INVESTIGATING THE INFLUENCE OF MANAGEMENT PRACTICES ON THE 
ASSEMBLY AND FUNCTION OF MICROBIAL COMMUNITIES 
 
 
 
 
 
 
 
 
 
 
 
by 
 
MAX W. SPENCER 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
A DISSERTATION 
 
Presented to the Department of Biology 
and the Division of Graduate Studies of the University of Oregon 
in partial fulfillment of the requirements 
for the degree of 
Doctor of Philosophy  
 
June 2023 
 
  
DISSERTATION APPROVAL PAGE 
 
Student: Max W. Spencer 
 
Title: Investigating the Influence of Management Practices on the Assembly and 
Function of Microbial Communities 
 
This dissertation has been accepted and approved in partial fulfillment of the 
requirements for the Doctor of Philosophy degree in the Biology by: 
 
Barbara Roy Chairperson 
Krista McGuire Advisor 
Andrew Kern Core Member 
David Garcia Core Member 
Raghuveer Parthasarathy Institutional Representative 
 
and 
 
Krista Chronister Vice Provost for Graduate Studies  
 
Original approval signatures are on file with the University of Oregon Division of 
Graduate Studies.  
 
Degree awarded June 2023 
  
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© 2023 Max W. Spencer  
 This work is licensed under a Creative Commons 
Attribution-NonCommercial-NoDerivatives 4.0 International License. 
 
3 
 
  
DISSERTATION ABSTRACT 
 
Max W. Spencer 
 
Doctor of Philosophy 
 
Department of Biology 
 
June 2023 
 
Title: Investigating the Influence of Management Practices on the Assembly and 
Function of Microbial Communities 
 
  
Microbial communities are integral to many ecosystem functions, including 
functions of interest to humans such as nutrient cycling and pathogenic infection. 
However, the influence of management intensity on microbial community assembly and 
functioning is poorly understood. High intensity management often reduces the overall 
diversity and biomass of fungi and bacteria; yet ecosystem function does not follow such 
a clear trend. Additionally, the timeframe in which management impacts microbial 
community and function is generally unknown. To address these gaps, I characterized 
microbial communities within logged and unlogged watersheds in the Western Cascade 
Mountain range and vineyards using different management intensities across a climate 
gradient in western Oregon, USA. Furthermore, I used wine fermentation as a proxy for 
microbial community functioning to measure functional differences between 
management intensities. I found that human management continued to have legacy 
effects upon microbial communities even five decades after cessation. I also found that 
management intensity had a clear influence on the organoleptic compounds found within 
Pinot noir wine samples. This work can deepen our understanding of the response of 
microbial communities and their functioning to human management. 
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CURRICULUM VITAE 
 
 
NAME OF AUTHOR:  Max W. Spencer 
 
 
GRADUATE AND UNDERGRADUATE SCHOOLS ATTENDED: 
 
 University of Oregon, Eugene, OR, USA 
 Grand Valley State University, Allendale, MI, USA 
 Grand Rapids Community College, Grand Rapids, MI, USA 
 
 
DEGREES AWARDED: 
 
 Doctor of Philosophy, Biology, 2023, University of Oregon 
 Bachelor of Science, Biology, 2015, Grand Valley State University 
 Associates of General Studies, 2012, Grand Rapids Community College 
 
 
AREAS OF SPECIAL INTEREST: 
 
 Microbial ecology 
 Community ecology 
 
 
PROFESSIONAL EXPERIENCE: 
 
 Lab Coordinator, Biology Learning Center, Grand Rapids Community College, 
2018 
 Professional Tutor, Biology Learning Center, Grand Rapids Community College, 
2015-2018 
 Curriculum Coordinator and Brewing Specialist, Siciliano’s Market, 2015-2018 
 Student Tutor, Grand Valley State University, 2013-2015 
  
 
GRANTS, AWARDS, AND HONORS: 
 
 Sonoma County Mycological Society Grant, University of Oregon, 2021 
Biology Graduate Employee Teaching Award, University of Oregon, 2021 
Mentoring, Academic Support and Scholarships for Science Students, Grand 
Valley State University, 2015 
Mentoring, Academic Support and Scholarships for Science Students, Grand 
Valley State University, 2014 
Student Summer Scholars Grant, Grand Valley State University, 2013 
 
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PUBLICATIONS: 
 
Spencer, M.W., Roy, B.A., Thornton, T.E., and McGuire, K.L. (2022). Logging has 
legacy effects on the structure of soil fungal communities several decades after 
cessation in Western Cascade forest stands. Frontiers in Forestry 6, 41. doi: 
/10.3389/ffgc.2023.791766 
 
Spencer, M.W., Dunn, J.P., and Szymczak, E.M. (2018). An inventory of Cicadellidae, 
Aphrophoridae, and Delphacidae (Hemiptera) in the Alvar Grasslands of the 
Maxton Plains, Michigan. The Great Lakes Entomologist 51, 18-25. doi: 
tgle/vol51/iss1/4 
 
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ACKNOWLEDGMENTS 
 
Funding for this work was provided in part by the Oregon Wine Board and the 
Sonoma County Mycological Association Graduate Scholarship.  
Thank you to my advisor Krista McGuire for taking a chance on me, for 
recognizing when I was unhappy and needed to change my focus to wine, and for always 
encouraging me to take breathers.  
Thank you to Kaye Shek for being a great collaborator, a fantastic unofficial 
mentor, and an even better friend. Thank you to Tiffany for working long hours alongside 
me to finish the H.J. Andrews project and sharing the stress of graduate school. Thank you 
to Bitty Roy for always allowing me to springboard my ideas, giving advice from, and 
reminding me to relax.  
I couldn’t have completed my dissertation without the support of my cohort and 
friends within the program: Kayla, Andrew, Lina, Monika, Murillo, Sabrina, Jess, and the 
rest of the IE2 graduate student community. Thank you to Terra who helped teach me all 
the procedures that I use daily in the lab. Thank you to all the students that aided me during 
their undergraduate studies: C.J., Grace, Blaine, Nathan, Alyssa, and many others.  
Last and most important of all, thanks to my spouse Ash for loving me, believing in 
me, and moving across the country to support my aspirations. And, of course, our sweet, 
sweet boys: Hal, Stormy, and Cosmo.  
 
 
 
 
 
 
 
 
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Dedicated to the younger me; you made it kiddo. 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
8 
 
  
TABLE OF CONTENTS 
Chapter Page 
 
I. INTRODUCTION  ................................................................................................... 14 
 Literature Cited  ..................................................................................................... 17 
II. THE LEGACY EFFECTS OF LOGGING ON SOIL AND  
LITTER MICROBIAL COMMUNITIES IN A WESTERN  
CASCADES FOREST STAND .................................................................................. 18 
 1. Introduction  ....................................................................................................... 18 
 2. Materials and methods ....................................................................................... 21 
 2.1 Site description  .......................................................................................... 21 
 2.2 Sample collection  ...................................................................................... 24 
 2.3 Soil and litter sample processing  .............................................................. 24 
 2.4 Library preparation  ................................................................................... 25 
 2.5 Sequence processing  ................................................................................. 26 
 2.6 Statistical analyses  .................................................................................... 26 
 2.7 Compilation of fruiting (sporocarp) studies  .............................................. 27 
 3. Results  ............................................................................................................... 28 
 3.1 Differences between management types  ................................................... 28 
 3.2 Mineral soil vs. litter .................................................................................. 33 
 3.3 Fruiting-body comparison  ......................................................................... 35 
 3.4 Physiochemical properties  ........................................................................ 35 
 4. Discussion  ......................................................................................................... 35 
 4.1 Legacies of logging  ................................................................................... 35 
 4.2 Mineral soil vs. litter  ................................................................................. 39 
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 4.3 Soil eDNA vs. sporocarp sampling  ........................................................... 40 
 5. Conclusion  ........................................................................................................ 41 
 Literature Cited  ..................................................................................................... 42 
BRIDGE I  ................................................................................................................... 49 
III. THE INFLUENCE OF MANAGEMENT ON THE MICROBIAL  
TERROIR AND METABOLOME OF WINE FROM OREGON VINEYARDS ...... 50 
 1. Introduction  ....................................................................................................... 50 
 2. Materials and methods  ...................................................................................... 53 
 2.1 Site descriptions  ........................................................................................ 53 
 2.2 Sample collection and processing  ............................................................. 54 
 2.3 Library preparation and sequencing .......................................................... 55 
 2.4 Sequence processing and analyses  ............................................................ 57 
 3. Results  ............................................................................................................... 58 
 3.1 Sources of microbial taxa in grape must  ................................................... 58 
 3.2 Influence of management and AVA on microbial taxa  ............................ 62 
 3.3 Changes in metabolites between treatments  ............................................. 64 
 4. Discussion  ......................................................................................................... 66 
 3.1 Taxa present in wine must and their sources  ............................................ 66 
 3.2 Microbial communities in high vs. low intensity vineyards  ..................... 67 
 3.3 Connecting management, microbial communities, and metabolites  ........ 69 
 5. Conclusion  ........................................................................................................ 71 
 Literature Cited  ..................................................................................................... 72 
BRIDGE II  .................................................................................................................. 80 
 
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IV. CHANGES SOIL MICROBIAL TIMESERIES IN AN OREGON  
VINEYARD REVEALS TEMPORAL SHIFTS FOLLOWING CHANGE  
TO BIODYNAMIC PRACTICES  .............................................................................. 81 
 1. Introduction  ....................................................................................................... 81 
 2. Materials and methods  ...................................................................................... 83 
 2.1 Site description  .......................................................................................... 83 
 2.2 Sample collection and processing  ............................................................. 84 
 2.3 Molecular methods  .................................................................................... 85 
 2.4 Timeseries data  ......................................................................................... 86 
 2.4 Sequence processing and analyses  ............................................................ 86 
 3. Results  ............................................................................................................... 88 
 3.1 Inter-varietal and inter-block variation in soil  
 microbial community composition  ................................................................. 88 
 3.2 Time series of soil microbial communities in vermentino and syrah  ....... 89 
 4. Discussion  ......................................................................................................... 92 
 5. Conclusion  ........................................................................................................ 95 
 Literature Cited  ..................................................................................................... 96 
V. Conclusion  ............................................................................................................. 102 
 Literature Cited  ..................................................................................................... 102 
 
 
 
 
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LIST OF FIGURES 
 
Figure Page 
 
 
1. Graphic representation of community assembly  ................................................... 15 
 
2. Map of H.J. Andrews Experimental Forest  .......................................................... 23 
 
3. Non-metric multidimensional scaling ordinations based on Bray-Curtis .............. 29 
4. Comparisons of ectomycorrhizal fungi communities  ........................................... 30 
5. Comparisons of saprotroph communities .............................................................. 31 
6. Comparisons of pathogen communities  ................................................................ 32 
7. Comparisons of fungal communities in soil and litter  .......................................... 34 
8. Relative contribution of source to sink samples for bacteria  ................................ 59 
9. Relative contribution of source to sink samples for fungi  .................................... 60 
10. Euler diagrams showing overlap of ASVs between sample types  ........................ 61 
11. Euler diagrams showing overlap of ASVs between high and low intensity .......... 63 
12. Differential abundance of fungal species and genera  ........................................... 64 
13. Plot of differentially abundant metabolites at the end of fermentation  ................ 65 
14. Plot of differentially abundant metabolites in bottled wine  .................................. 66 
15. Euler diagrams of taxonomic overlap between vermentino and syrah  ................. 88 
16. Euler diagrams of taxonomic overlap between years of sampling ........................ 90 
17. Fungal and bacterial taxa that vary significantly between years of syrah  ............ 91 
18. Fungal and bacterial taxa that vary significantly between years of vermentino  ... 92 
 
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LIST OF TABLES 
 
Table Page 
 
 
1. Management history for each watershed sampled within  
 the H.J. Andrews experimental forest .................................................................... 23 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
  
 
 
 
13 
 
  
CHAPTER I 
INTRODUCTION 
Community assembly is a framework that explains how local communities of 
organisms are formed and shaped through the understanding of dispersal, abiotic 
conditions, biotic interactions, and dynamic changes over time (Figure 1) (Young et al., 
2001; Nemergut et al., 2013; Leibold et al., 2016). Within the framework of community 
assembly there is a nebulous “regional species pool” that contains all species that could 
possibly disperse into the focus communities. In the real world the species pool might 
include species found in the surrounding environment as well as any species capable of 
airborne, waterborne, or other forms of long-distance dispersal (e.g., spores, seeds). Any 
species that manages to disperse into the focus communities must then survive abiotic 
and biotic “filters” to persist. Abiotic filters include all environmental parameters that 
may impede or aid the establishment of a species (nutrient pools and fluxes, climatic 
conditions, pH, light availability, etc.). Biotic filters include all direct or indirect 
interactions involving other individuals and/or species ranging from facilitation to 
competition. The community assembly process is ongoing; it is not a singular event. The 
species pool and filters can change over time as new species are introduced and the 
physical parameters of the environment change from species “functions” and discrete 
abiotic events (Leibold et al., 2016). Species’ functions encompass all organismal 
processes and behaviors that alter the physical or chemical conditions of the environment. 
As those conditions change, so do the filters. However, linking community composition 
to function is difficult, as function is a vague concept shaped by the context of the study 
system and the interest of the researcher (Vellend et al., 2014; Bier et al., 2015; Leibold 
14 
 
  
et al., 2016). Simplified systems, such as agroecosystems, are excellent for studying the 
link between community composition and function. Managed systems are lower in 
diversity, producing less noise within data, are widely replicated, have well-defined 
complexity gradients, allow for large-scale manipulations, and have measurable functions 
relevant to the system managers (Perfecto and Vandermeer, 2008; Wolfe and Dutton, 
2015). 
 
