil communities (Table 6, Table 7). However, only wood communities (not soil) were visually distinct upon investigation with DCA (Figure 19, Figure 20).

2.3.2.2 Pyrotag sequencing communities

DCA of bacterial pyrotag sequencing communities revealed separation based on site as well as decay status (Figure 21). Many of the bacterial OTUs specific to early decay were site- specific OTUs as well (Figure 22). Consistent with the T-RFLP approach, similarities between Chapleau and Mississauga bacterial communities were stronger than those between Mississauga and Haliburton (Figure 22). Fungal pyrotag sequencing revealed a similar trend, though less distinct, there wasn’t nearly as much difference between Chapleau fungal communities and Haliburton communities (Figure 23). There were still site specific OTUs that caused minor differentiation between Haliburton and Chapleau but site specificity of OTUs did not relate to decay, most OTUs in the ordination were specific to early decay, or generalists which were present in all decay stages (Figure 24).

2.3.3 Indicator Species

Many bacterial indicator species (species found to be significantly associated with a particular decay status through indicator species analysis) were of very low average relative abundance (abundance < 1%) were present in late decay, but not early decay in both Haliburton and Chapleau CWD. There was also a similar increase in abundance of OTU 8 in late decay samples (abundance > 1%). Some OTU’s only seemed to be important in one residue type; otu 2099 was almost 4% of the bacterial community composition of Haliburton residues, but was almost absent in Chapleau communities (Figure 25, Figure 26). The family Acidobacteriaceae as a group were a higher proportion of the bacterial community in Chapleau wood. OTUs within the Acidobacteriaceae appeared to have different decay specificities, at least in Chapleau residues: 10, 30, 46, 253, 342, 839 and 1347 appear to be associated with late decay; whereas, 19, 479, 2281 and 2387 represent a higher proportion of total community in early and mid decay (Figure 26, Table 8).

Most fungal indicator species were found in early decay. Some of these OTUs were large components of the total fungal community. The main indicator species in Haliburton were represented by two OTUs, 1539 and 1652 that represented averages of 9.5% and 4.7% of the total fungal community (Figure 27). The most abundant indicator species in Chapleau samples was represented by OTU 815 which was 7.4% of the total fungal community (Figure 28). OTUs

1539 and 836 were not identified effectively, but 1652 was identified as belonging to the genus Cuphophyllus (Table 9).

2.3.4 Wood Chemistry and Linkages to Community Structure

PCA ordination of wood chemistry from samples utilized for pyrotag sequencing showed a decay-class separation corresponding to Total N and P. This separation was more distinct in Haliburton samples, as the Chapleau early- and mid- decay stages ordinated together (Figure 29). When a broader set of samples was investigated, Chapleau communities did not separate by decay class (Figure 30). Wood chemistry, particularly total N and P, changed clearly with decay stage at Haliburton (Figure 31). RDA explorations of T-RFLP community structure and chemical variables identified phosphorus as the chemical driving differences in Chapleau inner wood and Haliburton communities (Figure 32, Figure 33). Sulphur was the chemical found to drive differences between Chapleau outer wood communities (Figure 34).

2.4 Discussion

2.4.1 Microbial community change through the decay process

Decaying wood is a dynamic system; throughout the decay process, the biological, physical and chemical characteristics of the wood are irreversibly transformed (Harmon et al., 2004; Heilmann-Clausen & Christensen, 2003; Maser et al., 1979). The T-RFLP based outer wood communities in Chapleau, and both the inner and outer wood communities in Haliburton clearly changed along this spectrum (Figure 19, Figure 20). Many nutrients, physical characteristics and biological parameters are involved in the relationship between microbial taxa and CWD decay status. Phosphorus is a limiting nutrient in the Haliburton forest, and also potentially in boreal soils, so it is not surprising that this had a relationship to community change in this study (Figure 32, Figure 33) (Gradowski & Thomas, 2006; Maynard & Paré, 2014). Also, microbial communities have been shown to respond to phosphorus gradients in soil, and thus it may follow that they were responding to them in wood as well (Cruz et al., 2009; DeForest & Scott, 2010). Phosphorus has been shown to increase through decay (Laiho & Prescott, 1999; Rudz, 2013), and contrary to phosphorus affecting the microbial communities, it could be that the organisms in the wood were altering the phosphorus content through active import Interestingly, sulphur was the most related to decay differences of the measured

53 elements/nutrients in the Chapleau wood outer surface microbial community structure (Figure 34). Sulphur is a key nutrient in certain essential amino acids and many secondary compounds and is in especially low concentrations in wood. Fungi have adapted to utilize to the sulphur bound in wood tissues, or import it from elsewhere (Schmalenberger et al., 2011). The forms of sulphur made available after fungal degradation of tissues could lead to further changes in microbial community structure. There may be more chemical stability within the Chapleau wood, as the chemical composition in these samples did not follow as linear a pattern associated with increasing decay as with Haliburton wood (Figure 30, Figure 31).

The nitrogen and phosphorus chemistry of CWD, in the context of the pyrotag sequencing derived community structure, appeared important, particularly as nitrogen and phosphorus proportionally increase in CWD through decay (Figure 30) (Hafner et al., 2005; Krankina et al., 1999; Laiho & Prescott, 1999, 2004; Rudz, 2013). It may be that bacterial communities were more strongly linked to these chemical changes because of their dependence on their immediate environment for nutrients. Fungi may be less responsive because they can access these elements from soils and thus their own complexity structure is linked to carbon source complexity, wood physical characteristics, and presence of rhizomorphs (Figure 24) (Kubicek, 2013; Stokland et al., 2012b). It was odd that fungi were not more responsive across decay spectra as fungal succession has been documented in past studies (Jönsson et al., 2008; Kubartová et al., 2012; Rajala et al., 2011, 2012). There might be less late decay specific fungi because of the strategy of fungal growth. Fungi are often considered as greedy organisms; the life strategy of fungi involves initial seizure of resources and then monopolization of the use of that resource. Fungi have various adaptations that encourage this; hyphal growth allows for quick degradation of useful resources, and the retained hyphal structures store these resources and prevent other organisms from accessing them (Boddy, 2001; de Boer & van der Wal, 2008; Mueller & Foster, 2004). Fungi are also notorious producers of anti-biotic compounds, which they use to enhance their ability to dominate a resource until its utility has been extracted (Mueller & Foster, 2004; Susi et al., 2011). Once fungi exhaust these resources, they will then move onto a new, more nutrient rich environment, and organisms adapted to use the more difficult to degrade compounds that remain can become more prevalent. Though this relationship was not as readily apparent with ordination approaches, indicator species associated with particular decay states of wood demonstrated an apparent association of fungal OTUs with early

54 and mid decay, and many bacterial OTUs with late decay stages (Figure 25, Figure 26, Figure 27, Figure 28).

