Drivers of Community Composition in Wood-Inhabiting Fungi and Implications for Wood Decay in Temperate Forests
by Amy Marie Milo
B.A. in Biology, May 2007, University of Virginia M.S. in Biology, May 2009, University of Virginia
A Dissertation submitted to
The Faculty of The Columbia College of Arts and Sciences of The George Washington University in partial fulfllment of the requirements for the degree of Doctor of Philosophy
May 20, 2018
Dissertation directed by
Amy E. Zanne Associate Professor of Biological Sciences The Columbian College of Arts and Sciences of The George Washington University certifes that
Amy Marie Milo has passed the Final Examination for the degree of Doctor of Philosophy as of
March 2, 2018. This is the fnal and approved version of the dissertation.
Drivers of Community Composition in Wood-Inhabiting Fungi and Implications for Wood Decay in Temperate Forests
Amy Marie Milo
Dissertation Research Committee:
Amy E. Zanne, Associate Professor of Biology, Dissertation Director
John Lill, Associate Professor of Biology, Committee Member
Melissa McCormick, Research Scientist, Smithsonian Institution, Smithsonian Environmental Research Center, Committee Member
ii © Copyright 2018 by Amy Marie Milo All rights reserved
iii Acknowledgements
I would like to thank my advisor, Amy E. Zanne, for all of her guidance, patience, and support over the past 6 years, as well as my committee, John Lill, Melissa McCormick, Keith
Crandall, and Nathan Kraft, for the dedication of their time and suggestions while crafting the projects that form this dissertation. I would also like to acknowledge the Department of Biological
Sciences at the George Washington University for its support, as well as the fnancial support of the Harlan family that funded me over the course of my studies.
I would like to thank all of the people that contributed to this research, including the many collaborators that made the research possible: Shawn Fraver, Daniel Lindner, Tony D’Amato, and
Brian Palik for allowing me to be part of the Minnesora decay stakes project; Sean McMahon,
Melissa McCormick, Jess Shue, and Rutuja Chitra-Tarak for access to the Smithsonian
Environmental Research Center facilities and data. I am grateful for all of the help I received in completing feld and lab work, including the wonderful undergraduates who came to the lab from
George Washington University and beyond: Rachel Milner, Abigail Carter, Henry Betts, Joe
Dawson, and Justin Hachey; and for all of the amazing people that have formed part of the Zanne lab during my time there: Brad Oberle, Darcy Young, Mariya Shcheglovitova, Marissa Lee,
Habacuc Flores, and Roy Rich. I want to acknowledge the supportive community of graduate students, post-docs, and technicians at George Washington University, especially Sarah Josway,
Mariana Abarca-Zama, Lily Hughes, Joey Steigler, Aidan Manubay, João Tonini, Catriona
Hendry, Karen Poole, and the EcoEvoBioGeo Discussion group. Many thanks to Tara Scully and
David Morris for providing valuable experience and guidance in teaching. I would also like to thank my Master’s advisor, Henry M. Wilbur, and his wife, Becky Wilbur, for setting me on this path so many years ago at the University of Virginia.
iv Finally, I would like to thank my parents, Sue and Joe Milo for their never-ending love and support, and for always provdiding a refuge to take a break from it all, and my husband,
Patrick Sullivan, for sticking it out through countless trips to DC and for never letting me give up.
v Abstract of Dissertation
Drivers of Community Composition in Wood-Inhabiting Fungi and Implications for Wood Decay in Temperate Forests
Globally, forests account for over half of the carbon stored in terrestrial ecosystems. A substantial portion of this carbon is incorporated into woody plant tissues that have long (year to hundreds of years) residence times in forest ecosystems after plant death. Wood inhabiting fungi play a critical role in the global carbon cycle as the primary decomposers of woody plant biomass in forests. Fungi are some of the only organisms capable of breaking down lignin and cellulose, the primary building blocks of plant woody tissues, the the action of enzymes that specifcally target these macromolecules. The composition of fungal communities depends on environmental conditions and the quality of the woody substrate, and in turn afects how quickly wood decay proceeds. These organisms are sensitive to changes in the environment, showing changes in fruiting phenology related to climate change, and in fruiting abundances related to forest management. My dissertation seeks to explore the relationship between fungal community composition and the wood decay process in the context of anthropogenic changes to the environment. First, I used a large-scale forest harvesting experiment to address the question of how diferent harvesting practices infuence the fungal community and wood decay rates. I found that both fungal communities and wood decay rates were structured more strongly by wood species than harvest regime two years post harvest, but that the type of forest harvest might impact the ability of fungi to replace initial wood-decayers as wood decay progresses. Second, I combined repeated surveys of wood fruiting fungi with assays for enzyme activity to determine how fungal fruiting and enzyme activities are infuenced by environmental conditions, and whether these two fungal activities are well-coordinated. I found that both activities respond strongly to temperature, but over diferent timescales. Fruiting and enzyme activities were
vi signifcantly but weakly correlated, indicating that we cannot extrapolate how fungal ecosystem function might be changing in response to climate change based on changes in fruiting phenology.
Finally, I used amplicon sequencing of fungal communities within wood to relate the wood- inhabiting fungal community to enzyme activity to determine how much of the variation in enzyme activity can be attributed to the fungal community and whether certain fungal taxa were particularly related to high enzyme activities. Fungal community composition explain 12 to 26% of the variation in enzyme activity levels, and 14 fungal taxa were related to high enzyme activities. Thus, despite the high diversity of these communities, there are species that might be more important than others for ecosystem function, and these species should be targeted for future studies.
vii Table of Contents
Acknowledgments ……………………………………………………………………………….. iv
Abstract of Dissertation …………………………………………………………………………. vi
List of Figures …………………………………………………………………………………… ix
List of Tables …………………………………………………………………………………….. x
Chapter 1: Introduction …………………………………………………………………………... 1
Chapter 2: Composition and function of wood-inhabiting fungal communities respond to retention harvesting in aspen mixedwood forests in Minnesota, USA …………………………… 7
Chapter 3: Environmental infuences on fruiting body and extracellular enzyme phenologies of wood-inhabiting saprotrophic fungi …………………………………………………………….. 26
Chapter 4: Key players in wood-inhabiting fungal communiites infuence extracellular enzyme activities ………………………………………………………………………………………… 53
References ………………………………………………………………………………………. 74
Appendix 1 ……………………………………………………………………………………… 87
viii List of Figures
Figure 2.1 .………………………………………………………………………………………. 23
Figure 2.2 .………………………………………………………………………………………. 24
Figure 2.3 ……………………………………………………………………………………….. 25
Figure 3.1 ……………………………………………………………………………………….. 48
Figure 3.2 ……………………………………………………………………………………….. 49
Figure 3.3 ……………………………………………………………………………………….. 50
Figure 3.4 ……………………………………………………………………………………….. 51
Figure 3.5 ……………………………………………………………………………………….. 52
Figure 4.1 ……………………………………………………………………………………….. 71
Figure 4.2 ...……………………………………………………………………………………... 72
Figure 4.3 ……………………………………………………………………………………….. 73
Figure A1.1 ……………………………………………………………………………………... 89
Figure A1.2 ……………………………………………………………………………………... 90
ix List of Tables
Table 3.1 ………………………………………………………………………………………… 43
Table 3.2 ………………………………………………………………………………………… 44
Table 3.3 ………………………………………………………………………………………… 47
Table 4.1 ………………………………………………………………………………………… 69
Table 4.2 ………………………………………………………………………………………… 70
x Chapter 1: Introduction
My dissertation concerns the community ecology and ecosystem function of wood- inhabiting fungi. This is a diverse group of organisms that is critical to the cycling of carbon and other nutrients in forest ecosystems. These fungi have a range of abilities to degrade plant tissues through the action of extracellular enzymes, which can be measured within wood to determine how much degradation of plant tissues is happening in situ. Wood-inhabiting fungi are sensitive to changes in their environment caused by human activities. In my dissertation, I explore the impacts on the fungal community of forest harvesting, which changes the availability of woody substrate to fungal colonizers, and the implications of changes in fungal phenology due to changing climates.
In my three data chapters, I explore how fungal communities infuence wood decay and enzyme activities that lead to wood decay. My results highlight the importance of this group of organisms to wood decomposition, and the need for more attention to be paid to the processes by which fungi degrade wood to determine the implications of changing environmental conditions to fungal community composition and ecosystem function.
Approximately 30% of the world’s terrestrial ecosystems are forests (Bonan 2008), containing over 3 trillion trees (Crowther et al. 2015) and which store half of the world’s terrestrial carbon (Malhi 2002). A signifcant portion of this carbon is tied up within plant woody tissues (Pan et al. 2011), from which it is released primarily through the activities of wood decaying fungi (Cornwell et al. 2009). Large pieces of woody debris have residence times in temperate forests ranging from years to hundreds of years. The rate at which this substrate decays is determined by the climate, the traits of the wood species, and the community of fungi that assembles to decay it (Weedon et al. 2009). The fungal community also determines where the carbon stored in deadwood will end up, whether it is incorporated into fungal tissues, respired into
1 the atmosphere, or converted to organic residues that have long storage times in forest soils.
There are two major pathways that fungi use in wood decomposition. Generally, fungi break down the substrate that they inhabit by secreting enzymes that break organic polymers into their monomer units, which are then absorbed through active transport into fungal cells. White rot fungi are fungi that have the ability to enzymatically degrade lignin as well as cellulose and hemicellulose. This results in a more complete breakdown of the woody substrate, as the lignocellulose matrix that makes up woody tissue is completely degraded. Brown rot fungi, on the other hand, use a Fenton-like reaction to modify the lignin matrix to access the cellulose embedded within. This results in a characteristic residue that is stable within soils for up to hundreds of years. The brown rot characteristic is a derived trait that has evolved independently in several fungal lineages (Floudas et al. 2012). Brown rot is more common in gymnosperm wood and at higher latitudes, while white rot is more commonly found in angiosperm wood. Recent studies of rot residues caused by fungi and genomic capabilities of several species have indicated that these two pathways are endpoints on a spectrum of fungal wood decay systems (Floudas et al.
2015; Riley et al. 2014; Schilling et al. 2015).
In this dissertation, I employ two methods of assessing wood decay activity by communities of fungi: directly by measuring wood mass loss, and indirectly by measuring enzyme activities within wood over time. The enzymes that I target in the second method are mostly involved in white rot decay pathways, with two enzymes involved in cellulose degradation that are restricted in brown rot lineages. The cellulose degradation pathway begins with randomly cleaving crystalline cellulose through the action of endoglucanase, followed by cellobiohydrolase (CBH) that cleaves two to four glucose units from the resultant chains. These di- and tetramers are broken into individual glucose molecules by ß-glucosidase (BG). In chapters two and three, I assay
2 decaying wood samples for the action of cellobiohydrolase and ß-glucosidase, two white rot associated enzymes. I assay for two additional hydrolytic enzymes: ß-xylosidase, which targets hemicellulose molecules composed of xylose, the most common sugar in angiosperm wood after glucose, and N-acetyl-glucosaminidase (NAG), which targets a polysaccharide common in fungal cell walls, and chitin. Through the action of NAG, fungi can ofset the scarcity of nitrogen in woody tissues by recycling fungal tissues (Kaiser et al. 2014; Shoji and Craven 2011). Genes encoding for both of these enzymes are more widespread across the fungal tree than those for BG and CBH (Floudas et al. 2012). Assaying for these enzymes provides insight into what substrates fungi are targeting during the decay of woody tissues.
Wood-inhabiting fungi are sensitive to changes in their environment, with habitat degradation due to forest management and dramatic shifts of fungi in response to changing climates having been observed in European studies for the past decade. What remains unclear is how these changes in fungal species occurrences afects their ecosystem function of wood decomposition. Over the past two hundred years, intense forest management practices have resulted in a reduction of the available deadwood in European forests (Junninen and Komonen
2011; Siitonen 2001). Efective forest management seeks to harvest trees before they naturally senesce, maximizing the productivity of the landscape, but resulting in fewer inputs into the pool of deadwood available for wood-inhabiting fungi. In particular, fungi that are dependent upon large-diameter, well-decayed logs are impacted the most, with several species listed as endangered or threatened in European countries (Hattori, Yamashita, and Lee 2012; Hottola and Siitonen
2008). In the United States, few studies have assessed the impacts of forest management on fungi
(but see Brazee et al. 2012; Lindner, Burdsall, and Stanosz 2006), and we do not track the viability status of any fungal species under the national Environmental Species Act.
3 Our understanding of the impacts that climate change has on fungi also comes from
Europe, where several long-term datasets have shown that fungal phenology has changed in response to warmer climates. The frst of these, Gange et al. (2007), showed that the time during which fungi were observed fruiting in a single location in Great Britain had expanded by two weeks between the 1950’s and the early 2000’s. Since then, several studies have shown similar changes in fungal phenology (Büntgen et al. 2013; Kauserud et al. 2008, 2010; Kauserud,
Colman, and Ryvarden 2008), with some of the most dramatic shifts occurring in saprotrophic fungi (Boddy et al. 2014). These changes in fungal fruiting activity suggest that there should also be changes in how and when fungi acquire resources (Kauserud et al. 2012), with implications for rates of nutrient cycling in forests that are not well-understood.
In my second chapter, I set out to determine how two diferent forest harvesting practices impact fungal communities in wood and wood decay rates. In a collaborative project with the
University of Minnesota and the USDA Forest Service, I used a wood decay experiment conducted in concert with a large-scale tree harvesting experiment to determine whether retaining a small patch of intact forest within an otherwise clearcut matrix (aggregate retention forestry) would maintain fungal communities and wood decay rates that were closer to unharvested forests than complete clearcuts. I observed small changes in fungal communities that were related to the harvest method, with fungal communities from clearcuts and retention stands signifcantly diferent from each other, and signifcantly higher wood mass loss in clearcuts than in retention stands. Wood species was important in both community composition and wood mass loss, with distinct fungal communities inhabiting aspen and balsam fr wood, and little mass loss observed in balsam fr. The relationship between fungal diversity and mass loss in the experiment suggested that the efect on the fungal community might increase with time from the harvest, as the loss of
4 pre-existing deadwood from the clearcut sites leads to a lack of fungi to colonize the deadwood created during the harvest as it increases in decay. Further work is needed to confrm this result, and to determine the efect repeated harvest may have on fungal communities in these systems.
