Science, Irving K. Barber Faculty of (Okanagan)
Earth, Environmental and Geographic Sciences, Department of (Okanagan)
DSpace
UBCO
Browne, Emily
Master of Science - MSc
University of British Columbia
Upland forests typically are a sink for atmospheric methane (CH₄) because well-drained soils are inhabited by high-affinity methanotrophic bacteria. However, CH₄ commonly is generated in trees experiencing heartwood rot caused by fungal pathogens. To date, studies of CH₄ emissions associated with heartwood rot have focused primarily on deciduous trees. The objective of this study was to investigate CH₄ fluxes in upland conifer forests situated in the Montane Spruce (MS), Engelmann Spruce—Subalpine Fir (ESSF), and Coastal Western Hemlock (CWH) Biogeoclimatic zones of southern British Columbia. The study aimed to determine whether CH₄ fluxes in upland forests differ between sites, tree species, trees bearing conks, and other bark anomalies. Trees were surveyed in 10 x 10 m quadrats (n=37) for species, location, diameter at breast height, and evidence of stem fungal infection. A portable gas analyzer and sealed chambers were used to measure CH₄ flux from tree stems having a diameter >20 cm, conks resulting from fungal pathogens, stem anomalies, and the soil surface. The flux rate was determined from the change with time of the methane concentration in the chamber headspace. Methane flux was measured from 126 trees and 21 soil plots. Tree species (Picea engelmanii, Abies lasicarpa, Thuja plicata, Tsuga heterophylla, Abies amabilis, Populus balsamifera) from all three sites exhibited both emission and uptake of CH₄ with no significant differences between species. Tree stem fluxes were small (MS mean = 0.006 ± 0.059 µmol m-² h-¹, ESSF mean = -0.034 ± 0.057 µmol m-² h-¹, CWH mean = -7.0x10-⁵ ± 0.046 µmol m-² h-¹) relative to rates of soil uptake of atmospheric CH₄ (mean = -2.475 ± 0.801 µmol m-² h-). Stem fluxes from conifers at the ESSF site differed from those at the MS and CWH sites likely due to a greater prevalence of conks and fungal infection at ESSF. Bark anomalies and conks did not exhibit higher rates of CH₄ flux in contrast to the elevated emissions from similar features on deciduous tree species reported in other studies. Soil was a consistent and larger sink for atmospheric CH₄ than tree stems at all three sites and consequently, the mature forest sites collectively were net sinks for CH₄.
Methane fluxes in mature upland conifer forests in three biogeoclimatic zones in southern British Columbia by Emily Browne B.Sc., University of Windsor, 2021 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE COLLEGE OF GRADUATE STUDIES (Earth and Environmental Sciences) THE UNIVERSITY OF BRITISH COLUMBIA (Okanagan) April 2024 © Emily Browne, 2024 ii The following individuals certify that they have read, and recommend to the College of Graduate Studies for acceptance, a thesis entitled: Methane fluxes in mature upland conifer forests in three biogeoclimatic zones in southern British Columbia submitted by Emily Browne in partial fulfillment of the requirements of the degree of Master of Science. Dr. Edward Hornibrook, Irving K. Barber Faculty of Science Supervisor Dr. Adam Wei, Irving K. Barber Faculty of Science Supervisory Committee Member Dr. Alyse Kiesser, School of Engineering Supervisory Committee Member Dr. Sean Smuckler, University of British Columbia-Vancouver University Examiner iii Abstract Upland forests typically are a sink for atmospheric methane (CH4) because well-drained soils are inhabited by high-affinity methanotrophic bacteria. However, CH4 commonly is generated in trees experiencing heartwood rot caused by fungal pathogens. To date, studies of CH4 emissions associated with heartwood rot have focused primarily on deciduous trees. The objective of this study was to investigate CH4 fluxes in upland conifer forests situated in the Montane Spruce (MS), Engelmann Spruce—Subalpine Fir (ESSF), and Coastal Western Hemlock (CWH) Biogeoclimatic zones of southern British Columbia. The study aimed to determine whether CH4 fluxes in upland forests differ between sites, tree species, trees bearing conks, and other bark anomalies. Trees were surveyed in 10 x 10 m quadrats (n=37) for species, location, diameter at breast height, and evidence of stem fungal infection. A portable gas analyzer and sealed chambers were used to measure CH4 flux from tree stems having a diameter >20 cm, conks resulting from fungal pathogens, stem anomalies, and the soil surface. The flux rate was determined from the change with time of the methane concentration in the chamber headspace. Methane flux was measured from 126 trees and 21 soil plots. Tree species (Picea engelmanii, Abies lasicarpa, Thuja plicata, Tsuga heterophylla, Abies amabilis, Populus balsamifera) from all three sites exhibited both emission and uptake of CH4 with no significant differences between species. Tree stem fluxes were small (MS mean = 0.006 ± 0.059 µmol m-2 h-1, ESSF mean = -0.034 ± 0.057 µmol m-2 h-1, CWH mean = -7.0x10-5 ± 0.046 µmol m-2 h-1) relative to rates of soil uptake of atmospheric CH4 (mean = -2.475 ± 0.801 µmol m-2 h-). Stem fluxes from conifers at the ESSF site differed from those at the MS and CWH sites likely due to a greater prevalence of conks and fungal infection at ESSF. Bark anomalies and conks did not exhibit higher rates of CH4 flux in contrast to the elevated emissions from similar features on deciduous tree species iv reported in other studies. Soil was a consistent and larger sink for atmospheric CH4 than tree stems at all three sites and consequently, the mature forest sites collectively were net sinks for CH4. v Lay Summary Methane is a potent greenhouse gas and a key contributor to global warming. Deciduous trees are known to emit methane through stem pores when infected with heartwood rot or by transporting methane from water-saturated soil. In contrast, the potential for conifer trees to emit or transport methane has not been studied extensively to date. This project investigated the capacity for conifer trees to emit methane from bark features such as wounds or fungal infections under different environmental conditions. The stems of coniferous tree species at three sites in British Columbia were determined to release small amounts of methane to the atmosphere and to consume methane from the air. Bark features attributed to fungal infection did not exhibit higher rates of methane release. Soil in the forest sites consistently consumed more methane than conifer trees emitted and consequently, all forest sites were a net sink for atmospheric methane. vi Table of Contents Abstract ......................................................................................................................................... iii Lay Summary ................................................................................................................................ v Table of Contents ......................................................................................................................... vi List of Tables ................................................................................................................................ xi List of Figures ............................................................................................................................. xiii List of Abbreviations ................................................................................................................. xvi Acknowledgments ..................................................................................................................... xvii Dedication ................................................................................................................................. xviii 1.0 Introduction ..............................................................................................................................1 1.1 Overview and Rational ..................................................................................................... 1 1.2 Global CH4 Budget............................................................................................................ 3 1.3 Microbial Aspects ............................................................................................................. 5 1.4.1 Methane in soil: Wetlands ......................................................................................... 6 1.4.2 Methane In Soil: Well-drained uplands ..................................................................... 7 1.5 Methane In Trees .............................................................................................................. 9 1.5.1 Methanogenesis: Microbial activity, heartwood rot, and fungal infection ................ 9 1.5.2 Methanotrophy: In the heartwood and on the bark surface ..................................... 10 1.5.3 Wetland Forests: Biotic and abiotic controls on tree-mediated CH4 emissions ...... 11 1.5.4 Wetland Forests: Tree structures/adaptations .......................................................... 12 1.5.5 Upland Forests: Tree stem CH4 fluxes in upland forests ......................................... 14 1.5.6 Upland forests: Tree stem-to-atmosphere interactions ............................................ 19 vii 1.5.7 Upland forests: Conifer tree species ........................................................................ 20 1.6 Tree Chambers ................................................................................................................ 23 1.7 Research Statement ......................................................................................................... 25 1.7.1 Research Objectives ................................................................................................. 26 2.0 Site Description ......................................................................................................................28 2.1 Site Selection .................................................................................................................. 28 2.2 Montane Spruce (MS) ..................................................................................................... 29 2.3 Engelmann Spruce –Subalpine Fir.................................................................................. 31 2.4 Coastal Western Hemlock............................................................................................... 32 3.0 Methods .................................................................................................................................. 34 3.1 Quadrat Sampling ........................................................................................................... 34 3.1.1 Whole Tree Survey .................................................................................................. 36 3.2 Environmental Parameters .............................................................................................. 37 3.3 Gas Flux Measurements .................................................................................................. 37 3.3.1 Tree Chambers ......................................................................................................... 38 3.3.2 Soil Chambers .......................................................................................................... 38 3.4 Coring ............................................................................................................................. 39 3.5 Data Processing ............................................................................................................... 40 3.5.1 Minimum Detectable Flux ....................................................................................... 40 3.5.2 Whole Tree Interpolation ......................................................................................... 41 3.5.3 Statistical Analysis ................................................................................................... 42 3.5.4 Scaling Tree Fluxes.................................................................................................. 42 3.5.5 Scaling Soil Fluxes .................................................................................................. 43 viii 3.5.6 Scaling to the Study Area ........................................................................................ 43 4.0 Results and Discussion on Methane Fluxes From Upland Conifer Trees ........................45 4.1 Background ..................................................................................................................... 45 4.2 Tree Stem CH4 Fluxes By Site and Species.................................................................... 46 4.2.1 Montane Spruce (MS) .............................................................................................. 46 4.2.2 Engelmann Spruce – Subalpine Fir (ESSF) ............................................................. 47 4.2.3 Coastal Western Hemlock (CWH) ........................................................................... 