2.4.2 Site specificity of microbial communities

There were clear differences between microbial communities across the 3 forests studied, a pattern that was expected at least for early decay residues. Not only did the sites have different climate conditions that could impact the moisture status of wood, but soil characteristics and species of decaying wood was different across sites as well (Figure 18). The combination of environment, decay status and original residue types relate to CWD types that differ in chemistry and carbon content. The similarities between Chapleau and Mississauga, especially as revealed by pyrotag sequencing analysis could potentially be because of the similar precipitation rates between these two sites, or because both were old growth forests (Figure 22), but whatever the reason, it is intriguing that the sugar maple residues of the same decay state generally had disparate microbial communities between Haliburton and Mississauga. Harvesting history has been shown to effect future CWD decomposition rates and decomposer communities, so the lack of harvesting in Chapleau and Mississauga is a plausible explanation for the similarities in communities across these sites (Kebli et al., 2012; Shorohova et al., 2008). Perhaps this was the case, where similar species that are resistant to the stresses introduced by harvesting have thrived in both environments. If not caused by land-use management, similar communities might also be a response to the similar precipitation regimes on these sites (both were modestly drier than Haliburton). The dependency on environmental moisture is especially true of bacteria in which the similarity between Chapleau and Mississauga communities was apparent in pyrotag sequencing data. The lack of clear fungal response to these differing conditions could be because of the reduced dependence of fungi on environmental moisture through the water scavenging adaptations of many fungal species (Boddy & Watkinson, 1995; Marjanovic & Nehls, 2008; Mueller & Foster, 2004). Bacteria are dependent on their immediate environment, whereas fungi can often import water over distances to support growth in a water-scarce, nutrient rich substrate (Boddy & Watkinson, 1995; van Hees et al., 2006).

2.4.2.1 Spatial considerations of microbial community structure within logs

The discussion of wood decomposer communities is usually in the context of whole-log processes (Brunner & Kimmins, 2003; Kebli et al., 2012; Rajala et al., 2010, 2011). Interestingly however, it appears that CWD microbial communities and decay processes were different in different parts of the log, but patterns were not consistent across forest types or tree species. This was illustrated by the difference between inner and outer communities within Haliburton Sugar Maple CWD, but not Chapleau Jack Pine CWD (Figure 19, Figure 20). There are numerous potential reasons for why these differences were seen. The residues were dissimilar, being from different species of wood, which most likely have different decay dynamics due to chemical and physical differences. Different tree species have been shown to have different decay rates and community composition under controlled conditions (Freschet et al., 2012; Prewitt et al., 2014). The lack of change as decay progressed in inner Chapleau CWD tissue may have been due to the difficulty of colonization of the inner areas, due to the structure of the woody material (Freschet et al., 2012). Perhaps Acer heartwood was more easily penetrable to fungi than Pinus and thus allowed for the different community compositions seen through the decay classes in both inner and outer Haliburton CWD (Harmon et al., 2004; Schwarze, 2007).

The environment surrounding CWD residues could also influence the decay process. The surface of late decay CWD in Chapleau was consistently colonized by mosses whereas moss cover on Haliburton residues was inconsistent. This could have helped introduce mycorrhizal hyphae, possibly advancing the decay of CWD outer surfaces (Botting & DeLong, 2009; Kauserud et al., 2008). Outer surfaces are also exposed to multiple avenues of inoculation: deposition of spores from air and precipitation; contact with soils; transmission through insects and animal contact. Though the surfaces of Chapleau CWD later came to be dominated by bryophytes, perhaps the past exposure of outer surfaces provided a sufficient reservoir of biodiversity to that successive community dynamics were possible throughout the decay process. Exposure changes wood characteristics on the outer surfaces of wood through physical as well as biological processes. The loss of bark, which is significantly different in structure to interior wood would be one of these alterations (Harmon et al., 2004; Maser et al., 1979). Another physical process would be leaching, as interior wood could have more stability than exterior surfaces, particularly in early decay stages where water penetration into wood is limited without

56 prolonged exposure to extremely wet conditions (Edwards & Jarvis, 1982; Freschet et al., 2012; Prewitt et al., 2014; Susi et al., 2011). The moisture present in these external surfaces during wetting events could increase the competitiveness of bacterial species, and increase their diversity and activity on these surfaces (Greaves, 1971; Harmon et al., 2004).

2.4.2.2 Influence of site characteristics on decay stage indicator species

The characteristics of the wood species likely contributed to the different responses of indicator species within Chapleau and Haliburton wood residues. The prevalence of Cuphophylis during early decay of Haliburton residues may indicate it was acting as a saprotroph, though the genus is thought to be associated with mosses, moss growth is more common in later stages of decay and this genus was reduced in later decay stages, nor was it as present in heavily moss covered Chapleau samples (Figure 27, Figure 28, Table 9) (Botting & DeLong, 2009; Harmon et al., 2004; Maser et al., 1979). The increased variety of early decay stage indicator species in Haliburton could have occured because of the different primary agents of mortality in each ecosystem. Haliburton historically, was high-graded, leaving poor quality seed stock that resulted in a forest of trees susceptible to disease, so there might be more plentiful hyphal inoculation possibilities for facultative necrotrophs as well as more CWD sourced from necrotrophic fungi. The most abundant early decay organism was part of the Leotiomycetes, a family of fungi that contains many plant-disease causing species (Figure 27) (Sanoamuang et al., 2013; Wang et al., 2006). The main agent of mortality in Chapleau is wind and fire, and few trees with indications of fungal infection were seen. It makes sense that Sistotrema was in higher abundance in Chapleau wood, as Sistotrema are saprobic on wood; it is able to monopolize the as yet uncolonized CWD from physically introduced mortality events (Adair et al., 2002; Hibbett & Donoghue, 2001). Other prevalent fungal OTUs seen in the Haliburton CWD could be parasitic fungi that have not been fully characterized. This may indicate that the mechanism of mortality could be a large influence on the degradation of these residues in later years, which has been found in a previous studies (Kebli et al., 2012; Stokland et al., 2012a). If this was the cause of the smaller amount of influential fungi in Chapleau residues, providing mostly wood of slash- origin to a forest may reduce fungal diversity in future CWD communities. If these areas were to be reclaimed as natural forest areas, the natural mortality of trees could also be affected through a reduction in necrotrophic fungi. The Acidobacteriaceae were likely only important in Chapleau Pine CWD because of its acidic characteristics, or perhaps because of the unique phenols in the

57 tissues. Perhaps the nature of Chapleau CWD has restricted the types of bacteria that can flourish in these materials, which has allowed the Acidobacteriaceae to diversify and adapt to the different environments through the decay process. Those Acidobacteriaceae species present in early decay could be fulfilling some of the same functions as Burkholderia in Haliburton CWD. It is difficult to determine their function, as the Acidobacteriaceae are particularly underrepresented in pure culture, despite being detected in many acidic environments (Parte et al., 2010). Burkholderia could be using the free sugars in freshly necrotic tissue, and those released by fungal enzymes. The plentiful sugars could induce nitrogen fixation activity that is present in members of Burkholderia, should conditions allow (Brenner et al., 2005). It is also possible that Burkholderia may have been present as an endophytic bacteria during the live activity of the wood, and be present in larger numbers because of its previous association with the live plant. Burkholderia is a known endophyte of willow and aspen, and has been cultivated from cutting of trees (Pirttilä & Frank, 2011). Endophytes cultured from species of crop plants have exhibited antifungal associations with plants, and the presence of bacteria introduces an element of resource competition that can inhibit fungal growth (Paul et al., 2013; Pirttilä & Frank, 2011). The particular Burkholderia species may have been an endophyte particularly adapted to taking advantage of early fungal degradation of wood tissues. In both systems it is likely that the different suites of organisms were contributing similar functions through the decay process, though those activities are conjecture, as the characterization of microbial species are far from complete.