In my third chapter, I conducted monthly surveys of fungi fruiting on wood in combination with enzyme assays on wood samples taken at the same time period to ask how the phenology of fungal fruiting and enzyme activities were related. I used multivariate general linear models to determine over what time scales both fruiting and enzyme activities responded to air temperature and precipitation. These models indicated that fall-fruiting fungi respond more strongly to temperatures averaged over two months, while enzymes responded to weekly temperatures. Spring-fruiting fungi were more sensitive to temperature and precipitation over shorter time scales, as well. Fruiting body abundances peaked on average 4 weeks after enzyme activities peaked, with the diferences in peak activity being signifcant in 2014 and 2016. Finally, fruiting body abundances were only weakly correlated with enzyme activities taken during the same sampling event. Taken together, these results suggest that fungal enzymatic activity responds to environmental cues over diferent time scales than fungal fruiting, and that fruiting may not be a good indicator of enzymatic activity. Therefore, more work is needed in order to understand the implications of observed changes in fungal fruiting phenology for wood decomposition and nutrient cycling in forest ecosystems.
Finally, my fourth chapter builds upon the results of my second chapter to dig into how the fungal community within a log infuences the enzyme activities observed. I used high- throughput amplicon sequencing to characterize fungal communities from the same samples used for enzyme assays. Despite the evidence for seasonal shifts in fruiting fungi from Chapter 3, I observed no seasonal pattern in the DNA community data. Instead, fungal communities showed
5 separation due to wood species and decay class, confrming that fungal communities are shaped by wood species (Purahong et al. 2018), and turnover slowly as wood decay progresses (Boddy
2001). Fungal community composition explained between 12% and 26% of the observed variation in enzyme activity, and I identifed 14 species that were associated with high enzyme activity. Of these, those that were associated with high cellulose-degrading activities were Basidiomycota and were saprotrophic. However, the fungal taxa that were associated with NAG and BX were more likely to be Ascomycota, and were less likely to be wood saprotrophs. These results underscore the complexity of the fungal community within wood; while a few species may be responsible for driving wood decomposition, there are also important species interactions, and these may be occurring between taxa that are not performing the same environmental function.
This dissertation seeks to directly link fungal communities with their ecosystem function, wood decay. In Chapter 2, I directly measured wood mass loss over two years, and used a structural equation model to examine the interactions between environmental conditions, harvest, fungal community richness and wood mass loss. I discovered important interactions between harvest practices, fungal community richness and mass loss that may have implications for how these communities recover from disturbance over time. In the following chapters, I used repeated observations of the fungal community at multiple scales, fruiting bodies and DNA sampling of wood, to determine how the fungal community is related to wood decay activity, measured as enzyme activities within the wood. This work is novel in the fne temporal scales over which the fungal community and enzyme activities were observed, and in that it directly links the fungal community to its enzyme activities within wood. These methods provide important insights into the functioning of fungal communities, and raise many avenues for further research.
6 Chapter 2: Composition and function of wood-inhabiting fungal communities respond to retention harvesting in aspen mixedwood forests in Minnesota, USA
Abstract
Wood-inhabiting fungi play a central role in nutrient cycling in forests by decomposing wood. Many of these fungi are sensitive to forest harvesting practices, but the efects of harvesting on overall community structure and function are poorly understood. Retention forestry, in which some trees are left unharvested, has the potential to ofset negative impacts of clearcutting by preserving mature trees and existing deadwood. We used a large-scale live-tree retention experiment to determine whether wood-inhabiting fungal communities and wood decay rates of
Populus tremuloides (aspen) and Abies balsamea (fr) varied among aggregate retention, clearcut, and unharvested stands in aspen mixedwood forests in northern Minnesota, USA. We collected mass loss and fungal community data from standard-sized wood stakes decayed for two years following harvest. Harvest signifcantly afected environmental conditions, which were correlated with changes in wood mass loss and fungal diversity in aspen stakes. Mass loss was negatively correlated with fungal diversity in fr stakes and in aspen stakes in clearcuts, but positively correlated in aspen stakes in unharvested and retention stands, indicating the relationship between fungal diversity and mass loss is context dependent. Fungal communities sampled after two years of decay were distinct between aspen and fr stakes. Harvest treatment had an additional, though small, efect on fungal community composition in stakes, with stake mass loss, soil temperature, and fne woody debris biomass also contributing to community structure. Our results indicate that aggregate retention practices may mitigate the impact of forest harvest on wood-inhabiting fungi by maintaining older deadwood sources of inoculation. Our results suggest the diversity of tree species retained should be considered by forest managers when they wish to promote fungal
7 diversity in harvested areas.
Introduction
Wood-inhabiting fungi are the primary agents of wood decomposition in forests, playing a critical role in carbon and nutrient cycling (Cornwell et al. 2009). The importance of fungal communities in regulating decay rates has been repeatedly demonstrated (Bradford et al. 2014,
Meier et al. 2014), although the direction of the relationship between fungal diversity and decay rates remains unclear (Toljander et al. 2006, Dickie et al. 2010, Fukami et al. 2012, Peay et al.
2013, Yang et al. 2016, but see Lindner et al. 2011, Valentin et al. 2014, van der Wal et al. 2015, and Baber et al. 2016). Studies have shown large diameter, moderately decayed pieces of woody debris harbor the highest fungal diversity, but small diameter woody debris also contributes signifcantly to the overall richness of an area (Kruys and Jonsson 1999; Nordnn et al. 2004;
Stokland, Siitonen, and Jonsson 2012). Forest management typically reduces amounts of woody debris by harvesting trees before they senesce into the deadwood pool and by damaging existing woody debris during harvest (Hottola and Siitonen 2008). The reduction in this critical resource following a century or more of intensive forest management has decreased fungal diversity in forests in northern Europe (Siitonen 2001; Junninen and Komonen 2011), and diferences in fungal communities can be observed for up to 80 years after harvest in temperate hardwood ecosystems in North America (Lindner, Burdsall, and Stanosz 2006).
Managers worldwide are retaining mature canopy trees at the time of harvest (often called retention forestry, sensu Lindenmayer et al. 2012) in an efort to maintain biodiversity and structural complexity in forests historically managed with clearcutting-based systems (Franklin et al. 1997; Gustafsson et al. 2012). The ability of retention forestry to mitigate efects on the abundance and diversity of many groups of organisms – birds, saproxylic beetles, amphibians,
8 small mammals, ectomycorrhizal fungi – has been demonstrated (reviewed in Fedrowitz et al.
2014); however, its utility in maintaining wood-inhabiting fungal diversity has not been investigated, despite the importance of these organisms in ecosystem function.
Additionally, the impact of retention harvesting on ecosystem processes, such as carbon and nutrient cycling, has been identifed as an urgent knowledge gap in our assessment of retention forestry’s potential benefts (Gustafsson et al. 2012). Forest harvesting can alter carbon and nutrient cycling by changing environmental conditions on the forest foor that mediate cycling rates or by disrupting microbial communities that depend on resources from intact trees.
Harvesting elevates soil moisture and soil temperatures (Keenan and Kimmins 1993; Nilsson et al.
2008), both of which are expected to increase turnover rates (Covington 1981). However, there is evidence that forest harvesting reduces rates of leaf litter decomposition (Blair and Crossley 1988;
Prescott 1997; Yanai, Currie, and Goodale 2003), although these efects have not been observed in woody debris. Furthermore, it is unclear whether any changes in decomposition rates are caused by environmental changes from tree removal, changes to wood-inhabiting fungal communities, or a combination of both (Keenan and Kimmins 1993).
Maintaining the continuity of forest physical and demographic structure, including mature and senescing trees that will recruit to the deadwood pool (Junninen, Penttilä, and Martikainen
2007) as well as existing deadwood, is a core justifcation for retention forestry (Palik and
D’Amato 2017). Since wood-inhabiting fungi are dependent on deadwood and are the primary agents that recycle this material through the ecosystem, assessing how these organisms respond to forest harvest and tree retention is critical for assessing the ability of retention forestry to meet biodiversity and ecosystem functioning goals. Wood-inhabiting fungi may respond positively to forest harvest: the amount of deadwood available may increase in the short term if logging slash
9 (branches, tops, small diameter stems) is left on harvested sites. On the other hand, aggregate tree retention may retain an important source of fungal inoculum by reducing the loss of large woody debris in various stages of decay within retention patches.
Because of their central role in carbon and nutrient cycling, as well as their contribution to native biodiversity, we focused this study on the impact of retention forestry on wood- inhabiting fungi diversity and function. We used a large-scale, replicated forest harvest experiment in northern Minnesota, USA, that includes aggregate tree retention, clearcut, and unharvested controls, and intentionally creates a range of retained logging slash. This design allowed us to ask:
1. How do harvest treatments infuence the wood-decay fungal community and wood decay rates? and 2. What is the relationship between fungal community diversity and decay rates? We answered these questions by using standardized wood stakes, decayed for two years, then collected and analyzed for mass loss and fungal community composition using high-throughput DNA sequencing. If wood-inhabiting fungi conform to predictions from retention forestry, we expect elevated species richness in retention stands and decreased species richness in clearcut stands compared to unharvested forest, with distinct communities in intact forest and clearcuts and members of both communities present in retention stands (Fedrowitz et al. 2014), and that these changes in diversity will have implications for wood decay rates.
Methods
Study sites
Sites used in this study form part of a larger experiment designed to assess impacts of harvesting systems on site productivity and nutrient cycling in aspen mixedwood forests (see
Kurth et al. 2014). Four sites of approximately 40 ha were located within St. Louis County,
Minnesota, USA. Site elevation ranged from 395 – 428 m with slopes between 0 – 8%. The study
10 area has a continental climate, with mean winter temperature of -16 ˚C and mean summer temperature of 26 ˚C. Mean annual precipitation is between 660 – 710 mm, with 75% falling May
– October. Stands originated after clearcut harvesting 55 – 68 years ago and were dominated by
Populus tremuloides, with lesser amounts of Betula papyrifera, Acer rubrum, and Fraxinus nigra.
Abies balsamea, Picea mariana , and Picea glauca were commonly occurring conifer species.
Harvesting treatments were replicated across the sites in a randomized complete block design, with sites as blocking factor. Each site consisted of 7 stands of approximately 4.1 ha randomly assigned a harvest treatment: clearcut, aggregate tree retention, and unharvested control.
Aggregate tree retention consisted of two roughly circular patches of approximately 0.1 ha (~5% of the stand area) located within an otherwise clearcut stand in which all stems > 2.54 cm in diameter at 1.3 m height were removed. Harvests were conducted in February 2010 on frozen ground and snowpack. An additional slash retention treatment was applied to harvested stands, with the intent of leaving 100%, 20%, and 0% slash. However, due to operational difculties in achieving these strict percentages during harvests (Klockow et al. 2013), a range of slash was retained within these treatments. For this reason, we have chosen to evaluate this treatment as a continuous variable in terms of coarse woody debris (CWD, pieces 7.6 cm diameter) and fne woody debris (FWD, pieces < 7.6 cm diameter) biomass remaining on the stands after harvest
(Supplementary Information, Fig. A1.1).
Soil temperature and soil moisture were measured on six 400 m2 circular plots randomly located within each stand. In retention stands, one plot was located within each aggregate retention patch, with the remaining four plots located in the clearcut matrix, while the six plots were distributed uniformly within clearcut and uncut stands. Soil temperature and moisture were measured once the ground thawed after harvest in May 2010. Plot-level measurements were
11 averaged across each stand, and average growing season (May – October) soil moisture (mL·g1) and temperatures (°C) were calculated for the frst two years after harvest. All six plots were used for clearcut and unharvested control stands, while only the two plots within the patch of retained trees were used for aggregate retention stands.
CWD biomass (Mg·ha-1) was measured by the line intercept method (Brown 1971), using three 20-m transects radiating equiangularly from plot centers. FWD biomass (Mg·ha-1) was measured in subsections of these transects: 1-m segments for the one-hour size class, 2-m segments for the 10-hour class, and 4-m segments for the 100-hour class (see Brown 1971 for class defnitions).
Decay stakes
We manufactured standard sized wood stakes from two species, P. tremuloides (aspen, the dominant overstory species) and A. balsamea (fr, the most common conifer species), from untreated, knot-free, locally harvested lumber to a size of 2.54 cm x 2.54 cm x 20 cm. Stakes were dried at 75 ˚C for eight days and weighed before being randomly assigned to a stand for deployment at the edge of two (aggregate retention) or three (clearcut and control) of the plots, for a total of 18 plots per site. Four of each stake species were placed in each plot in June 2010 and secured with 10.2 cm landscaping staples attached to individually numbered aluminium tags. In
June 2014, two years after deployment, we collected one stake of each species from each plot, leaving remaining stakes for future collections.
In the lab, stakes were sampled for DNA by removing the exterior layer of wood with sterile razor blades at three locations (2.54 cm from each end and in the middle) and drilled using a 3.175 mm DNA-sterile bit. Sawdust was stored in a 2% CTAB bufer (Lindner and Banik 2009) at -20 ˚C until DNA extraction occurred. The drilled stakes were dried at 75 °C for one week and
12 weighed. The fnal dry mass of the stakes was adjusted to account for the small amount lost to sawdust collection using each stake’s measured density. This corrected dry mass was subtracted from the initial dry mass to determine mass loss.