47 4.2.3 Methane Fluxes Between Sites. ............................................................................... 50 4.2.4 Methane Flux Differences Between Tree Species. .................................................. 51 4.2.5 Methane Flux Relationships with Environmental Variables ................................... 52 4.3 Global Comparison of Tree Stem CH4 Fluxes ................................................................ 53 4.4 Environmental Variables Driving Tree Stem CH4 Fluxes .............................................. 57 4.4.1 Soil moisture ............................................................................................................ 57 4.4.2 Tree Stem Moisture .................................................................................................. 58 4.4.3 Soil and Air Temperature......................................................................................... 58 4.5 Differences in Angiosperm and Gymnosperm Species .................................................. 59 4.6 Coniferous Species-Specific Differences ....................................................................... 60 4.7 Summary ......................................................................................................................... 61 5.0 Results and Discussion on Conks, Bark Anomalies, and Heartwood Rot in Upland Conifer Tree Stems ......................................................................................................................63 5.1 Background ..................................................................................................................... 63 5.2 Results ............................................................................................................................. 64 5.2.1 Heartwood Rot ......................................................................................................... 64 ix 5.2.2 Presence of Conks on tree Stems ............................................................................. 67 5.2.3 Whole Tree Survey .................................................................................................. 68 5.2.4 Tree Condition ......................................................................................................... 69 5.3 Methanogenesis in the Heartwood of Tree Stems .......................................................... 70 5.4 Microbial Communities on the Bark Surface of Tree Stems .......................................... 72 5.5 Spatial Heterogeneity of CH4 Fluxes on the Bark Surface ............................................. 73 5.6 Conks: Connection between heartwood, bark surface, and atmosphere ......................... 74 5.7 Summary ......................................................................................................................... 76 6.0 Results and Discussion on the Net Methane Exchange in Upland Conifer Forests .........78 6.1 Background ..................................................................................................................... 78 6.2 Soil CH4 Fluxes............................................................................................................... 79 6.3 Soil Temperature and Soil Moisture ............................................................................... 80 6.4 Methane Flux Comparison and Scaling .......................................................................... 81 6.5 Rates of Soil Uptake of Atmospheric CH4 ..................................................................... 85 6.6 Scaling Methane for the Whole Ecosystem .................................................................... 87 6.7 Spatial Interpolation and Net Ecosystem CH4 Flux ........................................................ 87 6.8 Summary ......................................................................................................................... 90 7.0 Conclusions ............................................................................................................................ 91 7.1 Conclusions ..................................................................................................................... 91 7.2 Limitations ...................................................................................................................... 93 Bibliography .................................................................................................................................95 Appendices ................................................................................................................................. 107 Appendix A: ....................................................................................................................... 108 x Appendix B: ........................................................................................................................ 116 Appendix C: ........................................................................................................................ 129 Appendix D: ........................................................................................................................ 133 Appendix E: ........................................................................................................................ 134 Appendix F: ........................................................................................................................ 138 xi List of Tables Table 1.1 Atmospheric methane sinks and source estimated using a bottom-up approach. Adapted from Saunois et al. (2020). All units are in Tg CH4 yr-1……………………...………... 4 Table 2.1 Summary of site characteristics…………………………………………...………… 33 Table 2.2 Tree species present in study quadrats………………………………….…………… 33 Table 3.1 Quadrat survey for sites………………………………………...…………………… 36 Table 4.1 Summary of tree stem CH4 flux measurements. Values are reported in units of µmol CH4 m-2 h1………………………………………………………………………………... 49 Table 4.2 Summary of CH4 uptake and emissions for trees and individual stem measurements at each study site ('no flux’ = flux below MDF). Totals are presented in the number and the percentage of the total for that site…………………………………………………………….... 50 Table 4.3 Comparison between the tree stem methane fluxes from this study and various other published studies………………………………………………………………………………... 56 Table 5.1 Trees from the quadrat surveys showing evidence of heartwood rot…………………65 Table 5.2 Tree CH4 flux for CWH Q2 trees before and after tree cores were collected. All units are in µmol m-2 h-1………………………………………………………………………... 66 Table 5.3 Summary of methane fluxes measured from Picea engelmannii infected with conks at the EESF site. All units are in µmol m-2 h-1…………………………………………………….. 67 Table 6.1 Summary of soil CH4 flux measurements. All units are in µmol m-2 h-1……………. 49 Table 6.2 Scaled tree and soil methane fluxes for each quadrat and site. The net methane flux per quadrat is the soil flux minus the tree flux. All units are in µmol · h-1…………………….. 84 Table 6.3 Comparison between the soil methane fluxes measured in this study and reported xii by Brachmann et al. (2020) for other sites in British Columbia. Note the change in flux units from other tables and figures that were made to facilitate the comparison…………………….. 86 xiii List of Figure Figure 2.1 Site map of British Columbia with the approximate location of each quadrat survey…………………………………………………………………………………………... 28 Figure 2.2 Quadrat maps for the three study sites (a) Montane Spruce, (b) Engelmann Spruce – Subalpine Fir, (c) Coastal Western Hemlock and a photograph of typical conditions at each site. The quadrat maps are oriented with the access road parallel to the bottom edge. Each individual sampling quadrat is 10 x 10 m with the total study area at each site being ~100 x 100 m………………………………………………………………... 29 Figure 3.1 Quadrat map of Q3 in the CWH site. Maps like the one shown were prepared for every quadrat at the three study sites. The 37 quadrat maps in total are presented in Appendix A. ……………………………………………………………………………………. 35 Figure 3.2 The diagram on the right shows the area of the tree that was sampled. The red rectangle would wrap around the diameter of the tree. The diagram on the left shows the location of all 20 flux measurements taken around the stem of the tree. ……………………… 37 Figures 3.3 Flux chambers used to capture trace gas emissions from tree stems and ground surfaces: (a) tree stem chamber attached to a conifer sealed with pottery clay, and (b) soil chamber sealed to the ground with a knife edge insert used to trap soil gas fluxes……………. 39 Figure 4.1. Box plot showing CH4 fluxes for tree stem measurements at each site. The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data and the dots represent data points. ……………………………………………………………….………………………….. 51 xiv Figure 4.2 Boxplot of the CH4 fluxes for each tree species presented by site (blue = CWH, yellow = ESSF, grey = MS). The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data. ……………………………………………………………….………………. 52 Figure 5.1 Boxplot of methane fluxes for tree stems that showed, and did not show, evidence of heartwood rot separated by site (blue = CWH, yellow = ESSF, grey = MS). The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data………………… 65 Figure 5.2 Tree methane fluxes from the whole tree survey interpolated on the tree surface. White dots indicate bark anomalies where branches previously existed. Black dots indicate The location of cracks and wounds in the bark. Warmer colors indicate areas of methane emission and cooler colors indicate areas of methane uptake. The axial bar shows how methane flux rates change with height. ………………………………………………………… 69 Figure 6.1 Soil CH4 uptake and soil temperature for each quadrat at the (a) MS, (b) ESSF, and (c) CWH sites. Methane uptake (µmol m-2 h-1) rates are shown in blue and soil temperature (°C) is plotted in orange. The two data types are plotted on a common y-axis, which was possibly by representing soil uptake rates for methane as positive fluxes into the soil. Note that the y-axis scale for MS data differs from ESSF and CWH to accommodate the higher soil temperatures in July. The box shows the interquartile range and the line within the box is the mean values. Lines extending from the box represent the first and last quartiles of data. ……………………………………………………………… 81 xv Figure 6.2 Boxplot showing methane fluxes for trees and soils at the three study sites (blue = CWH, yellow = ESSF, grey = MS). The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data. ……………………………………………………………... 82 Figure 6.3 Panels a to c show the topography at the three study sites with the black square outlining the 100 x 100 m area that contained the study quadrats. Contour maps to the right of each topographic map show the spatial interpolation of the scaled methane fluxes for each site, which is the net sum of soil methane uptake and tree stem fluxes. The contour maps were created using an inverse distance weighted interpolation to estimate how methane uptake varied throughout the landscape. Darker areas represent the lowest methane uptake rates and lighter areas have the highest methane uptake rates. Each panel has an individual graded scale for methane uptake rates. The direction of the north area varies because the topographic and interpolated maps are oriented with the site access road on the south side…... 