2.4.3 Consistency in the decay process between distinct forest types

Despite unique decomposer community components in early decay, between site similarities were present and became more prevalent in later decay stages. The elevation of bacterial OTUs shared between sites that occurred in late decay may have been due to initially distinct unique structures becoming more structurally similar as residues become highly decayed (Figure 25, Figure 26). The decreased relative abundance of many late decay bacterial indicator species could be because of competition for remaining resources, a broader suite of remaining organic substituents or introduction of greater surface areas and diversities of microenvironments within the late decay log (Chen et al., 2001; Harmon et al., 2004; Krankina et al., 1999; Maser et al., 1979). It is likely that a complex combination of these physical and biological characteristics allowed a later decay stage CWD to host such high microbial diversity. The increased presence

58 of Methylocystis in late decay wood could be an indication that late stage wood experiences periods of water excess, as this genus requires methane or methanol as a carbon source, and biogenic methane is most commonly produced in anoxic environments (Benstead & King, 1997; Dedysh et al., 2007; Le Mer & Roger, 2001). Early decay stages could possess insufficient porosity to enable the water saturation required for methane production or the organisms responsible could require nutrients in forms present only in later decay stages. Fungi seem to be more important in early decay which corresponds with the common life strategy of many fungi: grab the “good” resources and then use them to reproduce (Boddy, 2001). Competition could be controlling the abundance of those species without the ability to effectively penetrate into wood tissues, and break down the easily available simple sugars still present in early decay wood (Boddy, 2001; Harmon et al., 2004; Jönsson et al., 2008; Maser et al., 1979). Bacteria may just be more competitive when it comes to breaking down the more recalcitrant compounds remaining in late decay residues. In summation, decay seems to be guided first by important degradative activities of site-specific fungal species, and then move towards a bacterial dominated process with less site specificity.

2.4.4 Interaction of wood and soils through decay stages

The differences between soil communities in Chapleau and Haliburton were generally expected as the different soil types, vegetation and climate conditions all have the potential to influence microbial communities (Evans & Wallenstein, 2011, 2014; Lukow et al., 2000; Siira- Pietikäinen et al., 2001; Steinweg et al., 2012; Tiessen et al., 1994). It was strange that the diverse characteristics of CWD at different decay stages were not influencing the soil communities in direct contact with them. It may have been the case that T-RFLP did not have the resolution necessary to detect small phylogenetic differences that may have been occurring, as T- RFLP fragments can represent multiple species, because closely related species can produce similarly sized fragments, whereas high throughput techniques (such as 454 and Illumina sequencing) can discriminate unique sequences more effectively (Hafner & Groffman, 2005; Rudz, 2013; Spears & Holub, 2003). Also, because soil cores were taken from the side and angled so as to not disturb CWD that was also being sampled too thoroughly, they could have been influenced more heavily by litter deposition (i.e. a sample more directly under the log may have been more influenced by the CWD decay processes. This finding of dissimilar communities still showed that close contact of soils and CWD did not heavily influence microbial community

59 composition. The lack of notable effects on the surrounding soils may have been because of localization of fungal explorative structures such as rhizomorphs or hyphal aggregates in the sampling area (Fransson et al., 2004; Lehto & Zwiazek, 2011; Tedersoo & Kõljalg, 2003). It could also be because the magnitude of CWD leachates was not sufficient or consistent enough to have a transformative effect on the microbial communities in adjacent and underside soils. CWD in clearcut conditions may have a greater effect because of the loss of regular litter inputs from soils and become a more important resource for nutrient provision to soils in the short term (Hafner et al., 2005). Though, this is not necessarily the case as was seen the study outlined in Chapter 1 and others (Kataja-aho et al., 2011; Siira-Pietikäinen et al., 2001).

2.5 Conclusion

Microbial diversity and community structure in decaying wood is influenced by time/decay stage, location within the log, environment and residue type. There are distinct community compositions in different environments, residue types and decay states. The organisms involved are likely important contributors to the nutrient cycling involved with CWD in forest environments. There is truly such variation in microbial communities within CWD that the estimation of biodiversity losses is a daunting task. There may have been some resilience in community structure, through those species that are retained throughout the decay process and those that appear to be present in diverse environments, but specific fungal organisms seemed to be important to the initiation of decay. The bacterial species that become important in later decay stages may source from nearby litter, and could be less affected by intensified wood removal processes over time. Early decay organisms may not be dispersed as effectively in a management system with less wood debris supply to the forest floor. Soils may not provide the refuge of biodiversity that is necessary to maintain important organisms, as those assessed in this study did not seem to have any response to the decay status of the wood they were in contact with. This emphasizes the necessity of retaining these features in forests lest we disturb decay cycles, potentially altering nutrient dynamics and decay rates of CWD.

Chapter 2 Figures

Figure 18: Weighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from wood samples from only inner and outer wood samples.

Figure 19: Weighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from outer Jack Pine wood sampled in Chapleau.

Figure 20: Unweighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from inner and outer Sugar Maple wood sampled in Haliburton.

Figure 21: Bacterial pyrotag sequencing communities of DNA isolated from CWD samples in Chapleau, Mississauga and Haliburton displayed using detrended correspondence analysis. OTUs displayed in the ordination are coloured based on their presence or absence within a specific site (Chapleau, Mississauga and Haliburton). Site fonts are coloured based on the decay state represented by the pooled sample.

Figure 22: Bacterial pyrotag sequencing communities of DNA isolated from CWD samples in Chapleau, Mississauga and Haliburton displayed using detrended correspondence analysis. OTUs displayed in the ordination are coloured based on their presence or absence within a specific decay class. Site fonts are coloured based on the decay state represented by the pooled sample.

Figure 23: Fungal pyrotag sequencing communities of DNA isolated from CWD samples in Chapleau, Mississauga and Haliburton displayed using detrended correspondence analysis. OTUs displayed in the ordination are coloured based on their presence or absence within a specific site (Chapleau, Mississauga and Haliburton). Site fonts are coloured based on the decay state represented by the pooled sample.

Figure 24: Fungal pyrotag sequencing communities of DNA isolated from CWD samples in Chapleau, Mississauga and Haliburton displayed using detrended correspondence analysis. OTUs displayed in the ordination are coloured based on their presence or absence within a specific decay class. Site fonts are coloured based on the decay state represented by the pooled sample.

Early Mid Late (0−10 years) (1−18 years) (4−40 years)

1164 Sphingomonadaceae 255 563 Rhodospirillaceae 1954 135 Acetobacteraceae 177 278 158 1024 694 Bradyrhizobiaceae 1167 2257 Beijerinckiaceae 121 316 67 889 Methylocystaceae 233 8 159 Beijerinckiaceae 411 Methylocystaceae 832 Bradyrhizobiaceae 33 Beijerinckiaceae 48 197 Hyphomicrobiaceae 51 Methylocystaceae 1960 1339 Caulobacteraceae 249 550 Xanthomonadaceae 42 Fabaceae 760 Sinobacteraceae 501 Hyphomicrobiaceae 160 300 Burkholderiaceae 2099 342 1347 839 30 588 15 352 1832 848 46 1449 253 10 Acidobacteriaceae 409 36 1161 1308 475 127 136 611 846 479 19 2387 2281 581 138 129 290 Koribacteraceae 123 61 470 147 Streptomycetaceae 107 Enterobacteriaceae 306 Conexibacteriaceae 325 Clostridiaceae 224 Peptococcaceae 185 76 Chitinophagaceae 204 Hydrogenothermaceae 0.00 0.01 0.02 0.03 0.04 0.00 0.01 0.02 0.03 0.04 0.00 0.01 0.02 0.03 0.04

Figure 25: Average relative abundance of Bacterial OTU’s from pyrotag sequencing of DNA isolated from Haliburton CWD residues. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

Early Mid Late

Figure 26: Average relative abundance of Bacterial OTU’s from pyrotag sequencing of DNA isolated from Chapleau CWD residues. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