DNA extraction and sequencing
We extracted DNA following Lindner and Banik (2009). Briefy, sawdust in CTAB was incubated for two hours at 65 °C. DNA was precipitated using isopropanol at -80 °C for 10 minutes, and the pellet was rinsed with 70% ethanol. DNA clean up was accomplished using
GeneClean III kits (MP Biomedicals). To sample for fungal diversity, we amplifed the internal transcribed spacer using primers ITS1F and ITS4 (Martin and Rygiewicz 2005). Amplifcation was performed in triplicate using GoTaq Hot Start Polymerase (Promega) and products pooled to reduce PCR bias when sequencing.
Amplicon library preparation was performed according to a double-indexed barcoding protocol developed by IBEST Genomics Resources Core at the University of Idaho (Moscow, ID).
Barcodes provided by IBEST were ligated to a 1:15 dilution of initial PCR products using GoTaq
Hot Start Polymerase (Promega). DNA was quantifed on a BioTek Synergy HTX Multi-Mode
Reader using a high sensitivity Quant-iT dsDNA assay kit (Invitrogen). Samples were pooled according to DNA concentration (100 ng, 20 ng and 10 ng), and sequencing was performed on an
Illumina MiSeq calibrated to sequence 300 base pair paired-end amplicon fragments at IBEST.
Sequences were de-multiplexed using a script provided by IBEST
(http://github.com/msettles/dbcAmplicons/). Sequences were assembled with PEAR (Zhang et al.
2014), and unassembled reads were trimmed to remove poor quality bases at the tail end of the forward sequence. Assembled reads and trimmed forward reads were used for downstream analysis. Using USEARCH (Edgar 2013), sequences were screened to have a maximum
13 probability of an erroneous base pair of one. Sequences were clustered into operational taxonomic units (OTUs) using a 97% similarity threshold and singletons were discarded. OTUs were mapped against the UNITE ITS1 database (November 2015; Koljalg et al. 2014) using the RDP classifer at a 50% probability cut-of (Wang et al. 2007). OTUs were mapped back to raw reads to create an
OTU × sample matrix. OTUs present in negative controls were removed from each sample and samples with fewer than 100 sequences were discarded from further analyses.
Data analysis
We used a structural equation model (SEM) to evaluate the relative importance of forest harvest treatment and environmental variables (soil temperature, soil moisture, CWD biomass, and FWD biomass) on percentage mass loss and fungal diversity in stakes, using lavaan (Rosseel
2012). Fungal diversity was calculated from the OTU data for each stake using the Chao 1 richness estimator (Chao 1984) with the function estimateR in vegan (Oksanen et al. 2017). We also calculated Shannon diversity, Simpsons diversity, and performed rarefaction on our samples
(to 250 and 1,000 OTUs), but since results of these metrics did not difer from each other, we present only Chao1 index results below. We evaluated whether percentage mass loss afected fungal diversity, as the decay state of wood is a proxy for conditions experienced by fungi, such as resource availability. To evaluate diferent responses of aspen and fr stakes, we included wood species as a grouping variable in the model. Signifcant covariances between soil temperature, soil moisture, CWD biomass, and FWD biomass were included in the model, and harvest treatments were coded as continuous such that higher values indicated increased severity of harvest (i.e., control < retention < clearcut).
To further explore relationships between fungal diversity, mass loss, wood species, harvest treatment, and environment, we built general mixed efects models with stand nested within site as
14 a random factor using function mixed in the afex package (Singmann et al. 2016). With fungal diversity as the response variable, we included wood species, mass loss, environmental variables, and harvest treatment as main efects, as well as the interaction of wood species with all other main efects, followed by the removal of non-signifcant interactions to achieve our fnal model. A second mixed efects model, with percentage mass loss as the response variable, included wood species, harvest treatment, and all environmental variables as main efects, with the addition of signifcant interaction efects between wood species and other main efect variables. We performed post-hoc Tukey HSD tests with the glht function from the multcomp package (Hothorn,
Bretz, and Westfall 2008).
Fungal community analyses were performed on the OTU table using non-metric multidimensional scaling (NMDS) ordination based on presence-absence matrices with the
Jaccard distance metric, using the metamds function within the vegan package (Oksanen et al.
2017). The efect of wood species, tree retention, and the suite of environmental variables on the ordination was explored with the envfit function. To confrm the efect of treatment environmental variables on community structure, we also performed a permutational ANOVA using the function adonis from the vegan package (Oksanen et al. 2017).
We applied a natural log transformation to CWD biomass, FWD biomass, and percentage mass loss to meet assumptions of mixed model ANOVAs, and transformed values were centered to the mean and scaled by dividing by the standard deviation for use in the SEM. All analyses were performed in R (version 3.3.2; R Core Team 2016).
Results
Overall treatment and environmental efects
The fnal SEM indicated that harvest treatment strongly afected the environmental
15 variables measured (see also Fig. A1.2) and that environment infuenced fungal diversity and percentage mass loss of aspen but not fr stakes (chi-squared = 0.98; Fig. 2.1). For aspen stakes, fungal diversity was negatively afected by FWD biomass (z = -2.34, P = 0.02), and percentage mass loss was related positively to soil moisture (z = 2.14, P = 0.03) and negatively to CWD biomass (z = -2.80, P < 0.01). The only model element that signifcantly afected fr fungal diversity was percentage mass loss (negative, z = -3.90, P <0.001).
Harvest efects on mass loss
Percentage mass loss ranged from 1% to 17% in fr and from 2% to over 57% in aspen.
The percentage mass loss over two years from stakes was afected by the interaction between
wood species and harvest treatment (F5,133.1 = 103.38, P < 0.001; Fig. 2.2). Percentage mass loss of aspen was signifcantly higher than fr across all treatments (Tukey HSD, P < 0.01). Harvest treatment had no efect on fr mass loss (P > 0.99), while aspen stakes in clearcut stands had signifcantly higher mass loss than those in retention stands (P < 0.001).
Harvest efects on fungal community structure
After quality control we obtained approximately 1.5 million sequences clustered into
1,220 OTUs from a total of 134 stakes with which to perform community analyses. NMDS ordination converged on a solution with three axes (stress = 0.1780,). The frst and second axes separated aspen and fr stakes, with aspen stakes associating negatively with the frst axis and positively with the second (Fig. 2.3a). Retention stands associated negatively and clearcuts positively along the third axis (Fig. 2.3b). Percentage mass loss was negatively correlated with the frst axis, soil moisture and temperature were positively correlated with the second, and FWD biomass, soil moisture and temperature correlated negatively with the third. The permutational
ANOVA showed a signifcant efect of wood species (R2 = 0.05, P = 0.001), harvest treatment (R2
16 = 0.025, P = 0.001), and percentage mass loss (R2 = 0.01, P = 0.001) on the structure of fungal communities.
Relationship between mass loss and fungal diversity
There were two signifcant interactions in the fnal mixed efects model examining factors
infuencing fungal diversity in stakes: between percentage mass loss and wood species (F1, 121 =
6.62, P = 0.01) and between harvest treatment and wood species (F2, 104 = 4.77, P = 0.01). Fungal diversity in fr and aspen stakes from clearcut stands was negatively related to percentage mass loss, while aspen stakes from retention and unharvested stands had a slight positive relationship with percentage mass loss (Fig. 2.2). Percentage mass loss, wood species, and harvest treatment were also signifcant main efects (P < 0.05). In post-hoc tests, aspen stakes from unharvested stands had lower diversity than stakes (both species) from all other stands, except for aspen stakes in clearcuts, which also had signifcantly lower diversity than aspen stakes in retention stands.
There were no signifcant diferences in diversity among fr stakes, or between aspen stakes in retention stands and fr stakes.
Discussion
Previous studies on the efect of tree retention on target groups of organisms have shown that retaining mature trees in harvested stands can efectively maintain biodiversity when compared to clearcuts (reviewed in Fedrowitz et al. 2014 and Gustafsson et al. 2012). Our results indicate that communities of wood-inhabiting fungi may also beneft from aggregate live-tree retention forestry. In our experiment, retention stands mitigated environmental changes compared to clearcuts by maintaining similar levels of FWD biomass and soil moisture as unharvested stands, and being intermediary for soil temperature and CWD biomass between unharvested and clearcut stands (Fig. A1.2). Altered environmental conditions among harvest treatments resulted
17 in diferences in fungal community composition, and for aspen stakes, lower mass loss and a distinct relationship between fungal diversity and mass loss in aggregate retention stands compared to those in clearcut stands.
Responses of fungal community to harvest treatments
We detected small but signifcant diferences among harvest treatments in fungal communities inhabiting experimental decay stakes. Fungal communities were less variable in both types of harvest stands, as demonstrated by their smaller dispersion in ordination space than controls (Fig. 2.3b), suggesting that each harvest type favored a subset of fungal taxa.
Nevertheless, the amount of variation in fungal community composition explained by harvest treatment was relatively small, and there was overlap among fungal communities found in both harvested and unharvested treatments. The strength of the harvest efect on fungal community composition may be weakened due to the dependence of wood-inhabiting fungi on deadwood and creation of this substrate during forest harvest. On our sites, the amount of woody debris increased on clearcut and retention stands compared to unharvested stands (Fig. A1.1c, d), providing more available resource for fungi to inhabit and from which to colonize stakes. Previous work at these sites indicates that small diameter woody debris (<5 cm diameter) is a particularly important driver of polyporoid fungal community composition (Brazee et al. 2012). Indeed, stakes from harvested stands showed higher fungal diversity at early stages of decomposition (Fig. 2.2). Thus, forest harvesting may create conditions favorable to fungal growth and activity, while also increasing substrate availability, thereby potentially ofsetting any negative impacts of habitat disruption on fungal community composition, at least in the short term.
Response of wood decay to harvest treatments
Although many studies have addressed the benefts of tree retention for maintaining
18 biodiversity, much less is known about how tree retention afects ecosystem processes
(Gustafsson, Kouki, and Sverdrup-Thygeson 2010). Our results demonstrated that wood decomposition rates varied by harvest treatment for aspen stakes, but not for fr stakes two years after harvest. Since gymnosperms are known to decay more slowly than angiosperms (Weedon et al. 2009), it is not unexpected that these two species exhibited diferent mass loss patterns. Conifer species can show a long lag period between recruitment onto the forest foor and onset of mass loss (Fraver et al. 2013), and two years after harvest fr stakes may still have been in such a lag phase. This may partially account for the lack of any harvest treatment or environmental variable efect observed in fr stakes.
Aspen stakes, however, difered in percentage mass loss between treatments, with stakes on clearcuts losing signifcantly more mass than those on retention stands, although neither harvest treatment difered signifcantly from unharvested stands (Fig. 2.2). This disparity is likely due to environmental changessince there wasa signifcant positive relationship between mass loss and soil moisture in the SEM (Fig. 2.1), and soil moisture was signifcantly elevated in clearcut stands overaggregate retention stands (Fig. A1.2b). Lower rates of mass loss mean that retention patches have greater carbon storage times, and may serve as resource for fungi and other saproxylic organisms longer.
Relationship between diversity and decay
Increased diversity on sites with tree retention compared to unharvested and clearcut stands is one of the predictions of retention forestry (Fedrowitz et al. 2014), and we observed this pattern in aspen decay stakes (Fig. 2.2), and it increased with greater mass loss. While aspen stakes in both clearcut and retention stands showed higher diversity at lower levels of mass loss relative to unharvested stands, stakes in retention stands increased in diversity with increasing
19 mass loss, while stakes in clearcut stands decreased. As early colonizing fungal species decay substrates, they deplete resources, and eventually render conditions unsuitable for themselves, leading to their displacement by successor species (Boddy 2001, Stokland Siitonen, and Jonsson
2012). This pattern in our study may indicate that while clearcut areas are rich in early colonizing species, these are not being readily replaced by successor species, whose successful dispersal may be limited by the lack of nearby advanced-decayed substrates. By maintaining pre-harvest deadwood, retention stands may have larger impacts on fungal diversity and community composition as time from harvest increases when substrates created during harvest enter more advanced decay stages and early colonizing fungal taxa are readily replaced by fungi from debris in more advanced stages of decay.
While negative relationships between percentage mass loss and fungal species richness has been attributed to increased competition (Dickie et al. 2012; Fukami et al. 2010; Yang et al.
2016), there is also evidence that fungal diversity increases rates of mass loss. For instance, Meier et al. (2014) demonstrated the strong infuence of fungal richness and community composition on wood decay rates. Our results show that rather than following a general rule, the relationship between fungal community diversity and mass loss rates is context dependent. We observed changes in direction and magnitude of the correlation between diversity and mass loss depending on wood substrate species, as well as forest harvest treatment (Fig. 2.2), indicating that a host of exogenous factors can infuence fungal community function.
Importance of maintaining woody debris diversity
Our results indicated that wood species had the largest efect on structuring fungal communities and on mass loss; harvest treatment was of secondary importance. These fndings highlight an important consideration for retention forestry – the quality and diversity of the
20 resource retained has implications for both biodiversity and ecosystem function. There was very little overlap between fungal communities found inhabiting aspen and fr stakes, suggesting that fungal biodiversity can be increased by maintaining a diversity of tree species, and therefore woody debris substrates, in managed forests (Heilmann-Clausen, Aude, & Christensen 2005). As industrial forestry tends to simplify the composition and structure of forest systems (Gustafsson et al. 2012), the ability of retention forestry to maintain tree diversity (Rosenvald & Lõhmus 2008;
Roth 2012) translates to increased diversity in wood-inhabiting fungal communities. Furthermore, because the wood species had very diferent rates of mass loss, maintaining a diversity of woody species may also increase the diversity of woody debris in diferent decay stages. The fungi most vulnerable to forest management are those species dependent upon moderate to well-decayed wood (Heilmann-Clausen & Christensen 2004). Our results highlight the importance of fostering resource diversity in order to maintain fungal diversity.