89 xvi List of Abbreviations CWH: Coastal Western Hemlock DBH: Diameter at breast height ESSF: Engelmann Spruce – Subalpine Fir MDF: Minimum detectable flux MS: Montane Spruce OD: Outer diameter UGGA: Ultra-portable greenhouse gas analyzer xvii Acknowledgements I would like to thank Ishy Ghataura for her dedicated work in rough conditions during the 2022 field season. I would also like to thank Marcus Savery for his guidance and assistance in the field and with maintaining equipment in the 2022 field season. I extent my sincerest gratitude to Riley Donaldson, and my dad, Tony Browne, for their willingness to volunteer their time for my 2023 field season and for all their support along the way. This work would not be possible without the hard work and support of my passionate field team. I would also like to thank Emily Moore for her constant support and guidance throughout my thesis. Lastly, I would like to acknowledge and thank my supervisor and committee members, Dr. Edward Hornibrook, Dr. Adam Wei, and Dr. Alyse Kiesser, for their expertise and guidance throughout this research. xviii Dedication I dedicate this work to my loving family, for providing me with endless guidance, love, and support. To my parents, Liza Browne and Tony Browne, for believing in me and for all they have done to make this possible. And to my sister, Sara Browne, for paving the way and showing me that hard work and dedication pay off. 1 1.0 Introduction 1.1 Overview and Rational Methane (CH4) is a greenhouse gas that is 28 times more potent than carbon dioxide (CO2) and is important to understand to help mitigate climate change as part of the global CH4 budget (Jeffrey et al., 2021; Saunois et al., 2020). While CH4 is produced in many natural environments, wetlands are thought to be the largest source of natural CH4 emissions (Hornibrook & Aravena, 2009), accounting for 20% of global CH4 flux (Hornibrook & Aravena, 2009; Saunois et al., 2020). Methane is produced by microbial activity during the decomposition of organic matter under anaerobic conditions (Schoell, 1988). Methanogens are archaea that produce CH4 in anoxic soil which can be released by diffusion, ebullition, or plant-mediated transport (Pangala et al., 2014). Trees and other wetland plants enhance CH4 emissions by serving as a conduit between anoxic soil, where CH4 is produced, and the atmosphere by enabling CH4 to bypass methane-consuming (methanotrophic) bacteria at the air-water interface (Megonigal & Guenther, 2008a; Vargas & Barba, 2019). Methane that is produced in the soil and transported through the tree stem is emitted through the aerenchyma and lenticels that serve as gas exchange for tree respiration and photosynthesis. As CH4 is produced in the soil, it travels upward through the aerenchyma and is released through the lenticels at decreasing concentrations with stem height (Barba et al., 2019; Jeffrey et al., 2021; Pangala et al., 2013). Upland forests are not as well-studied as wetlands and are not currently accounted for in the global CH4 budget. Trees in upland forests are rooted in well-drained aerobic soils and can only access anoxic soil with their deep tap roots. The global CH4 budget reports upland forest soils as 2 a net CH4 sink (Saunois et al., 2020) because of the strong methanotrophy occurring in the soil. However, these systems are more complex and have been reported to switch from a sink to a source of atmospheric CH4 periodically due to soil water content, plant physiology, soil chemistry, and microbial communities. (Megonigal & Guenther, 2008). This complexity is poorly characterized in upland forests and is likely due to CH4 emissions in periodically moist soil (Wang et al., 2017), and could be potentially further complicated by trees potential to emit methane, partially offsetting CH4 uptake by soil. Methane in upland trees is produced and concentrated in the tree stem through heartwood rot which is the product of wood degradation through a chain of processes involving fungi, bacteria, and archaea (Barba et al., 2019; Covey & Megonigal, 2019; Hietala et al., 2015; Jeffrey et al., 2021; Mukhin & Voronin, 2007). This process begins with fungal infection of a tree, which is common and widespread amongst upland forests (K. R. Covey et al., 2012; Wang et al., 2017). Methane emissions from upland trees have recently been reported (Barba et al., 2019, 2021; Covey et al., 2012; Gorgolewski et al., 2022; Wang et al., 2016, 2017), however, the mechanisms associated with the emission of CH4 through parts of the tree are less clear (Megonigal & Guenther, 2008b; Wang et al., 2017). Most of the studies that have been conducted in upland forests are on deciduous trees, and the potential for coniferous trees to emit internally produced CH4 is unclear (Machacova et al., 2020; Wang et al., 2021a). Canada has approximately 362 million hectares of forest, with 68% consisting of coniferous species (Canada, 2015). The potential for conifer species to emit CH4 is uncertain and may currently be a significant unquantified source of CH4 in the global CH4 budget. There is also uncertainty about how this potential source might respond to future changes in global climate that may accelerate rates of microbial activity or the spread of fungal pathogens. 3 1.2 Global CH4 Budget Methane gas is a greenhouse gas that has a high global warming potential due to its ability to absorb Earth’s emitted infrared radiation (Saunois et al., 2020; Stavert et al., 2022). Over a 100-year time scale, CH4 has a global warming potential that is 28-36 times more potent than carbon dioxide (CO2; Stavert et al., 2022). Currently, sources of atmospheric CH4, from both natural and anthropogenic sources, are greater than sinks of atmospheric CH4, creating an imbalance where concentrations are increasing year to year. Concentrations of earth atmospheric CH4 have been recorded for centuries through air extracted from ice cores (Lan et al., 2021) and there have been natural fluctuations in atmospheric CH4 concentrations. However, since the industrial revolution, atmospheric concentrations have been rising due to anthropogenic sources of CH4 which include agriculture and waste, fossil fuels, and biomass and biofuel burning (Table 1.1; Badr et al., 1992; Lan et al., 2021; Stavert et al., 2022). As of 2018, the atmospheric mixing ratio of CH4 reached 1857 ppb, which is approximately 2.6 times greater than the pre-industrial concentration (Saunois et al., 2020). This increase in atmospheric CH4 concentration year-to-year is contributing to global climate change and the potential for devastating impacts on ecosystems worldwide, in particular, at high northern latitudes. 4 Table 1.1 Atmospheric methane sinks and source estimated using a bottom-up approach. Adapted from Saunois et al. (2020). All units are in Tg CH4 yr-1. 2000 - 2009 2008 - 2017 Mean Range Mean Range Wetlands 147 102-179 149 102-182 Other Natural Sources 222 143-306 222 143-306 Total Natural Sources 369 245-485 371 245-488 Agriculture and waste 192 178-206 206 191-223 Fossil Fuels 110 94-129 128 113-154 Biomass and Biofuel burning 31 26-46 30 26-40 Total Anthropogenic Sources 334 321-358 366 349-393 Total Chemical Loss 595 489-749 595 489-749 Soil Uptake 30 11-49 30 11-49 Total Sink 625 500-798 625 500-798 Sources 703 566-842 737 594-881 Sinks 625 500-798 625 500-798 Imbalance 78 112 Natural sources of CH4 exist and contribute to the atmospheric CH4 concentrations. Natural sources of atmospheric CH4 include vegetated wetlands and inland water systems, land geological sources, wild animals, termites, thawing terrestrial and marine permafrost, and oceanic sources (Table 1.1; Saunois et al., 2016, 2020). Natural wetlands are the greatest natural source of atmospheric CH4 contributing a mean of 149 Tg CH4 yr-1 (range = 102 – 182 Tg CH4 yr-1) from a 2008-2017 global CH4 budget report (Saunois et al., 2020). The main source of CH4 in natural wetlands is tree-mediated emissions of CH4 produced in the soil (Covey & Megonigal, 2019). Most natural sources of CH4 are through microbial processes; however, some sources are through plant material and combustion which are considered chemical production of CH4 (Conrad, 2009). The natural production of CH4 via microbial metabolism is discussed in more detail in the sections below. 5 Methane sinks counteract some of the CH4 sources by consuming/oxidizing atmospheric methane. The main CH4 sinks include chemical losses from the troposphere and stratosphere and soil uptake. Methane uptake by soil is a microbial process that is discussed in more detail in the sections that follow. In recent reports, sinks of atmospheric CH4 are lower than those sources of atmospheric CH4, creating an imbalance, where CH4 concentrations are growing year to year. According to the 2008-2017 report, the imbalance of CH4 sources and sinks resulted in an atmospheric growth of 16.8 Tg CH4 yr -1 using a top-down approach (Saunois et al., 2020). 1.3 Microbial Aspects In environments where sources of atmospheric CH4 are microbially driven, CH4 is produced through a series of microbial metabolic activities decomposing organic matter, predominantly in the absence of oxygen (Buan, 2018; Conrad, 2007; Murrell & Jetten, 2009). Methanogens are the terminal microorganisms of the series where they occupy anaerobic niches in natural environments and anthropogenic landscapes (Murrell & Jetten, 2009). The process of transforming organic matter to CH4 gas is achieved by a complex microbial community in which methanogenic archaea completes the final stage once other microorganisms higher in the chain of decay break down complex substrates into a consumable form. At the terminal stage, archaea metabolize H2, CO2, acetate, and formate producing CH4 to gain their energy (Blaut, 1994; Conrad, 2007; Murrell & Jetten, 2009). The microorganisms that supply substrates to the methanogenic archaea are usually fermenters and are predominantly bacteria, but protozoa and fungi can also contribute to the degradation process (Bryant, 1979; Conrad, 2007). Methane-oxidizing bacteria, also known as methanotrophs, counteract some of the CH4 production by consuming CH4 in aerobic environments (Conrad, 2007; Hanson & Hanson, 1996). These 6 bacteria break down CH4 using activated oxygen to produce methanol, which is the first stage of biological CH4 oxidation and involves unique enzymes. 1.4.1 Methane in soil: Wetlands In wetland forests, the primary source of CH4 production is in anaerobic wetland soils, then CH4 gets transported through the tree roots and out the tree stems, into the atmosphere (Jeffrey et al., 2020). Many studies have reported that spatial and temporal variations in CH4 emissions are mainly from soil temperature and water table depth (Jeffrey et al., 2020; Pangala et al., 2013, 2015; Sjögersten et al., 2020; Terazawa et al., 2015, 2021). The water table depth will change the soil conditions and alter the rates of oxidation and production of CH4 while changing between aerobic and anaerobic conditions (Terazawa et al., 2015). While the water table is low, aerobic conditions are created which are favored by methanotrophic bacteria. These methanotrophs act as a sink for CH4 (Rusch and Rennenberg, 1998), reducing the concentrations of CH4 transported up the tree stems. Alternatively, a higher water table creates anaerobic conditions in poorly drained wetland soils which are essential for microbial methanogenesis (Jeffrey, Maher, Tait, et al., 2021; Pangala et al., 2013; Sjögersten et al., 2020). Environmental variables can impact the concentrations of CH4 produced in soil. Soil temperature influences CH4 production and diffusion since microbial metabolic activities have an optimal temperature in which they occur. The effect of soil temperature in result alters the tree stem emissions and was found to be one of the main contributing factors in the emission fluxes throughout many studies (Pangala et al., 2015; Terazawa et al., 2015; Terazawa et al., 2021). Additionally, soil pore water was used to investigate the variability in CH4 emissions through trees in some studies since it would alter the oxygen concentrations in soil (Pangala et al., 2015; Terazawa et al., 2015; Terazawa et al., 2021). Concentrations of CH4 in soil pore water were 7 found to be a factor in determining methane fluxes from tree stems in a temperate forest for Alnus glutinosa and Betula pubescens tree species (Pangala et al., 2015). Tropical wetlands which have nutrient-rich soil from organic matter have high concentrations of CH4 in shallow pore water (Pangala et al., 2013) which contributes to the tree stem emissions in various tropical tree species. Pitz et al. (2018) found that soil moisture did not significantly contribute to the magnitude of CH4 emissions for various temperate tree species. This study investigated the CH4 emissions from tree stems along a groundwater gradient from wetland ecosystems to upland forest ecosystems. The results revealed that soil moisture reduced gas diffusion rates through soil pore spaces which favored diffusion of gases through woody tissues (Pitz et al., 2018). Therefore, the concentrations of methane produced in soil are driven by environmental variables which include soil temperature and soil pore water. 