Early Mid Late (0−10 years) (1−18 years) (4−40 years)

323 1670 1488 Dothideomycetes 373 551 1675 1297 1539 0.095 9 854 Leotiomycetes 792 901 1599 814 Lecanoromycetes 422 1754 865 1567 755 Schizosaccharomyces 753 748 483 Lecanoromycetes 949 1503 Pezizomycetes 97 492 Leotiomycetes 1575 Lecanoromycetes 664 1024 Pezizomycetes 1153 Saccharomycetes 352 Dothideomycetes 1127 363 1648 1724 775 Leotiomycetes 1526 Archaeorhizomycetes Ascomycota 1730 1338 1741 1415 Archaeorhizomycetes 1135 222 643 Archaeorhizomycetes 558 43 Leotiomycetes 295 310 Leotiomycetes 430 Archaeorhizomycetes 555 608 Sordariomycetes 850 199 1616 578 887 894 Sordariomycetes 665 893 84 930 1362 Dothideomycetes 8 1826 Leotiomycetes 472 1576 Sordariomycetes 1841 Dothidiomycetes 354 787 Eurotiomycetes 413 Sordariomycetes 1379 1693 Eurotiomycetes 790 1328 Agaricomycetes Basidiomycota 319 1479 Sordariomycetes 1655 Ascomycota 1091 981 Agaricomycetes 1545 473 Basidiomycota 75 718 Agaricomycetes 336 Eurotiomycetes 140 Pezizomycetes 1832 1577 Eurotiomycetes Ascomycota 556 190 Leotiomycetes 1243 Agaricomycetes 1120 1437 339 1652 0.047 239 671 1494 437 441 836 815 1 89 Agaricomycetes 771 613 Basidiomycota 104 923 205 1769 1715 1844 1710 1147 1653 945 146 Agaricomycetes 793 307 955 169 Chytridomycetes Chytridomycota 111 24 Agaricomycetes Basidiomycota 30 Conthreep 359 Dacrymycetes Cilophora 223 Microbotryomycetes 414 Tremellomycetes 355 Dacrymycetes 1765 183 67 389 Basidiomycota 688 Wallemiomycetes 1803 391 Dacrymycetes 810 Pucciniomycetes 276 Agaricomycetes 136 0.000 0.005 0.010 0.015 0.020 0.025 0.000 0.005 0.010 0.015 0.020 0.025 0.000 0.005 0.010 0.015 0.020 0.025

Figure 27: Average relative abundance of Fungal OTU’s from pyrotag sequencing of DNA isolated from Haliburton CWD residues. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

Early Mid Late

323 1670 1488 Dothideomycetes 373 551 1675 1297 1539 9 854 Leotiomycetes 792 901 1599 814 Lecanoromycetes 422 1754 865 1567 755 Schizosaccharomyces 753 748 483 Lecanoromycetes 949 1503 Pezizomycetes 97 492 Leotiomycetes 1575 Lecanoromycetes 664 1024 Pezizomycetes 1153 Saccharomycetes 352 Dothideomycetes 1127 363 1648 1724 775 Leotiomycetes 1526 Archaeorhizomycetes Ascomycota 1730 1338 1741 1415 Archaeorhizomycetes 1135 222 643 Archaeorhizomycetes 558 43 Leotiomycetes 295 310 Leotiomycetes 430 Archaeorhizomycetes 555 608 Sordariomycetes 850 199 1616 578 887 894 Sordariomycetes 665 893 84 930 1362 Dothideomycetes 8 1826 Leotiomycetes 472 1576 Sordariomycetes 1841 Dothidiomycetes 354 787 Eurotiomycetes 413 Sordariomycetes 1379 1693 Eurotiomycetes 790 1328 Agaricomycetes Basidiomycota 319 1479 Sordariomycetes 1655 Ascomycota 1091 981 Agaricomycetes 1545 473 Basidiomycota 75 718 Agaricomycetes 336 Eurotiomycetes 140 Pezizomycetes 1832 1577 Eurotiomycetes Ascomycota 556 190 Leotiomycetes 1243 Agaricomycetes 1120 1437 339 1652 239 671 1494 437 441 836 815 0.074 1 89 Agaricomycetes 771 613 Basidiomycota 104 923 205 1769 1715 1844 1710 1147 1653 945 146 Agaricomycetes 793 307 955 169 Chytridomycetes Chytridomycota 111 24 Agaricomycetes Basidiomycota 30 Conthreep 359 Dacrymycetes Cilophora 223 Microbotryomycetes 414 Tremellomycetes 355 Dacrymycetes 1765 183 67 389 Basidiomycota 688 Wallemiomycetes 1803 391 Dacrymycetes 810 Pucciniomycetes 276 Agaricomycetes 136 0.000 0.005 0.010 0.015 0.020 0.025 0.000 0.005 0.010 0.015 0.020 0.025 0.000 0.005 0.010 0.015 0.020 0.025

Figure 28: Average relative abundance of Fungal OTU’s from pyrotag sequencing of DNA isolated from Chapleau CWD residues. OTU’s shown were determined to be significant (p < 0.05) using indicator species analysis.

1.5 Early

Mid 1.0 TotalC Late

TotalNP 0.5

0.0 Ca Mg PC2 − 0.5

K − 1.0

− 1.5 Chapleau

Haliburton − 2.0 −2 −1 0 1 2 3 PC1

Figure 29: PCA of chemical characteristics representing pooled CWD samples used for pyrotag sequencing analysis.

Figure 30: PCA of chemical characteristics representing pooled Chapleau CWD samples used for T-RFLP analysis.

Figure 31: PCA of chemical characteristics representing pooled Haliburton CWD samples used for T-RFLP analysis.

Figure 32: Forward selection RDA of Chapleau Inner T-RFLP-based communities against chemical data.

Figure 33: Forward selection RDA of Haliburton T-RFLP-based communities against chemical data.

Figure 34: Forward selection RDA of Chapleau outer T-RFLP-based communities against chemical data.

Chapter 2 Tables

Table 5: Haliburton and Chapleau wood chemistry as percentage of elements by dry weight. Values are mean of results percentage from samples with standard error in parentheses. Site Decay Total N Total C P K Ca Mg Stage Chapleau 1 0.21(0.05) 51.31(0.64) 0.012(0.003) 0.029(0.003) 0.23(0.08) 0.026(0.005) 2 0.26(0.06) 52.16(0.75) 0.013(0.005) 0.033(0.005) 0.32(0.12) 0.028(0.005) 3 0.28(0.06) 51.11(0.38) 0.016(0.004) 0.043(0.010) 0.20(0.04) 0.023(0.002) 4 0.65(0.09) 52.97(0.38) 0.035(0.006) 0.062(0.008) 0.30(0.05) 0.034(0.004) 5 1.00(0.22) 52.00(0.78) 0.057(0.015) 0.065(0.013) 0.43(0.08) 0.051(0.012) Haliburton 1 0.11(0.01) 47.87(0.30) 0.009(0.001) 0.218(0.023) 0.27(0.05) 0.037(0.005) 2 0.05(0.01) 48.41(0.21) 0.008(0.001) 0.168(0.026) 0.38(0.05) 0.037(0.006) 3 0.37(0.09) 49.17(0.30) 0.022(0.003) 0.097(0.020) 0.76(0.19) 0.066(0.019) 4 0.62(0.05) 49.43(2.28) 0.037(0.007) 0.132(0.023) 1.64(0.33) 0.123(0.026) 5 1.42(0.10) 45.99(1.51) 0.067(0.004) 0.077(0.008) 1.61(0.27) 0.085(0.017)