Conclusions
Wood-inhabiting fungi perform a vital ecosystem service in forests by breaking down dead woody tissue, thereby cycling carbon and nutrients. Fungal communities are strongly structured by the type of woody substrate available to them, and these communities respond diferently to forestry practices that alter substrate type and quantity. Aggregate tree retention may beneft fungal communities by maintaining structural and functional complexity. The potential benefts of aggregate tree retention may be more important with increasing time since harvest, as harvest-created woody substrate becomes unsuitable for primary fungal colonizers, and successor species can colonize from more decayed woody debris located in aggregate retention patches.
Given the importance of wood species in structuring wood decay fungal communities, retention practices should strive to retain multiple tree taxa on site to support a broad array of fungal
21 species and functions in harvested stands.
Data Accessibility
Our sequencing data will be housed with the NCBI Sequence Read Archive, and accession numbers provided here upon publication. Mass loss and environmental data from the study will be archived with the Dryad Digital Repository.
22 Figure 2.1. Structural equation model showing signifcant (P < 0.05; solid lines) and marginally signifcant (P < 0.15, dotted lines) relationships between forest harvest treatment, wood-inhabiting fungal diversity, and percentage mass loss in aspen and fr stakes. Numbers next to the lines indicate standardized correlation coefcient.
23 Figure 2.2. Percent mass loss interacted signifcantly with both decay stake species and harvest treatment in afecting Chao1 estimate of fungal diversity. Regression lines indicate the relationship between the diversity estimator and percentage mass loss for the three harvest treatments (control, solid line, solid circle; retention stands, dashed line, crosses; clearcuts, dotted lines, open circles) and both wood species (fr, black; aspen, gray).
24 Figure 2.3. The NMDS ordination demonstrated variation of decay stake fungal communities across 3 axes. Signifcant efects are represented by ellipses around 95% confdence intervals of wood species and treatment centroids, and environmental variables are indicated by arrows. a)
NMDS axes 1 and 2 show separation of fungal communities by wood species (aspen, grey; fr, black) and percentage mass loss, soil temperature and moisture. b) NMDS axes 1 and 3 show separation due to harvest treatment (CN – control, closed circles; RT – retention, crosses; CC – clearcut, open circles), with signifcant efects of percentage mass loss, fne woody debris biomass
(FWD), soil temperature and moisture.
25 Chapter 3: Environmental infuences on fruiting body and extracellular enzyme phenologies of wood-inhabiting saprotrophic fungi
Abstract
Fungal phenologies are responsive to temperature changes due to climate change, but the consequences of these changes for fungal ecosystem function remain unknown. By breaking down woody debris, wood-inhabiting saprotrophic fungi are critical players in global nutrient cycles. To date, saprotrophic fungi have demonstrated some of the largest phenological shifts among fungi in the past decades. We surveyed fungal fruiting bodies on woody debris monthly for four years in a mixed-deciduous forest in the mid-Atlantic, USA, and took samples of wood substrate to assay for extracellular enzyme activity at the same time points. We frst examined the seasonality of communities of fruiting fungi and enzyme activity potentials. Second, using multivariate linear regressions we determined what environmental conditions (rainfall and temperature) at diferent time scales best predicted these activities for all fungi as well as fungi binned by seasons. Finally, we quantifed the degree to which fungal fruiting body and enzymatic activities were related to one another. We found distinct communities of fungi fruiting in spring, summer, and fall/winter, with peak fruiting abundances occurring in early September, on average. Peak activity for extracellular enzymes occurred signifcantly earlier than fruiting in 2 out of 3 years by an average of 4 weeks before peak fruiting body abundance. Temperature had the largest infuence on both fruiting and enzymes, but fruiting responded to temperature over a longer timescale (2 months) than did enzymatic activities (1 week). Enzyme activities had a weak positive relationship with fruiting body abundances. Our results demonstrate the importance of measuring the phenology of fungal activities other than fruiting to gain insight into how fungal ecosystem functions may be changing in response to climate change.
26 Introduction
For the past two decades it has been widely recognized that global climate change has substantial efects on the distribution and life cycles of both plants and animals (Parmesan and
Yohe 2003). Observed changes in the timing of species life history events -- phenological shifts -- in plants and animals are widely accepted as a response to climate change. These shifts have the potential to disrupt important species associations (Visser and Both 2005), facilitate species invasions (Wolkovich and Cleland 2011), promote disease outbreaks (Wu et al. 2016, McMichael et al. 2006), and destabilize critical human enterprises, such as agriculture (Chakraborty and
Newton 2011). Diferent guilds of fungi are crucial components of global ecosystems, impacting nutrient cycles with saprotrophs being the primary decomposers of organic matter, facilitating nutrient uptake and exchange with plant communities as mycorrhizal symbionts, and acting as agents of disease and mortality as pathogens. Changing climates are known to impact fungi by causing shifts in fruiting abundance (Büntgen et al. 2013; Büntgen, Kauserud, and Egli 2012), active fruiting period (Boddy et al. 2014; Gange et al. 2007; Kauserud et al. 2008), and host associations (Gange et al. 2011).
Since the initial observation that fungal fruiting patterns are shifting in accordance with warming climates, this phenomenon has been observed in several long-term datasets based on fruiting body survey records (Büntgen, Kauserud, and Egli 2012; Kauserud et al. 2012; Kauserud et al. 2008) as well as collection dates from fungaria records (Diez et al. 2013). These studies suggest that while shifts have occurred across fungi, the largest shifts have occurred within the saprotrophic fungal guild (Boddy et al. 2014). While mycorrhizal fungi may be constrained by the phenology of their plant associates (Garbaye, Courty, and Bre 2007; Voříšková et al. 2014), from which they receive carbohydrates, saprotrophic fungi are free-living and perhaps more responsive
27 to climatic cues. Additionally, as there is little to no seasonal pattern in the inputs into the deadwood pool, wood-inhabiting saprotrophic fungi (WIF) do not need to match their phenology to a seasonal resource pool, such as might be true for litter saprotrophic fungi and annual leaf fall in deciduous forests. This independence from phenological constraints make WIF ideal for investigating fungal phenological responses to climate cues, as they should be less constrained to match their phenology to other species with which they associate and more responsive to weather conditions.
The majority of the work in fungal phenology has focused on fruiting bodies or sporocarps, the macroscopic reproductive structures produced by some fungi to disperse spores
(Andrew et al. 2017; Boddy et al. 2014). As such, these studies have not observed other fungal activities that contribute directly to ecosystem function e.g., decomposition of organic matter.
However, as fruiting bodies must necessarily be supported by such decay activities, they may serve as indicators of recent decay. Saprotrophic fungi break down complex organic molecules through the action of extracellular enzymes, which target specifc molecules present in the substrate through which they grow. It is through the action of these enzymes that the decay of organic matter takes place and by which fungi obtain carbon and nutrients for growth and reproduction. Fruiting is a nutritionally expensive activity for fungi (Heaton, Jones, and Fricker
2016; Schmit 2002), therefore the ability of saprotrophic fungi to produce fruiting bodies must be dependent on the action of these enzymes. Given the recent changes observed in fungal fruiting phenology attributed to changing climates and the importance of extracellular enzyme activity to fungal growth and nutrient cycling, it is important to understand the phenology of fungal extracellular enzyme activity in natural systems to evaluate the impact that changing climates may have on fungal-driven nutrient cycling.
28 We know relatively little about the proximate drivers of fungal fruiting and enzyme activity, particularly in wood decay systems. There are several reasons for variation in when a fungus produces a fruiting body: to optimize conditions for spore dispersal (Norros et al. 2012,
2014) and establishment, to escape natural enemies (A’Bear et al. 2013), or to escape limiting resources, or once energy acquisition requirements have been met (Kauserud et al. 2010).
Variation among fungal species in the timing of their fruiting could indicate that they are responding to diferent combinations of these pressures, and therefore using diferent environmental cues. The degree to which fruiting and enzyme activities are linked, or respond to the same environmental cues, is unknown. We expect extracellular enzyme activity to be occurring where any metabolically active fungus exists, but that the level of activity of various enzymes should be driven by which enzymes fungi produce, depending on their nutritional requirements (i.e. carbon, nitrogen; Wallenstein and Weintraub 2008), the substrate that is accessible in the environment (i.e. lignin, cellulose, chitin), and the environmental conditions that facilitate enzymatic reactions (i.e. temperature driving reaction rates, or water availability to disperse reactants; Gilbertson 1980). Due to the latter, we expect that enzyme activities will have stronger responses to short-term environmental conditions, while fruiting activities may respond to conditions over longer time periods, which integrate conditions for resource acquisition necessary to produce sporocarps.
In this study, we focused on WIF in a mixed-deciduous forest in the mid-Atlantic, USA.
We combined monthly surveys for fruiting bodies and sampling of wood substrate to look for coordination between fungal fruiting activity and fungal extracellular enzyme activity. We seek to answer the questions: 1) Do fruiting communities and enzyme activities show seasonal patterns?
2) What weather variables infuence these activities and over what time scales? 3) Are fruiting and
29 enzyme activities correlated? Based on previous work, we expected to see distinct spring and fall communities of fruiting WIF, with peak fruiting abundance in the fall (Halme and Kotiaho 2012;
Sato, Morimoto, and Hattori 2012). Due to their role in resource acquisition, extracellular enzyme activities should peak before the fall fruiting peak. We explored the relationship between both fruiting and enzyme activity and temperature and precipitation over the course of the growing season, and investigated the sensitivity of fungal activities to immediate environmental conditions through to capturing seasonal changes at longer time scales. Temperature and precipitation are environmental measurements that are commonly taken around the world, and are known to afect both fungal fruiting (Diez et al. 2014; Pinna et al. 2010) and enzymatic activities (Baldrian et al.
2013a) individually. The timing of both fungal fruiting and extracellular enzyme activity has been demonstrated to be sensitive to environmental conditions that occur months to days previous to fruiting (Baldrian et al. 2013b; Diez et al. 2014; Ovaskainen et al. 2013), therefore we test the relationships between fungal activities and temperature and precipitation over multiple time scales. We do not expect fruiting and enzyme activities from the same sampling period to be strongly correlated with one another, which will limit our ability to extrapolate how fungal ecosystem function might be shifting due to changing climates along with fruiting activity. This work provides important insight into the cryptic phenology of fungal extracellular enzyme activity, which can help predict the impact of changing climates on fungal ecosystem function.
Methods
Study site
The study was carried out at the 16 ha Center for Tropical Forest Science Forest Global
Earth Observatory (ForestGEO) forest dynamics plot at the Smithsonian Environmental Research
Center in Edgewater, MD, USA. The plot ranges from dry upland habitats, dominated by
30 Liriodendron tulipifera (Magnoliaceae), Fagus americana (Fagaceae), and Quercus spp.
(Fagaceae) to wet lowland habitats characterized by Platanus occidentalis (Platanaceae). The plot has a mean annual temperature of 15°C, and receives an average of 120.2 cm of rain per year.
Fruiting body surveys
Fruiting bodies were surveyed monthly during the growing season between June 2013 and
December 2016. Deadwood was searched on four 2 m wide x 400 m long transects running from north to south and spaced every 100 m within the plot. Each transect also included four 10 m x 20 m plots spaced 70 – 80 m along the transect, resulting in an area of 840 m2 of area searched per transect. Fresh fruiting bodies (determined by the color of the expanding margin, the pliability of the sporocarp, and the absence of insect or other damage) occurring on wood, as well as their location along the transect and several substrate characteristics were recorded. Substrates were measured for horizontal diameter at their large end or, if standing, at 1.37 m using 95 cm timber calipers (Haglof, Sweden), and classifed according to a 5-stage decay class system. In this classifcation, decay class 1 is freshly downed wood with bark and fne branches intact, and decay class 5 is well-decayed wood with no bark, no branches or only branch stubs, and wood that has lost its structural integrity (Sollins 1982). The presence or absence of bark was also noted. Field identifcations of fruiting bodies were made when possible, otherwise fruiting bodies were collected and returned to the lab for identifcation through microscopic examination and DNA barcoding.
DNA barcoding was completed for voucher specimens of all identifed species, as well as for representatives of morphologically grouped collections. DNA was extracted following the protocol of Lindner and Banik (2009). Briefy, samples from the spore surface of fruiting bodies were stored in a 2x CTAB solution, and DNA was extracted using ethanol precipitation followed
31 by clean-up using MP Biomedicals GeneClean kit. We amplifed the ITS fungal barcoding region using primers ITS1F and ITS4 (Martin and Rygiewicz 2005), and Sanger sequencing was performed at the Laboratory of Analytical Biology at the Smithsonian National Museum of
Natural History. Sequence traces were edited and aligned using Geneious v. 8.0. Sequences were identifed through BLAST search against the NCBI nucleotide database.
Enzyme assays
Beginning in March 2014, we collected wood samples from 10 logs (>30 cm diameter) distributed along the transects in the ForestGEO plot in tandem with the fruiting body surveys.
Logs were sampled using a 12 mm increment borer (Haglof, Sweden) and stored in 15 mL centrifuge tubes on ice until they could be returned to the lab, late that day. On the same day as collection, samples were weighed and subsamples of approximately 5 g were taken and placed in the drying oven at 75°C for at least one week and reweighed to determine moisture content of the logs. The remainder of the wood samples were stored at -80°C until enzyme activity was assayed.
Enzyme assays were conducted following recommendations in German et al. (2011). We used fuorimetric assays to measure the activity potentials of 4 hydrolytic enzymes: beta- glucosidase (BG), beta-xylosidase (BX), cellobiohydrolase (CBH), and N-acetyl-glucosaminidase
(NAG). These enzymes break down cellulose, hemicellulose, both cellulose and hemicellulose, and the fungal polysaccharide chitin, respectively. Wood cores were ground in liquid nitrogen using a mortar and pestle, followed by mechanical pulverization in a MM400 Mixer Mill (Retsch,
Germany), as suggested for ligneous tissue (Yockteng et al. 2013). Assays were performed on 0.2
+/- 0.01 g of the frozen wood dust suspended a 1% sodium acetate bufer at a pH of 5. Slurries were added to microplates containing enzyme-specifc substrates bound to methylumbelliferyl and allowed to incubate for 2 hours at the average temperature on the day samples were collected.