Figure 1. Graphic depiction of community assembly. Species disperse from a regional 
species pool into local communities where they encounter abiotic and biotic filters. A 
species must be able to survive the filter conditions to establish and persist within a local 
community.  
 
Through the lenses of community assembly and ecological disturbance, human 
management can be viewed as an attempt to manipulate biotic and abiotic filters to 
15 
 
  
prevent the establishment of undesirable species (weeds, pathogens, herbivores, 
predators) and encourage the facilitation and growth of desirable species (crops, 
livestock, ornamentals, lumber trees, etc.). Management varies dramatically in both 
intensity and frequency, depending on the needs of the target species and the practices 
chosen by the manager. Management practices involve altering the physical and chemical 
properties of the environment, fitting within the definition of disturbance (Borics et al., 
2013). Examples of physical alterations include the removal of biomass, compaction or 
disruption of soils, and irrigation; while chemical alterations include the addition of 
fertilizers, use of biocides, and changes to organic inputs. Ultimately, disturbances, 
including those caused by management, directly impact the process of community 
assembly and community functions by altering abiotic and biotic filters.  
In this dissertation I discuss the influence of variation in management intensities 
on microbial communities in two managed plant communities common in Oregon, USA: 
logging in forest stands of the Western Cascades mountain range, and vineyards 
comprised of Vitis vinifera grape vines for wine making. This work is separated into three 
chapters. Chapter I focuses on the long-term legacy effects of logging on fungi in mineral 
soil and litter layers several decades after logging has ended at the Long-Term Ecological 
Research (LTER) forest known as H.J. Andrews. The second chapter explores the 
influence of management intensities (high and low) on bacteria, fungi, and their function 
in vineyards across several growing regions in Oregon. The third chapter is a case study 
of one vineyard from Applegate Valley that utilizes a timeseries of soil microbial data 
during and after converting the vineyard from high intensity management practices to low 
intensity management practices. 
16 
 
  
LITERATURE CITED 
Bier, R.L., Bernhardt, E.S., Boot, C.M., Graham, E.B., et al. (2015). Linking microbial 
community structure and microbial processes: an empirical and conceptual 
overview. FEMS Microbiology Ecology 91, fiv113. doi: 10.1093/femsec/fiv113 
 
Borics, G., Várbíró, G., and Padisák, J. (2013). Disturbance and stress: different meaning 
in ecological dynamics? Hydrobiologia 711: 1-7. doi: 10.1007/s10750-013-1478-
9 
 
Leibold, M.A., Chase, J.M., and Ernest, S.K.M. (2016) Community assembly and the 
functioning of ecosystems: how metacommunity processes alter ecosystem 
attributes. Ecology 98, 909-919. doi: 10.1002/ecy.1697 
 
Perfecto, I, and Vandermeer, J. (2008). Spatial pattern and ecological process in coffee 
agroforestry. Ecology 89, 915-920. doi: 10.1890/06-2121.1 
 
Nemergut, D.R., Schmidt, S.K., Fukami, T., O’Neill, S.P., et al. (2013) Patterns and 
processes of microbial community assembly. Microbiology and Molecular 
Biology Reviews 77, 342-356. doi: 10.1128/MMBR.00051-12 
 
Vellend, M. LaJoie, G., Bourret, A, Murria, C., Kembel, S.W., and Garant, D. (2014) 
Drawing ecological inferences from coincident patterns of population- and 
community-level biodiversity. Molecular Ecology 23, 2890-2901. 
 
Wolfe, B.E., and Dutton, R.J. (2015). Fermented foods as experimentally tractable 
microbial ecosystems. Cell 161, 49-55. doi: 10.1016/j.cell.2015.02.034 
 
Young, T.P., Chase, J.M., and Huddelston, R.T. (2001) Comparing, contrasting and 
combining paradigms in the context of ecological restoration. Ecological Rest. 19, 
5-18. doi: 10.3368/er.19.1.5 
 
 
 
 
 
 
 
 
17 
 
  
CHAPTER II 
THE LEGACY EFFECTS OF LOGGING ON SOIL AND LITTER MICROBIAL 
COMMUNITIES IN A WESTERN CASCADES FOREST STAND 
 This manuscript writing and core ideas are my own and resulted from discussions 
with co-authors. I led the fieldwork team including C.J. Paulino, G. Ridder, J. Cothern, 
and K. Evens. Tiffany Thornton and I performed all molecular laboratory work. Tiffany 
also wrote the methods section. Bitty Roy compiled the sporocarp data, wrote the 
sporocarp results, and critically revised the manuscript. Lucas Silva provided site 
selection assistance, put us in contact with the HJA rangers, and critically revised the 
manuscript. Krista McGuire provided funding, aided in developing the experimental 
design, and critically revised the manuscript.  
 From: Spencer, M. W., Roy, B. A., Thornton, T. E., Silva, L. C. R., McGuire, K. 
L. 2023. Logging has legacy effects on the structure of soil fungal communities several 
decades after cessation in Western Cascade Forest stands. Frontiers in Forests and Global 
Change 6, 791766. doi: ffgc.2023.791766 
1. Introduction 
Logging is one of the most pervasive disturbances to forests. Around 3.5 billion 
m3 of wood is harvested around the globe annually, affecting 29 million hectares of 
forest (Pepke, 2010; Crowther et al., 2015). While improvements to harvesting and 
processing technologies have made the logging industry more efficient, demands for 
timber products have increased in recent decades and that trend is predicted to continue 
(Zhou et al., 2005; Nepal et al., 2021). Logging not only affects tree cover, but also 
disrupts belowground soil communities that are essential for forest ecosystems. Fungi are 
18 
 
  
especially vulnerable to logging-associated disturbances due their formation of 
multicellular bodies known as mycelia that ramify through their substrate, and their 
reliance upon plant hosts—alive or dead, depending on the species—as their source of 
carbon. Fungal communities play critical roles in the functioning of forest ecosystems, 
including structuring plant communities (Gams, 2007), driving nutrient fluxes 
(DelgadoBaquerizo et al., 2016), influencing forest productivity, and sequestering carbon 
(Fr ˛ac et al., 2018).  
Three main functional groups of fungi important to forest functioning are 
mycorrhizal fungi, pathogens, and decomposers (Ehrenfeld et al., 2005; Baldrian, 2016). 
Mycorrhizal fungi take up nutrients from the soil for their host plants in exchange for 
photosynthetically derived carbon (Bagyaraj and Ashwin, 2017). These fungi are also 
able to protect their hosts from pathogenic infections and increase stress tolerance (Jayne 
and Quigley, 2014; Kanekar et al., 2018; Gonthier et al., 2019). There are two broad 
categories of mycorrhizal fungi, arbuscular mycorrhizal fungi (AMF), and 
ectomycorrhizal fungi (ECMF). AMF are widely distributed and associate with >80% of 
plant species, while ECMF associate with ∼2% of plant species, primarily large woody 
species that dominate temperate and some tropical forests (Aerts, 2003). Pathogenic fungi 
attack their host plant species, which adds nutrients and carbon to resource pools (Maron 
et al., 2011). Decomposers—or saprotrophs—break down organic matter by releasing 
extracellular enzymes, increasing access to nutrients for other organisms (Bagyaraj and 
Ashwin, 2017). Decomposers can also suppress pathogenic fungi, reducing mortality of 
vulnerable host plants (Adnan et al., 2019). Many soil fungi also produce fruiting bodies 
(e.g., mushrooms) to generate and release sexual spores. Prior to next generation 
19 
 
  
sequencing, studying fungal communities primarily consisted of collecting these 
reproductive structures for identification. It is useful to compare sequences found in 
belowground environmental DNA (eDNA) studies with macrofungi collections to 
discover what proportion of species are shared between different methods. Not all fungi 
produce fruiting bodies, therefore sequencing of DNA in soil can reveal species absent 
from aboveground collections. On the other hand, aboveground collections can fill in 
gaps resulting from biases in DNA extraction, PCR amplification, and sequencing. 
Studies comparing fruiting body surveys to soil sequencing surveys typically find 
between 10 and 20% overlap, indicating the importance of utilizing both styles of survey 
(Gardes and Bruns, 1996; Dahlberg et al., 1997; Baptista et al., 2015).  
The Pacific Northwest of the US is dominated by ectomycorrhizal trees and 
continues to be one of the most active logging regions in North America. Recent 
estimates of logging in the area show an annual average of seven million tons of lumber 
are harvested in Oregon (ORFI, 2020), and nine million tons are harvested in Washington 
per year (Arnold, 2004). Increases in efficiency of lumber use and conservation efforts 
have protected some old-growth areas in these areas, but demand continues to drive 
increases in harvesting (Pepke, 2010). Two major logging techniques are selective 
logging and clearcutting. The impacts of each technique on soils are similar, but 
clearcutting effects tend to be far more extreme. The use of large vehicles and machines 
in both techniques results in compaction of soil and heavy metal pollution (Hartmann et 
al., 2014). Openings in the canopy allow more light to reach the soil, which decreases 
moisture and increases temperature. The removal of trees also eliminates hosts for 
symbionts and alters the chemistry of soil as litter inputs are reduced. Erosion is also far 
20 
 
  
more likely, which can further alter soil chemistry and nutrient pools. Knowledge about 
the impacts of logging on belowground communities will not only increase understanding 
the role of disturbance in fungal community assembly but could also aid in active forest 
management and replanting efforts.  
To evaluate the consequences of logging legacies on fungal composition, we 
sequenced fungi from soils in clearcut, selectively logged, and old-growth forest in the 
Pacific Northwest. We separately sampled soil (Oi—Oe) and litter because the litter layer 
is often ignored but contains a substantial amount of fungal biomass (Lim et al., 2010; 
Frey et al., 2011; Hu et al., 2019). Furthermore, the layers may differ in the composition 
of fungal functional guilds (McGuire et al., 2013). We also compared belowground 
communities to aboveground fruiting body composition. We addressed the following 
questions: (1) How do soil and litter fungal communities vary between forest stands with 
different logging histories? (2) What proportion of fungi are found both and belowground 
within the forest? (3) How do fungal communities in the litter layer differ from those in 
the soil? 
2. Materials and methods 
2.1 Site description 
Our study sites were located within The H. J. Andrews Experimental Forest 
(hereafter referred to as HJA; 44.2◦N, 122.2◦W), a mid-elevation Pacific Northwest 
conifer forest dominated by Pseudotsuga menziesii (Douglas-fir) and Tsuga heterophylla 
(Western hemlock) with stands of varying age up to 700 years. The annual mean 
temperature is 8.6◦C, the mean annual precipitation is 2,289 mm/yr. HJA was established 
in 1948 by the United States Forest Service. The original focus of the research being 
21 
 
  
conducted within HJA was to manipulate forest operations such as logging and forest 
regeneration to optimize the efficiency of the timber industry. Due to this history, the 
watersheds within HJA were subjected to logging of varying intensity at different times. 
We sampled three watersheds with different logging legacies within HJA—watershed 6 
(WS6), watershed 7 (WS7), and watershed 8 (WS8) (Figure 1). WS6 and WS7 were both 
previously logged: WS6 was clearcut in 1974 with 9% road coverage and was replanted 
with P. menziesii seedlings in 1976, while WS7 had 60% of its overstory harvested in 
1974 with 0% road coverage, the rest of the canopy harvested in 1984, and a final 
thinning in 2001 to ensure ∼4 m between each tree. WS8 was never logged, it was left as 
an old-growth forest control for comparison with other watersheds from the inception of 
HJA (Table 1). However, it did experience a fire that burned up to 70% of the standing 
trees in the 1850s, though this is still considered an old-growth forest within this system. 
The watersheds lie within the transition between western hemlock (Tsuga heterophylla) 
and silver fir (Abies amabilis) zones. WS6 was planted with 100% P. menziesii at a 
density of 383 stems/ha with a basal area of 70 ± 10 m2/ha. WS7 was planted with 100% 
P. menziesii at a density of 333 stems/ha with a basal area of 75 ± 4.8 m2/ha, which was 
reduced to 220 stems/ha in 2001. The understory of all three watersheds is comprised of 
vine maple (Acer circinatum), two Oregon grape species (Mahonia aquifolium and M. 
nervosa), whipplea (Whipplea modesta), salal (Gaultheria shallon), and rhododendron 
(Rhododendron macrophyllum). The overstory in WS8 is a mix of P. menziesii, T. 
heterophylla, A. amabilis, and western red cedar (Thuja plicata) with nearly one-third of 
individuals being greater than 450 years of age and the remaining around 125 years or 
younger (Andrews, 2022). 
22 
 
  
 
 
Figure 1. Map of H. J. Andrews Experimental Forest—a Long Term Ecological 
Research Site (LTER) in Lane and Linn County, Oregon, USA. Includes coloration of 
three watersheds with different management histories that were sampled for soil fungi. 
Watershed 6, clearcut in 1974; watershed 7, canopy harvest in 1974, 1984, and thinning 
in 2004; watershed 8, unlogged. 
 