Table 6: Adonis significance and variance explained for TRFLP communities based on binary tables of bacterial and fungal community. Data is displayed within the cells as p-value/F2 value. Dataset Category Location on Site Decay Class log Complete 0.001/0.038 0.001/0.037 0.001/0.025 Wood only 0.001/0.010 0.001/0.041 0.001/0.042 Soil only NA 0.001/0.079 0.001/0.067 Haliburton Wood 0.286/0.013 NA 0.001/0.286 Chapleau Wood 0.001/0.023 NA 0.001/0.085 Chapleau Inner Wood NA NA 0.029/0.127 Chapleau Surface Wood NA NA 0.001/0.067 Chapleau Soil NA NA 0.005/0.063 Haliburton Soil NA NA 0.001/0.178

Table 7: Adonis significance and variance explained for TRFLP communities based on abundance tables of bacterial and fungal community. Data is displayed within the cells as p- value/F2 value. Dataset Category Location on Site Decay Class log Complete 0.001/0.035 0.001/0.033 0.001/0.027 Wood only 0.001/0.013 0.001/0.044 0.001/0.041 Soil only NA 0.001/0.074 0.004/0.067 Haliburton Wood 0.130/0.015 NA 0.001/0.075 Chapleau Wood 0.001/0.029 NA 0.001/0.780 Chapleau Inner Wood NA NA 0.153/0.122 Chapleau Surface Wood NA NA 0.002/0.139 Chapleau Soil NA NA 0.004/0.168 Haliburton Soil NA NA 0.013/0.067

Table 8: OTUs identified using NCBI BLAST of Bacterial indicator species of decay status determined from pyrotag sequencing of CWD from Chapleau, Mississauga and Haliburton. Matches below 90% identity were not included.

Percent OTU BLAST Identification e_value identity 8 Methylocystis echinoides 91.55 2.00E-165 10 Terriglobus saanensis SP1PR4 95.04 0 15 Acidobacterium capsulatum ATCC 51196 93.75 0 19 Granulicella mallensis MP5ACTX8 95.1 0 30 Acidobacterium capsulatum ATCC 51196 93.07 2.00E-179 33 Beijerinckia derxii subsp. Derxii strain DSM 2328 94.9 0 36 Terriglobus roseus strain KBS 63 98.69 0 46 Acidobacterium capsulatum ATCC 51196 93.36 0 48 Rhodoplanes elegans strain AS130 95.76 0 51 Methylocystis heyeri strain H2 91.49 4.00E-174 61 Acidobacterium capsulatum ATCC 51196 92.69 0 76 Chitinophaga ginsengisegetis 92.61 0 107 Yersinia ruckeri 97.46 0 121 Methylocystis heyeri strain H2 94 0 123 Acidobacterium capsulatum ATCC 51196 91.8 2.00E-179 127 Edaphobacter aggregans 97.41 0 129 Candidatus Koribacter versatilis Ellin345 90.95 3.00E-176 135 Kozakia baliensis strain Yo-3 94.38 0 136 Granulicella tundricola 98.14 0 138 Acidobacterium capsulatum ATCC 51196 90.36 9.00E-177 147 Stretpacidiphilus carbonis strain JL 415 99.22 0 158 Afipia birgiae 34632 97.35 0 159 Beijerinckia mobilis 96.37 0 160 Rhizomicrobium electricum 91.04 2.00E-101 177 Acidisoma tundrae 94.95 0 197 Rhodoplanes elegans strain AS131 94.65 0 233 Beijerinckia derxii subsp. Derxii strain DSM 2329 92.15 2.00E-177 249 Asticcacaulis benevestitus 93.78 0 253 Terriglobus roseus strain KBS 63 96.7 1.00E-168 255 Sphingomonas oligophenolica 97.35 0 278 Kozakia baliensis strain Yo-4 92.37 0 300 Burkholderia sediminicola 95.98 1.00E-180 306 Conexibacter woesei 90.89 0 316 Methylocystis echinoides 93.26 0 325 Clostridium cellulolyticum 94.87 0.0000007 342 Terriglobus saanensis SP1PR4 92.41 0 352 Terriglobus saanensis SP1PR4 93.56 0

409 Terriglobus roseus strain KBS 64 98.65 0 411 Methylocystis heyeri strain H2 94.33 0 470 Acidobacterium capsulatum ATCC 51196 92.92 0 475 Granulicella mallensis MP5ACTX8 97.59 2.00E-177 479 Granulicella mallensis MP5ACTX8 96.83 0 550 Rhodanobacter umsongensis 97.83 0 563 Skermanella aerolata 91.73 5.00E-167 581 Acidobacterium capsulatum ATCC 51196 93.1 0 588 Acidobacterium capsulatum ATCC 51196 95.71 0 611 Edaphobacter aggregans 96.02 0 694 Bradyrhizobium oligotrophicum 96.48 0 832 Methylocapsa acidiphila strain B2 95.06 0 839 Granulicella tundricola 91.67 0 846 Acidobacterium capsulatum ATCC 51196 93.75 0 848 Edaphobacter aggregans 95.55 0 889 Beijerinckia derxii subsp. venezualae 93.39 0 1024 Afipia birgiae 34633 95.13 0 1161 Granulicella mallensis MP5ACTX8 98.44 0 1164 Novosphingobium lentum NBRC 107847 96.18 3.00E-150 1167 Afipia birgiae 34634 97.12 0 1308 Granulicella mallensis MP5ACTX8 97.42 0 1339 Brevundimonas nasdae 94.12 0 1347 Acidobacterium capsulatum ATCC 51196 93.97 0 1449 Acidobacterium capsulatum ATCC 51196 95.22 0 1832 Granulicella mallensis MP5ACTX8 93.75 0 1954 Acidisphaera rubrifaciens 95.6 0 1960 Caulobacter segnis 95.78 0 2099 Burkholderia sordidicola 99 0 2257 Methylovirgula ligni 98.12 1.00E-175 2281 Granulicella mallensis MP5ACTX8 93.59 0 2387 Terriglobus saanensis SP1PR4 93.15 0

Table 9: OTUs identified using NCBI BLAST of Fungal indicator species of decay status determined from pyrotag sequencing of CWD from Chapleau, Mississauga and Haliburton. Matches below 90% identity were not included. OTU BLAST Identification Percent e_value identity 8 Davidiella tassiana 97.32 0 30 Pseudoplatyophrya nana 98.5 0 43 Cyttaria hookeri 98.24 0