32 Plates were read with a Synergy HTX multi-mode microplate reader (Biotek, USA) with an excitation and emission wavelength of 420/450 nm. After the initial reading was taken, 0.2 uL of
NaOH was added to the wells to raise the pH and the plates were read again after 2 minutes.
Weather data
Environmental conditions, including temperature and precipitation, were recorded on a canopy tower in the middle of the ForestGEO plot on a continuous basis. We used these data to calculate average temperature and cumulative precipitation across a range of timescales. Starting from the day of the survey, we included the previous day, previous week, previous month, and previous two months in order to capture fungal response to short- and long-term environmental conditions. Since we expected these variables to be highly correlated with one another, we performed a cluster analysis using Pearson’s r to determine the extent of the correlation.
Data Analysis
We performed a non-metric multidimensional scaling ordination (NMDS) to examine how the composition of fungal fruiting communities changed seasonally using the metamds function within the vegan package (Oksanen et al. 2017). For this analysis, we defned the start and end of seasons using climatological seasons (starting dates: spring, March 1; summer, June 1; fall, September 1; winter, December 1). All fungal species that were observed more than once in the surveys were included in the analysis. We used Bray-Curtis distances to separate observations of 87 fungal species across each transect observation (N = 99). We mapped 95% confdence ellipses around seasonal centroids on the resulting ordination and performed permutational
ANOVAs on the distance matrix to look for signifcant changes in seasonal community centroids using adonis, and confrmed that signifcant diferences were not due to diferences in dispersion using permadisp, both functions from the vegan package (Oksanen et al. 2017). To compare
33 fruiting abundances with enzyme activities across the three years of overlapping data, we used t- tests to look for signifcant diferences in the month in which peak annual activity occurred between the hydrolytic enzymes and fruiting body abundances each year.
To determine which environmental factor best explained changes in fruiting communities and enzyme activities, we used the manyglm (fruiting abundances) and manylm (enzyme activity potentials) functions from the mvabund package (Wang et al. 2017) to build multivariate models of species or enzymes response to environmental conditions. We looked at average temperature
(°C) and cumulative rainfall (mm) over a series of time windows: the day, the week, the month, and two months previous to the survey or sample collection. We used AIC values to compare across single factor (either temperature or precipitation) generalized linear models with the binomial distribution (fruiting bodies) and linear models (enzyme activity potentials) at all four time scales. Species that fruit at diferent times of the year may have diferent relationships to weather conditions than the community as a whole. Therefore, we subset the fruiting community by season based on the species’ NMDS scores and tested for sensitivity to weather variables in the same manner as for the community as a whole.
We used linear models with the enzyme activity potential of each hydrolytic enzyme as the independent variable to determine whether fruiting and enzyme activity potentials were correlated within the same sampling period. We then used partial linear regression to determine whether including fruiting body abundances signifcantly improved the model ft over a reduced model that included only the transect in which the log was located.
For all analyses, each transect was treated as an independent observation of fruiting body abundance and community composition, giving a sample size of 4 for fruiting body observations for each time period. Enzyme activities for the four hydrolytic enzymes were assessed on 14 logs
34 over the three year period, with at least 10 logs in each sample, except for the frst sampling point
(March 2014), when only 7 logs were sampled. Activity potentials were ln(x + 1) transformed to meet the assumption of normality for linear models. All analyses were performed in R version
3.4.1 (R Core Team 2017).
Results
We collected fruiting body abundance data from over 3,300 observations of 99 species over 33 sampling periods. The NMDS converged on a 3-axis solution (stress = 0.21) and demonstrated separation between fruiting communities in spring, summer, and winter/fall (Fig.
3.1). Spring and summer communities were separated along the frst axis, while spring and summer were separated from winter/fall communities along the second axis. The permutational
ANOVA confrmed signifcant diferences among spring, summer, and fall/winter communities
(F3,95 = 5.11, P < 0.001), and there was no signifcant diference in the dispersal of these communities in ordination space (P > 0.05), indicating distinctive community centroids.
Enzyme activity potentials were highest for BG, followed by CBH and NAG, and lowest for BX (Fig. 3.2). All four enzymes varied in activity potential across the three years, with peak enzyme activity occurring in week 28 (BG, BX, CBH) and week 30 (NAG), on average, which corresponds to mid- to late July. Fruiting body abundances also varied across the year, with peak abundances occurring in week 35 (early September), averaged across the four years of fruiting body data. Comparing the month of peak fruiting abundance with peak activity potentials for the four enzymes, t-tests showed that BG, BX, and CBH peaked signifcantly earlier than fruiting bodies in 2014 and 2016, while NAG peaked signifcantly earlier than fruiting bodies only in 2016
(Table 3.1, P < 0.05). In 2015, there were no diferences between when enzymes and fruiting bodies peaked.
35 There was higher correlation among average temperatures across time slices than among cumulative precipitation values (Fig. 3.3), with correlations increasing with the more proximate timescales (i.e. correlation between previous day and previous week > between previous day and previous month). Multivariate model fts improved most when average temperature from any time window was included in the model for both entire fruiting communities and enzymes (Fig. 3.4), but there were diferences in the timescale that improved model ft the most. Models of fruiting body composition were improved most by average temperature at two months, followed by week, previous day, and month, while the model ft for enzyme activities was most improved by average temperature from the previous week, month, previous day, and two month timescales. For both fruiting bodies and enzyme activities, precipitation improved the models most over the two month timescale, with precipitation amounts worsening model ft at all other timescales for fruiting bodies. Model fts were slightly improved by cumulative precipitation over the previous month for enzyme activities.
Of the 99 species included in the fruiting body multivariate models, 83 could reliably be assigned to the season in which they typically fruit based on their NMDS scores. Of these, 27 fruited mostly in the fall and winter, 22 were generalists and fruited throughout the year, 21 fruited in the summer, and 13 in the spring (Table 3.2). In multivariate models, the fall/winter and generalist fungi had the most similar results to the overall fruiting community (Fig. 3.5), with temperature across time scales having a positive efect on model ft, particularly at longer time scales. Summer fruiting fungi also showed a strong positive response to temperature, although the relationship to timescale was reversed, with shorter timescales improving model ft more than longer. The spring fruiting community, however, responded very diferently than the community
36 as a whole, with much more sensitivity to precipitation as well as to temperature over short time scales.
Finally, we found signifcant positive correlations between fruiting body abundances and enzyme activity potentials for all four of the hydrolytic enzymes assayed. Partial linear regression revealed that including fruiting body abundances in the models resulted in a small increase in model ft (Table 3.3) and this increase was signifcant for all of the enzymes, except for BG
(increased adjusted R2 values: 0.03 (BX), 0.04 (NAG, BG), and 0.06 (CBH).
Discussion
In this study, we looked for seasonal variation in fungal fruiting and extracellular enzyme activities. To our knowledge, this is the frst study that has examined patterns of enzyme activity levels within years in woody debris, and the frst study that has attempted to link fruiting activity to other cryptic fungal activities that are important for ecosystem function. Our fndings indicate that these fungal activities are only loosely related and that they respond to environmental cues at diferent time scales. Fruiting fungi responded to environmental cues diferently depending on the season in which they fruited, indicating that they were using diferent strategies to optimize when fruiting occurs. These results suggest that more work is needed to understand the implications of climate change for fungal phenology and ecosystem function.
We found defnite patterns in seasonal activity for both fruiting and all four hydrolytic enzymes (Figs. 1 & 2). The NMDS of fruiting showed distinct communities fruiting in the spring, summer, and fall/winter seasons, indicating that fungi were partitioning the fruiting season among species. Fungi may fruit at diferent times during the year for many possible reasons: to escape from natural enemies, to reduce competition for the colonization of new substrates, to maximize dispersal distance of spores, to allow enough time for the accumulation of resources before
37 fruiting, or in response to limiting resources where they are located (Büntgen, Kauserud, and Egli
2012). Our results suggest that while fungi fruiting throughout the year or later in the growing season may be accumulating resources throughout the growing season, spring and summer fruiting fungi are maximizing colonization success by respond to more immediate environmental conditions (Fig. 3.5). This strategy may give them an advantage in colonizing new woody substrates that entered the system during the winter and early spring, for instance.
By breaking the fungal community into seasonal components, we found that diferent segments of the fungal community responded to diferent environmental conditions over diferent timescales (Fig. 3.5). Spring-fruiting fungi in particular responded very diferently to environmental conditions than did fungi that fruited later in the year. Spring fruiting fungi were most responsive to recent weather conditions, with the previous day’s temperature showing the most infuence on model ft, followed by the precipitation and average temperature over the previous week. Since these encompass the frst fungi that fruit after temperatures climb above freezing, they may be especially dependent on rapid responses to favorable conditions. While early fruiting can be risky, as frost risks remain, these fungi may capitalize on resources they accumulated during the previous growing season (Kauserud et al. 2010b) and early access to new resources in the environment exposed over the winter months. Summer-fruiting fungi continued to have a strong association with the previous day’s temperature, with the efect of temperature decreasing as the timescale lengthened. Fall/winter fruiting fungi and generalist fungi represented over half of the fungi in the survey and drove the overall community patterns. For both the complete fungal community and fall/winter fungi average temperature over two months improved the model ft the most, and precipitation had relatively little power to improve the models, except at the longest timescale. Fall/winter-fruiting fungi may have been responding to longer-term
38 environmental conditions becaues this was the period over which the fungi were able to accumulate resources, with fruiting occurring when resources were sufcient or became limiting within the substrate.
Enzymatic activity potentials also varied seasonally during the 3 years they were assayed
(Fig. 3.2). This is the frst time that enzyme activity potentials have been investigated in wood across several years with regular sampling within years. Peak activity varied for both fruiting and enzymes from year to year, with fruiting activity peaking later in the year than enzyme activities for 2 out of the 3 years surveyed. Overall, this supports the hypothesis that enzyme activity precedes fruiting activity, since it is through the action of extracellular enzymes that fungi acquire resources, although it is possible for fungi to store and translocate energy and resources (Hobbie,
Macko, and Shugart 1999; Wells, Boddy, and Donnelly 1998). Our study included four extracellular enzymes, three of which (BG, BX, and CBH) target polysaccharides that make up plant cell walls, and the fourth which targets a fungal polysaccharide (NAG). We found slight diferences in when NAG activity was elevated compared to the other three enzymes (Table 3.1,
Fig.3.2), and this may have been due to diferences in fungal nutritional needs during the growing season. NAG is involved in the breakdown of fungal cell walls, which can arise from the opportunity to degrade intact or senesced hyphae of competitor species within the substrate, or from an individual recycling its own hyphal cells to reallocate resources within the substrate (Shoji and Craven 2011). These fungal tissues are a source of nitrogen, and high NAG activity may be associated with turnover of fungal communities within the woody substrate.
Both fruiting and enzyme activities were most infuenced by temperature across time scales. While fruiting activity, especially late-season fruiting activity, was best predicted by longer-term average temperatures, enzyme activities were best predicted by shorter- and medium-
39 term temperatures with previous week and day having the highest delta AIC values. Enzymes are temperature sensitive, with higher temperatures generally resulting in faster reaction times within an optimal temperature range for a specifc enzyme (German et al. 2012; Wallenstein, McMahon, and Schimel 2009). The assays we performed target suites of enzyme families that target particular substrates (cellulose, hemicellulose and chitin). Fungal species vary in the enzyme profles they are capable of producing. Additionally, fungal individuals can manipulate the enzymes produced from within their profle at a given moment to work better in the current environmental conditions. For example, fungi can produce enzymes that are less temperature sensitive during warmer conditions and more temperature sensitive in cooler conditions
(Wallenstein, McMahon, and Schimel 2009). It is not surprising then that enzyme activities should be most infuenced by more immediate environmental conditions.
Unexpectedly, both fruiting and enzyme activities were relatively unresponsive to precipitation. Aside from spring fruiting fungi, fruiting and enzyme activities did not respond to precipitation on short or medium timescales, and only cumulative precipitation over two months improved model ft for both fruiting and enzyme activities. Complementary to our work, a previous study found that higher growing season precipitation can result in delayed fruiting times for fall fruiting fungi (Diez et al. 2013). Cellulolytic wood decay is an aerobic process (Kirk and
Farrell 1987), meaning that the process is considerably slowed in highly saturated wood. Fungi therefore depend on water availability being within a certain optimal range, with enough water to allow enzymes and products to difuse around hyphae while still allowing for adequate oxygenation. At our study site, the ground was generally saturated in the spring and early summer, when high spring rainfall occurs and before evapotranspiration pressure is high. The response to precipitation for both fruiting and enzyme activity should be longer term, as substrates dry out as
40 the growing season progresses and become more suitable for fungal activities.
Despite having diferent patterns of activity levels across the year (Fig. 3.2, Table 3.1), fruiting body abundances and enzyme activity potentials were signifcantly positively correlated with one another at the same sampling period (Table 3.3), although note that BG was marginally signifcant. However, the amount of variation in enzyme activity explained was small, and the increase in variation explained only 3-6% on inclusion of fruiting bodies in the analysis. This result may be partially understood due to the similarities in the responses of these activities to cumulative precipitation over two months, and generally strong and positive relationship to temperature across time scales (Fig. 3.4). More strikingly, these results suggest that fruiting bodies may not be the best indicator of physiological activities of fungi within substrates. My results perhaps are unsurprising given that fruiting bodies and enzymes occur in diferent locations, with enzymes produced within wood matrix and bufered from external environmental conditions. It seems that fruiting depends on enzyme production and digestion of wood for fungi to accumulate resources to fruit; however, the timing of fruiting post-resource acquisition can vary depending on optimal environmental conditions for fruiting body development and spore dispersal.