 
Table 1. Management history for each watershed sampled within the H. J. Andrews 
experimental forest. 
 
23 
 
  
 
2.2 Sample collection 
All samples were collected on 15 November 2018. Within each watershed we 
sampled along three 100 m transects running east to west that were separated by 50 m 
north to south. Along each transect we collected five litter and five soil samples at 
randomized points (soil n = 45, litter n = 45). Litter samples consisted of collecting 
organic litter around the target point by hand while wearing EtOH-sterilized gloves to fill 
a sterile 500 mL bag. Soil samples consisted of 2.5 cm × 20 cm cores taken using an 
EtOH sterilized corer after the litter layer was removed. Samples were stored on ice for 
up to 12 h before being transferred to a −20◦C freezer until homogenization and DNA 
extraction. 
2.3 Soil and litter sample processing 
Soil samples were homogenized using EtOH and UV-sterilized 2 mm aperture 
sieves, and litter samples were homogenized using EtOH and UV-sterilized ceramic 
mortar and pestles. To facilitate homogenization, liquid nitrogen was poured onto the 
litter samples immediately before pulverizing. For each sample, 0.25 g and 3.0 g were 
weighed in sterile 2 ml Eppendorf tubes for DNA extraction and moisture analysis, 
respectively. An additional sterile 2 ml Eppendorf tube was filled and kept as an archival 
sample within a −80◦C freezer. All tubes were stored at −20◦C before use.  
The rest of each homogenized sample was air-dried at room temperature in a 
labeled paper bag. Air-dried soil samples were prepared for pH analysis by mixing a 1:2 
ratio of sample to deionized water. For soil samples, 10 g of soil was weighed in a glass 
beaker and mixed with 20 ml of deionized water. For litter samples a ratio of 1:8 was 
24 
 
  
used to compensate for absorption, 2.5 g of litter was weighed in a glass beaker and 
mixed with 20 ml of deionized water. Three readings per sample were recorded using a 
benchtop pH meter with a glass electrode, and the average value was calculated. Moisture 
was calculated for each sample by recording the weight of 3.0 g of sample in an 
aluminum weighing dish, drying the sample in a drying oven at 130◦C for 48 h, re-
weighing the sample, and subtracting the dried sample weight from the initial sample 
weight (Black, 1965). 
2.4 Library preparation 
High throughput sequencing was used to analyze fungal community composition 
for soil and litter samples. DNA was extracted from 0.25 g of each soil and litter sample 
using Qiagen DNeasy PowerSoil single-tube extraction kits, following the manufacturer’s 
protocol. The ITS1 region of the fungal rRNA gene was amplified using the ITS1-F 
(CTTGGTCATTTAGAGGAAGTAA) and ITS2 (GCTGCGTTCTTCATCGATGC) 
primer pair, the Nextra NX2 Illumina (San Diego, California) adapter, and Earth 
Microbiome barcoded primers. A master mix containing 12.5 µl GoTaq, 9.45 µl 
nuclease-free water, 0.5 µl forward (ITS1F) primer, 0.5 µl reverse (ITS2) primer, 0.5 µl 
Nextera (NX2) primer, and 0.05 µl BSA was added to the wells of a 96-well PCR plate. 
In each well, 1.0 µl of genomic DNA and 0.5 µl of a unique barcoded primer was added 
to the master mix for a total volume of 25 µl per well. PCR reactions were held in a 
thermal cycler at 94◦C for 1 min, with amplification proceeding for 35 cycles at 94◦C for 
30 s, 52◦C for 30 s, and 68◦C for 30 s, and a final elongation of 7 min at 68◦C.  
Amplicon concentrations were quantified according to a Quant-iT PicoGreen 
protocol (Thermo Fisher Scientific, Waltham, MA). DNA concentrations were quantified 
25 
 
  
on a Molecular Devices SpectraMax M5E multi-mode plate reader (San Jose, CA). 
Amplicons were pooled together according to quantification data from the PicoGreen 
assay to obtain an equimolar concentration of 1 ng of DNA per sample. The pooled 
library was purified using a Qiagen QIAquick PCR purification kit, according to the 
manufacturer’s protocol. The amplicon pool was sequenced using an Illumina MiSeq 
(San Diego, CA) instrument at the University of Oregon’s Genomics and Cell 
Characterization Core Facility (GC3F). 
2.5 Sequence processing 
Raw sequences were demultiplexed using the Quantitative Insights into Microbial 
Ecology (QIIME) pipeline (Caporaso et al., 2010). We then assembled an amplicon 
single variant (ASV) table using the DADA2 pipeline (Callahan et al., 2016). Taxonomy 
was assigned using the naïve Bayesian classifier from the Ribosomal Database Project 
(RDP) against the UNITE database (Koljalg et al., 2005; Cole et al., 2014). We used 
variationstabilizing transformation to normalize ASV counts (Anders and Huber, 2010). 
To assign functional guild classifications to each ASV we used the annotation tool 
FUNGuild set to its default parameters (Nguyen et al., 2016). Many ASVs tend to have 
multiple guilds assigned to them (e.g., saprotroph and pathogen). When performing our 
downstream analyses, we allowed for ASVs to be represented in multiple categorical 
subsets (i.e., the previous example would be counted once as a saprotroph and once as a 
pathogen). 
2.6 Statistical analyses 
Statistical analyses were performed in R (v3.6.3) using the vegan package 
(Oksanen et al., 2019). We used the Bray-Curtis dissimilarity statistic to quantify the 
26 
 
  
compositional dissimilarity between our sample communities. This statistic allowed us to 
visualize the difference between communities within a two-dimensional plot using non-
metric multidimensional scaling (NMDS). Additionally, to test for significance in our 
Bray-Curtis dissimilarity results we performed permutation analyses of variance 
(PERMANOVAs) comparing watershed, sample source (soil or litter), as well as 
physicochemical properties (pH, moisture). Additionally, to visualize compositional 
differences, we compared the relative abundances of functional guilds and the most 
abundant 15 genera found in each treatment. Figures were generated using ggplot2 
(Wickham, 2016). 
2.7 Compilation of fruiting (sporocarp) studies 
To gain an understanding of the total fungal community in the forest and because 
eDNA and sporocarp surveys capture different parts of the fungal community (Frøslev et 
al., 2019) we wanted to compare the fungal species represented by sporocarps (sexual) 
vs. those in the soil eDNA samples (largely asexual). We limited the lists to macrofungi 
[mushrooms in the general sense (Thiers and Halling, 2018), including boletes and corals 
as well as larger ascomycetes such as Helvella] that were identified to species. This list 
was then compared to a list of macrofungi collected from the LTER using existing data 
from the H. J. Andrews from the following publications and reports, and their associated 
datasets in the H. J. Andrews datafiles [Rhoades, 1972; Luoma et al., 1991 (TP109); 
Smith et al., 1996 (SA01401)] and iNaturalist (2022) observations through February 
2021. Sporocarps in the Rhoades (1972) study were collected opportunistically between 
October 1970 and April 1972, those in the Smith et al. (1996) study were collected twice 
per year for 4 years from three forest stands, one old growth, one rotation age and one 
27 
 
  
young-growth. The Luoma et al. (1991) study took place in 10 stands, with sampling 
occurring for about 6 weeks twice per year for 4 years. All the names in our final list 
(Supplementary Table 1) were verified in Index Fungorum; this process uncovered 
numerous typos and names changes which are enumerated in Supplementary Table 1. 
Many collections were identified only to genus; these were not included in our 
comparison with the soil eDNA because we had no way of determining which species 
they might match in the eDNA. 
3. Results 
3.1 Differences between management types 
While keeping litter and mineral soil separate, the fungal communities form 
separate clusters by management histories (clearcut, canopy + thinned, unlogged) based 
on Bray-Curtis dissimilarity (Figure 2). The difference between management types was 
significant for both mineral soil and litter using PERMANOVA (soil: df = 2, F = 1.84, R 
2 = 0.0825, p < 0.001; litter: df = 2, F = 2.32, R 2 = 0.104, p < 0.001). The composition 
of the genera that comprised each guild were distinct between management types 
(Figures 3–5A). Saprotrophs, pathogens, and ectomycorrhizal fungi were all significant 
when considered separately (Figures 3– 5C and Supplementary Table 2). Similarly, if the 
watersheds are grouped into a binary of logged and unlogged (Figure 6) all of these 
categories remain significantly different (all fungi: df = 1, F = 2.55, R 2 = 0.0291, p < 
0.001; ECMF: df = 1, F = 2.35, R 2 = 0.0275, p < 0.001; saprotroph: df = 1, F = 7.34, R 2 
= 0.0795, p < 0.001; pathogen: df = 1, F = 3.08, R 2 = 0.125, p < 0.001). 
28 
 
  
 
Figure 2. Non-metric multidimensional scaling ordinations based on Bray-Curtis 
dissimilarity between three forest stands with different logging histories sampled in H. J. 
Andrews Forest, Oregon. Included are ectomycorrhizal fungi, saprotrophs, and plant 
pathogens in soils (A–C), as well as ectomycorrhizal fungi, saprotrophs, and plant 
pathogens in litter (D–F). All PERMANOVA p-values are <0.001. Clearcut, watershed 6, 
canopy + thinned, watershed 7, unlogged, watershed 8. 
 
29 
 
  
 
Figure 3. Comparisons of ectomycorrhizal fungal (ECM fungi) communities in soil and 
litter of logged and unlogged forest stands within the H. J. Andrews Forest, Oregon, 
USA. Clearcut (CC), watershed 6, canopy + thinned (C + T), watershed 7, unlogged 
(UL), watershed 8. (A) Relative abundances of ECMF genera between 
watersheds. (B) Relative abundances of ECMF genera between soil and litter. (C) NMDS 
ordination of ECMF communities based on Bray-Curtis dissimilarities between soil and 
litter (PERMANOVA, p = 0.578). 
 
 
 
30 
 
  
 
Figure 4. Comparisons of saprotroph communities in soil and litter of logged and 
unlogged forest stands within the H. J. Andrews Forest, Oregon, USA. Clearcut (CC), 
watershed 6, canopy + thinned (C + T), watershed 7, unlogged (UL), watershed 
8. (A) Relative abundances of saprotroph genera between watersheds. (B) Relative 
abundances of saprotroph genera between soil and litter. (C) NMDS ordination of 
saprotroph communities based on Bray-Curtis dissimilarities between soil and litter 
(PERMANOVA, p = 0.028). 
31 
 
  
 
Figure 5. Comparisons of pathogen communities in soil and litter of logged and 
unlogged forest stands within the H. J. Andrews Forest, Oregon, USA. Clearcut (CC), 
watershed 6, canopy + thinned (C + T), watershed 7, unlogged (UL), watershed 
8. (A) Relative abundances of pathogen genera between watersheds. (B) Relative 
abundances of pathogen genera between soil and litter. (C) NMDS ordination of 
pathogen communities based on Bray-Curtis dissimilarities between soil and litter 
(PERMANOVA, p = 0.005). 
 
 
 
32 
 
  
3.2 Mineral soil vs. litter 
Based on Bray-Curtis dissimilarity, the overall fungal communities separated into 
distinct clusters for mineral soil and litter (Oi—Oe) samples (Figure 6C). We found that 
the difference between these groups is highly significant using PERMANOVA (df = 1, F 
= 13.92, R 2 = 0.141, p < 0.001). Some of these differences are apparent when comparing 
relative abundances of functional guilds and the genera that comprise them (Figures 3–
5B). Litter communities tend to contain more pathogens, while mineral soil communities 
contain more ectomycorrhizal fungi (ECMF; Figure 6A). When comparing the 
saprotrophs and pathogens between soil and litter samples there are significant 
differences (df = 1, F = 2.42, R 2 = 0.028, p = 0.008 and df = 1, F = 3.08, R 2 = 0.035, p 
= 0.007, respectively). There are not significant differences between soil and litter for 
ectomycorrhizal fungi (df = 1, F = 0.944, R 2 = 0.011, p = 0.574)—however, it was 
significant when accounting for an interaction with management history (df = 1, F = 1.63, 
R 2 = 0.019, p = 0.007). 
33 
 
  
 
Figure 6. Comparisons of fungal communities in soil and litter of three watersheds with 
varying logging histories within the H. J. Andrews Forest, Oregon, USA. Clearcut (CC), 
watershed 6, canopy + thinned (C + T), watershed 7, unlogged (UL), watershed 
8. (A) Relative abundances of functional guilds between watersheds for soil and litter. 
Functional guilds were assigned using FUNGuild analysis. (B) NMDS ordination of 
fungal communities based on Bray-Curtis dissimilarities comparing watersheds 
(PERMANOVA, p < 0.001). (C) NMDS ordination of fungal communities based on 
Bray-Curtis dissimilarities comparing soil and litter (PERMANOVA, p < 0.001). 
 