104 Sistotrema raduloides 95.41 2E-173 111 Botryobasidium simile 99.75 0 140 Sarcosphaera crassa 98.16 6E-179 169 Clydeae vasicula 99.75 0 190 Albotricha sp. AU_BD31 95.52 2E-108 205 Auricularia peltata 99.01 0 239 Psilocybe cyanescens 99.26 0 276 Steccherinum fimbratum 94.32 6E-173 310 Cyttaria hookeri 97.47 0 319 Hypocrea nigricans 95.64 9E-158 323 Tetrachaetum elegans 98.78 0 339 Archaeorhizomyces sp. FG15P2b 94.29 1E-162 430 Archaeorhizomyces sp. FG15P2b 97.95 0 437 Geminibasidium hirsutum 97.02 0 492 Blumeria graminis f. sp. Hordei 98.2 0 551 Tetrachaetum elegans 97.33 0 555 Phaeoacremonium australiense 99.74 0 556 Ascomycota sp. DIVA1 97.28 0 558 Blumeria graminis f. sp. Hordei 97.9 0 578 Tetrachaetum elegans 98.51 0 608 Lecythophora sp. 6-14c 99.26 0 613 Sistotrema raduloides 98.43 0 643 Archaeorhizomyces finlayi 98.53 8E-127 688 Geminibasidium hirsutum 95.04 2E-159 755 Schizoaccharomyces japonicus 99.11 9E-107 771 Sistotrema raduloides 98.77 0 775 Geniculospora sp. 12g-CH10 98.98 0 815 Sistotrema raduloides 99.26 0 850 Lecythophora sp. 6-14c 100 0 854 Gelatinomyces siamensis 97.28 0 901 Tetrachaetum elegans 99.51 0 945 Hyphodontia arguta 98.08 6E-122 949 Tetrachaetum elegans 98.28 0 955 Apicomplexa sp. BCS-2013c 96.62 7E-178 1024 Verpa bohemica 99.5 0 1120 Umbelopsis sp. I1A5 96.84 1E-143 1153 Dipodascopsis tothii 100 0 1415 Archaeorhizomyces finlayi 99.35 2E-152 1437 Archaeorhizomyces sp. FG15P2b 95.26 3E-100 1479 Acremonium psammosporum 97.45 9E-101 1494 Hygrocybe chloochlora 97.52 0 1503 Verpa bohemica 97.27 2E-165 1526 Archaeorhizomyces finlayi 98.5 0

1575 Gregorella humida 97.04 0 1576 Hypoxylon haematostroma 98.27 0 1599 Neoscytalidium dimidiatum 99.51 0 1652 Cuphophyllus pratensis 99.75 0 1655 Hypocrea nigricans 95.11 9E-107 1670 Capnobotryella sp. MA 4775 98.19 3E-125 1741 Archaeorhizomyces sp. FG15P2b 99.13 8E-108 1803 Lecythophora sp. 6-14c 94.77 7E-121 1826 Neofabrea sp. XY-2013 98.8 1E-118 1841 Botryosphaeria laricina 98.96 0

General Conclusion

Though there have been reports of microbial community and chemical differences between intensified harvesting systems compared to typical stem-only harvesting, only the most extreme form of intensification, blading, was different from stem-only harvests in the study performed in Chapter 1 (Hartmann et al., 2012; Thiffault et al., 2011). There are of course, studies that have reported a general harvesting effect, but without differences attributed to additional biomass removal treatment (Kataja-aho et al., 2011; Mariani et al., 2006; Tan et al., 2005). Currently, the effects of intensified harvesting are not consistent enough to make broad generalizations about the implementation of this type of intensified management. Though microbial communities between harvesting treatments were not apparent, the consistent difference between control and harvested sites demonstrated that harvesting disturbance changed the microbial community structure and function in harvested stands. However, the differences between harvested and fire sites showed that this change was not replicating the microbial community structure of the natural mortality event of fire. These were potentially important differences, as the Chapleau system is fire adapted and local vegetation and microbial communities likely require fire in order to undergo normal successional processes (Harmon et al., 2004; Stenlid et al., 2008). These differences could extend to survival or loss of important microorganisms that could modulate plant competition (Van Der Heijden et al., 2008). This could have been true in the sites sampled in this study, particularly since some of the OTUs exhibiting differences between harvested and fire sites were identified as a Bradyrhizobiaceae and Hyphomicrobiaceae, families of bacteria well known for having members with mutualistic plant symbiont characteristics (Denison & Kiers, 2011). It could be possible that differences could emerge after future years of wood degradation and altered nutrient input levels into the soils of the harvested plots, as the degradation of materials was minimal in the two years of sampling. Unfortunately, it could be years before changes similar to the study by Hartmann et al. (2012) emerge in Chapleau soils.

Microbial communities present during wood decay would not be informative when attempting to predict soil community changes from biomass removal (and large even age wood inputs from harvesting practices): microbial communities within wood of various decay states did not appear to have an observable relationship with the microbial communities in closely

84 associated soils. It may be that soils were not immediately impacted by wood removal, and only respond to long-term nutrient inputs (Hartmann et al., 2012; Thiffault et al., 2011). The above ground communities associated with CWD would be greatly impacted by harvesting intensification through alteration of the CWD distributions across the forest landscape. Decay was shown to be unique to an environment, regardless of residue type: it could be difficult to restore microbial communities in decaying CWD pools should there be sufficient disturbance. Harvesting events can damage existing CWD through mechanical alterations of the landscape, and the remaining (and reduced in intensified systems) slash materials are all fresh, un-colonized wood (Higgins & Lindgren, 2004; Morris et al., 1983). The decomposition communities associated with CWD on such a landscape could be altered compared to those exposed to natural disturbance events (Kebli et al., 2012; Shorohova et al., 2008; Stenlid et al., 2008). The loss of early successional decay species could be especially detrimental; decay of wood could be drastically altered by the lack of initial structural decomposition perpetrated by these species. In a landscape without trees and with reduced slash materials, necrotrophs requiring live trees could be particularly affected.

It appeared that the moisture regime of an environment had an effect on microbial community structure as well: the similarities between Mississauga and Chapleau wood may have been related to this. Moisture related impacts could be lessened in forest management systems such as selection and shelterwood cuts where site exposure post-harvest may be less extreme than in clearcut systems. The increased exposure of CWD materials could effect colonization by early decay organisms through introducing drying/rewetting stresses that may not exist in the buffered forest environment (Evans & Wallenstein, 2011). These factors could influence future decomposition communities in wood residues of harvested sites, possibly extending the time of decay if important early decay organisms are not present to begin structural decomposition processes. Reduced decomposition of wood residues would impact potentially important nutrient cycling processes and thus long-term soil characteristics (Kruys et al., 2002; Rudz, 2013; Spears & Lajtha, 2013; Spears & Holub, 2003).

The reduction of slash materials could also have positive effects from a management perspective, despite the potential negative ecological impacts. Possibly nectrotrophic organisms were some of the main fungal agents of early decay in Haliburton Forest, a reduction of slash materials could reduce the propagation of some of these organisms, and lower the potential for

85 infection in healthy trees (Stenlid et al., 2008). However, extensive reduction of these organisms could reduce available habitat by reducing the future availability of standing dead trees (Harmon et al., 2004; Maser et al., 1979). In contrast to the Haliburton environment, fungal diversity in Chapleau appeared to be more related to saprobic growth. The diversity of the saprobes responsible for this decay may be dependent on large scale wood inputs as the main natural disturbance events in boreal environments are wind and fire (Stenlid et al., 2008).

The study in Chapter 2 showed that there are inconsistencies between the decay of wood in different environments that could make policy regarding these ecological features especially challenging. The lack of microbial community consistency in the spatial context of Chapleau logs was not found in Haliburton logs: further investigation is needed to see how this may affect decomposition in these materials. The difference in the process of decomposition emphasizes the need to consider many site specific characteristics when discussing wood decomposition, particularly sources of wood inputs and site history (Kebli et al., 2012; Stokland et al., 2012a).

Into the future, the Island Lake site in Chapleau will be a valuable resource to study the effect of intensified biomass harvesting on interactions between CWD and soil microbial communities in a boreal context. In future years, as vegetation becomes more established and microbial communities might develop more discrete differences in response to the intensification gradient, the interactions of boreal vegetation and microbial communities could reveal important relationships modulating biogeochemical cycles. Also in future years, as wood decays, the effect of intensification on microbial succession through the decay spectrum should be assayed. Whether important decay organisms are impacted by the change in residue prevalence remains to be seen, but if these alterations were to occur, there could be ecological impacts affecting insects, plants and small animals (Harmon et al., 2004). Future microbial communities should be monitored to see if differences do emerge as seen by Hartmann et al., (2012), and what microbially modulated activities have significant contributions to the overall ecosystem functioning of the boreal forest.