1.4.2 Methane In Soil: Well-drained uplands Upland forests have well-drained soils where methanotrophs reside and oxidize atmospheric CH4 for energy (Brachmann et al., 2020; Feng et al., 2020; Le Mer & Roger, 2001). They are the main sinks for atmospheric CH4 (Dlugokencky et al., 2011), but the uptake rates have decreased by 77% from 1981 to 2010 due to increased precipitation (Ni & Groffman, 2018). The controls on CH4 uptake in soils include the diffusion rates of gases in soil and the biological oxidation rate (Curry, 2007; Feng et al., 2020). The uptake rates of CH4 are also impacted by environmental variables such as soil temperature, soil moisture, ground surface temperature, vegetation cover, soil type, and porosity (Ball et al., 1997; Billings et al., 2000; Brachmann et al., 2020; Brockett et al., 2012; Christiansen et al., 2017; Gu et al., 2016; Knief et al., 2005; Mazzola et al., 2022; Megonigal & Guenther, 2008a; Menyailo, 2003; Smith et al., 2003). Altitude was reported to change the rates of methanotrophy and a study found that recently deglaciated alpine 8 environments do not experience CH4 uptake due to the undeveloped soil layer and lack of microbial communities (Brachmann et al., 2020). The abundance and diversity of microbial communities are impacted by soil types and vegetation species (Billings et al., 2000; Brockett et al., 2012; Knief et al., 2005). Brockett et al (2012), investigated the structure of microbial communities across various biogeoclimatic zones in western Canada and found that tree species influence the microbial communities due to nutrient availability, soil pH, and abundance of bacterial-fungal biomass ratios. Organic matter content, which is driven by tree species and biogeoclimatic zones, is closely related to microbial communities in soil as well (Brockett et al., 2012). Another study found that tree density impacts the soil moisture and therefore the microbial communities' oxidation rates of CH4 (Mazzola et al., 2022). These studies observed differences in the CH4 uptake capability of soil due to variability in environmental conditions, but overall, each study observed measurable rates of CH4 uptake. Upland soils are not ubiquitous CH4 sinks because production and oxidation of CH4 in soil can occur simultaneously, causing a reduced net sink, and in some cases net emissions of CH4 (Brewer et al., 2018; Christiansen et al., 2017; Gu et al., 2016; Lohila et al., 2016; Megonigal & Guenther, 2008a; von Fischer & Hedin, 2002). Methanogenic bacteria can survive long periods in dry and oxidized soil and can become active during periods of wet conditions (Angel et al., 2012). Soil moisture is the main control on upland soil methanogenesis since it influences the abundance and diversity of the microbial communities (Brockett et al., 2012; Megonigal & Guenther, 2008a), but temperature also impacts the emission rates (Gu et al., 2016). Anoxic microsites can also occur in upland soils where CH4 is being produced in small areas of anaerobic soils due to water-filled soil pore spaces (Brewer et al., 2018). The presence of these 9 microsites and periods of emissions do not counteract all of the methane uptake by upland soils, however, the ability to quantify the emission rates would allow for a clearer understanding of the extent of upland soils' atmospheric CH4 sinks. 1.5 Methane In Trees Trees that reside in wetland environments are exposed to different conditions in comparison to those that reside in well-drained soils. There are a variety of physiological and environmental factors that vary between wetland and upland trees that contribute to the potential for tree and soil CH4 fluxes. The metabolic activity of microbial communities drives most of these differences. Trees have only recently been investigated for their role in global methane cycles, with a focus on wetland trees, and more recently, trees in upland forests, and is discussed in detail in the following sections. 1.5.1 Methanogenesis: Microbial activity, heartwood rot, and fungal infection Methane in upland trees is produced and concentrated in the tree stem through heartwood rot which is the product of wood being consumed and the substrate being transformed through a chain of processes involving fungi, bacteria, and archaea (Barba et al., 2019; Beckmann et al., 2011; Covey & Megonigal, 2019; Hietala et al., 2015; Jeffrey et al., 2021; Mukhin & Voronin, 2007). This process begins with fungal infection of a tree, which is common and widespread amongst upland forests (Covey et al., 2012; Wang et al., 2017). Lenhart et al, (2012) conducted experiments with saprotrophic fungi on several substrates, including wood, and found that some fungi can produce CH4 independently of archaea, even under oxic conditions. Although, this study was unique and conducted under ideal laboratory conditions, it has not been confirmed by other studies, or in situ and may be a controversial observation. Regardless, fungi play a key role in breaking down lignin in trees which contributes to the process of methanogenesis. 10 There are a variety of saprotrophic fungi that can lead to heartwood rot in various tree species and at different times. Many fungal species break down wood materials after a tree is dead, however, some species of fungus can lead to heartwood rot in trees that are alive. One example of a heartwood rot fungus, Phellinus pini, is known as a white pocket rot fungi and causes red ring rot in conifers and infects a large variety of coniferous tree species (Lohmus, 2016). It is responsible for loss due to decay to approximately 10% of interior stands and is one of the most destructive heart rots in North America (Manning, 2007). Once a tree is infected it can take up to 100 years for the fruiting body to form on the outside of the tree (Manning, 2007), and the decay may spread 1.2 m above and 1.5 m below the fruiting body (Glaeser and Smith, 2010). The infection can also extend to the sapwood and develop into larger roots and can be hazardous due to fall risk in cases of severe infection (Glaeser and Smith, 2010; Manning, 2007). This knowledge of how fungal pathogens behave, and the extent of rot potential can give insight into concentrations of CH4 produced in the heartwood and its potential to be released into the atmosphere, seeing that this particular fungus is widespread. 1.5.2 Methanotrophy: In the heartwood and on the bark surface Oxidation of CH4 is known to occur on tree stems because the bark surface is an aerobic environment and a suitable habitat for methanotrophs (Murrell & Jetten, 2009). Additionally, cavities in tree stems resulting from fungal rot or surface damage that extends to the heartwood of a tree also can be sufficiently aerobic for methanotrophs to function. Thus, methanotrophy is a natural process in tree stem CH4 cycling in both the heartwood and the bark surface. Bark-dwelling methanotrophs in tropical wetland trees have been reported to reduce CH4 emissions by 36 ± 5 %, acting as a partial sink for CH4 transported from the soil through the tree stem (Jeffrey et al., 2021). The presence of methanotrophs on wetland trees is also evident through their 11 impact on 13C/12C ratios in emitted CH4 relative to the soil CH4 pool. Biological isotope fractionation through the preferential use of lighter 12CH4 during methanotrophy causes heavier 13C to be enriched in unoxidized CH4 emitted from the tree stem relative to the source of CH4 in the root zone (Jeffrey et al., 2021). Terazawa et al. (2021) also reported CH4 stem emissions depleted in 13C by approximately 2-3% (or 20 to 30 permil (parts per thousand)) relative to pore water CH4 as a result of partial oxidation that occurred during transport through the tree stems. 1.5.3 Wetland Forests: Biotic and abiotic controls on tree-mediated CH4 emissions In wetland environments, CH4 is emitted through a variety of transport pathways including diffusion, ebullition, and plant-mediated, with plant-mediated emissions being the dominant pathway across studies (Covey & Megonigal, 2019). Studies that aim to quantify the net emissions from wetland trees have been conducted on a variety of species in various climatic regions including tropical, and temperate wetland forests (Jeffrey et al., 2021; Pangala et al., 2013; Terazawa et al., 2015; Gauci et al., 2010). There are a range of environmental variables that have been investigated alongside wetland tree stem CH4 emissions. Since the CH4 that is emitted from wetland tree stems is produced in the soil, environmental conditions of the soil impact the concentrations of CH4 being produced, and ultimately emitted into the atmosphere. Jeffrey et al. (2020), demonstrated that the CH4 emissions from wetland tree are impacted by the water table by conducting a survey that spans from a wet to dry zone and found that the emissions form trees in the wetter soil (where the water table is at the surface) had much higher daily CH4 emissions compared to those trees which were situated in soils that where the water table was lower. Observations from Terazawa et al. (2015), and Pangala et al. (2014), revealed similar results to those in Jeffrey et al.’s (2020) study. Further, Terazawa et al. (2015), also considered the effects of soil temperature and 12 porewater and found a correlation with tree stem methane emissions, suggesting these variables could potentially be impacting factors. Pangala et al. (2017) used stable carbon isotopes in tropical wetlands to determine the CH4 emission from tree stems and found stable isotope values that were consistent with the isotopic composition of soil water and indicated that it is the dominant CH4 source. Variability in the tree stem CH4 emissions between trees can be explained by physiological variables. Pangala et al. (2014), found that lenticel density on the tree stems was positively correlated with tree stem emissions of CH4. Pangala et al. (2015), observed that wood density was also correlated with tree stem emissions. This can further be explained by the tree stem emission pattern with increasing vertical height from the soil. It is commonly reported that the CH4 emissions from tree stems decrease with increasing height from the ground (Gauci et al., 2010; Jeffrey et al., 2020, 2021; Pangala et al., 2013, 2014, 2015; Pitz et al., 2018; Rusch & Rennenberg, 1998; Sjögersten et al., 2020; Terazawa et al., 2007, 2015, 2021). In studies related to wetland tree stem emissions of CH4, stable isotopes have been used to trace CH4 from the soil up the tree stem, to determine if diffusion or oxidation processes are driving emissions (Pangala et al., 2014). Examining the fractionation of the carbon isotope at various stem heights in comparison to the soil allows for a better understanding of the mechanisms that are occurring as CH4 is being transported through the tree stem (Jeffrey et al., 2021). 1.5.4 Wetland Forests: Tree structures/adaptations Trees that exist in ecosystems that are periodically or permanently flooded have adapted anatomical structures to allow for root survival under low oxygen conditions. When soil is flooded and the pore spaces become water-filled, gas exchange between trees and the atmosphere is restricted limiting the entry of oxygen to support aerobic respiration (Kozlowski, 1997). To 13 overcome this, wetland trees have adaptations such as hypertrophied lenticels, enlarged aerenchyma tissue, and development of adventitious roots to increase aeration of their root systems (Kozlowski, 1997; Rusch and Rennenberg, 1998; Pangala et al., 2013). These adaptations are similar to those of herbaceous aquatic plants, which have been well-studied for their ability to increase emission of CH4 from anaerobic wetland soil (Armstrong et al., 1996; Chanton and Whiting, 1996, Pangala et al., 2013; Pangala et al., 2017). These adaptations that enable trees that are rooted in wet soil to obtain oxygen (Gauci et al., 2010) also enhance emission of soil-produced CH4 (Pangala et al., 2013; Gauci et al., 2012; Terazawa; 2021). The transport of CH4 through aerenchyma and lenticels allows for a greater proportion of CH4 to bypass low-affinity soil methanotrophs, which form an effective barrier to diffusive emission of CH4 from the ground surface (Pangala et al., 2013). The density of lenticels on tree stems has been shown to correlate with the strength of CH4 fluxes and lenticel presence, abundance, degree of opening, type, and development stage all impact differences in fluxes between tree species (Pangala et al., 2014). Pangala et al. (2014) also determined that the transport of CH4 by molecular diffusion and release of CH4 to the atmosphere through the lenticels was the main pathway for CH4 fluxes. Sjogersten et al. (2020) confirmed these findings and observed that lenticel density impacted CH4 flux. The pathways and adaptations required for respiration in trees exposed to inundation have allowed for soil-produced methane to be released into the atmosphere. Each tree species may have slight variations of these adaptations which will cause variations in CH4 emissions between species (Pitz et al., 2018). As trees develop, these features that allow gas exchange also exhibit variation and young trees have been found to emit CH4 by orders of magnitudes higher than mature trees of the same species, but the mechanism was poorly 14 understood (Pangala et al., 2015). A study by Pitz et al. (2018) reported contrasting results, as the trees with larger diameters (more mature) had higher CH4 emissions, likely due to the larger and deeper rooting depths in anoxic soils. These contrasting results highlight the uncertainty surrounding the mechanisms for tree stem methane fluxes. Wood density also can impact tree stem CH4 fluxes. Tree cores are used to characterize specific wood density and also reveal the presence of heartwood rot, which can result in the production of CH4 inside tree stems (Pangala et al., 2013). The variation in wood density between species was reported to be correlated with the variation in CH4 fluxes and revealed that species with lower wood density transport more CH4 through the increased pore spaces (Pangala et al., 2015). 