 
34 
 
  
3.3 Fruiting-body comparison 
Our belowground sequences from the three sites yielded 144 macrofungi 
including those that form mushrooms, cups, crusts, and truffles that were identified to 
species. Our compilation of sporocarp data across the entire H. J. Andrews forest yielded 
380 species; however, only 34 (6.49%) were shared between the sporocarp and eDNA 
datasets for a total of 524 species found. The entire list, indicating the kind of data 
(eDNA or sporocarp) and the source of the information can be found in Supplementary 
Table 1. 
3.4 Physiochemical properties 
We characterized the pH and moisture content of our samples. Based on 
PERMANOVAs of the Bray-Curtis dissimilarities of communities, we found that pH and 
moisture had a significant effect on soil fungi (pH: df = 1, F = 2.63, R 2 = 0.0590, p < 
0.001; moisture: df = 1, F = 3.07, R 2 = 0.0682, p < 0.001) while it did not have a 
significant effect on litter fungi (pH: df = 1, F = 1.19, R 2 = 0.0282, p = 0.121; moisture: 
df = 1, F = 1.16, R 2 = 0.0275, p = 0.128). 
4. Discussion 
4.1 Legacies of logging 
Pseudo replication is a potential concern with our dataset, as our logging 
treatments are confounded with watershed identity. This may limit the generalizability of 
our data to other logging systems. However, due to the close geographic proximity, 
shared climate, similar topography, identical understory composition, and limited 
physical barriers we are confident that this effect is minimal. Logging practices vary in 
their effects on aboveground forest structure, and we found that differences in historical 
35 
 
  
logging also had significant legacy effects on belowground fungal composition. We saw 
large differences in some taxa between logged and unlogged forests, and these 
differences depended on the functional groups of the fungi. We also saw differences in 
fungal composition between logged forests depending on whether the forest was clearcut 
or selectively logged. Logging causes several consistent ecological changes in forest 
structure, composition, and function that the fungi are likely responding to. For example, 
the early stages of succession are lost due to replanting and since the tree ages are the 
same in replated forests, there are no snags, few downed logs and the canopy is open 
allowing more sunlight to hit the forest floor for about 30 years post cut (Franklin et al., 
2002). To simplify our discussion, we only consider the genera that had very large (2× or 
more) differences between sampling units in our soils and litter data.  
In soils, we found that ECM fungi showed strong compositional responses to 
logging legacies, which may have implications for forest regeneration trajectories since 
most of the trees in these sites depend on ECM fungi for growth and survival. We found 
that some fungal genera were more abundant in early to mid-successional forests whereas 
others were more restricted to unlogged, old growth forests. Since the dominant overstory 
tree hosts (P. menziesii) are the same in these sites, these results suggest that 
environmental filtering structures ECM fungal communities across sites due to the abiotic 
changes from historical logging practices. We found greater abundance (≥2×) of 
Piloderma and Russula in unlogged forests, which is consistent with other studies (Smith 
et al., 2016; Kranabetter et al., 2018) and we found more of the early successional 
Wilcoxina in logged forests (Dickie et al., 2013). Studies of sporocarps (fruiting bodies) 
have generally found that Inocybe species are largely early successional (Cripps, 1997 
36 
 
  
and refs. therein), which is consistent with our data. However, a study by Norvell and 
Exeter (2004) on sporocarps in the Oregon Coast range found no relationship between 
stand age after logging and Inocybe fruiting. The differences between the Inocybe studies 
could be the result of differences in methods; studies of sporocarps can only detect 
fruiting, not abundance of the taxon in the soil, whereas we could only detect abundance 
in the soil and our fruiting body survey was not concomitant—it was a compilation of 
more than 40 years of data. Similarly, the genus Cortinarius is considered to be later 
successional (Nara et al., 2003; Sun et al., 2015), but we found them to be more than 2× 
more abundant and more diverse in the soils of logged watersheds, with only 3/37 taxa 
restricted to the soils of the unlogged forest. Another study compared ECMF in logged 
and unlogged forests in Malaysia, finding similar trends in relative abundances of 
genera—nearly twice as many Russula in unlogged forests, and more Inocybe in logged 
forests. Together, these results suggest that some fungal clades may show similar trends 
due to logging regardless of location (McGuire et al., 2015).  
Plant pathogens were distinctly in their abundance and composition between the 
logged and unlogged sites, and our data suggest that these differences are potentially 
linked to differences in the abundance of litter. For example, Cylindrosympodium was 
greater than twice as abundant in the unlogged watershed, and this genus was also greater 
than twice as abundant in the litter. Post clearcutting, the logged watersheds were burned, 
which would have significantly reduced the litter relative to the unlogged watershed 
which also would have had larger and older trees. We note here that FUNGuild (Nguyen 
et al., 2016) classified Cylindrosympodium as a pathogen, but the particular species in our 
data were not determinable with the exception of C. lauri, which was not common. It was 
37 
 
  
originally isolated from Laurus leaves and was assumed to be plant pathogenic (Crous et 
al., 2007), but we found no evidence that it has been tested for pathogenicity in any study. 
While some species of Cylindrosympodium are thought to be plant pathogens (Crous et 
al., 2007), others appear to be involved in decomposition (Purahong et al., 2016; 
Hernandez-Restrepo et al., 2017). Deeper study of this abundant genus is warranted as its 
abundance in litter and in both our study and others (e.g., Jumpponen et al., 2010; Silva 
and Lambers, 2021) suggests it is important at the ecosystem scale. The higher 
abundance of pathogens could make seed recruitment difficult within the unlogged 
watershed but may also help maintain tree diversity as has been found in tropical systems 
(Song et al., 2021).  
We also saw differences between the individual logging treatments. The soil 
fungal communities of watershed 7 (selectively logged) were similar to watershed 6 
(clearcut), while the litter fungal communities were similar to watershed 8 (old growth). 
This may indicate that below-ground soil fungi are more vulnerable to impacts from 
logging than fungi found in the litter later. Another explanation may be that the process 
of selectively harvesting in watershed 7, rather than clearcutting, allowed for persistence 
of the original above-ground fungal community. This is in contrast with the above-
ground fungal community of watershed 6, which was likely wiped out when clearcut and 
burned with a novel assembly replacing it, perhaps representative of where the seedlings 
were sourced. These differences indicate that there are alternative states depending on the 
type of logging and regeneration strategy, and that above-ground and below-ground 
communities may exhibit different legacy responses (Rodriguez-Ramos et al., 2020). 
Even following planting, and a cessation of logging in watershed 6, 43 years before our 
38 
 
  
sampling there remains a legacy effect. This is indicative of potential changes in the 
function of the communities between logged and unlogged watersheds.  
Changes to the soil fungal community can impede or facilitate forest regeneration 
through their interactions with plants as they establish and mature. The seedlings of 
ectomycorrhizal plant species can rely upon common mycorrhizal networks (CMN) to 
assist in their establishment (Liang et al., 2021). Access to CMNs give seedlings a steady 
flow of nutrients, and—depending on the fungi involved—a source of fixed carbon from 
mature conspecifics in the network (Walder et al., 2012). Changes to the saprotroph 
community can further influence access to nutrients by altering decomposition. Thus, 
changes in the composition of these two guilds due to logging can influence the 
accessibility of nutrients and carbon for trees during early regeneration. This can 
ultimately slow down the process of regeneration and the storage of carbon within a 
forest (Allison and Treseder, 2008). 
4.2 Mineral soil vs. litter 
Soil and litter fungi had different compositional trends across the logging gradient 
due to the strong segregation of fungal functional groups across these horizons. Higher 
relative abundances of pathogens in the litter layer are likely due to the presence of 
pathogens within senesced host material that falls and constitutes the Oi layer (Wahdan et 
al., 2020). We saw the opposite trend with ECMF. ECMF can break down organic 
materials, but they are not as efficient as independent saprotrophs (Talbot et al., 2008). 
This explains why ECMF were detected in greater relative abundances in the mineral 
soil, as they forage for nutrient pools, rather than in the litter layer where the early stages 
of decomposition occur (Lindahl et al., 2007). This tendency may also explain why there 
39 
 
  
does not seem to be a distinct difference between the ECMF found in mineral soil and 
litter layers—it appears that the genera in the litter samples are comprised of a subset of 
the genera found in the mineral soil samples. The ECMF sequences found in the litter 
layer may be the exploratory hyphae of the belowground mycelium searching for nutrient 
sources (Rosinger et al., 2018) or may be spores from fruiting bodies.  
Saprotrophic fungal composition was also distinct between soil and litter, likely 
due to the different forms of organic material in the layers (Bödeker et al., 2016). The 
relative abundance of saprotrophs was also higher in litter samples. Early colonizers of 
senesced organic material are likely found in the litter layer, while those that are 
responsible for later stages of decay may be found in or near the mineral soil. ECMF and 
saprotrophs compete for the same organic resources, which further explains their spatial 
segregation (Talbot et al., 2008; Bödeker et al., 2016). 
4.3 Soil eDNA vs. sporocarp sampling 
Fungal sporocarps, e.g., mushrooms, are produced when sexual reproduction has 
occurred (Pelkmans et al., 2016), whereas fungal DNA in soils is primarily composed of 
asexual hyphae and asexual spores (Camenzind et al., 2022). The two datasets combined 
suggest that the 16,000 acres of the HJ Andrews Forest contain at least 524 species of 
macrofungi. Of these only a small subset of the soil eDNA dataset shared species with the 
compilation of sporocarp data from HJ Andrews (6.49%), which is somewhat less than 
the 10% overlap typically found between sporocarp and sequencing surveys (Dahlberg et 
al., 1997; Richard et al., 2005; Porter et al., 2008; Baptista et al., 2015). Interestingly, 
there were more macrofungi in collections/observations than in the eDNA, which is 
unusual. However, given that we surveyed a much larger area (the whole LTER) for 
40 
 
  
sporocarps than we collected soil eDNA from (several points in each of three 
watersheds), and the sporocarp surveys covered several years, the larger number of 
species in the sporocarp group is less surprising. It could be argued that we can’t compare 
the two data sets, even qualitatively, because they were not taken in exactly the same 
places and at the same time. However, studies of fruiting body formation indicate that a 
combination of environmental signals trigger fruiting and that these signals are species-
specific (Pelkmans et al., 2016). For this reason, it can take dozens of years or more to 
completely sample a plot in a forest. For example, a study in Switzerland by Straatsma et 
al. (2001) sampled mushrooms over a 21-year period in the same plots. Only 8/408 were 
found in all years and many only fruited once during the 21 years. Future studies would 
benefit from collecting and sequencing fruiting bodies as well as soils because that is the 
only way to directly and accurately compare the datasets, as identification errors can 
occur with morphological studies, fungal diversity is poorly documented, and species 
identification with high-throughput sequence data can be error-prone (Thines et al., 2018; 
Nilsson et al., 2019).  
5. Conclusion  
Our data show that logging can have a legacy effect upon fungal communities 
lasting decades, including guilds that are important for forest ecosystem processes. 
Planting following logging, as was done in our sites, may minimize some of these legacy 
changes, as there is less of a lag between the loss of hosts for the soil fungi and their 
reintroduction. However, planting may introduce novel fungi into the system, which can 
change the trajectory of regeneration for the fungal community and add its own legacy 
effect. Changes to the fungal community due to legacy effects can have consequences for 
41 
 
  
recovery following disturbance. Forest regeneration is an ongoing process with no real 
end—yet forests can reach stable states where the turnover of species is minimal. The 
plant communities of the forests in the Western Cascades can take decades or even more 
than a century following a disturbance to reach such a state depending on the severity 
(Franklin et al., 2002). Long term studies that track the regeneration of fungal 
communities in parallel to the regeneration of plants are severely lacking, limiting our 
understanding of how the communities influence one another. Further research is 
required to determine the impacts from the loss of particular fungal species on tree 
establishment and recruitment. 
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BRIDGE I 
In chapter II, I demonstrated that management has long term effects on soil 
microbial community assembly, but I did not link those changes to ecological functions. 
We can make assumptions about functional capacity using the taxa present; however, that 
is not equivalent to measuring a target function. In chapter II we investigate how 
variation in vineyard management intensity across several grape growing regions 
throughout Oregon influences microbial communities in soils, plant tissues, and wine. 
We then link community differences to variation in a function of interest: the production 
of organoleptic compounds during fermentation.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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CHAPTER III 
THE INFLUENCE OF MANAGEMENT ON THE MICROBIAL TERROIR AND 
METABOLOME OF WINE FROM OREGON VINEYARDS 
Contains unpublished co-authored material (with K. L. Shek, K.L. McGuire, K.M. 
Meyer, J.B. Weisz, and G.V. Jones). Conception of project by Krista McGuire, Katherine 
Shek, Jeremy Weisz, Kyle Meyer, and Greg Jones. Field collection was performed by 
me, Kaye Shek, or Jeremy Weisz. Lab work was performed by me and Kaye Shek. Data 
analysis was performed by me or Kaye Shek. The manuscript writing is mine with 
additions by Kaye Shek. Critical revision was provided by Krista McGuire. Editing 
assistance provided by Kyle Meyer, Jeremy Weisz, Greg Jones. 
1. Introduction 
An important aspect of wine, both commercially and scientifically, is the concept 
of terroir. Terroir refers to the environmental factors that contribute to the phenotype of a 
crop, including climate, topography, soil characteristics, and processing methods (Van 
Leeuwen and Seguin, 2007; González-Barreiro et al., 2015; Lucini et al., 2020). Not until 
recently have researchers considered the influence of microbial communities—the 
metabolic engines driving fermentation and shaping organoleptic properties of wine—as 
part of wine terroir (Bokulich et al., 2014; Gilbert et al., 2014; Bokulich et al., 2016; 
Belda et al., 2017; Morrison-Whittle and Goddard, 2018). It is now widely accepted that 
microbes affect the winemaking process from start to finish beyond merely creating 
ethanol (Morrison-Whittle and Goddard, 2018). Within the vineyard diverse communities 
of fungi and bacteria associated with the Vitis plant can alter nutrient availability, 
increase tolerance to stressors such as drought, lower rates of pathogenic infections, and 
50 
 