Conclusion
Fungi show seasonal patterns in both fruiting and enzymatic activities, with distinct communities of fungi fruiting in spring, summer, and fall/winter and peak fruiting and enzyme activity in September and July, respectively. Fruiting communities overall responded more to environmental conditions over two-month timescales than immediate conditions, except for spring-fruiting fungi, which may pursue diferent optimization strategies. Enzyme activities responded more to near-term temperature conditions, refecting both enzymatic sensitivity to temperature and the ability of fungi to optimize enzyme production for current environmental
41 conditions. Although I found a positive correlation between enzyme activity and fruiting body abundances, the relationship was weak. Therefore, it is not possible to extrapolate an estimate of the ecosystem function of fungi from fruiting body data. Instead, the phenology of enzyme production, as well as the temperature responsiveness of diferent enzymes, deserves closer scrutiny to determine how climate change may afect fungal driven processes such as wood decomposition in the future.
42 Table 3.1. Month of peak activity for the three years of overlapping fruiting and enzyme activity for four hydrolytic enzymes: BG, beta-glucosidase; BX, beta-xylosidase; CBH, cellubiohydrolase; and NAG, N-acetyl-glucosaminidase. t-tests were used to determine whether the distribution of peaks was signifcantly diferent between fruiting and each of the enzymes for each of the three years of overlapping data. Signifcant diferences (P < 0.05) are in bold.
Year Peak Fruiting Enzyme Peak Enzyme t-test P-value 2014 September BG July 3.3 0.029 BX July 3.3 0.029 CBH July 5.0 0.003 NAG August 2.2 0.076 2015 August BG July 0.7 0.529 BX June 1.2 0.273 CBH July 0.9 0.394 NAG June 1.5 0.171 2016 October BG May 9.3 < 0.001 BX April 7.6 < 0.001 CBH May 9.3 < 0.001 NAG May 6.7 < 0.001
43 Table 3.2 Season associated with the fruiting of 86 species of fungi observed between June 2013 and January 2017. Fungi that could not be identifed to genus with either morphological or DNA barcoding are identifed with a unique Morphological Taxonomic Number (MTN) within the study. The phylum and family of each species is also given.
Fruiting Season Species or MTN Phylum Family Generalist 0001-01 Basidiomycota – 0004-01 Ascomycota – 0005-01 Basidiomycota – Antrodia fragrans Basidiomycota Steccherinaceae Byssomerulius incarnatus Basidiomycota Phanerochaetaceae Ceriporia reticulata Basidiomycota Phanerochaetaceae Cerrena unicolor Basidiomycota Polyporaceae Datronia mollis Basidiomycota Polyporaceae Dentocorticium sp. Basidiomycota Polyporaceae Fomitiporia langlosii Basidiomycota Hymenochaetaceae Irpex lacteus Basidiomycota Meruliaceae Loweomyces fractipes Basidiomycota Steccherinaceae Oligoporus sp. Basidiomycota Polyporaceae Peniophora albobadia Basidiomycota Peniophoraceae Phellinus sp. Basidiomycota Hymenochaetaceae Polyporus alveolaris Basidiomycota Polyporaceae Steccherinum ochraceum Basidiomycota Steccherinaceae Stereum complicatum Basidiomycota Stereaceae Trametes conchifer Basidiomycota Polyporaceae Trametes elegans Basidiomycota Polyporaceae Trametes sp. 2 Basidiomycota Polyporaceae Tremella mesenterica Basidiomycota Tremellaceae Spring 0003-01 Basidiomycota – 0007-01 Basidiomycota – Artomyces pyxidatus Basidiomycota Auriscalpiacea Auricularia auricula Basidiomycota Auriculariaceae
44 Table 3.2 Contiued. Fruiting Season Species or MTN Phylum Family Spring (cont.) Calocera cornea Basidiomycota Dacrymycetaceae Daldinia chilidae Ascomycota Xylariaceae Ductifera pululahuana Basidiomycota Incerae sedis Exidia glandulosa Basidiomycota Auriculariaceae Galiella rufa Ascomycota Sarcosomataceae Kretzschmaria deusta Ascomycota Xylariaceae Megacollybia sp. Basidiomycota Marasmiaceae Nidularia pulvinata Basidiomycota Agaricaceae Perenniporia luteola Basidiomycota Polyporaceae Perenniporia rhizomorpha Basidiomycota Polyporaceae Polyporus varius Basidiomycota Polyporaceae Radulomyces confuens Basidiomycota Pterulaceae Sarcoscypha sp. Ascomycota Sarcoscyphaceae Schizophyllum commune Basidiomycota Schizophyllaceae Scutellinia sp. Ascomycota Pyronemataceae Xylaria sp. Ascomycota Xylariaceae Xylobolus frustulans Basidiomycota Stereaceae Yuchengia narymica Basidiomycota Polyporaceae Summer Bjerkandera adusta Basidiomycota Meruliaceae Coprinopsis variegata Basidiomycota Psathyrellaceae Dacrymyces chrysospermus Basidiomycota Dacrymetaceae Dacryopinax spathularia Basidiomycota Dacrymetaceae Fuscoporia gilva Basidiomycota Hymenochaetaceae Hohenbuehelia sp. Basidiomycota Pleurotaceae Marasmiellus candidus Basidiomycota Marasmiaceae Resupinatus sp. Basidiomycota Tricholomataceae Stereum ostrea Basidiomycota Stereaceae Stereum sanguinolentum Basidiomycota Stereaceae Trametes sp. 1 Basidiomycota Polyporacea
45 Table 3.2 Continued Fruiting Season Species or MTN Phylum Family Summer (cont.) Trichaptum biforme Basidiomycota Incertae sedis Fall/Winter 0002-01 Basidiomycota – 0006-01 Basidiomycota Hydnaceae Antrodiella sp. Basidiomycota Steccherinaceae Ascocoryne sp. Ascomycota Helotiaceae Bisporella citrina Ascomycota Helotiaceae Daedaleopsis confragosa Basidiomycota Polyporaceae Daedaleopsis sp. Basidiomycota Polyporaceae Exidia recisa Basidiomycota Auriculariaceae Ganoderma applanatum Basidiomycota Genodermataceae Ganoderma lucidum Basidiomycota Genodermataceae Gloeoporus dichrous Basidiomycota Meruliacaea Ischnoderma resinosum Basidiomycota Ischnodermataceae Junghuhnia nitida Basidiomycota Steccherinaceae Lentinellus sp. Basidiomycota Auriscalpiaceae Lycoperdon pyriforme Basidiomycota Agaricaceae Mycena haematopus Basidiomycota Mycenaceae Mycena leaiana Basidiomycota Mycenaceae Panellus stipticus Basidiomycota Marasmiaceae Perenniporia sp. Basidiomycota Polyporaceae Phanerochaete chrysorhiza Basidiomycota Phanerochaetaceae Phlebia radiata Basidiomycota Meruliaceae Phlebia tremellosa Basidiomycota Meruliaceae Physisporinus vitreus Basidiomycota Meripilaceae Pleurotus ostreatus Basidiomycota Pleurotaceae Plicaturopsis crispa Basidiomycota Amylocorticiaceae Trametes betulina Basidiomycota Polyporaceae Trametes versicolor Basidiomycota Polyporaceae
46 Table 3.3. Results of partial linear regression comparing a full model relating enzyme activity potentials to transect and fruiting body abundances to a reduced model including only transect as an independent variable. Signifcant P-values indicate that including fruiting body abundances in the model add a signifcantly improve the amount of variation accounted for by the model.
Enzyme Full Model R2 Reduced Model R2 P-value
BG F4, 54 = 3.084 0.109 F3, 55 = 2.690 0.069 0.052
BX F4, 54 = 3.159 0.113 F3, 55 = 2.327 0.083 0.026 CBH F4, 54 = 3.397 0.124 F3, 55 = 2.510 0.062 0.022 NAG F4, 54 = 2.277 0.070 F3, 55 = 1.599 0.026 0.040
47 Figure 3.1. Ordination of fungal fruiting communities between June 2013 and December 2016.
Each point represents a transect on a single day. Points are colored by season. Ellipses represent
95% confdence intervals around centroid means for each season.
48 Figure 3.2. Fruiting body abundance (Fb.abund; counts) and enzyme activity potentials (umol/uL/ hr; BG, beta-glucosidase; BX, beta-xylosidase; CBH, cellubiohydrolase; NAG, N-acetyl- glucosaminidase) each month 2014-2016 for four transects on the ForestGEO plot at the
Smithsonian Environmental Research Center, Edgewater, MD.
49 Figure 3.3. Cluster diagram of the relationship between average temperature (aveTemp) and cumulative precipitation (Precip) over four time periods – the day (PrevDay), week (Week), month
(Month), and two months (2Month) – previous to each sampling event – based on Pearson’s r2.
The position of the branching point between two variables on the y-axis indicates the level of covariation.
50 Figure 3.4. Multivariate responses of fungal fruiting communities (a) and hydrolytic enzyme activity potentials (b) to weather variables across four time periods: the previous day, week, month, and two months from when samples were taken. Green bars indicated average temperature variables (°C) over the given time period, and purple indicates cumulative precipitation over the given time period (mm). Higher delta AIC values indicate that the model ft is improved by the inclusion of the variable.
51 Figure 3.5. Multivariate responses of fungal fruiting communities to weather variables when the fruiting community is subset by the season in which it typically fruits. Higher delta AIC values indicate that the model ft is improved by the inclusion of the variable. Green bars indicated average temperature variables (°C) over a given time period, and purple indicates cumulative precipitation over the given time period (mm).
52 Chapter 4: Key players in wood-inhabiting fungal communities infuence extracellular enzyme activities
Abstract
As the primary agents of wood decomposition in forests worldwide, saprotrophic fungi are critical players in the global carbon cycle. The role of wood inhabiting fungal communities
(WIF) in determining wood decay rates is increasingly being recognized as molecular detection methods improve, and as climate and wood traits do not account for all of the variability in wood decomposition. However, microbial communities are highly diverse and show variability across a variety of timescales, contributing to the difculty in assessing the impact of WIF communities on wood decay. Fungi secrete extracellular enzymes to break down organic macromolecules to meet their nutritional requirements, resulting in decomposition of the substrate. We used high- throughput amplicon sequencing and extracellular enzyme assays to repeatedly sample decaying logs from a temperate mixed deciduous forest to determine the timescales for turnover in the WIF community, how much variation in enzyme activity could be explained by WIF community composition, and whether there were species that were strongly associated with high enzyme activity. Our data show that WIF communities are stable within and across years, with community turnover occurring as wood increases in decay. We demonstrate that WIF community composition explains from 12 to 25% of the variation in enzyme activity. Out of 69 commonly occurring operational taxonomic units, we identifed 14 that were associated with high enzyme activity, indicating that a few species were important for the overall ecosystem function of WIF communities.
Introduction
Forest ecosystems cover approximately 30% of the earth’s surface, and contain upwards of
53 3 trillion trees, that store an estimated 2.4 petagrams of carbon per year (Bonan 2008; Crowther et
al. 2015; Pan et al. 2011). Carbon is stored by incorporating CO2 into glucose molecules in plant tissues via photosynthesis, and is released through respiration from living plants, soils, and dead organic matter. The mass of deadwood in a temperate forest is roughly equivalent to the mass of tree leaves (Gough et al. 2007), and its release into the atmosphere is a small but signifcant part of the global carbon budget. As the main agents of wood decomposition, saprotrophic fungi play a critical role in the global carbon cycle (Cornwell et al. 2009). Wood decay rates depend on a combination of climate (Kahl et al. 2015), wood traits (Cornwell et al. 2008; Freschet, Aerts, and
Cornelissen 2012; Weedon et al. 2009), and the fungal community that assembles within the wood
(Kubartová, Ottosson, and Stenlid 2015; Van Der Wal, Ottosson, and De Boer 2015). The role that fungal community plays in regulating wood decay rates has been gaining more attention recently as detection methods have become increasingly powerful, and as recent work has found that climate and wood traits do not adequately predict wood decay rates (Bradford et al. 2014; Van
Der Wal, Ottosson, and De Boer 2015). Increasing our understanding of how fungal communities infuence decay rates is therefore essential in order to better incorporate this critical ecosystem process into global carbon models.
The communities of wood inhabiting fungi (WIF) that assemble with a piece of deadwood are structured by the wood itself, with many fungal species associating with particular wood species, decay classes, and sizes (Berglund et al. 2011; Purahong et al. 2018; Rajala et al.
2012). Traditionally, fungi have been characterized by whether they prefer angiosperm versus gymnosperm wood, although recent work indicates that there may be more species specifcity within these larger plant groups than previously thought (Purahong et al. 2018). WIF communities are also afected by the colonization order of fungi (Dickie et al. 2012; Fukami et al. 2010), which
54 can alter the trajectory of the subsequent community. Turnover in fungal communities occurs as the wood increases in decay and the environment becomes unsuitable for initial colonizers, who are subsequently replaced by later arriving competitors (Boddy 2001). As wood decomposition is a multi-year process, this turnover occurs slowly as decay increases; in contrast, fungal communities in soil (Voříšková et al. 2014; Žifčáková, Howe, and Baldrian 2016) and communities of fungi fruiting on wood (Chapter 2) experience seasonal turnover as environmental conditions change within the year. Whether wood inhabiting fungal (WIF) communities also experience seasonal turnover is unknown.
Wood is a nutritionally poor substrate, with high carbon to nitrogen ratios that make it difcult to degrade. Fungi secrete a suite of enzymes that target a variety of substrates to fulfl their nutritional requirements (Hanson et al. 2008; McGuire et al. 2010). In white rot fungi, oxidative enzymes break down lignin to provide access the carbohydrates (e.g. cellulose and hemicellulose) that make up plant cell walls, which are then targeted by hydrolytic enzymes as a source of carbon (Peralta et al. 2017). The process of breaking down cellulose is initiated by endocellulases, which cleave the crystalline cellulose structure in random locations.