1.5.5 Upland Forests: Tree stem CH4 fluxes in upland forests Trees in upland forests are rooted in well-drained soils and potentially access anoxic soil only with their deep tap roots. In global CH4 budgets upland forest soils as represented as a net CH4 sink (Saunois et al., 2020); however, these systems are more complex and have been reported to switch from a sink to a source of atmospheric CH4 periodically (Megonigal & Guenther, 2008a). This complexity is poorly understood in upland forests and the variables which lead to potential CH4 production and emissions are stem moisture (Wang et al., 2017), soil moisture (Pitz & Megonigal, 2017), soil temperature (Li et al., 2020a), tree species (Barba et al., 2019; Covey & Megonigal, 2019; Pitz & Megonigal, 2017), the microbial communities in the tree stem and the soil (Covey et al., 2012; Li et al., 2020a; Welch et al., 2019), and potentially other environmental and physical variables. Methane emissions in upland trees have been reported on multiple occasions (Barba et al., 2021; Covey et al., 2012; Gorgolewski et al., 2022, 2023; Li et al., 2020; Machacova et al., 2013, 2016; Maier et al., 2018; Pitz et al., 2018; S. Pitz & Megonigal, 2017; Sabrekov et al., 2021; Vainio et al., 2022; Vargas & Barba, 2019; Wang et al., 2016, 2017, 2021; 15 Warner et al., 2017; Welch et al., 2019). However, there are large variations between studies in their results and the proposed mechanisms for potential production and emission of CH4. Details of these studies and the variation they exhibit will be examined further in the paragraphs that follow. An early study of upland trees (Covey et al., 2012) suggested that CH4 emissions from trees on well-drained soils were likely due to fungal infections that produce CH4 in the tree stem through heartwood rot. This study investigated mature trees (80-100 years old) in a well-drained, oak-dominated forest in Connecticut, USA. Methane was sampled from the inside of the tree after drilling to the core and removing gas using a vacuum-sealed vial (Covey et al., 2012). Species included (Pinus strobus (eastern white pine), Tsuga canadensis (eastern hemlock), Quercus rubra (red oak), Betula lenta (black birch), and Acer rubrum (red maple). The mean production potential of CH4 (μg CH4 m−2 s−1 ) were 0.067, 0.115, 0.107, 1.555, and 1.239 respectively. They found that the species with the highest CH4 concentrations in the heartwood were also those that were most susceptible to heart rot fungal infections. These findings indicate that CH4 production may be occurring more rapidly in trees that are infected by fungal pathogens. Pitz and Megonigal (2017) and Warner et al., (2017) conducted similar studies and investigated mature, temperate upland forests (tree species from Pitz and Megonigal (2017): Liriodendron tulipifera, F. grandifolia, C. tomentosa (Lam.) Nutt., Q. velutina Lam., Q. michauxii Nutt., Acer rubrum and Liquidambar styraciflua; Tree species from Warner et al. (2017): Fagus grandifolia, Quercus spp., Liriodendron tulipifera, and Acer spp.) in Maryland, USA. These studies used static chambers that fit over the bark of trees and did not drill into the tree center. The emissions exhibited a diurnal and seasonal component (Pitz and Megonigal, 2017) and variation occurred amongst individual trees and species (Pitz & Megonigal, 2017; Wang et al., 2016; Warner et al., 16 2017). Maier et al., (2018) investigated mature Beech forests in Germany and the Czech Republic, and only the forest plot in the Czech Republic exhibited CH4 emissions from the tree stems, with one tree experiencing very high emissions, that increased with stem height, in comparison to the other four trees. These studies (Maier et al., 2018; Pitz & Megonigal, 2017; Warner et al., 2017) did not conclusively lead to the source of the CH4 (production in the soil or the heartwood). The environmental variables tested were not correlated with emissions and therefore the source of the CH4 could not be identified (Pitz & Megonigal, 2017). Warner et al., (2017) additionally investigated CH4 emissions in coarse woody debris from deadfall which was highly variable. Methane fluxes in coarse woody debris were correlated with C/N and suggested there is a switch from emission to consumption of CH4 during the decay process. The synthesis of these studies raises questions regarding the mechanisms behind the variation in CH4 emissions from tree stems in upland forests. Moist heartwood and low wood density in P. davidiana in a semi-humid warm-temperate forest in Mentougou District, China created favorable anoxic conditions for CH4 production which was not seen in the bark and sapwood, or dead wood (Wang et al., 2016, 2017). The observed depletion of 13C CH4 in the wet heartwood indicated CO2 reduction in the heartwood suggesting it is produced by methanogenic archaea (Schoell, 1988.; Wang et al., 2016), along with the high correlation between CH4 concentration and water content in the heartwood indicates the production of CH4 in the heartwood (Wang et al., 2017). The concentration of CH4 in the heartwood of these trees was not highly correlated with the emissions of CH4 from the surface of the bark, which is likely due to the ability of the stem to diffuse the CH4 into the atmosphere, which could be facilitated by cracks, holes, and other defects (Gorgolewski et al., 2022; Jeffrey et al., 2021; Wang et al., 2016). The study also investigated CH4 in the soil and reported that CH4 17 emissions from tree stems appeared to be mainly produced within the heartwood of the tree as opposed to production in the soil and transported through the tree stem (Wang et al., 2016). Mean annual emissions from Populous davidiana of 85.3 and 103.1 μg CH4 m−2 h−1 on a trunk surface area basis were observed, however, this study also investigated C. cathayensis, which had notably darker wood samples indicating presumed rot, but very little CH4 emissions were observed in comparison to P. dividiana (Wang et al, 2016). The concept of CH4 production within the tree stem rather than transport from the soil through the tree stem is further supported by Welch et al. (2019), where an upland forest in Panama, Central America was studied from wet to dry season (tree species: Simarouba amara (Aubl.), Heisteria concinna). The soil exhibited changes in CH4 flux from the wet to dry season, as the soil acted as a CH4 sink during the dry season and switched to a CH4 source during the wet season. The tree stems however, did not exhibit this same seasonal pattern, as the tree stems showed mostly positive fluxes in both seasons (Welch et al., 2019), which could indicate that CH4 emissions in these trees are produced within the tree and not the soil. These observations of CH4 emissions that are likely from internally produced CH4 are not ubiquitous across studies. In contrast, a study conducted in Chesapeake Bay, Maryland, on a gradient of soil moisture from wetland, transitional zone, to upland forests (tree species: Liriodendron tulipifera (tulip poplar), Fagus grandifolia (beech), Liquidambar styaciflua (sweetgum), and Quercus (Oak)) found a strong negative correlation between the depth to the groundwater and the CH4 flux from the tree stem (Pitz et al., 2018), which suggests that the CH4 was being produced in the soil and transported and emitted through the tree stem, however, was complicated by variation in tree physiology, soil temperature, and other environmental conditions. Machacova et al. (2013) conducted a study on Fagus sylvatica in a greenhouse and 18 also found that the CH4 emitted was likely produced in the soil and found no evidence of internal production. Both of these studies indicate that upland trees may also have the potential to emit soil-produced CH4, however, it is important to note that environmental conditions would play a large role and there needs to be a greater focus on the CH4 fluxes from the soil as well in these forests. Wang et al., (2020) investigated upland forests in boreal (tree species: Larix gmelina, Pinus sylvestris var. mongolica, Betula platyphylla, Pinus pumila, and Picea koriensis), temperate (tree species: Quercus wutaishanica, Betula platyphylla, and Acer mono), and tropical and subtropical zones in China, and found no substantial tree stem emissions in boreal forests, and only 9.8% and 1.8% of tree stems to emit substantial concentrations of CH4 (in this study substantial was considered to be ≥ 10 000 μL−1 of CH4). The concentrations of CH4 in the heartwood were dependent on soil moisture and tree species and were highly correlated with heartwood moisture. Given that soil moisture is a driving factor for CH4 production and emissions in wetland forest trees', this may indicate that moisture is an important indicator of a tree's ability to produce and emit CH4. Tree stems in upland forests have had measurable CH4 emissions from a variety of tree species and environmental conditions. There are however still key research gaps in the mechanisms of the emissions, and the preferred environmental conditions for the production and emission of tree stem CH4. Further, many studies on upland forests that experience wet periods cannot conclude if the CH4 is produced in the soil and being transported by the tree, or if the CH4 is being produced internally in the heartwood, which raises a need for soil to be studied in addition to trees. 19 1.5.6 Upland forests: Tree stem-to-atmosphere interactions Mechanisms of the emissions in upland trees are still unclear. One potential mechanism for the internally produced CH4 to be emitted into the atmosphere is through wounds and bark anomalies (Gorgolewski et al., 2022; Jeffrey et al., 2021). Gorgolewski et al. (2022) studied an upland forest in Central Ontario, Canada (tree species: Acer saccharum Marsh., Fagus grandifolia Ehrh., Betula alleghaniensis Britt., Picea mariana Britton, Sterns and Poggenburg, Tsuga canadensis Carrière, and Abies balsamea) and used a closed chamber to investigate CH4 emissions over the bark wounds. Wounds were categorized into major or minor, and trees with major wounds were found to have higher CH4 emissions even in unblemished areas (Gorgolewski et al., 2022). This trend was also seen in a study in a subtropical lowland, where bark anomalies were hotspots for CH4 emissions, which allowed CH4 from the center of the tree to bypass methanotrophs in the bark and allow for easier diffusion (Jeffrey et al., 2021). Other wounds, including fungi, were investigated and fungi growing on the tree showed the greatest CH4 emission from this study, followed by wet scars, where cavities had the lowest emissions (Gorgolewski et al., 2022). Therefore, there is a need for a better understanding of the mechanism behind tree stem CH4 emissions. Determining if all trees have the potential to emit CH4, or only those individuals that contain bark anomalies and wounds would aid in estimating CH4 fluxes on larger scales. Additionally, Gorgolewski et al. (2022) investigated the branches of trees (excluding foliage) and observed CH4 emissions rates similar to that from the stem. The source of CH4 was likely through heartwood rot in both the stem and branches and the similar emissions rates could be due to the thinner bark on branches allowing for CH4 to diffuse more easily even though there was less heartwood in comparison to the tree stem (Gorgolewski et al., 2022). When looking at the 20 foliage excluding the branches, it acts as a net sink for CH4 during the day, accounting for up to 38% of net ecosystem CH4 uptake, and approximately 62% of soil uptake in this forest (Gorgolewski et al., 2023a). Uptake in the canopy was suggested to be due to CH4 consumption by bacteria inhabiting foliage (Gorgolewski et al., 2023a; Putkinen et al., 2021a). Tree stems have also switched from sinks to sources periodically and this switch could be due to methanotrophs on the bark surface (Jeffrey et al, 2021, Machacova et al., 2021, Gorgolewski et al., 2022), or due to soil moisture changes and upland forests having access to soil produced CH4 through their tap roots periodically (Wang et al., 2016). Temporal variation also occurs on both diurnal and seasonal scales. Barba et al. (2021) observed CH4 in tree stems is highly variable over short periods and switches from sinks to sources. Additionally, seasonal variation occurs, particularly in regions that seasonally reach cold temperatures where microbial metabolic activity is reduced. The temporal variation in CH4 fluxes is driven by the metabolic activity of the microbial communities that change with the environmental conditions they exist in. The variety of studies shows the complexity of CH4 fluxes in upland trees and how it is impacted by many factors such as soil temperature, soil moisture, tree species, bark thickness, seasonal and diurnal variation, spatial variation in a single tree, and a forest stand, wounds and bark anomalies, and many other factors. 