  
regulate the synthesis of plant metabolites (Bagyaraj and Ashwin, 2017; Kundu et al., 
2022).  
After grapes are harvested from the vineyards the entire process of fermentation is 
driven primarily by communities of microbes (Morrison-Whittle and Goddard, 2018). 
While most modern wineries rely on commercial yeasts, typically Saccharomyces 
cerevisiae, many are beginning to adopt spontaneous fermentation methods (sometimes 
referred to as “natural” wines) or some combination of the two (Jolly et al., 2014). 
Spontaneous fermentation relies on inoculation of post-harvest grape must with microbes 
from the environment. The source of these “wild” microbes is thought to be a 
combination of grapes, other plant tissues, airborne dispersal in the vineyard and winery, 
harvesting and winery equipment, and staff (Morrison-Whittle and Goddard, 2018; Pinto 
et al., 2015). Despite this assumption, few studies have sought to quantify the relative 
contribution of potential inoculation sources to the fermentation of wine throughout the 
fermentation process, with most to date primarily focused on the grape microbiome 
(Bokulich et al., 2016; Gilbert et al., 2014; Vitulo et al., 2019). 
Bacteria and fungi, particularly yeasts, present during fermentation can have a 
massive influence on the organoleptic properties of wine. Through their diverse 
metabolic capacities microbes can convert flavorless precursors within grapes into 
volatile compounds, such as terpenoids and thiols, which contribute to aroma and flavor 
(González-Barreiro et al., 2015; Parker et al., 2017). They can also synthesize metabolites 
de novo, including alcohols, esters, phenols, and organic acids. These organoleptic 
compounds associated with microbial metabolisms are core to what we understand as the 
basic palate of wine and can include compounds that are characteristic of different 
51 
 
  
varietals of wine (Bokulich et al., 2014; Anesi et al., 2015; González-Barreiro et al., 
2015; Parker et al., 2017). Furthermore, different consortia of microbes present in wine 
can result in distinct flavor profiles, which indicates that understanding microbial terroir 
is part of understanding wine terroir (Jolly et al., 2014).  
Understanding how different aspects of terroir influence microbial communities is 
relatively unexplored—especially the effects of management (Agarbati et al., 2019; 
Burns et al., 2016; Hendgen et al., 2018; Vitulo et al., 2019). The common agricultural 
practices of tilling, biocide application, irrigation, addition of chemical fertilizer, and 
others disrupt the physical and chemical aspects of an environment (Peterson et al., 
2018). Understanding how management practices influence microbial communities in 
agroecosystems is essential for understanding the organoleptic properties of wine, and, 
importantly, for improving the resiliency of modern crop production to further 
disturbance. As it stands, modern agriculture is not sustainable into the future due to the 
reliance upon fossil fuels, limited quantities of mineral fertilizers, use of synthetic 
biocides, loss of topsoil to erosion, water scarcity, and the emergence of novel pathogens 
(Cordell and White, 2011; Foley et al., 2005; McDonald and Stukenbrock 2016). 
Sicrobes can help alleviate some of these issues by reducing the reliance upon non-
organic inputs for fertilization and biocide use, tilling, and irrigation if given the chance 
(Garcia-Orenes et al., 2016; Peterson et al., 2018; Nosheen et al., 2021). The use of 
microbes to increase crop resiliency is of special interest to perennial crops such as V. 
vinifera, which has seen a recent surge in managers switching to more sustainable 
practices within the United States (Castellini et al., 2017; Baiano, 2021).  
52 
 
  
In the state of Oregon, USA there are approximately 1000 wineries across 21 
distinct growing regions known as American Viticultural Areas (AVAs) and sub-AVAs. 
The AVA system assigns appellation of origin to regions of wine grape growing that 
share geographic, climatic, and geological aspects that contribute to a shared identity of 
terroir. Oregon AVAs encompass a wide range of climates in a gradient from warm and 
dry in the south to cold and wet in the north. There also exists a wide variety of 
management styles that range from high to low intensity and input. Many low intensity 
vineyards follow strict standards to gain certifications such as Demeter Biodynamic or 
LIVE Sustainable, whereas high intensity vineyards frequently apply chemical fertilizers, 
biocides, and practice tillage. Biodynamic vineyards are certified through the Demeter 
International standard while other vineyards are certified through LIVE, which is a 
Pacific Northwest organization that bases its program on international Integrated 
Production standards (Castellini et al., 2017). This makes Oregon a perfect natural 
laboratory to test for the simultaneous influences of climate and management on terroir 
and microbes within vineyards and wineries. We set out to answer three main questions: 
1) How does management intensity alter microbial communities in vineyards independent 
of climate? 2) What is the relative contribution of different microbial sources (soil, bark, 
leaf, and grape) to wine fermentation communities? 3) Are detectable differences in 
fermentation communities correlated with differences in microbial metabolite diversity? 
2. Materials and methods 
2.1 Site descriptions 
Our sites consisted of seven pairs of vineyards in four different American 
Viticultural Areas (AVAs) throughout western Oregon, USA. Therefore, we ensured that 
53 
 
  
every AVA we sampled included a minimum of one high intensity management site and 
one low intensity management site to account for variation in abiotic conditions. The 
AVAs we selected were Rogue Valley (two pairs of vineyards; n = 4), Willamette Valley 
(three pairs; n = 6), Columbia Gorge (one pair; n = 2), and Umpqua Valley (one pair; n = 
2). These AVAs represent a temperature and precipitation gradient ranging from warm 
and dry in the southernmost region to cool and wet in the north. Average temperatures 
decrease and average precipitation increases from our southernmost vineyard in Rogue 
Valley (~42.3°N, 64cm rainfall, mean of 13.3°C) to the northernmost vineyard in 
Columbia gorge (~45.6°N, 110cm rainfall, mean of 8.9°C).  
2.2 Sample collection and processing 
At each vineyard we sampled within a singular block to minimize any influence 
from variation in rootstock, grape varietal, or soil characteristics between blocks. We 
chose blocks of Pinot noir where available, and syrah if not due to its ubiquitous nature in 
southern Oregon, to keep the varietal consistent between vineyards. Within the selected 
block we sampled five random vines and took one leaf, one cluster of grapes, one strip of 
bark approximately 15cm in length, and a 10cm deep by 2.5cm wide soil core near the 
base. Samples were collected using either EtOH sterilized gloves or EtOH sterilized soil 
cores and placed in a sterile whirlpak bag. Once collected, samples were placed on dry 
ice before being stored in a -20°C freezer until processing and extraction. A subset of the 
sampled vineyards (n = 10) also harvested, crushed, and fermented grapes from the 
blocks we targeted separate from the rest of their crop. They provided five samples from 
must (freshly crushed grapes) as well as five samples from the end of fermentation 
(~0°Bx). This allowed us to sequence and track microbial community contributions of 
54 
 
  
both bacteria and fungi from tissue to wine; additionally, it allowed us to perform 
metabolomic assays on the must and wine to link microbial community to changes in 
metabolic products. 
Soils were hand homogenized using EtOH and UV sterilized sieves prior to 
extraction to ensure even sampling from within each entire core. We used approximately 
250mg of each soil sample for extraction with Qiagen (Venlo, Netherlands) Dneasy 
PowerSoil Pro Kits. Leaf and bark samples were crushed in sterile mortars using liquid 
nitrogen. We stripped the skins from three randomly selected grapes from each cluster for 
extraction. We extracted DNA from all plant tissues with Qiagen Dneasy Plant Pro Kits 
using approximately 100mg of homogenized material per sample.  
Must and wine samples were prepared using a modified protocol (Bokulich et al., 
2014). We centrifuged 10 mL of each must and wine sample at 4,000xg before decanting. 
We then mixed and rinsed the resulting pellet with 1000uL of cold PBS, each rinse 
followed by centrifuging at 8,000xg and decanting. Samples were then immediately run 
through the Qiagen Dneasy Plant Pro Kits protocol. Additionally, three 1.5 mL technical 
replicates of must, fermenting wine, and bottled wine from ten of the vineyards (n = 30) 
were sent to the West Coast Metabolomics Center at the University of California, Davis 
for primary metabolite analysis using gas chromatography with time-of-flight mass 
spectrometry (GC TOF-MS). 
2.3 Library preparation and sequencing 
All samples were prepared for amplicon sequencing using a two-stage PCR 
protocol referred to as iTag: the first stage amplifies the genomic region of interest using 
forward and reverse primers attached to heterogeneity spacers to increase base-call 
55 
 
  
diversity within a sequencing run; the second stage ligates combinatorial dual indexes for 
multiplexing, as well as i5 and i7 adapters for binding to the Illumina NovaSeq flow cell. 
We used the ITS1f and ITS2 primer pair to amplify the internal transcribed spacer (ITS) 
amplicon to characterize fungi, the 515F and 806R primer pair to amplify the 16S 
ribosomal RNA (16S) amplicon to characterize bacteria, and the 1333 and 1647 primer 
pair to amplify the 18S ribosomal RNA (18S) amplicon to characterize yeasts.  
We carried out PCR1 in 20µl reactions composed of 2µl of genomic DNA, 0.5µl of 
10µM forward primer, 0.5µl of 10µM, 10µl GoTaq Green MasterMix (Promega), 0.1µl 
of 100µg/µl bovine serum albumin (BSA), and 6.9µl nuclease free water. ITS and 18S 
PCR1 conditions: Denaturation of DNA at 94°C for 3 mins; 30x amplification cycles of 
45 sec at 94°C, 1 min at 54°C and 90 sec at 72°C; finished with a 10 min extension at 
72°C. 16S PCR1 conditions: Denaturation at 94°C for 3 mins; 30x amplification cycles 
of 45 sec at 94°C, 1 min at 50°C and 90 sec at 72°C; finished with a 10 min extension at 
72°C. 
We conducted PCR2 in 25µl reactions composed of 3µl PCR1 product, 2µl of 
barcoded primers, 10µl GoTaq Green MasterMix (Promega), 0.1µl of 100µg/µl BSA, and 
9.9µl nuclease free water. Conditions: denaturation at 94°C for 3 mins; 12x amplification 
cycles of 45 sec at 94°C, 1 min at 52°C and 90 sec at 72°C; finished with a 10 min 
extension at 72°C. PCR2 products were sequenced on an Illumina NovaSeq 6000 at the 
Genomics and Cell Characterization Center (GC3F) at the University of Oregon. To 
obtain the longest reads available on the NovaSeq 6000 we used paired end 2x 150 base 
pair chemistry.  
2.4 Sequence processing and analyses 
56 
 
  
We demultiplexed and removed primers from raw sequences using the HTStream 
pipeline (v.1.3.3; Settles, 2022). We then processed trimmed reads through the dada2 
pipeline (v.1.24.0; Callahan et al., 2016) in the R environment; this pipeline filters, 
denoises, and merges the reads, assigns taxonomy to each unique amplicon sequence 
variant (ASV), and constructs an ASV counts by sample matrix for each dataset—ITS, 
16S, 18S. For taxonomic assignment we used >97% alignment with the UNITE database 
for our ITS sequence data (v9.0; Nilsson et al., 2018), the SILVA database for our 16S 
sequences (v138.1; Quast et al., 2013), and the PR2 database for our 18S sequences 
(v4.14.1; Gillou et al., 2013). To normalize our microbial and metabolomic data, we used 
the negative binomial variance stabilizing function of the DESeq2 R package (v1.38.3; 
Love et al., 2014). We did not rarify prior to normalization, as rare taxa are known to 
influence fermentations in other studies (Zhao et al., 2022).  
All analyses were performed in the R environment (v4.2.2; R Core Team, 2021). 
We used the vegan R package (v2.6.4; Oksanen et al., 2022) to calculate Bray-Curtis 
dissimilarities between our samples. We then used vegan to run permutation analyses of 
variance (PERMANOVAs) based on those previously generated Bray-Curtis dissimilarity 
matrices. We created Euler diagrams using the ps_euler function in the MicEco R 
package (v0.13.1; Liu et al., 2021). We also used the fast expectation-maximization 
microbial source tracking (FEAST) R package (v0.1.0; Shenhav et al., 2019) to calculate 
relative contributions from source samples (soils and tissues) to sink samples (must). 
FEAST uses an expectation-maximization algorithm to determine the most likely ratio of 
source contributions on a specified sink. To test the significance in community variation 
between treatments we used analysis of compositions of microbiomes with bias 
57 
 
  
correction (ANCOM-BC, v1.6.4; Lin and Peddada, 2020). ANCOM-BC is a form of 
differential abundance analysis created to account for sampling biases inherent to 
microbiome amplicon datasets by estimating the sampling fraction which is the ratio of 
true abundance versus sample abundance based on the observed counts using a linear 
regression approach. We removed any taxa that had <10 reads in our total data set before 
the ANCOM-BC analysis as rare taxa can easily produce false positives. We then used 
the differential abundance analysis function in DESeq2 to identify metabolites within 
Pinot noir must and wine samples that were significantly different between management 
types. In our analyses we used the metabolites identified by DESeq2 that were significant 
(p < 0.05 and FDR < 0.05), named, and had a |log2|fold change of ≥1. The ggplot2 
package (v3.4.0; Wickham, 2016) was used to generate figures.  
3. Results 
3.1 Sources of microbial taxa in grape must 
Using the source tracking abilities of the FEAST package we found that within 
our data the majority of ITS and 16S ASVs from fresh must samples are likely derived 
from grape samples or “unknown” sources (Figures 1 and 2 respectively). Grapes had a 
mean contribution of 31% for fungi and 68% for bacteria while unknown had a mean 
contribution of 48% for fungi and 20% for bacteria. Additionally, based on the FEAST 
results and Euler diagrams using presence / absence, wine samples share fungal and 
bacterial taxa with all the tissues that we sampled as well as soil (Figure 3). 18S showed a 
similar pattern using the Euler diagram approach, though we were unable to run source 
tracking on that dataset due to the low richness of ASVs in the data. 
 