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Appendix 1: Extended Methods

Laboratory methods

DNA Extraction

DNA was extracted from samples with the PowerPlant® DNA Isolation kit (MO BIO Laboratories Inc., Carlsbad, CA). There were deviations from the protocol. Sample incubation at 65°C was performed in “heating block”. Homogenization was performed using a 5 minute cycle on a 16 tube MiniBeadbeater TM (Biospec Products Inc., Bartlesville, OK). Samples were stored frozen for analysis at a later date.

PCR

PCR reactions were performed in an MWG AG Biotech Primus 96+ Thermocycler. Previously determined optimized cycles were utilized for the amplification of bacterial 16S ribosomal DNA, fungal 18S ribosomal DNA and archaeal 16S ribosomal DNA sequences. The primers and cycle specifications can be found in Table 1.

PCR reactions were run through a 0.15% Agarose gel for 45 minutes at 120 V. Gels were then stained with ethidium bromide for 20 minutes and visualized using 302 nm wavelength UV light in a UVP High Performance UV Transilluminator. Alternatively, gels were stained with SYBRsafeTM or REDsafeTM before being visualized..

Table 10: PCR cycle parameters and primers utilized for amplification of hypervariable regions from bacterial 16S ribosomal DNA, archaeal 16S ribosomal DNA and fungal 18S ribosomal DNA Targeted Sequence PCR cycle Primers Bacterial 16S 94°C – 5 min Eu27f AGA GTT TGA TCM TGG CTC AG

105 ribosomal gene 30 cycles of (Bräuer et al., 2006) 94°C- 1 min Eu1492R ° ACG GYT ACC TTG TTA CGA 50 C- 1 min CTT 72°C- 2 min (Bräuer et al., 2006) Final extension 72°C- 10 min Fungal 18S 94°C – 5 min Fu817f 30 cycles of TTA GCA TGG AAT AAT RRA ribosomal gene ATA GGA 94°C- 1.5 min (Edel-Hermann et al., 2004) 53°C- 1 min Fu1536r 72°C- 2 min ATT GCA ATG CYC TAT CCC CA Final extension 72°C- 10 min (Edel-Hermann et al., 2004) Archaeal 16S 94°C – 5 min Ar109f 30 cycles of ACK GCT CAG TAA CAC GT ribosomal gene (Ramakrishnan, 2001) ° 94 C- 1 min Ar912r 52°C- 1 min CTC CCC CGC CAA TTC CTT TA 72°C- 2 min (Ramakrishnan, 2001) Final extension 72°C- 10 min

T-RFLP

Bacterial and fungal ribosomal gene segments were amplified using the cycle and primer sets indicated Table 1 with a 5’ end 6-carboxyfluorescein (6-FAM) modification on the Eu27f and Fu1536r primers. The PCR products were then purified using the GenEluteTM PCR Clean- up Kit (Sigma-Aldrich Canada Co., Oakville, Ontario). Digestions of gene products were completed as described in Table 2. T-RFLP analysis was completed at the Agriculture and Food Laboratory in Guelph (University of Guelph Agriculture and Food Laboratory, Guelph, ON). T- RFLP data was preprocessed in R using a custom function created using the algorithm described by Ishii, Kadota, & Senoo, (2009) with slight modifications to accommodate the data format supplied by Guelph. The cutoff distance was set to 2bp and the final output was expressed as proportion total peak height per TRF by sample (Kaplan & Kitts, 2003).

Table 11: Restriction enzyme digestion conditions for T-RFLP analysis of hypervariable regions of bacterial 16S ribosomal DNA, archaeal 16S ribosomal DNA and fungal 18S ribosomal DNA. Purpose Enzyme Sequence Reaction conditions Reference (5’ – 3’) Archeal TaqI T’CGA 3 U at 65°C for 2 hours: with 1 µl (Lueders & 10x incubation buffer and 1 µl

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(Ar109f, bovine serum albumin in a total Friedrich, 2000) Ar912r) volume 10 µl. Bacteria MspI C’CGG 37°C for 3 hours, combine with 1 (Lukow et al., (Primers µl 10x incubation buffer and 1 µl 2000) bovine serum albumin = total Eu27f, volume 10 µl. Eu1492r) Fungi AluI and AG’CT 5 U of the restriction enzymes in a (Edel-Hermann et

(Fu817f MboI ‘GATC final volume of 10 µl for 3 h at al., 2004) 37°C. and Fu1536r)

Pyrotag sequencing

Amplicon pyrotag sequencing (bTEFAP) was originally described by Dowd et al. (2008) and has been utilized in describing a wide range of environmental and health related microbiomes including the intestinal populations of a variety of sample types and environments, including cattle (Callaway et al., 2010; Dowd et al., 2008; Williams et al., 2010). In a modified version of this process, 16S universal Eubacterial forward primer (AGRGTTTGATCMTGGCTCAG) was used to amplify fragments from the bacterial 16S rRNA gene and 18S universal Fungal forward primer (TTAGCATGGAATAATRRAATAG) was used to amplify fragments from the Fungal 18S rRNA gene. A single-step 30 cycle PCR using HotStarTaq Plus Master Mix Kit (Qiagen, Valencia, CA) were used under the following conditions: 94oC for 3 minutes, followed by 28 cycles of 94oC for 30 seconds; 53oC for 40 seconds and 72oC for 1 minute; after which a final elongation step at 72oC for 5 minutes was performed. Following PCR, all amplicon products from different samples were mixed in equal concentrations and purified using Agencourt Ampure beads (Agencourt Bioscience Corporation, MA, USA). Sequencing was performed at MR DNA (www.mrdnalab.com, Shallowater, TX, USA) utilizing Roche 454 FLX titanium instruments and reagents and following manufacturer’s guidelines.

454 data was analyzed in the quantitative insights into microbial ecology (QIIME) pipeline (Caporaso et al., 2010). 454 data was quality filtered QIIME default parameters (quality score = 25, min length=200, max length = 1000). Additional quality filtering and OTU clustering

107 was performed with the Usearch 5.2.236 program, which utilizes the UCHIME algorithm to identify chimera sequences for removal against the gold.fa dataset (Edgar, 2010; Edgar et al., 2011). De novo OTU picking with uclust was used to form the representative OTU dataset (Edgar, 2010). Bacterial taxonomy was assigned using the RDP classification algorithm against the Greengenes 13_5 database 97% confidence rep set (Wang et al., 2007; McDonald et al., 2011). Muscle was used to produce a denovo alignment of all OTU sequences (Larkin et al., 2007). Aligned sequences were filtered (entropy 0.10, gap fraction 0.80) and made into a phylogenetic tree using muscle (Edgar, 2004). Fungal taxonomy was assigned using the RDP classification algorithm against the 97% Silva database for the eukaryotic 18S ribosomal DNA gene (Quast et al., 2013; Wang et al., 2007). Muscle was used to produce a denovo alignment of all OTU sequences, which were then filtered (entropy 0.10, gap fraction 0.80) and used to make a phylogenetic tree with the muscle algorithm (Edgar, 2004).

Gene Identification

Indicator species sequences collected from cloning experiments were entered into the BLAST genome database and correlated to the closest matching documented sequence (Madden, Tatusov and Zhang, 1996). Closest match for species identification was determined using NCBI Blast using the Blastn algorithm with an expect threshold of 10, word size 11, match/mismatch Scores 2,-3, Gapcosts: Existence 5, Extension 2.