1.5.7 Upland forests: Conifer tree species The studies discussed above, regarding upland trees, (except some individual trees from Wang et al., 2021 and Covey et al., 2012) were conducted on deciduous tree species. There is a large gap in research surrounding conifer tree species and whether they have the same capabilities to produce and emit CH4 on the same scale as deciduous trees in upland forests. Few studies have 21 included conifer tree species (Covey et al., 2012; Machacova et al., 2016; Vainio et al., 2022; Wang et al., 2021a). A 50-year-old stand of Scots pine (Pinus sylvestris) was investigated for CH4 fluxes from the stem and shoots of trees in both wet and dry areas. Shoots emitted more CH4 (mean = 0.050 μg m−2h−1) than the stems (mean = 0.005 μg m−2h−1), and wet plots (mean = 0.100 μg m−2h−1) emitted significantly more CH4 than dry plots (mean = 0.013 μg m−2h−1) (Machacova et al., 2016). It is assumed that in wet conditions, CH4 produced in the deep soil gets transported through the stomata in the tree and emitted mainly by the shoots, and in dry conditions the CH4 is mainly produced in the heartwood (Machacova et al., 2016). Another study looked at Norway spruce (P. abies) in a forested fen, and Downy birch (B. pubescens) in an upland forest and a forested fen (Vainio et al., 2022) in southern Finland. The trees in the upland forest emitted less CH4 than those in the forested fen, and the Norway spruce emitted less CH4 than the birch. The spruce trees showed higher CH4 emission in the shoots than the stem (Vainio et al., 2022) which agreed with the findings of Machacova et al. (2016). The birch tree emissions followed trends with soil temperature, but this pattern was not observed in the spruce trees (Vainio et al., 2022), which indicates that CH4 is mainly produced in the soil and transported through the tree. Wang et al. (2021) also studied a variety of conifer species that were present in the boreal forest site and concluded that CH4 flux from tree stems was insignificant. Covey et al. (2012) included conifer species in their study and observed CH4 emissions in Eastern hemlock, but no emissions in Eastern white pine, however, the study drilled into the heartwood of the tree rather than measuring the flux occurring on the bark surface. Norway Spruce was also studied in upland forests in eastern Norway, where some trees were infected by white-rot fungi (Hietala et al., 2015). This study drilled into trees to collect CH4 gas 22 concentrations and determined that healthy trees and infected trees that had wood decay had CH4 concentrations close to ambient air. Methane concentrations in spruce trees have shown spatial variation (Hietala et al., 2015; Machacova et al., 2020), which is predicted for upland trees with the potential of heartwood rot (Barba et al., 2019). The few studies that include conifer species (Covey et al., 2012; Hietala et al., 2015; Machacova et al., 2016; Vainio et al., 2022; Wang et al., 2021a) had varying results and should be further investigated to determine the role that conifer species play in the global CH4 cycle. The current research surrounding upland conifer trees' CH4 fluxes is limited to a small variety of species and small sample sizes. The studies conducted by Covey et al. (2012) and Hietala et al. (2015) both drilled into the heartwood of trees, which does not indicate the contribution to atmospheric CH4, it only indicates the internal gas concentrations of the trees. The study conducted by Vaninio et al. (2022) was conducted mostly in bogs, which would not give insight into upland conifer species. Machacova et al. (2016) reported a mean tree stem CH4 flux from Pinus sylvestris of 0.005 µg·m2·h-1 in boreal upland forests, however, the sites included wet and dry plots, only 100m apart, which would create uncertainty surrounding the mechanisms behind the CH4 production (produced in the soil and transported through the tree stem versus produced in the tree stem). Additionally, the sample size was six trees, which would not be representative of a whole forest system and lacks soil flux measurements to give context to the contribution of the flux. Although these studies have given insight into the potential for conifer trees to emit CH4, there is still a large knowledge gap in understanding the role that upland conifer forests play in the global CH4 budget. 23 1.6 Tree Chambers With the increased interest in tree stem gas emissions a variety of chamber methods have been designed and tested in temperate and tropical forests, with each making new improvements to adapt to certain tree species (Gauci et al., 2010; Jeffrey et al., 2021; Pangala et al., 2013; Rusch & Rennenberg, 1998; Siegenthaler et al., 2016). There was a need to design a chamber that differs from traditional static ground chambers that allows for gas flux measurements on the complex surfaces of tree stems. Ground chambers are generally larger volumes and are placed on a surface rather than attached and tethered to it. Obtaining gas fluxes from tree stems which stand vertically and have curved surfaces was not possible using the existing chambers. The goal of using stem chambers is to capture the gas exchange between the atmosphere and the bark surface of the tree stem. Samples can be collected as individual samples using a syringe and vial, or they can be collected as a flow-through method where the gas passes through a container and is collected or is connected to a real-time analyzer where gas concentrations are measured in the field. The design of the chamber is customized to the application and sample collection method. The methods used to quantify gas fluxes from tree stems into the atmosphere use a chamber or sleeve-like device that attaches to the tree stem and captures the gas escaping from the lenticels. Two main groups of these chambers include full circumference, where the chamber goes around the full diameter of the tree enclosing a section of the stem, or partial circumference, where only a portion of the stem diameter is covered and collecting the gas from the stem. Within the group of full circumference chambers, each study used a similar chamber design with slight modifications for best fit on certain species. Rusch and Rennenberg (1998) designed a cylindrical static chamber with rubber seals that enclose the chamber around an alder tree stem to ensure there is no exchange of gas with the atmosphere. One end of the cylinder had an attached fan and 24 magnet. Gauci et al. (2010) created a similar design with a cubic gas chamber that fits around an alder tree stem with rubber seals and acrylic sides. These two chamber designs were further modified by Pangala et al. (2013) to include hinges and spring clips to enclose the tree stem, with a gas-impermeable foam strip to seal the chamber against the tree stem. The chambers continuously logged the internal pressure, temperature, and humidity, and were equipped with a pressure regulator. Recent studies have adapted and improved the approach of Pangala et al. (2012) to create flux chambers that are more compact, easier to deploy, and able to accommodate a greater range of tree diameters. Seigenthaler et al. (2016) created a semi-rigid chamber that fits around the diameter of the tree stem or a small fraction of the stem for larger trees. This method uses pre-shaped gas-impermeable plastic that has a natural curving tendency to fit around the tree stem and uses adhesive gas-impermeable foam to seal the plastic chamber/sleeve around the tree stem and straps that fit around to increase the stability. This method reduced the closure period of the chamber to improve the linear fitting of initial rates and reduced the size of the chamber to reduce the potential for poor mixing when the gas exchange surface and chamber volume ratio are too large (Siegenthaler et al., 2016). Another chamber design by Brechet et al. (2021) created an automated flexible chamber, similar to Seigenthaler et al. (2016), made of rectangular gas-impermeable plastic and neoprene strips, which is held tightly against the tree stem with straps. Another method is SNIFF (a ‘small nimble fine-scale flux’) which is a lightweight device that attaches to a small section of the tree stem and measures the gas emissions (Jeffrey et al., 2020). This method is more flexible as it fits various tree species or tree diameters. It is a small, portable, cylindrical chamber that seals onto a tree stem using a white potting clay ring that molds to the shape of the tree. However, this chamber method allows for the collection of rapid 25 tree stem emissions on only a small section of the tree and does not give results for the whole diameter of the tree at specific heights (Jeffrey et al., 2020). There are a variety of different tree stem chamber adaptations, with the design of the chamber specific to the application of the research. There has been a shift towards chamber designs that are more flexible and versatile to allow for a sealed fit over a variety of individual trees. These methods allow for larger sample sizes in studies which improves the quality of the data and makes results more applicable to whole forests. 1.7 Research Statement This preliminary study aims to investigate CH4 fluxes in mature upland conifer forests in three sites situated in different biogeoclimatic zones in British Columbia, Canada to determine the extent to which tree stem CH4 fluxes from conifers may offset, or enhance, the soil sink for atmospheric CH4 in upland forests. Fungal pathogens are widespread in conifer forests (Lomus, 2016; Manning, 2009), which can lead to the formation of heartwood rot and conditions that are favorable for methanogenesis in tree stems (Bryant, 1979; Conrad, 2007). In contrast to conifer forests, CH4 formation and release due to heartwood rot in deciduous trees is comparatively well-studied (Gorgolewski et al., 2022; Pitz et al., 2018; Vainio et al., 2022). This study is intended to begin to address the knowledge gap for tree stem methanogenesis in upland conifers and CH4 oxidation on the surface of conifer stems. Both processes have the potential to impact current estimates of net ecosystem CH4 fluxes in upland forests, which are primarily based on soil uptake of atmospheric CH4 by high-affinity methanotrophic bacteria. There are an estimated 3.47 million km2 of forest in Canada (Statistics Canada, 2015) and approximately 0.64 million km2 of forest in BC, of which 90% is coniferous and 43% is classified as old-growth (i.e., between 120 to 140 years old in non-coastal environments; British 26 Columbia, 2012). The forest area in Canada is significant on a global scale and needs to be more accurately characterized in terms of trace gas fluxes associated with biogeochemical processes that respond to ongoing climate change or forestry operations and other anthropogenic land management practices. 1.7.1 Research Objectives The overarching objective of this study was to determine the extent to which upland conifer trees and the presence of heartwood rot due to fungal infection might be an important uncharacterized component of the global CH4 cycle. The study was conducted in three sites, each situated in a different biogeoclimatic zone to better assess the potential impact of differences in annual temperature and moisture on CH4 production and consumption in upland trees and soils. These environmental parameters are established drivers of seasonal and interannual differences in microbial methanogenesis and methanotrophy in other ecosystems that are key contributors to the atmosphere-biosphere exchange of CH4. Their potential impact has yet to be quantified in upland forests, in particular, concerning tree stem CH4 fluxes from conifers. Production of CH4 in trees does not necessarily lead to CH4 emissions from stem surfaces. A further objective of this study was to investigate for the first time the possibility that conks inhabiting the stems of fungal-infected trees may serve as porous conduits that enable or enhance the release of CH4 from decaying heartwood. The role of other types of stem surface wounds and anomalies in CH4 exchange with the atmosphere also was investigated. In parallel with measurements of tree stem CH4 fluxes in upland conifer forests, soil uptake of atmospheric CH4 was quantified to enable calculation of net CH4 emission in study quadrats. The goal was to quantify the relative contribution of trees and soils to net CH4 emissions in upland forests to determine the extent to which tree stem fluxes might increase or decrease the CH4 sink 27 currently attributed to upland conifer forests in global CH4 budgets. The primary aim of the study is to investigate, for the first time, CH4 emission characteristics of upland trees, with a particular interest in whether fungal-infected trees exhibit higher rates of CH4 flux. The specific research questions are: 1) Do stem surfaces of mature coniferous trees situated on well-drained soil in upland forests emit or consume CH4? 2) Can conks inhabiting trees infected with a heart rot fungus be a conduit for transporting stem-produced CH4 to the atmosphere? 3) Are CH4 fluxes from conifer tree stems associated with bark wounds or other surface anomalies, and the health condition of the tree? 4) Does inclusion of CH4 fluxes from tree stems significantly alter estimates of net CH4 exchange in well-drained upland conifer forests? 