Cellobiohydrolase then cleaves of 2 to 4 glucose units, which are broken into glucose molecules through the action of ß-glucosidase. Hemicellulose is embedded within the cellulose matrix, and can be composed of several diferent sugars in addition to glucose, giving it a more irregular shape than crystalline cellulose. The most abundant sugar after glucose, xylan, composed of xylose monomers, is broken down through the action of ß-xylosidase. Fungi also use hydrolytic enzymes to recycle their own or their competitors’ tissues to gain nitrogen by breaking down chitin, a component of fungal cells walls, through the action of N-acetyl-glucosaminidase (Kaiser et al.
55 2014). As the agents of wood decomposition, we can use enzyme activities as an instantaneous measure of how much decay is occurring within a substrate.
The composition of fungal communities afects wood decay rates because fungi are not equal in their ability to break down woody substrates. Not all fungi have the genetic ability to produce all enzymes, particularly those targeting lignin and cellulose. Even fungi that are wood saprotrophs have evolved diferent strategies to break down the recalcitrant lignocellulose matrix.
(Eastwood et al. 2011; Floudas et al. 2012; Riley et al. 2014) Brown rot, for instance, is a derived mode of wood decomposition that relies on a Fenton-like reaction that employs free radicals to modify lignin structure and gain access to cellulose and hemicellulose (Floudas et al. 2012). The number of copies of genes that encode oxidative and hydrolytic enzymes correlates with fungal lifestyles, with white rot wood saprotrophs having many more copies of genes coding for cellobiohydrolases and glucosidases than mycorrhizal, mycoparasitic, or brown rot saprotrophic species (Eastwood et al. 2011; Floudas et al. 2012; Riley et al. 2014). Most wood saprotrophic fungi are in the phylum Basidiomycota, within which the ability to decay wood is found in several families, most notably the Polyporaceae which contains numerous white and brown rot genera.
Wood saprotrophs are also found in the phylum Ascomycota; these generally cause soft rot, which is localized around the fungal hyphae and not widespread within the substrate, and does not cause large amounts of mass loss (Worrall, Anagnost, and Zabel 1997).
There has been debate about how much the diversity of fungal communities impacts their functioning in the ecosystem. Highly diverse fungal communities are often assumed to have high levels of functional redundancy (Strickland et al. 2009), and high levels of competition that inhibits decay (Boddy 2000; Hiscox et al. 2014; Yang et al. 2016). Species interactions within the fungal community are important, as these communities are diverse and individuals must compete
56 for space and resources within the wood. In experimental settings, fungal interactions tend to be idiosyncratic, with some species facilitating, some inhibiting, and some with no impact on decay
(Venugopal et al. 2018, Betts unpublished data). Amplicon sequencing of fungal communities using the most common barcoding regions (ITS1 & 2, Koljalg et al. 2014) does not provide any information on the functional abilities of the fungal community. Therefore we must combine sequencing with other methods to determine what species might be most important in driving wood decay in situ.
We used repeated sampling of logs in a mid-Atlantic mixed deciduous forest for amplicon sequencing and extracellular enzyme assays to address the following questions: 1) Over what time scales does the fungal community within wood turnover? 2) How much variation in the activity of hydrolytic enzymes is explained by the fungal community composition? And 3) Are there fungal taxa that are associated with high enzyme activity? Based on previous work in soils and in fruiting fungi, we expected to see a small amount of intra-annual variation in WIF communities, but greater variation between years and across decay classes as succession occurs. We expect fungal communities to explain some variation in enzyme activities, although there will be high levels of noise in the community data for several reasons. Not all fungi that are captured in amplicon sequencing surveys of WIF communities are active within the wood; dead tissues, dormant species, unestablished spores, as well as soil or pathogenic fungi that have been brought in by insects, can all be present in genetic surveys but not be contributing to fungal activity (Baldrian et al. 2012). We used the relationship between community composition and enzyme activities to identify fungi that may be highly active within the substrate and important to the overall decay process. We expected these taxa to be well represented in existing databases, since they are likely to have been cultured, and they have economic signifcance for lumber, paper, and biofuel
57 industries (Bahmachari 2017). We expected these taxa to be from known wood saprotrophic white rot Basidiomycete lineages, as fungi in these clades are known to possess genes for the assayed enzymes, and are considered to be responsible for the majority of wood decomposition in terrestrial systems.
Methods
Study site & Sampling
We selected eight logs from the 16 ha Center for Tropical Forest Science Forest Global
Earth Observatory (ForestGEO) forest dynamics plot at the Smithsonian Environmental Research
Center in Edgewater, MD, USA. Logs were a minimum of 40 cm diameter at the large end to allow for repeated sampling, and represented a range of decay stages, from freshly fallen to well- decayed. The logs included hardwood species: Liriodendron tulipifera (N = 5), Carya spp. (N =
3), and Prunus serotina (N = 1). We assessed the decay state of the logs by classifying them using a 5 decay class system (Sollins 1982), where 1 is a freshly fallen log, and 5 is a log in the fnal stages of decay. We sampled logs in the middle of their length and halfway between their top and bottom using a 12 mm increment borer (Haglof, Sweden). Samples were collected 12 times in
2015 and 2016 to capture all four seasons: March, May, July/August, October, December, January.
We placed samples in 15 mL centrifuge tubes in the feld and kept them on ice until we returned to the lab. In the lab, subsamples were weighed before being dried at 75°C for at least one week, then reweighed to determine moisture content at harvest. Samples were stored at -80°C until enzyme activity could be assayed and DNA extracted.
Enzyme Assays
We conducted fuorimetric assays to measure the activity potentials of 4 hydrolytic enzymes: ß-glucosidase (BG), ß-xylosidase (BX), cellobiohydrolase (CBH), and N-acetyl-
58 glucosaminidase (NAG) following recommendations from German et al. (2011). These enzymes break down polysaccharides, with BG, BX, and CBH targeting cellulose and hemicellulose, the major components of plant cell walls, and NAG breaking down chitin, which is found in fungal cell walls. We ground the frozen wood cores in liquid nitrogen using a mortar and pestle, followed by 2 minutes of mechanical pulverization in a MM400 Mixer Mill (Retsch, Germany), as suggested for ligneous tissue (Yockteng et al. 2013). Assays were performed on 0.2 +/- 0.01 g of the frozen wood dust suspended a 1% sodium acetate bufer at a pH of 5. We added 200 uL of the slurry to microplates containing enzyme-specifc substrates bound to methylumbelliferyl and incubated for 2 hours at the average temperature on the day samples were collected. Plates were read with a Synergy HTX multi-mode microplate reader (Biotek, USA) after two hours with an excitation and emission wavelength of 420/450 nm. After the initial reading was taken, 0.2 uL of
NaOH was added to the wells to raise the pH and the plates were read again after 2 minutes.
DNA Extraction and Sequencing
We extracted DNA from the pulverized wood samples following the protocol in Lindner and Banik (2009). Briefy, wood was incubated in 2x CTAB for two hours at 65°C. DNA was precipitated in cold isopropanol at -80°C for 10 minutes, and the pellet was rinsed with 70% ethanol. DNA clean-up was accomplished using GeneClean III kits (MP Biomedicals). We used the fungal specifc primers ITS1F and ITS4 (Martin and Rygiewicz 2005) to amplify the internal transcribed spacer unit of rDNA. We amplifed each sample in triplicate using Q5 high fdelity taq polymerase (New England BioLabs) and pooled the products to reduce PCR bias when sequencing. We included a synthetic mock community provided by the USDA Forest Service
Center for Forest Mycology lab (Madison, WI), as well as water and CTAB negative controls to assure the quality of sequencing in downstream analysis.
59 We prepared the amplicon libraries according to a double-indexed barcoding protocol developed by IBEST Genomics Resources Core at the University of Idaho (Moscow, ID).
Barcodes provided by IBEST were ligated to a 1:10 dilution of the PCR products using Q5 high fdelity taq polymerase (New England BioLabs) in two rounds of PCR reactions. DNA was quantifed on a BioTek Synergy HTX Multi-Mode Reader with a high sensitivity Quant-iT dsDNA assay kit (Invitrogen). We pooled samples into high and low quality pools at approximately equal concentrations based on quantifcation. Sequencing was performed on an
Illumina MiSeq calibrated to sequence 300 base pair paired-end amplicon fragments at IBEST.
We used scripts provided by IBEST to de-multiplex the double barcoded samples
(http://github.com/msettles/dbcAmplicons/). We assembled overlapping sequences with PEAR
(Zhang et al. 2014), and trimmed unassembled reads to remove poor quality bases at the tail end of the forward sequence. We concatenated assembled reads and trimmed forward reads to use in downstream analysis. We used the open source VPARSE pipeline (Rognes et al. 2016) for the remainder of the sequence processing. We fltered out sequences that had a maximum probability of an erroneous base pair of 1 based on the phred scores of the individual base pairs then dereplicated the dataset to remove identical sequences. To reduce the chance of creating spurious operational taxonomic units (OTUs), we discarded sequences that had no identical match before clustering using a 97% similarity threshold. OTUs were matched back to raw reads to create an
OTU × sample matrix. We removed all samples with fewer than 100 sequences, as these were considered failed. We then examined the sequences present in the negative and positive (mock community) controls to perform quality controls on the OTU table. We had no OTU with more than 5 sequences in the negative controls, and no unexpected OTUs in the mock community with
60 more than 5 sequences, so to be conservative, we set all OTUs with fewer than 5 sequences in a sample to 0. Our fnal matrix included 86 samples and 1089 OTUs.
We performed taxonomic assignments for consensus sequences for each OTU using the
Ribosomal Database Project (RDP) classifer with the native UNITE ITS1 database (Koljalg et al.
2014) at a 50% probability cut-of (Wang et al. 2007). We assigned functional guild information to OTUs with the FUNGuild tool (Schilling 2015). As FUNGuild assigns functional information at the genus level, only fungi that classify to the genus level in the RDP classifer can be assigned a function.
Data analysis
All analyses were conducted in R version 3.4.1 (R Core Team 2017). We performed community analysis with a variety of functions from the vegan package (Oksanen et al. 2017). We examined the structure of fungal communities within logs using NMDS ordination with metamds, and looked for clustering of samples due to log, log decay class, log species, season, and year in which samples were collected with envfit. We performed a permutational ANOVA with adonis to determine whether any of these variables signifcantly afected wood fungal communities. We also looked for correlations between samples and enzyme activity levels using envfit and adonis. For the adonis models, we stratifed the model such that the permutations were conducted within each log to account for the repeated sampling over time.
We used weighted average partial least squares regression (WAPLS) to address the question of how much variation in enzyme activity could be explained by fungal community composition. To ensure that we had repeated observations of OTUs across samples, we subset our complete OTU table to include only the OTUs that were present in at least 10% of samples. Our resulting OTU table contained 84 samples and 69 OTUs. We performed single variable WAPLS
61 regressions for the activities of each of the hydrolytic enzymes assayed using a series of functions provided in the rioja package (Juggins 2017). First, we performed the regressions using WAPLS, followed by model validation using the “leave one out” method with crossval, and signifcance testing with rand.t.test. We then extracted the species coefcients from the frst components of the
WAPLS models to identify taxa that were associated with high levels of enzyme activities. For each of the enzymes, we determined the mean coefcient value and selected taxa that were within one standard deviation of the mean. We then examined this short list of taxa for taxonomic and guild assignments to see whether high enzyme activity was due to Basidiomycetes and saprotrophs
Results
We obtained 1.5 million sequences from 86 samples of 8 logs sampled 12 times (March,
May, July/August, October, December, January) over two years, 2015 and 2016. The NMDS ordination showed that although variation in fungal community composition over time occurred, there was no seasonal or directional change over time. We did observe signifcant clustering of samples from individual logs, and separation in fungal communities that corresponded to log species and decay class (Fig. 4.1). The permutational ANOVA showed that the fungal community was signifcantly afected by decay class and log species (Table 4.1). When we added enzyme activity potentials to the models, CBH and NAG positively loaded along the frst axis, along with lower decay classes and Carya spp. logs (Fig. 4.1b). All four enzyme activities correlated signifcantly with the fungal communities in the permutational ANOVA (Table 4.1).
We performed four WAPLS regressions to determine how much variation in extracellular enzyme activity could be explained by fungal community composition. Cross-validation of the models indicated that only the frst dimension held explanatory power. The frst dimension
62 explained 12.1 (NAG), 17.2 (BG), 19.4 (CBH), and 26.8 (BX) percent of variation in enzyme activities (Fig. 4.2), although the model was signifcant only for BX (P = 0.026) and marginally signifcant for CBH (P = 0.09). We extracted the species coefcients from each of the four models and determined which fungal species had high coefcients in models for each of the four enzymes
(Fig. 4.3). We selected all fungal taxa that had coefcient values greater than one standard deviation from the mean, which resulted in 14 OTUs. Of these, several OTUS were highly (> 2 standard deviations from the mean) associated enzyme activities: 5 with all four enzymes and 1 with BX. The remaining OTUs were between 1 and 2 standard deviations; 4 of these were associated with NAG, 2 with BG, and 1 with CBH and BX, and 1 with BX and NAG (Fig. 4.3,
Table 4.2).
We examined these OTUs for their taxonomy and functional classifcations through RDP and FUNGuild. Of those fungi that we could assign a functional guild, all of them were saprotrophic. All fve of the OTUs that were associated with high activities in all four enzymes were Basidiomycetes in the Polyporaceae family. Two of these were identifed as species of
Perenniporia, and identifed as white rot wood saprotrophs in FUNGuild (Fig. 4.3, Table 4.2). The two species associated with high BG activity were another Polyporaceae, and a wood saprotroph,
Sistotrema (Hydnaceae). The fungus associated with high CBH and BX activities classifed as an
Armillaria, which is a cord forming Basidiomycete that is a white rot saprotroph. Two of the fungi associated highly with NAG were Basidiomycetes, one that classifed to the class Agaricomycetes and the other to the order Polyporales, which is within Agaricomycetes. Agaricomycetes contains many saprotrophic lineages, including the Polyporaceae; note however, Polyporaceae is a functionally diverse clade. Of the remaining fungi, 2 associated with high NAG activity and 1 with high BX activity were classifed within the Ascomycota. One of these, a fungus from the order
63 Hypocreales that was associated with high BX and NAG activities, was identifed as a saprotrophic microfungus in FUNGuild. The OTU that was strongly associated with high BX activity was classifed as a member of the family Trichocomaceae, which are a generally saprotrophic clade. Finally, one of the fungi associated with high NAG activity was classifed to the Sordariales, an order of Ascomycetes that is known to have some wood saprotrophs.