58 
 
  
 
Figure 1. Relative contributions of source samples (bark, soil, leaf, grape, unknown) to 
sink samples (must) for bacteria using the 16S amplicon in Oregon, USA vineyards. 
Relative contributions were calculated using the fast expectation-maximization microbial 
source tracking (FEAST) package for R. (A) = individual vineyards; (B) = average by 
management (H for high intensity, L for low intensity). 
 
 
 
 
 
 
 
59 
 
  
 
Figure 2. Relative contributions of source samples (bark, soil, leaf, grape, unknown) to 
sink samples (must) for fungi using the ITS amplicon in Oregon, USA vineyards. 
Relative contributions were calculated using the fast expectation-maximization microbial 
source tracking (FEAST) package for R. (A) = individual vineyards; (B) = average by 
management (H for high intensity, L for low intensity). 
 
 
 
 
 
 
 
 
60 
 
  
 
Figure 3. Euler diagrams showing overlap of microbial amplicon sequence variants 
(ASVs) between sample types taken from vineyards within Oregon, USA. The “wine” 
category is a mix of must samples and fermented samples. (A) = fungi (ITS), (B) = 
bacteria (16S), (C) = yeasts (18S). 
 
 
 
 
 
 
61 
 
  
3.2 Influence of management and American Viticultural Area on microbial taxa 
Management and AVA had a significant effect, including a strong interaction 
between the two, on the composition of fungi, bacteria, and yeasts when considering each 
sample type separately (supplemental table 1). Euler diagrams based on the presence / 
absence of ASVs within our data showed several patterns of note. When considering 
AVAs, ITS and 18S data show a distinct geographic pattern with geographically adjacent 
AVAs and sub-AVAs overlapping, while there is little to no overlap between distant 
AVAs (Supplemental Figure 1). This is in contrast with the 16S data where there was 
overlap between all AVAs, regardless of geographic distance. All three amplicons 
showed large differences in ASVs between high and low management regardless of the 
varietal (Figure 4). Based on ANCOM-BC, four genera and eight species of fungi 
identified via ITS were differentially abundant within soil (Figure 5) and only one genus 
within wine. Two genera of bacteria were differentially abundant in soils and none in 
wine. No genera or species were differentially abundant for our 18S data.  
and fermented samples. (A) = fungi (ITS), (B) = bacteria (16S), (C) = yeasts (18S). 
62 
 
  
Figure 4. Euler diagrams showing 
overlap of microbial amplicon 
sequence variants (ASVs) between 
vineyards within Oregon, USA 
with high (H; n = 7) or low (L; n = 
8) intensity management practices. 
(A) = fungi (ITS), (B) = bacteria 
(16S), (C) = yeasts (18S). 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
63 
 
  
 
Figure 5. Differential abundance of fungal species and genera comparing the change in 
low management intensity vineyards relative to high intensity vineyards in Oregon, USA. 
Log fold change was calculated using analysis of compositions of microbiomes with bias 
correction (ANCOM-BC, v1.6.4; Lin and Peddada, 2020).  
 
3.3 Changes in metabolites between treatments 
Our differential abundance analysis on the metabolomic data of Pinot noir 
samples revealed named metabolites that are significantly different (adjusted p-value or 
FDR < 0.05) based on management intensity in fermented (Figure 6) and bottled (Figure 
7) wine. In fermented wine we found ten metabolites that were more abundant in high 
intensity samples, and twelve metabolites more abundant in low intensity samples. In 
bottled wine we found eight metabolites more abundant in high intensity samples, and 
eighteen metabolites more abundant in low intensity samples.  
 
64 
 
  
 
Figure 6. Volcano plot of differentially abundant metabolites in wine at the end of 
fermentation from Pinot noir grapes in Oregon, USA. Log2 fold change is comparing the 
change in abundance of metabolites from low management intensity vineyards relative to 
the abundance in high management intensity vineyards. Adjusted p-value is also known 
as the false discovery rate (FDR). Blue indicates a greater abundance in low management 
intensity samples, red indicates a greater abundance in high management intensity 
samples. The cutoff for inclusion of metabolites is adjusted p-value < 0.05 and |log2 fold 
change| ≥ 1. 
 
65 
 
  
 
Figure 7. Volcano plot of differentially abundant metabolites in bottled wine from Pinot 
noir grapes in Oregon, USA. Log2 fold change is comparing the change in abundance of 
metabolites from low management intensity vineyards relative to the abundance in high 
management intensity vineyards. Adjusted p-value is also known as the false discovery 
rate (FDR). Blue indicates a greater abundance in low management intensity samples, red 
indicates a greater abundance in high management intensity samples. The cutoff for 
inclusion of metabolites is adjusted p-value < 0.05 and |log2 fold change| ≥ 1.  
 
4. Discussion 
4.1 Taxa present in wine must and their sources 
We found that the primary source of microbial taxa in the fresh must is from 
grapes and unknown sources, implying that the grape microbiome is the most important 
to manipulate for fermentation purposes. However, our results also show that leaves and 
bark are sources of bacterial or fungal inoculum. These findings indicate that surveying 
the grape microbiome does not capture the full set of microbial taxa that can disperse into 
and influence the fermentation. Additionally, due to the high contribution of unknown 
66 
 
  
sources to must communities in our study, further sampling of alternative sources is 
necessary. These “unknown” sources are likely airborne dispersal, contact with 
equipment, and contact with personnel (Whittle and Goddard, 2017; De Roos et al., 
2019). It is surprising that soil was estimated to contribute very little to must, as the 
overlap in ASVs between soil, plant tissues, and wine samples was large. This result 
indicates that while soil may not directly inoculate the must it may act as a refugia for 
microbial taxa during the yearly dormancy V. vinifera experiences during winter. Fungi 
and bacteria can lie in dormancy within soils for varying lengths of time, even if their 
typical substrate or host is not present (Warren et al., 2019; Joergensen and Wichern, 
2018). 
4.2 Microbial communities in high vs. low intensity vineyards 
Our microbial data revealed that there were significant compositional differences 
between management intensities for soil, wine, and all plant tissues demonstrating that 
management influences microbial terroir, which provides a basis for the variation that we 
see in organoleptic compounds. While compositions were significantly different, the 
signal seems to be weak at a genus and species level based on differential abundance 
analysis. This indicates that any differences in composition are primarily due to species 
that are rare within our dataset. The one exception to this is soil fungi where the 
mycorrhizal genus Glomus, and several genera of pathogens and saprotrophs varied based 
on management. Multicellular fungi tend to have slow growth cycles relative to bacteria 
and single cell fungi, this makes them particularly susceptible to physical disturbances 
like tilling (Shi et al., 2019). Glomus and other arbuscular mycorrhizal fungi (AMF) are 
also composed of long, multi-nucleate hyphae. This physical structure likely explains 
67 
 
  
why Glomus was higher in abundance in low intensity vineyard soils (van der Heyde et 
al., 2017). AMF aid V. vinifera vines with nutrient uptake, water stress, and protection 
from plant pathogens (Jayne and Quigly, 2014; Bagyari and Ashwin, 2017). Higher 
abundance of Glomus signals that low intensity vineyards may be more resilient in the 
face of outside disturbance. Of the plant pathogens highlighted in our differential 
abundance analysis, the species Neonectria lugdunensis was the only one of major 
concern to vineyards. Neonectria was higher in abundance in lower intensity vineyards 
likely due to limited use of fungicides. Neonectria is part of suite of fungi that cause 
“black foot disease” which blocks vascular tissues and can kill vines under water stress 
(Agustí-Brisach and Armengol, 2013).  
We found that the most abundant bacterial genera and fungal taxa tied to 
fermentation and organoleptic compound production did not vary between management 
intensity, implying that variation in metabolites are due to less common taxa or 
intraspecific variation. The relevant yeasts we found include several Candida spp., Pichia 
spp., Metschnikowia spp., Saccharomyces spp., Schizosaccharomyces japonicus, and 
Wickerhamomyces spp. The relevant bacteria in our data include Acetobacter spp., 
Gluconobacter spp., Lactobacillus spp., and Oenococcus oeni. None of these genera or 
species were significantly different between management intensities. However, some 
were excluded from the differential abundance analysis as they were rare within our 
dataset. This further supports that differences within our metabolomic data are due to rare 
taxa or variation in the chemical composition of the must prior to fermentation. 
Alternatively, differences in the organoleptic metabolites may be due to intraspecific 
variation. Most bacterial and fungal taxa have highly modular genomes and metabolisms, 
68 
 
  
which could explain differences in concentrations of metabolites associated with 
microbial metabolisms (Legras et al., 2018; McCarthy et al., 2019). Additionally, higher 
disturbance intensity can lower genetic diversity and cause functional differences 
between sites (Banks et al., 2013) Untargeted genomic sequencing is required to further 
investigate this possibility in the future.  
4.3 Connecting management, microbial communities, and metabolites 
We found a higher abundance of amino acids in high intensity wine ferment 
samples, perhaps due to higher levels of nitrogen in vineyards receiving chemical 
fertilizers (Ough and Bell, 1980; Smit et al., 2014; Döring et al., 2022). Amino acids can 
influence the flavor of wine by being catabolized into alternative compounds such as 
higher alcohols and carboxylic acids via transamination (Styger et al., 2011). The three 
primary amino acids in wine involved with these transformations are leucine, isoleucine, 
and valine—two of which are more abundant in our high intensity ferment samples, 
leucine and isoleucine (Styger et al., 2011). Within our data, higher concentrations of 
bacteria or yeast-derived carboxylic acids and higher alcohols were almost exclusively 
found in low intensity samples, except for citric acid in bottled high intensity wine. This 
seems counterintuitive, as amino acids were higher in the high intensity ferment. 
However, it may indicate that the “maturity” of the fermentation was further along in the 
low intensity wine samples and any free amino acids were already consumed. This 
explanation is supported by the higher concentration of α-ketoglutarate in low intensity 
ferment samples which is made in the first step of the transamination reaction. Another 
explanation may be that the yeasts present in high intensity fermented samples were not 
as effective at creating these compounds, as the amino acids can be used in other 
69 
 
  
pathways and protein synthesis (Ough et al., 1980; Styger et al., 2011). The compounds 
can also be made as a byproduct of glycolysis, avoiding the requirement of amino acids 
(Styger et al., 2011).  
We also saw significant variation in organic acids between management 
intensities in both fermented and bottled wine samples. Organic acids play a key role in 
the organoleptic properties of wine. Being acids, many of them increase the perception of 
“sourness” in wine (Chidi et al., 2018; Valerio et al., 2004; Vicente et al., 2022). 
Sourness can take on different forms (i.e., vinegar versus lemon juice) which can add 
complexity and depth to a wine’s palate depending on the identity and relative 
concentration of the acids. Two of the acids in our data come exclusively from bacterial 
and yeast metabolic processes (lactic acid and phenyllactic acid) two are exclusively from 
plant processes and three from either plant or microbial sources (citric acid, propionic 
acid, and pyruvic acid) which indicates strong microbial influence on the acid profile 
(Bauer et al., 2018). Most of these acids saw an increase in low intensity wine samples; 
all except caffeic acid and citric acid in the bottled wine samples. These variations in acid 
profile suggest that the perception of acidity and acid-associated flavors is different 
between high and low intensity wine in our study.  
Several other miscellaneous bacteria and yeast derived metabolites highlighted by 
our analyses imply that low intensity bottled wine may possess stronger notes of fruit and 
flowers, while the high intensity bottled wine may possess a more complex bitterness—
all of which are favorable in Pinot noir (Singleton and Ouch, 1962; MacNeil, 2022). 
These miscellaneous organoleptic relevant metabolites include the disaccharides 
trehalose and gentiobose which are produced via microbially derived enzymes acting on 
70 
 