28 2.0 Site Description 2.1 Site Selection This study was conducted in southern British Columbia, Canada on the traditional and unceded territories of the Syilx, Cayuse, Umatilla, Walla Walla, and Homalco Peoples. Sites are situated in three different biogeoclimatic zones and are named according to these zones: Montane Spruce (MS), Englemann Spruce – Subalpine Fir (ESSF), and Coastal Western Hemlock (CWH), which occur on a moisture gradient of wet to dry (Figure 2.1). All sites contained trees greater than 50 years old and resembled natural mature forests with more than one tree species and trees of a variety of tree stem diameters. The selection criteria included a minimum area of 100 x 100 m, minimal anthropogenic disturbance, a forest buffer of at least 10 m to reduce forest edge effects, and well-drained soils. Sites were accessed through resource roads and quadrat surveys were conducted on foot (Figure 2.2) to reduce disturbance to ground cover at the sites. Topographic maps of each site with the study area outlined in black are presented in Figure 2.1. The black squares correspond to the quadrat maps and site images in Figure 2.2. The bottom edge of the quadrat maps (Figure 2.2) runs parallel to the access road for each site. Figure 2.1. Site map of British Columbia with the approximate location of each quadrat survey. 29 Figure 2.2. Quadrat maps for the three study sites (a) Montane Spruce, (b) Engelmann Spruce – Subalpine Fir, (c) Coastal Western Hemlock and a photograph of typical conditions at each site. The quadrat maps are oriented with the access road parallel to the bottom edge. Each individual sampling quadrat is 10 x 10 m with the total study area at each site being ~100 x 100 m. 2.2 Montane Spruce (MS) The MS site is located in the Okanagan Valley, British Columbia in the Montane Spruce Biogeoclimatic Zone (Meidinger & Pojar, 1991) and the subzone dry and mild (MS dm variant 1; Province of British Columbia, 2021; Ministry of Forests, 1999). The MS Biogeoclimatic Zone experiences short dry summers and cold winters. It is an elevation transitional zone between the Engelman Spruce – Subalpine Fire zone and the Interior Douglas fir zone. The dominant tree species typically are Lodgepole pine, Engelman Spruce, Subalpine Fir, and Douglas Fir. Montane Spruce (MS) Engelmann Spruce – Sub Alpine Fir (ESSF) Coastal Western Hemlock (CWH) (a) (b) (c) 30 However, this subzone (dry and mild) and variant (1) are dominated by Lodgepole pine, Engelmann Spruce, and Subalpine fir, and covers 0.19% of the province's land cover (Hamann et al., 2005). The MS study site (50.052409°N, 119.251814°W), situated at an elevation of 1370 to 1425 m, is underlain by Brunisolic Gray Luvisol soil, which is a well-drained loam and native soil for the area (Government of Canada, 2019; Province of British Columbia, 2018). The total annual precipitation for the biogeoclimatic subzone and variant is 643 mm and the mean annual temperature is 3.2°C (Table 2.1; Province of British Columbia, 2021). Although the environmental variables and geographical location would categorize this site as a Montane Spruce Forest, it is more representative of a transition between the MS and ESSF biogeoclimatic zones due to its species composition. This forest may not be at its full maturity and therefore should not be compared to other Montane Spruce biogeoclimatic zones that are fully developed. However, for consistency, the term Montane Spruce (MS) will be used throughout this research since that is how it is classified according to the British Columbia government classification of the site (British Columbia, 1999). The site contains a mature coniferous forest stand, having an average stem diameter of 35.5 cm (Table 2.1) from 3 different tree species (Table 2.2). It is surrounded by cut blocks of various ages. The forest shows evidence of anthropogenic disturbances, such as a trail running along the north side and a road that runs east along the study area (Figure 2.2a). During sampling in 2022, there was noise disturbance from logging trucks and recreation vehicles in the surrounding area. Data were collected during daylight hours in September and October of 2022, and all trees studied were cored the following year in the spring of 2023. 31 2.3 Engelmann Spruce –Subalpine Fir The second site is located in the Engelmann Spruce – Subalpine (ESSF) biogeoclimatic zone (Meidinger & Pojar, 1991), subzone moist hot (mh) (also known as moist warm – mw) in the Southern Interior, East Kootenay boundary area of British Columbia (49.196784°N, 118.089276°W; Province of British Columbia, 2021; Ministry of Forests, 1999). The ESSF biogeoclimatic zone experiences cold and long winters and is a subalpine boreal climate regime at high elevations on almost all major mountains in BC, covering 15.3% of the province. Engelmann Spruce is the dominant canopy species and Subalpine Fir is the dominant understory species. In the mw subzone, Subalpine Fir is the dominant species followed by Englemann Spruce, covering 0.32% of provincial land (Hamann et al., 2005). The study site was situated in a mature forest having an estimated age range of 160-200 years old and an average tree diameter of 41 cm from three different tree species (Table 2.2). Consistent with the ESSF biogeoclimatic zone, the dominant tree species were Engelman Spruce and Subalpine Fir, with a minor presence of Western Red Cedar. Site elevation ranged from 1440 to 1480 m. The total annual precipitation for the biogeoclimatic subzone and variant is 1599 mm, and the mean annual temperature is 2.3°C (Table 2.1; Province of British Columbia, 2021). Soil at the site is a well-drained, sandy loam (Orthic Dystric Brunisol; A. and Canada, 2013; Government of Canada, 2019; Province of British Columbia, 2018). The site was a mature conifer stand exhibiting conks on some trees (Figure 2.2b). It was surrounded by cut blocks of a range of ages. Data was collected during daylight hours in July of 2023. All trees studied were cored immediately following flux measurements. 32 2.4 Coastal Western Hemlock The third study site was situated in the Coastal Western Hemlock (CWH) biogeoclimatic zone and a very dry maritime subzone (xm variant 1; Meidinger & Pojar, 1991; Province of British Columbia, 2021; Ministry of Forests, 1999) on the central east side of Vancouver Island (49.430899°N, 123.74823°W; Figure 2.1). The CWH biogeoclimatic zone is the wettest zone in BC, and as such is highly productive with natural disturbance from wind but few wildfires resulting in generally older forests (Meidinger & Pojar, 1991). Dominant species are Western Hemlock and Western Red Cedar in the xm1 subzone and variant with Western Hemlock accounting for approximately half the trees, followed by Western Red Cedar and Amabilis Fir. The variant constitutes 0.49% of BC provincial land cover (Hamann et al., 2005). Cook Creek runs through the site, which hosts a mature conifer forest stand having an average tree diameter of 40.5 cm for Western Hemlock, Douglas Fir, and Western Red Cedar (Table 2.2). The area has an elevation of 220 m. The total annual precipitation for the biogeoclimatic subzone and variant is 1427 mm and the mean annual temperature is 9.3°C (Table 2.1; Province of British Columbia, 2021). Soil at the site is a Gleyed Dystric Brunisol, which is an imperfectly drained silt loam (Canada, 2013; Government of Canada, 2019; Province of British Columbia, 2018). There are anthropogenic disturbances from recreation vehicles and heavy traffic associated with logging activity in the surrounding area (Figure 2.2c). Data were collected during daylight hours in July of 2023, and all trees studied were cored immediately following the flux measurements. 33 Table 2.1 Summary of site characteristics. Table 2.2 Tree species present in study quadrats. Site Subzone and Variant % Land Cover of Variant Average Stem Diameter (cm) Average Elevation (m) Mean annual Temperature (°C) Mean Annual Precipitation (mm) MS dm 1 0.19 35.5 1370 3.2 643 ESSF mw 0.32 41.0 1480 2.3 1599 CWH xm 1 0.49 40.5 220 9.3 1427 Site Tree Species (Common Name) Tree Species (Latin Name) MS Engelmann Spruce Picea engelmanii Subalpine Fir Abies lasicarpa Western Red Cedar Thuja plicata ESSF Engelmann Spruce Picea engelmanii Subalpine Fir Abies lasicarpa Western Red Cedar Thuja plicata CWH Western Hemlock Tsuga heterophylla Amabilis Fir Abies amabilis Western Red Cedar Thuja plicata Cottonwood Populus balsamifera 34 3.0 Methods 3.1 Quadrat Sampling Trees were sampled using a random quadrat method consisting of 10 x 10 m quadrats. This approach ensures that measurements were conducted systematically and allowed for quantification of site characteristics for comparison between sites. The quadrats were treated as subsamples of each site and were not conventional treatment or control plots. The quadrats were situated a minimum of 10 m from the forest's edge to reduce edge effects that could impact the potential for a tree to respond to environmental variables or to be infected by fungus. All quadrats were surveyed within a 100 x 100 m forest block. All trees that had a diameter of 20 cm or larger, and trees located within a quadrat or on the boundary of the north or east side of the quadrat, were measured and included in the study. This sampling criteria does introduce bias in the species included since mature trees that have small diameters are excluded from this sampling (example Lodgepole Pine species). However, the selection of trees with larger diameters was necessary to exclude immature individuals from the majority of other species included in the study. Detailed site maps were drawn at each quadrat which showed the approximate size and location of each tree included in the study, the location of the trees that were too small to be included, the approximate size and location of fallen logs, and any other features in the quadrat (Figure 3.1; example of one quadrat map from MS Q6; figures for all quadrats are included in Appendix A). 35 Figure 3.1. Quadrat map of Q3 in the CWH site. Maps like the one shown were prepared for every quadrat at the three study sites. The 37 quadrat maps in total are presented in Appendix A. The Montane Spruce (MS) site had 20 quadrats containing a total of 61 trees that met size and location threshold requirements (Table 3.1). The MS quadrats contained from one to six trees with an average of three trees per quadrat. In the MS site four measurements were conducted on the stem of each tree: one each on the north, east, south, and west-facing sides at 135 cm height (diameter at breast height (DBH)). The Engelman Spruce-Subalpine Fire (ESSF) site had seven quadrats containing 30 trees (Table 3.1). The ESSF sites had an average of four trees per quadrat and a range of one to five trees per quadrat. The Coastal Western Hemlock (CWH) site consisted of ten quadrats and a total of 35 trees that met the conditions for inclusion in the study (Table 3.1). There was an average of four trees per quadrat and a range of one to six trees within the CWH quadrats. In contrast to the MS site, two measurements were conducted per tree at the EESF and CWH sites: one each on the north- and south-facing aspects at 135 cm height. The variation in the number of quadrats at each site was attributed to site access and availability. Additional flux measurements were made over wounds, conks, and bark anomalies below a 36 height of 200 cm, as well as trees with conks that fell outside of the quadrats were measured but only included in an analysis on methane fluxes from conks (included in chapter 5) Trees included in the field study were identified to the species level. Measurements of physical characteristics included diameter at breast height (DBH; approximately 135 cm from ground level; Covey, 2012), general condition, note of any anomalies or damage to the tree stem, and the presence of conks or other signs of fungal infection. Each tree was categorized into groups (i) alive, (ii) unhealthy, or (iii) dead based on their overall condition. The assessment of condition was based on the state of the bark surface and the amount of tree foliage. If trees had no foliage and no sign of new branches sprouting, they were considered dead. Trees with major wounds to the bark and/or little foliage, and no signs of new growth were considered unhealthy. Trees that had no substantive damage and green foliage were considered alive. Table 3.1 Quadrat survey for sites. Site Number of Quadrats Number of Trees Number of tree Measurements Number of Soil Plots MS 20 61 235 (4 per tree) 13 ESSF 7 30 65 (2 per tree + anomalies) 4 CWH 10 35 77 (2 per tree + anomalies) 4 Total 37 126 337 21 3.1.1 Whole Tree Survey One tree from the MS site was selected for additional measurements to investigate axial and radial variations in CH4 flux. Twenty flux measurements were conducted at random locations along the stem (Figure 3.2). The selected tree (T2 from quadrat 6) also was measured in the full quadrat survey on a different day using normal methods. This tree was selected because 37 preliminary results in the real-time field measurements showed evidence of CH4 emissions from the stem during the quadrat survey. Other trees showed similar potential CH4 emissions, but T2 was selected at random from the collection of trees showing potential CH4 emissions. Figure 3.2 The diagram on the right shows the area of the tree that was sampled. The red rectangle would wrap around the diameter of the tree. The diagram on the left shows the location of all 20 flux measurements taken around the stem of the tree. 3.2 Environmental Parameters Environmental parameters including air temperature and soil temperature were recorded at each quadrat during the time that gas flux measurements were performed. Soil temperature was measured using a Hanna HI temperature probe. 