Discussion
The fungal community within decaying wood has been gaining increasing attention recently as an important factor in determining rates of wood decomposition (Van Der Wal,
Ottosson, and De Boer 2015). While it has long been thought that climate controls rates of decomposition from local to global scales, Bradford et al. (2016) observed in a decay feld experiment across a range of latitudes that climate was a poor predictor, and the fungal biomass was an important predictor of wood decay rates at regional scales. Fungal communities are shaped by the characteristics of the wood they inhabit, with species (Purahong et al. 2018, Chapter 2) and degree of decay (Berglund et al. 2011; Rajala et al. 2012) both important factors driving community assembly. Here we tested for these potential fungal community composition impacts on decay by repeatedly sampling fungal community composition and enzyme activity over two years. We assessed the temporal scale over which turnover in WIF communities occurred and how these communities afected enzyme activities. We observed that fungal communities were strongly structured by wood species, and that communities did not show much turnover over short time scales, but long term change as wood decomposition increased did occur. Our results indicated that the fungal community was an important determinant of decay activity within wood, and demonstrated that a few fungal taxa may be responsible for the majority of the decay activity occurring.
64 Timescale of fungal community turnover
In our study, we repeatedly sampled a set of logs to determine the most important time scales over which WIF communities changed. Our previous work on fungal fruiting bodies in this system (Chapter 3) showed distinct seasonal communities of fruiting fungi. These results are similar to the seasonal shifts in soil fungal communities detected molecularly (Voříšková et al.
2014), leading us to expect that there would be seasonal changes in fungal communities within wood. Nevertheless, we did not observe any seasonal or yearly patterns of change in fungal communities when sampled by high-throughput amplicon sequencing, indicating that the variability we observed within each year was a more likely due to spatial heterogeneity of fungal taxa within the wood, or an artifact of random amplifcation during PCR.
While we did not detect environmentally driven turnover, we did observe structuring of the community by the decay class of the log, likely due to the succession of fungal communities as decay increased (Boddy 2001), and a strong efect of wood species. This is in agreement with recent work showing that fungi are more wood species specifc than previously thought (Purahong et al. 2018), with species having preferences even within the well-established angiosperm and gymnosperm divides. However, the efect of wood species and decay class could not be entirely separated, since all of the most decayed logs were L. tulipifera, due to forest history and log availability at our study site. Since we cannot know how long the logs from which we sampled had been on the forest foor, decay classes provide a useful proxy for length of decay, with later decay classes representing longer decay times. Therefore, the signifcant efect of decay class indicates that community turnover in naturally recruited deadwood occurs over long (multi-year) time scales.
Community and enzyme activity
65 Much of the discussion of the infuence that the fungal community has on wood decay rates centers on measures of richness and diversity, with no consensus emerging over whether higher or lower diversity results in increases in decay rates (Hoppe et al. 2016; Kahl et al. 2015;
Toljander et al. 2006; Yang et al. 2016). Our previous work has shown that the relationship between diversity and decay is context dependent (Chapter 2). We instead explored the relationship between fungal community composition and enzyme activities and found that the fungal community explained 10 – 30% of the variation in enzyme activity (Fig. 4.2). By parsing the community based on the WAPLS coefcients, we drilled down into the fungal community to extract key players. Here we showed that individual members of the community may have large efects on decay. Out of our initial pool of over 1,000 OTUs, we found that only 69 were found in
10% or more of samples, and of these only 14 were associated with high levels of enzyme activities. Five of these 14 were associated with all four enzymes, and were all classifed as
Polyporaceae in the Basidiomycota phylum. Two of these were further classifed to a genus,
Perenniporia, a known saprotrophic genus (Fig. 4.3, Table 4.2). Most likely, the other three OTUs are saprotrophic as well, since the family Polyporaceae is largely composed of fungi that grow and fruit on wood (Gilbertson 1980). Two other OTUs were associated with cellulose-targeting BG, and one with cellulose-targeting CBH and hemicellulose targeting BX. These were all classifed as
Basidiomycetes, one was classifed to Polyporaceae, and two were classifed down to genera
(Sistotrema and Armillaria) that are known saprotrophs. These three enzymes (BG, CBH, BX) target plant polysaccharides, and are directly involved in wood decomposition.
While we have made large strides in identifying likely key fungal decayers in our system, the classifcation methods we employed could only identify 4 of these enzymatically associated fungi to genus; this illustrates the need for more work on populating reference databases such as
66 UNITE (Koljalg et al. 2014) with ITS sequences and FUNGuild (Schilling 2015) with ecological guild information. Furthermore, this dearth of basic information on numerous OTUs underscores the difculty of assembling representative experimental communities to test diversity-decay relationships that are relevant to natural systems. We cannot select the taxa if the key players in those systems have not been identifed. However, the number of key taxa identifed in our study is within the same order of magnitude as the number of species typically used in experimental studies, 4 – 40 (e.g., Dickie et al. 2012; Maynard et al. n.d.; Venugopal et al. 2018). While these numbers are much lower than the overall richness observed in naturally assembling WIF communities, our results add credence to the assumption that a few species are responsible for most decay.
In contrast to the cellulose-targeting enzymes, several OTUs associated with high enzyme activity for hemicellulose-targeting BX and chitin-targeting NAG classifed as members of the
Ascomycota (Fig. 4.3). As few taxa in this clade are saprotrophs, it is unlikely that a direct link between the presence of these taxa and high enzyme expression is due to nutrient acquisition via wood decomposition. These fungi might instead be making a living by decomposing dead or senescing fungal tissues produced by other fungal species, or by breaking down sugars released from their lignocellulose matrix by other fungi. Of the four Ascomycota associated with these two enzymes, three of the OTUs classifed to clades that are known to include saprotrophs, but are not necessarily wood saprotrophs (Hypocreales and Sordariales, Table 4.2). All six of the fungi associated with NAG and BX activity were also only classifed to the higher taxonomic levels
(family and above), such that we can infer less about their function. Indeed, only one was present in the FUNGuild database, a Hypocreales associated with NAG and BX that is a saprotrophic microfungus. By acting as mycoparasites of either fungal tissue or fungal resources, these taxa
67 may represent important species interactions that limit the ability of other fungi to decompose wood efciently. Clearly, the diversity of fungal communities within wood is in need of further exploration when even key players are not found in the most commonly used fungal databases, particularly those that may be involved in activities other than wood decay.
Conclusion
Wood inhabiting fungal communities did not show patterns of seasonal or yearly change, but did show evidence of community turnover as decay class of the logs increased. There was also an efect of log species that persisted as decay progressed, but the efect of species and decay class could not be entirely resolved in this study. Future work should target wood species in various stages of decay to disentangle these efects. Fungal community composition was important in explaining variation of extracellular hydrolytic enzyme activities, indicating that models of wood decomposition that do not account for the fungal community might not adequately capture the variation in decay rates that are possible on a landscape. Finally, by combining molecular sampling of fungal communities with measuring fungal function through enzyme assays, we were able to identify several fungal species that were likely to be key players in wood decomposition.
Thus, studies that seek to understand the efect of fungal communities on wood decay rates can potentially simplify the system from the high species richness that generally characterizes these communities to focus on species that are likely to be most important for species interactions and decay activity.
68 Table 4.1. Results of permutational ANOVAs to determine how time, wood characteristics, and enzyme activity potentials structured wood-inhabiting fungal communities in 8 logs sampled at 12 time points between 2015 and 2016. Permutations were stratifed by the individual logs to account for the repeated measures. Signifcant P-values are in bold.
Factor df F P Season 3 0.89 0.76 Year 1 1.01 0.39 Decay Class 3 4.53 0.01 Species 2 4.32 0.01 Covariate df F P BG 1 2.57 0.01 BX 1 2.40 0.01 CBH 1 1.98 0.01 NAG 1 1.43 0.03
69 Table 4.2. Fourteen fungal taxa were associated with high levels of enzymatic activity. Their taxonomic classifcation, associated enzyme and functional guild assignment, if any, are shown below. Enzymes are: BG, beta-glucosidase; BX, beta-xylosidase; CBH, cellobiohydrolase; NAG, N-acetylglucosaminidase.
OTU Phylum Class Order Family Genus Functional Guild Associated Enzymes 1 Basidiomycota Agaricomycetes Polyporales Polyporaceae Perenniporia Wood saprotroph BG, BX, CBH, NAG
3 Basidiomycota Agaricomycetes Polyporales – – NAG
14 Basidiomycota Agaricomycetes Cantharellales Hydnaceae Sistotrema Wood saprotroph BG
30 Basidiomycota Agaricomycetes Agaricales Physalacriaceae Armillaria Wood saprotroph BX, CBH
39 Ascomycota Sordariomycetes – – – NAG
83 Ascomycota – – – – NAG
90 Ascomycota Sordariomycetes Hypocreales – – Saprotroph BX, NAG
102 Ascomycota Eurotiomycetes Eurotiales Trichocomaceae – BX
1317 Basidiomycota Agaricomycetes Polyporales Polyporaceae – BG, BX, CBH, NAG
1381 Basidiomycota Agaricomycetes Polyporales Polyporaceae – BG
1678 Basidiomycota Agaricomycetes Polyporales Polyporaceae – BG, BX, CBH, NAG
1954 Basidiomycota Agaricomycetes Polyporales Polyporaceae Perenniporia Wood saprotroph BG, BX, CBH, NAG
2268 Basidiomycota Agaricomycetes – – – NAG
2872 Basidiomycota Agaricomycetes Polyporales Polyporaceae – BG, BX, CBH, NAG
70 a.
b.
Figure 4.1. The NMDS ordination of fungal communities showing clustering of samples due to a. log identity and b. log species. The color of the points indicates the decay class of the log, while the shape indicates log species. There was signifcant clustering of the samples due to log identity, log decay class, and log species. The correlation with the enzyme activities of CBH and NAG is shown by the arrows in panel b.
71 * .
Figure 4.2. The amount of variation in enzyme activities explained by the WAPLS models as indicated by the model R2 value for each of the hydrolytic enzymes. Model signifcance is indicated by a star (P < 0.05), or a dot (P < 0.1).
72 Figure 4.3. Venn diagram of 14 fungal taxa that associated with high levels of enzyme activity labelled with the family to which each OTU classifed using the UNITE database (Koljalg et al.
2014) with the RDP classifer (Cole et al. 2014). Colored ellipses indicate to which enzyme the taxa was associated. Taxa in gray are Ascomycota, taxa in black are Basidiomycota. Stars indicate that the taxa did not classify to the family level, and the lowest level of classifcation is presented instead. Taxa in bold were classifed as saprotrophs with FUNGuild (Nguyen et al. 2016).
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86 Appendix 1: Supplemental Information for Chapter 2
Slash Retention Treatments and Efect on Environmental Variables
Each stand in which tree harvest occurred was additionally assigned to one of three levels of slash retention, with the goal of evaluating how harvesting tree tops and branches for the production of bioenergy might impact biodiversity on the sites. The levels of slash retention were as follows: 100%, 20%, and 0% slash retained. These treatment levels were crossed with tree removal treatments in a fully factorial design.
The efect of slash retention on soil temperature, soil moisture, coarse and fne woody debris biomass was evaluated using general linear mixed models with the function mixed in the afex package (Singmann et al. 2016) with stand nested within site as a random factor. Signifcant diferences among treatment levels were evaluated using post-hoc Tukey HSD tests.
Although all environmental variables measured showed signifcant changes on harvested plots when compared to unharvested controls, there were no signifcant diferences among the slash retention treatment levels (100%, 20% and 0% slash retention; Fig. A1.1).
Efect of tree harvest on environmental variables
To test how tree harvest treatment afected the environmental conditions experienced by fungal communities, we used general linear mixed models. The efect of each treatment on four environmental variables (soil temperature and moisture, CWD biomass and FWD biomass) was evaluated using the function mixed in the afex package in R (Singmann et al. 2016) with site as a random factor. Signifcant diferences among treatment levels were evaluated using post-hoc Tukey
HSD tests.
Tree harvest treatment had signifcant efects on all the environmental variables measured
87 on the sites. Clearcuts had signifcantly higher soil temperature (F2,65.14 = 41.27, p < 0.0001;
Fig.A1.2a) than controls, while soil moisture was signifcantly higher in clearcuts than in
aggregate retention stands or controls, which did not difer (F2,64.27 = 14.59 p < 0.0001, Fig.
A1.2b). Clearcuts also exhibited higher FWD biomass (F2,66 = 70.49, p < 0.0001, Fig. A1.2c) than both control and retention stands, while CWD biomass was higher in clearcuts than in controls
(F2,66 = 5.65, p = 0.005) but not signifcantly diferent from retention stands (Fig. A1.2d.). For soil temperature, moisture, and coarse and fne woody debris biomass, retention stands succeeded in maintaining the characteristics of unharvested stands. Although soil temperature and CWD biomass appeared to be higher in retention stands than in controls, the diferences were not signifcant.
88 Figure A1.1. Mixed efect models followed by Tukey HSD tests showed that although there were signifcant diferences between harvested sites and controls for all environmental variables (P <
0.02). There were no signifcant diferences among slash removal treatments on our experimental sites (P > 0.2). Diferent letters indicate signifcant diferences between treatment levels.
89 Figure A1.2. Efect of harvest treatment on (a) soil temperature, (b) soil moisture, (c) FWD biomass, (d) CWD biomass. Asterisks represent signifcant treatment diferences (*** P < 0.001;
** P< 0.01).
90