  
carbohydrates present in must (Ruiz-Matute et al., 2009; López et al., 2018). These 
disaccharides are typically bitter and may add to the complexity of flavor. Disaccharides 
are higher in the high intensity bottled wine which indicates the presence of yeasts that 
produce more β-glycosidases and β-xylosidases or a greater abundance of complex 
carbohydrates (López et al., 2018). The only significantly different higher alcohol is 
phenylethanol, which is higher in our low intensity bottled samples. Phenylethanol 
confers a floral or fruity character to wine and is produced via yeast metabolism (Jolly et 
al., 2014). Higher concentration of phenylethanol is associated with the presence of non-
Saccharomyces yeasts, such as Hanseniaspora, Pichia, or Wickerhamomyces, all of 
which were present in our wine samples (López et al., 2018). Ethyl succinate is the only 
ester we found varied between our samples with greater abundance in low intensity 
bottled samples. Esters within wine are perceived as fruity and are created through the 
metabolisms of lactic acid bacteria or yeast (Lasik-Kurdyś et al., 2018; Garde-Cerdán et 
al., 2021). Except for the disaccharides these compounds are higher in abundance in low 
intensity bottled samples.  
5. Conclusions 
Our data indicates that management intensity in vineyards has an influence on 
microbial community composition; furthermore, the differences in microbial composition 
are correlated to changes in metabolites related to the organoleptic properties of wine. As 
the wine industry is one that sells a product focused on flavor and aroma, understanding 
the nuances of variation in organoleptic compounds is important. While we cannot make 
judgements upon “quality”, as that is a deeply subjective concept, our results can inform 
winemakers of potential changes to expect when shifting management styles. However, 
71 
 
  
these results might not be the same for other varietals, as our metabolite analysis was 
entirely focused on Pinot noir samples. Future data on other varietals is essential to 
understanding whether the patterns in our data are consistent. Additionally, variation in 
the function of organoleptic compound production during fermentation seems to be 
linked to differences in rare microbial taxa rather than those more abundant. This reflects 
the72ominationn of fermentation by a select few species with rather cosmopolitan 
distributions. Grape must and wine are extremely harsh environments that favor a narrow 
range of microbial taxa. Any rare taxa or genotype that survives the beginning of 
fermentation can leave a unique signature on the final metabolome and organoleptic 
characteristics. Further study of rare or ”inconsequential” taxa in fermentations may lead 
to greater insight into the microbial contribution upon terroir.  
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BRIDGE II 
I demonstrated in chapter III that management intensity can significantly alter the 
compositions of bacterial and fungal communities present within vineyards and 
fermentations; furthermore, variation in the microbial communities was linked to changes 
in the organoleptic properties of fermented wine. In chapter IV we will explore how 
changes to management intensity can rapidly influence microbial communities within 
vineyard soils and the implications of those effects.  
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
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CHAPTER IV 
THE INFLUENCE OF MANAGEMENT ON THE MICROBIAL TERROIR AND 
METABOLOME OF WINE FROM OREGON VINEYARDS 
Contains unpublished co-authored material (With Blaine Pappa and Krista McGuire). 
Conception of project by myself and Krista McGuire. Fieldwork performed by me and 
Blaine Pappa. Molecular lab work performed by myself or Blaine Pappa under my 
supervision. Analysis by me or Blaine Pappa under my supervision. The writing is mine 
with critical revision by Krista McGuire.  
1. Introduction 
Wine is a multibillion-dollar international industry and commodity that plays a 
key role in cultural identity for many regions. Cultivating grapes (Vitis vinifera) for that 
industry, like all agricultural industries, is facing a growing concern of security in the 
face of threats such as climate change, topsoil erosion, dependence on chemical 
fertilizers, dependence on biocides, and water scarcity (Foley et al., 2005; McDonald et 
al., 2016; Peterson et al., 2018). Grape vines are particularly susceptible to these threats 
due to their perennial nature and long production lifespans. It takes up to three years for a 
V. vinifera vine to produce its first harvest and a vine can continue to produce fruit for up 
to fifty years (Strik, 2019). Because of this reality, grape producers must consider both 
short and long-term strategies for increasing crop security. Many have begun to adopt 
methods focusing on sustainability in a bid to increase the resistance and resiliency of 
their vines by treating their vineyards as ecosystems (Castellini et al., 2017; Nosheen et 
al., 2021). A key focus of modern sustainable agricultural movements falls under the 
umbrella of “soil health”. This nebulous concept shares common target parameters across 
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movements, including increasing biodiversity of microbes and invertebrates, increasing 
soil organic matter, increasing nutrient availability, as well as maximizing the benefits of 
other physical and chemical parameters (Tahat et al., 2022).  
Most of the various aspects of soil health are invariably linked to and driven by 
the ecological functions of plants and microbial communities. Plants are the primary 
source of organic inputs into soil through the shedding of litter, release of exudates, and 
death. Those inputs can alter the chemistry of the soil and increase concentrations of 
carbon and nutrients (van der Putten et al., 2013). Soil microbial communities feed upon 
these inputs, breaking them down and freeing up recalcitrant nutrients (van der Putten et 
al., 2013). A group of mutualistic fungi known as mycorrhizal fungi can further increase 
plant access to nutrients by shuttling nitrogen and phosphorus to their hosts in exchange 
for carbon (Bagyari and Ashwin, 2017; Rillig et al., 2023). Furthermore, mycorrhizal 
fungi can reduce water stress by increasing the surface area of roots and preventing water 
loss to the soil (Kamayestani et al., 2019). Mycorrhizal fungi and saprotrophic, or 
decomposer, fungi also compete with pathogens for resources and host access, which can 
reduce rates of infections (Kardol et al., 2015). Some bacterial taxa can also fix nitrogen 
from the atmosphere creating bioavailable forms that can be accessed by other organisms 
including crop plants. Microbial biomass also tends to be recalcitrant, increasing the 
organic matter in the soil and acting as a carbon sink for carbon sequestration. 
Additionally, from a winemaking perspective, soil can act as a refugia for fungi and 
bacteria that are associated with fermentation (Setati et al., 2012). Many bacteria and 
fungi can enter states of dormancy for long periods of time (Joergensen and Wichern, 
2018; Warren et al., 2019). Dormancy strategies include creating spores as well as 
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creating compounds that help resist environmental variation or attacks from other 
microbial taxa (Kearns and Shade, 2018). This allows bacteria and fungi to survive 
through the winter and persist year to year to act as a source of inoculum. 
Despite their importance to all aspects of soil health and fermentation, little is 
known about the influence of management practices upon the composition and 
functioning of microbial communities within vineyards. Studies have found that the use 
of high intensity management practices such as tilling, biocide application, and chemical 
fertilizer application reduces the diversity and abundance of bacteria and fungi within 
agroecosystems (Kihara et al., 2012; Agarbati et al., 2019; Barreiro et al., 2022); 
furthermore, low intensity practices encourage microbial diversity and abundance, 
leading to soil that is more resistant to disturbances (Thiele-Bruhn, et al., 2012; Garcia-
Orenes et al., 2016; Chen et al., 2020). We worked with a vineyard that shifted from high 
intensity to low intensity management practices in 2018 to see if there were detectable 
differences in soil fungi and bacteria by the 2021 harvest season. We wanted to address 
three main questions using the dataset: 1) are there detectable changes in communities of 
soil bacteria and soil fungi following a dramatic change in management strategy? 2) if 
there are changes, how quickly do they occur and are they temporally stable? and 3) how 
much intra-vineyard variation exists in communities of soil bacteria and soil fungi 
between blocks of the same grape varietals? 
2. Methods 
2.1 Site description 
Troon Vineyard (42°18’18”N, 123°13’15”W) is located within the Applegate 
Valley, a sub-American Viticultural Area (AVA), nestled within the Rogue Valley AVA 
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of southern Oregon, USA. AVAs are a system of appellation in the USA that group 
vineyards with shared climate, geology, and other physical characteristics that sum up to 
a shared identity. The Applegate Valley AVA is situated around 500m above sea level on 
south facing slopes between the Cascade Mountains and the Siskiyou Mountains. This 
results in a warm-summer Mediterranean climate with a mean annual temperature of 
12.5°C and a mean annual precipitation of 50-65cm. Troon has a unique management 
history. The first vines were planted in 1972, and until 2018 Troon used a high intensity 
“conventional” approach to management—including biocide, tilling, and chemical 
fertilizer use. After 2018, the vineyard began using a low intensity “biodynamic” 
approach. As of 2023, Troon is both Demeter Biodynamic certified and Regenerative 
Organic Certified, indicating practices that includes permaculture crop cover, presence of 
livestock, organic only inputs of fertilizer, no tilling, and extremely limited biocide use 
(Castellini et al., 2017; Regenerative Organic Certified, 2019).  
2.2 Sample collection and processing 
All sampling was performed during the harvest season on October 15th, 2021. To 
test for variation between blocks of vines we selected and sampled the two most common 
varietals grown by Troon, syrah (n = 4) and vermentino (n = 3). Within each block we 
took a 10 cm deep by 2.5 cm radius soil core using an EtOH sterilized soil auger within 
0.5 m of the base of five random vines from five random rows (n = 5 per block). Row and 
vine were picked via a random number generator. Samples were placed on dry ice for 
transit and stored at -20°C at the University of Oregon until processing. We hand 
homogenized the soils using EtOH and UV sterilized gloves and scoops to increase 
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sampling evenness in each core. We then used approximately 250mg of each sample for 
extraction with Qiagen DNeasy PowerSoil Pro Kits.  
2.3 Molecular methods 
All 2021 samples were prepared using a two-stage PCR protocol known as iTag. 
The first stage uses paired forward and reverse primers attached to heterogeneity spacers 
to amplify regions of interest within the genome; the second stage ligates combinatorial 
dual indexes for multiplexing, as well as i5 and i7 adapters for binding to the Illumina 
NovaSeq flow cell. We characterized fungi using the ITS1f and ITS2 primer pair to 
amplify the internal transcribed spacer (ITS) region, and bacteria using the 515F and 
806R primer pair to amplify the 16S ribosomal RNA (16S) region.  
We carried out PCR1 in 20µl reactions composed of 2µl of genomic DNA, 0.5µl 
of 10µM forward primer, 0.5µl of 10µM, 10µl GoTaq Green MasterMix (Promega), 
0.1µl of 100µg/µl bovine serum albumin (BSA), and 6.9µl nuclease free water. ITS 
PCR1 conditions: denaturation of DNA at 94°C for 3 mins; 30x amplification cycles of 
45 sec at 94°C, 1 min at 54°C and 90 sec at 72°C; finished with a 10 min extension at 
72°C. 16S PCR1 conditions: Denaturation at 94°C for 3 mins; 30x amplification cycles 
of 45 sec at 94°C, 1 min at 50°C and 90 sec at 72°C; finished with a 10 min extension at 
72°C. 
We then carried out PCR2 in 25µl reactions composed of 3µl PCR1 product, 2µl 
of barcoded primers, 10µl GoTaq Green MasterMix (Promega), 0.1µl of 100µg/µl BSA, 
and 9.9µl nuclease free water. Conditions: denaturation at 94°C for 3 mins; 12x 
amplification cycles of 45 sec at 94°C, 1 min at 52°C and 90 sec at 72°C; finished with a 
10 min extension at 72°C. 
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2.4 Timeseries data  
Previous soil microbial data from the 2018, 2019, and 2020 harvest seasons were 
provided by Biome Makers and Troon. The data from these samples were originally used 
to look at the microbial profile of the soil for management purposes. 2018 was the last 
year that Troon used high intensity management practices before switching to Demeter 
Biodynamic and Regenerative Organic practices. For each block included in the 
timeseries data, three “spoonfuls” of soil were collected by Troon in a sterile tube, 
repeated three times per block. Samples were sent to WineSeq (now part of Biome 
Makers) for DNA extraction, amplification, and sequencing on an Illumina MiSeq. 
Collection and processing methods are described in further detail in Belda et al., 2017. 
We ran the raw FASTQ files through the same pipelines as our 2021 data to reduce 
variation due to data processing technique. We set them to an equal truncation length as 
our data (200 bp) and merged the sequences before removing chimeras and assigning 
taxonomy. We then subset the samples to contain only syrah and vermentino blocks to 
match our 2021 data. This data includes 17 syrah samples (two from 2018, nine from 
2019, six from 2020) and 14 vermentino samples (four from 2018, six from 2019, and 
four from 2020).  
2.5 Sequence processing and analyses 
We demultiplexed and removed primers from raw sequences using the HTStream 
pipeline (v.1.3.3; Settles, 2022). We then processed trimmed reads through the dada2 
pipeline (v.1.24.0; Callahan et al., 2016) in the R environment; this pipeline filters, 
denoises, and merges the reads, assigns taxonomy to each unique amplicon sequence 
variant (ASV), and constructs an ASV counts by sample matrix for each dataset—ITS, 
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16S, 18S. We then assigned taxonomy with a cutoff of 98% similarity. For taxonomic 
assignment we used the UNITE database for our ITS sequence data (v9.0; Nilsson et al., 
2018), the SILVA database for our 16S sequences (v138.1; Quast et al., 2013), and the 
PR2 database for our 18S sequences (v4.14.1; Gillou et al., 2013).  
All analyses were performed in the R environment (v4.2.2; R Core Team, 2021). 
We created Euler diagrams to reveal the overlap of shared microbial taxa between factors 
using the MicEco package (v0.13.1; Liu et al., 2021). To normalize our microbial data for 
comparisons between varietals and blocks in our 2021 samples we used the negative 
binomial variance stabilizing function of the DESeq2 R package (v1.38.3; Love et al., 
2014). We used the vegan package to calculate Bray-Curtis dissimilarity matrices and ran 
permutation analyses of variance (PERMANOVAs) on the resulting matrices.  
To normalize our microbial data for timeseries analysis we performed a centered 
log ratio transformation and then detrended the result, also known as regression 
normalization (Coenen et al., 2020). We excluded any taxa that were not present in a 
minimum of 80% of samples or had an average relative abundance of less than 0.5% of 
the community for the rhythmicity analysis. We then used a combination of the R 
packages RAIN (Rhythmicity Analysis Incorporating Non-parametric methods, v1.30.0; 
Thaben and Westermark, 2014) and BiomeHorizon (v1.0.0; Fink, 2023) to test for 
statistically relevant rhythmic patterns in microbial community composition and to create 
longitudinal horizon plots for individual taxa across the four years of sampling. 
Originally developed for analyzing circadian rhythm transcriptomic data, RAIN can be 
used for any longitudinal or independent repeated measurement dataset. Traditional 
longitudinal statistics, such as Fourier, are often difficult to apply to large biological 
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datasets (Thaben and Westermark, 2014; Coenen et al., 2020). These datasets typically do 
not match traditional distribution families, even once log transformed, contain many 
structural zeroes, and can contain large amounts of noise (Coenen et al., 2020). Unlike 
other methods, RAIN is non-parametric and allows for independently rising and falling 
datasets that do not fit frequency patterns (e.g., sine, cosine). RAIN uses a rank test based 
on Jonckheere-Terpstra that detects monotonic slopes between rising and falling sections 
of data regardless of shape or symmetry. The primary assumption of RAIN is that the data 
is stationary (i.e., has the same mean sequences across all samples). This can be an issue 
if there is seasonality in sampling, or if sequencing runs have dramatically different 
performance. Our analysis attempts to avoid this issue in two ways: first, samples from 
all years were taken during the harvest season in the vineyard to avoid seasonal effects; 
second, the normalization method we used stabilizes the means across samples (Coenen 
et al., 2020).  
3. Results 
3.1 Inter-varietal and inter-block variation in soil microbial community composition 
Neither varietal nor block had a significant effect on the community composition 
for our 2021 data (p > 0.5 for both) and there was no significant interaction between the 
two. Furthermore, soil microbial communities showed a large amount of overlap between 
all vermentino and syrah samples from all four years (Figure 1) out of 1,417 fungal taxa 
767 were shared, 292 were unique to vermentino, and 358 were unique to syrah. For 
bacteria, out of a total of 953 taxa 651 were shared, 163 were unique to vermentino, and 
139 were unique to syrah.  
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Figure 1. Euler diagrams of taxonomic overlap in soil microbial communities between 
blocks of vermentino (yellow) and syrah (red) wine grape varietals. The values are of 
ASVs identified to species and agglomerated at the species level. (A) overlap of fungal 
taxa using ITS sequencing. (B) overlap of bacterial taxa using 16S sequencing.  
 