Statistical Analysis

T-RFLP community analysis

Differences between T-RFLP microbial communities in residues will be compared using DCA with the vegan package provided by R statistical software. OTU peaks were analyzed for presence/absence in samples as those species that could be detected by T-RFLP were assumed to be a large component of the total microbial community (Oksanen et al., 2013; R Core Team, 2013) An adonis test using an eigenvalue method was performed to determine whether there were significant differences between groupings (Oksanen et al., 2013). The variance explained by each factor was used to determine which subsets of data should be further investigated. Relationships between community composition and sample physical and chemical characteristics were determined using an eigenvalue based dbRDA where axis were determined using forward selection.

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Pyrotag sequencing analysis

454 data was analyzed in QIIME pipeline and R packages phyloseq, indicspecies and vegan (Caceres & Jansen, 2013; McMurdie & Holmes, 2013; Oksanen et al., 2013; R Core Team, 2013) Indicator species were identified using the Indval command in the indicspecies package. Only those species that were significantly associated with a treatment or combination treatments (p-value < 0.05) were retained in the dataset utilized for chart and figure construction. Richness and diversity were calculated in vegan. Relationships between community composition and sample physical and chemical characteristics were determined using an eigenvalue based dbRDA where axis were determined using forward selection.

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Appendix 2: Chapter 1 Additional Figures

Figure 35: Weighted DCA of TRFLP community structure based on bacterial 16S, fungal 18S and archaeal 16S genes for DNA isolated from mineral soil samples collected in 2012 in Island Lake biomass harvesting trial sites.

Figure 36: Weighted DCA of TRFLP community structure based on bacterial 16S, fungal 18S and archaeal 16S genes for DNA isolated from LFH soil samples collected in 2012 in Island Lake biomass harvesting trial sites.

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Figure 37: Weighted DCA illustrating TRFLP community structure based on bacterial 16S, fungal 18S and archaeal 16S genes. Points represent soil samples from different sites sampled within a given sampling year. Confidence ellipses of 95% were placed around clusters by year.

Figure 38: PCA of SIR and hydrolase activity based metabolic profiles from Island Lake Biomass trial mineral soils sampled in 2012.

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Figure 39: Shannon Diversity indices of Bacterial pyrotag sequencing community data for Chapleau mineral soils sampled in 2013.

Figure 40: Shannon Diversity indices of Bacterial pyrotag sequencing community data for Chapleau LFH soils sampled in 2013.

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Figure 41: Shannon Diversity indices of Fungal pyrotag sequencing community data for Chapleau mineral soils sampled in 2013.

Figure 42: Shannon Diversity indices of Fungal pyrotag sequencing community data for Chapleau LFH soils sampled in 2013.

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IMB

CMB

800 FMHB

TMNB

SMNB

TONBFMNB 600

SONB TMHBSOHB COB SMHBIOB

TOHB Species 400 200 0

0 1000 2000 3000 4000 Sample Size

Figure 43: Pyrotag sequencing rarefaction curves from Bacterial soil samples. Samples are described using letter designations in the following order treatment (I- Fire, B- Bladed, S- Stumped, F- Full Tree, T- Tree length, C- control), soil type (O- LFH, M- mineral 1-10cm), herbicide treatment (H- treated, N- non-herbicide), the B designates a Bacterial sample.

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700 BF

FMNF 600

TMNF FONF

SOHF CMF 500 SONF TOHFFOHF

SMHF COF IOF

400 FMHF TONF

SMNF Species 300

TMHF 200 100 0

0 5000 10000 15000 Sample Size

Figure 44: Pyrotag sequencing rarefaction curves from Fungal soil samples. Samples are described using letter designations in the following order treatment (I- Fire, B- Bladed, S- Stumped, F- Full Tree, T- Tree length, C- control), soil type (O- LFH, M- mineral 1-10cm), herbicide treatment (H- treated, N- non-herbicide), the F designates a Fungal sample. coloured based on presence (black points) and absence (grey X’s) in different harvesting regimes.

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Figure 45: Chao richness of Archaeal T-RFLP community data for Chapleau soils sampled in 2012. Differences were not found to be significant (p < 0.05) using ANOVA with a Tukeys post- hoc test.

Figure 46: Shannon diversity indices of Archaeal T-RFLP community data for Chapleau soils sampled in 2012. Differences were not found to be significant using ANOVA with a Tukeys post-hoc test.

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Figure 47: Shannon diversity indices of Bacterial T-RFLP community data for Chapleau soils sampled in 2012. Bars indicated with an asterisk (*) were significantly different from one another (p < 0.05).

Figure 48: Chao richness of Bacterial T-RFLP community data for Chapleau soils sampled in 2012. Bars indicated with an asterisk (*) were significantly different from one another (p < 0.05).

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Figure 49: Chao richness of Bacterial T-RFLP community data for Chapleau soils sampled in 2013. Differences were not found to be significant (p < 0.05) using ANOVA with a Tukeys post- hoc test.

Figure 50: Shannon Diversity indices of Bacterial T-RFLP community data for Chapleau soils sampled in 2013. Differences were not found to be significant (p < 0.05) using ANOVA with a Tukeys post-hoc test.

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Figure 51: Chao richness of Fungal T-RFLP community data for Chapleau soils sampled in 2012. Differences were not found to be significant (p < 0.05) using ANOVA with a Tukeys post- hoc test.

Figure 52: Shannon Diversity indices of Fungal T-RFLP community data for Chapleau soils sampled in 2012. Bars indicated with an asterisk (*) were significantly different from one another (p < 0.05).

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Figure 53: Chao richness of Fungal T-RFLP community data for Chapleau soils sampled in 2013. Differences were not found to be significant (p < 0.05) using ANOVA with a Tukeys post- hoc test.

Figure 54: Shannon Diversity indices of Fungal T-RFLP community data for Chapleau soils sampled in 2013. Differences were not found to be significant (p < 0.05) using ANOVA with a Tukeys post-hoc test.

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Appendix 3: Chapter 2 Additional Figures

Figure 55: Unweighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from wood samples.

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Figure 56: Unweighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from wood samples from only inner and outer wood samples.

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Figure 57: Unweighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from soil samples beneath sampled wood.

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Figure 58: Weighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from inner and outer Jack Pine wood sampled in Chapleau.

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Figure 59: Weighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from soils below Jack Pine wood sampled in Chapleau.

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Figure 60: Weighted DCA of Bacterial 16S and Fungal 18S T-RFLP communities from soils below Sugar Maple wood sampled in Haliburton.

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Figure 61: PCA of chemical characteristics representing pooled CWD samples used for T-RFLP analysis.

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Figure 62: Chao richness of Chapleau wood bacterial pyrotag sequencing communities.

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Figure 63: Shannon diversity of Chapleau wood bacterial pyrotag sequencing communities.

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Figure 64: Chao richness of Haliburton wood bacterial pyrotag sequencing communities.

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Figure 65: Shannon diversity of Haliburton wood bacterial pyrotag sequencing communities.

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Figure 66: Chao richness of Chapleau wood fungal pyrotag sequencing communities.

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Figure 67: Shannon diversity of Chapleau wood fungal pyrotag sequencing communities.

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Figure 68: Chao richness of Haliburton wood fungal pyrotag sequencing communities.

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Figure 69: Shannon diversity of Haliburton wood fungal pyrotag sequencing communities.