3.2 Time series of soil microbial communities in vermentino and syrah   
There were many unique fungal and bacterial taxa in each year of syrah and 
vermentino soil samples (Figure 2). Our 2021 samples appear to have the largest number 
of unique taxa for both bacteria and fungi with 155 unique bacteria and 550 unique 
fungi—85% and 96% of 2021 taxa respectively. 2018-2020 showed far more overlap in 
taxa, but each year still had between 22% and 45% unique taxa for fungi and bacteria. 
  
 
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Figure 2. Euler diagrams of taxonomic overlap in soil microbial communities between 
years of sampling syrah and vermentino varietal blocks. The values are of ASVs 
identified to species and agglomerated at the species level. (A) overlap of fungal taxa 
using ITS sequencing. (B) overlap of bacterial taxa using 16S sequencing.  
 
Because of this large separation of 2021 from the other years, we performed our 
periodicity analysis twice; once with the data for all four years (Figures 3 and 4; 
Supplemental Figures 1-2), and again excluding the data from 2021 (Supplemental 
Figures 3-6). Based on our analysis vermentino had 14 fungal taxa and 31 bacterial taxa 
that significantly varied between all four years (p-value < 0.01 from RAIN analysis), 
while syrah had 19 fungal taxa and 36 bacterial taxa that significantly varied between all 
four years. Significant taxa decreased to three fungi and seven bacteria for vermentino 
and three fungi and eight bacteria for syrah after restricting the results to taxa present in 
>75% of samples. Restricting the analysis to 2018-2020 removed two fungal taxa and ten 
bacterial taxa from vermentino, while it removed nine fungal taxa and removed fifteen 
bacterial taxon from syrah.  
 
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Figure 3. Fungal (A) and bacterial taxa (B) present in >75% of samples that significantly 
vary (p < 0.05, FDR < 0.05) between sampling years according to rhythmicity analysis 
incorporating non-parametric methods (RAIN) in blocks of Vitis vinifera var. syrah grape 
vines. Visualized using BiomeHorizon package. Band color and thickness indicates 
percent changes of relative abundances. Negative values are mirrored above the x-axis.  
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Figure 4. Fungal (A) and bacterial taxa (B) present in >75% of samples that significantly 
vary (p < 0.05, FDR < 0.05) between sampling years according to rhythmicity analysis 
incorporating non-parametric methods (RAIN) in blocks of Vitis vinifera var. vermentino 
grape vines. Band color and thickness indicates percent change of relative abundances. 
Negative values are mirrored above the x-axis. 
 
4. Discussion 
We found that neither the varietal nor the block number itself had a significant 
effect upon the community composition, indicating that intra-vineyard variation of soil 
microbial communities can be negligible. Other studies have found intra-vineyard 
variation is primarily due to sampling depth rather than block location—the exception 
being blocks with soils from different origins (Liang et al., 2019). This seems to indicate 
that when sampling soil for inter-vineyard studies it is not necessary to sample every 
block—unless there are considerable differences in edaphic characteristics between 
blocks.  
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We found that the RAIN package and analysis were easy to implement with our 
dataset. RAIN has quickly become a widely used method for analyzing longitudinal and 
repeated measurement data within the microbiological community to analyze community 
composition, genetic expression, and protein production (Coenen et al., 2020). RAIN has 
been implemented in human gut microbiome studies to test for periodic changes in 
community composition (Reitmeier et al., 2020), in marine and freshwater studies to 
examine diel effects on phototrophic and heterotrophic microbes and their gene 
expression (Linz et al., 2019; Coesel et al., 2021), and in studies focusing on the 
interactions of microbes with insects (Trinh et al., 2021). These studies all use similar 
sequencing-based datasets across longitudinal or repeated sampling schemes with varying 
windows—some 24 hours, some days, some weeks. Based on our use, we think that 
RAIN is a usable alternative to more traditional parametric methods; however, it is far 
more computationally expensive. Users may find it difficult to run without access to 
high-end hardware or access to a high-performance computing cluster. The major 
concern for our dataset, and other microbial datasets, is that RAIN was designed to be 
used for datasets with moderate amounts of time points (10-100) whereas our dataset 
contained fewer time points (Thaben and Westermark, 2014). Further experiments and 
comparative analyses are necessary to understand if RAIN is suitable for such low 
numbers of time points.  
While there is clear variation in microbial communities between years, it is 
difficult to determine if those differences are due to management or climatic factors. The 
2019 and 2021 samples display similar shifts in abundance for many taxa relative to 2018 
and 2020. While some of these changes were likely caused by adopting new management 
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practices, the more dramatic shifts can also be explained by the fact that Troon 
experienced severe to extreme droughts during the 2019 and 2021 growing seasons 
(Supplemental Figure 7). However, the 2020 growing season also experienced drought 
and does not show these patterns. Some taxa show a steady increase or decrease in 
abundance over the four years without massive shifts during drought and are better 
candidates for further exploration. These taxa include the fungi Seimatosporium vitis, 
Cystofilobasidium macerans, and the bacterial genera Bacillus, Blastococcus, 
Mesorhizobium, Methylobacterium, Nocardiodes, Paenibacillus, Solirubrobacter, and 
Sphingomonas. The fungus S. vitis increased in later years and is related to several 
grapevine trunk diseases (Camele and Mang, 2019) which may be of concern as fungal 
pathogens can increase in concentration following the cessation of fungicide use. The 
other fungal species that decreased in 2020 and 2021, C. macerans, may play a role in 
releasing pectinases during pressing the must and early fermentation, which assists 
fermentation and clarification (Bezus et al., 2022).  
We found several bacterial and fungal taxa only present in the 2020 and 2021 
samples that are related to the functions of nutrient cycling, pathogenic infection, and 
fermentation; all of which influence soil “health” and the production of wine. The 
nitrogen fixing bacterial genus of Rhizobium (van Rhyn and Vanderleyden, 1995) and 
nitrifying genus of Nitrospira (Daims et al., 2015) were only found in 2020 and 2021 
samples. Further, two species of the mycorrhizal genus Glomus (Kamayestani et al., 
2019) were only found in 2021 samples. These groups together indicate the potential for 
higher N fixation and uptake in the later soils. Several species of yeast related to 
fermentation and organoleptic compound production were only present in 2020 and 2021 
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samples, including five Candida spp., two Metschnikowia spp., two Mrakia spp., and 
Schizosaccharomyces pombe. These four genera are known to release flavor precursor 
compounds from V. vinifera during fermentation and synthesize de novo flavor 
compounds (Jolly et al., 2014; Parker et al., 2018). The presence of these species 
indicates that the flavor and aroma profile of wine produced in 2021 from Troon may 
contain a higher diversity of organoleptic compounds. Several pathogenic fungi from the 
genera Alternaria, Botrytis, Colletotrichum, and the taxon Armillaria mellea were also 
only found within the latter two years of sampling—potentially increasing the risk of 
crop loss. Alternaria, Botrytis and Colletotrichum can cause rot of leaves and grape 
bunches (Steel et al., 2011; Peng et al., 2012; Stranska et al., 2022), while A. mellea 
attacks the roots of V. vinifera vines (Devkota and Hammerschmidt 2020). The invasion 
of novel pathogens into the vineyard is most likely due to the decreased use of fungicides 
and is of concern for future grape production. However, the dynamics of pathogenic 
infection are difficult to predict solely on the presence of pathogenic taxa due to the 
presence of taxa that are known to lower rates of infection. Fungal saprotrophs, 
mycorrhizal fungi, and rhizosphere associated bacteria have all been shown to reduce 
infection rates; all of which are common within our dataset (Reynolds et al., 2003; 
Richards et al., 2020). Further study is required before making strong predictions about 
pathogen dynamics following a shift in management intensity within vineyards.  
5. Conclusions 
While severe drought conditions in 2019 and 2021 made it difficult to find other 
trends, our data does reveal that several taxa associated with nutrient cycling and 
organoleptic compound production increased from 2018 to 2021. These results indicate 
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that the ability of the microbial community in soil to cycle nitrogen is greater after 
shifting to low intensity practices. Further, the soil was more hospitable to a broader 
range of microbes beneficial to fermentation and the production of organoleptic 
compounds based on the presence of several unique taxa in later samples. However, the 
greater abundance of pathogens in the soil may be a concern for grape production over 
time. In the future, incorporating additional vineyards that shift management practices is 
essential for increasing the generalizability of our results.  
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CHAPTER V 
CONCLUSION 
 In this dissertation I have demonstrated that the intensity of human management 
practices influences the composition and function of microbial communities within 
logged forests and Vitis vinifera vineyards. Further, we saw that those impacts can persist 
for decades after cessation but can be reversed using practices that promote the 
recruitment and growth of microbial taxa. Understanding the correlation between 
management intensity and microbial community functioning is essential for increasing 
the resilience of contemporary human managed ecosystems. Additionally, changes to 
microbial functions can be of direct interest to producers and consumers of agricultural 
products, as those functions can change the organoleptic characteristics. Results from 
other microbial studies reveal that management intensity has similar effects in other 
systems, such as grazed grasslands (Piton et al., 2019; Zhang et al., 2022) and wheat 
agroecosystems (Hartman et al., 2018). These studies imply that the relationship between 
management intensity and microbial community composition may be universal.  
In future studies a finer gradient of management intensity could reveal the trend 
of the relationship between intensity and changes to microbial community composition 
and/or function within these systems. Additionally, functions other than organoleptic 
compound production may not have the same correlation with management intensity, 
including nutrient cycling, water stress mitigation, and pathogen protection. 
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