3.3 Gas Flux Measurements Flux measurements of CH4 from tree stems and soil were conducted using a Los Gatos Research Ultra-Portable Greenhouse Gas Analyzer (UGGA). The UGGA also measures water vapor concentration to correct CH4 mixing ratios for reporting on a dry atmosphere basis (Aerodyne Research Inc.). The UGGA analyzer was calibrated daily in the field using a certified Praxair gas standard that was stored and transported in aluminum polymer-coated gas bags. Gas flux measurements from tree stems and the ground surface were conducted using static chambers that are described in section 3.4. 38 3.3.1 Tree Chambers The tree chamber design was adapted from Jeffrey et al. (2020)’s small nimble fine-scale flux (SNIFF) approach, which consisted of a lightweight device that attaches to a small section of the tree stem to capture trace gas emissions. Other chamber designs used larger chambers (Gauci et al., 2010; Pangala et al., 2013; Rusch & Rennenberg, 1998; Siegenthaler et al., 2016); however, the small SNIFF chamber was adopted because it is more adaptable and portable for use on a greater variety of tree sizes and species (Jeffrey et al., 2020). The chambers in this study were sealed to tree stems using white pottery clay that molded to the shape of the tree. The tree chambers were small and rectangular, having a volume of 0.3 L covering a stem area of 0.0087 m2 (Figure 3.3 a), which enabled rapid internal mixing of gas within the system when air was circulated at a rate of ~150 cm3 min-1 using the diaphragm pump in the LGR UGGA. A second larger tree chamber covering a surface area of 0.0305 m2 with a volume of 1.5 L was used to measure trace gas flux from bark anomalies and conks that were too large to be enclosed in the 0.3 L SNIFF chamber. Chambers were connected to the UGGA using ¼” outer diameter (OD) polyethylene tubing. Gas concentrations (CH4, CO2, and H2O) were measured in real-time every second during a total closure time of five minutes per chamber deployment. 3.3.2 Soil Chambers Soil CH4 flux measurements were conducted at 21 of the 37 quadrats (MS: 13 quadrats, ESSF: 4 quadrats, CWH: 4 quadrats) using the UGGA analyzer and a ground flux chamber. The soil flux measurements were conducted on even ground after loose debris was cleared from the area, but no soil or vegetation disturbed or displaced. The soil chamber (Figure 3.3b) consisted of a 5 L metal chamber having a 0.14 m2 open surface that was sealed to the ground during gas flux 39 sampling. The chamber had a one-inch-long edge blade that penetrated the soil to create a gas-tight seal during flux measurement. In each quadrat, the soil chamber was deployed over the same spot five consecutive times to measure an average CH4 soil flux for the quadrat. Each chamber deployment had gas measurements collected in 1-second increments with a closure time of five minutes. Sufficient time was permitted between each chamber deployment to flush the chamber and tubing until the UGGA baseline reading returned to the concentration of CH4 in ambient air. Fifteen of the quadrats did not have soil fluxes collected due to uneven ground or ground cover that would not permit deployment of the chamber with a tight seal to the ground surface. Soil moisture and temperature were measured near the area where each gas flux measurement was conducted. Seven to ten soil moisture readings were recorded and averaged to obtain the reported soil moisture value. One soil temperature reading was recorded during the time of soil flux measurements. Figures 3.3 Flux chambers used to capture trace gas emissions from tree stems and ground surfaces: (a) tree stem chamber attached to a conifer sealed with pottery clay, and (b) soil chamber sealed to the ground with a knife edge insert used to trap soil gas fluxes. 3.4 Coring All trees were cored at 135 cm (the same height as the flux measurements) after all flux measurements were completed. A 5.1 mm increment borer was used to extract a tree core perpendicular to the bark, spanning (a) (b) 40 from the bark to the pith of the stem. The core was analyzed for signs of heartwood rot which could be identified through discoloration of the heartwood, or breakdown and gaps in the heartwood from decomposition. 3.5 Data Processing Raw data from the UGGA was exported and used to calculate a methane flux for each chamber deployment. The Ideal Gas Law (eq. 1) was used to calculate μmol[𝐶𝐻4] from the atmospheric mixing ratios (ppmv) measured using the UGGA where P is the atmospheric pressure (atm), V is the volume of the chamber (L), R is the Ideal Gas Law constant (L ∙ atm ∙ 𝑚𝑜𝑙−1 ∙ 𝐾−1), and T is the ambient temperature (𝐾) eq. 1 𝑛 =𝑃𝑉𝑅𝑇 The CH4 flux was calculated using eq. 2, where Mm is the molar mass of CH4 (16.043 g/mol), and S is the surface area of the opening of the chamber (m2). Slope was calculated from the CH4 concentrations over the duration of the flux measurements and measured in µmol. The (1106) is for the purpose of converting between µmol and mol units. eq. 2 Flux [CH4] = (𝑆𝑙𝑜𝑝𝑒 𝑜𝑓 𝜇𝑚𝑜𝑙[CH4] ∙1106) (𝑀𝑚[CH4] ∙ 1000) (1𝑆) 3.5.1 Minimum Detectable Flux Minimum detectable flux (MDFSE) was calculated for each sample under the specific measurement conditions. The calculation for MDFSE (eq. 3) is specific to high-frequency measurements and was developed for laser-based analyzers by Christiansen et al. (2015) similar to the UGGA analyzer used in this study. In eq.3, AA is the analytical accuracy of the instrument 41 (ppm), tc is the closure time for the chamber deployment (h), V is the volume of the chamber (m3), P is atmospheric pressure (Pa), S is the surface area of the opening of the chamber (m2), R is the Ideal Gas constant (𝑚3∙𝑃𝑎𝐾∙𝑚𝑜𝑙), T is ambient temperature (K), and Ps is the sampling periodicity (h), which in this study was once per second. eq. 3 𝑀𝐷𝐹𝑆𝐸 = (𝐴𝐴𝑡𝑐 √𝑡𝑐𝑃𝑠)(𝑉𝑃𝑆𝑅𝑇) An example of the MDFSE calculation using eq. 3 for tree stem CH4 fluxes at MS site is: eq. 3 𝑀𝐷𝐹𝑆𝐸 = ( 0.0030.0314√0.03140.00023) (0.0003 ∙ 1013000.00871 ∙ 8.314 ∙ 279.95) MDFSE = 0.003128 µmol ∙ m-2 ∙ h-1 Flux = 0.010318 µmol ∙ m-2 ∙ h-1 MDFSE < Flux The determined MDFSE is an order of magnitude less than the measured CH4, which provides certainty that the observed flux is significant and above the detection limit of the UGGA analyzer in combination with the size of chamber that was deployed. Fluxes that were lower than the calculated MDF were reported as 0 µmol m-2 h-1. 3.5.2 Whole Tree Interpolation Photos were taken of all sides of tree T2 in quadrat 6 used in the whole tree survey and pieced together to create a 2D map of the tree surface under which the 20 chamber deployment placements were conducted. The image was digitized, and the flux values were assigned to 42 polygons that represented the corresponding chamber placements in QGIS. An inverse distance weighted interpolation was performed to predict the flux for unmeasured areas, using the surrounding flux values (QGIS, 2024). Knots, branches, cracks, and wounds were overlayed to visualize the patterns of bark anomalies versus CH4 fluxes. 3.5.3 Statistical Analysis Basic R version 4.1.1 was used for statistical analysis and data visualization. Data analysis included ANOVA analysis and linear modeling to test the significance between groups. The linear mixed effects model compared the CH4 fluxes for various nested qualitative variables (example: differences between species within each site) to investigate differences between groups with a 95% confidence interval. All statistical tests used in this study require sampling to be random, each sampling unit is independent to avoid pseudo replication, the data is continuous and linear, and homogeneity of residuals, and the data is normally distributed (Schielzeth et al., 2020; St»hle & Wold, 1989). All of these assumptions were met for the statistical tests included in the study. 3.5.4 Scaling Tree Fluxes The measured tree stem CH4 fluxes were scaled to estimate total fluxes for the surface of entire tree stems. The multiple fluxes measured for each tree were averaged for one representative flux per tree. The surface area was calculated using the measured tree stem diameter at breast height and a conservative height estimate of 15 m. The average flux values were then multiplied by the overall surface area to obtain an estimated flux for each tree. A spatial interpolation analysis was performed for the study site area. The flux value for each quadrat was the average of all trees contained in the quadrat and the methods for the spatial interpolation followed those from the whole tree survey (section 3.1.1). 43 3.5.5 Scaling Soil Fluxes The five soil flux measurements for each quadrat were averaged to obtain one representative flux value and associated uncertainty for the quadrat. For quadrats where soil CH4 flux measurements could not be conducted, a representative flux value was calculated from the average of all soil fluxes from other quadrats at the site. To scale the average soil flux for each quadrat to the entire quadrat area, the detailed quadrat maps (Appendix A) were used to estimate the surface area where soil-atmosphere exchange of trace gas could occur. That area value is the total area of the quadrat minus the area of tree trunks, fallen logs, large boulders, streams, and any other obstructions that covered soil. The soil area was then multiplied by the average flux in each quadrat. A spatial interpolation analysis was performed using the scaled flux values from the quadrats that had flux measurements taken in the fields. It excluded quadrats where soil CH4 fluxes were estimated from average CH4 fluxes in other quadrats. Methods for the spatial interpolation were the same as those used in whole tree surveys (section 3.1.1). 3.5.6 Scaling to the Study Area A representative CH4 flux for each of the three study areas (CWH, ESSF, and MS) was calculated from the scaled tree CH4 fluxes for each quadrat subtracted from the estimated soil flux for the corresponding quadrat. An additional interpolation analysis was performed using the methods outlined in section 3.1.1 to characterize spatial variability of CH4 fluxes at each study area. The spatial variability was overlayed with a topographical map to investigate potential spatial flux patterns that vary with the slope of the site. These values are taken to be representative of CH4 fluxes for each biogeoclimatic subzone (and variant where applicable) for mature forests during the year and month that the flux surveys were conducted. It is important to 44 note that the goal of scaling these sites is to investigate potential CH4 contributions of tree stems in mature upland conifer forests and that this is a preliminary study which would need further research to apply findings to these biogeoclimatic zones and larger forest areas. 45 4.0 Results and Discussion on Methane Fluxes From Upland Conifer Trees 4.1 Background Trees in wetland environments emit methane (CH4) that is produced in the soil through special adaptations that allow for respiration during periods of inundation (Kozlowski, 1997; Rusch and Rennenberg, 1998; Pangala et al., 2013). Since trees in upland environments are rooted in well-drained aerated soil that typically consumes rather than produces CH4, the trees normally have not developed adaptions that allow for the transportation and emission of soil CH4. However, there is still potential for trees in upland environments to emit CH4 to the atmosphere as a result of heartwood rot in tree stems initiated by fungal infection (Bryant, 1979; Conrad, 2007). Low oxygen conditions within the decaying heartwood can lead to production of CH4 that is either stored in the tree stem, consumed by methanotrophs, or emitted into the atmosphere (Covey & Megonigal, 2019). Most studies to date of tree stem CH4 production and fluxes in upland forests have been conducted on deciduous trees (Barba et al., 2019; Beckmann et al., 2011; Covey & Megonigal, 2019; Hietala et al., 2015; Jeffrey, Maher, Chiri, et al., 2021; Mukhin & Voronin, 2007). The potential for upland conifer trees to produce and emit CH4 has not been extensively investigated. Covey et al. (2012) reported CH4 in the heartwood of a fungal-infected conifer, however, the tree had been cored to sample gases from decaying heartwood and the study did not attempt to quantify CH4 flux from the tree stem. Machecova et al. (2016) reported minor emissions of CH4 from a small sample of conifers included in a study that compared tree stem CH4 fluxes in wet and dry plots. The mechanisms of CH4 production and emissions from the trees were unclear and it could not be concluded that the CH4 in dry plots was not being produced in the soil during wet periods. 46 This chapter reports outcomes of field campaigns that aimed to determine whether CH4 coniferous trees situated in well-drained soils in upland forests emit CH4. A variety of conifer species were investigated in three sites, situated in different biogeoclimatic zones in British Columbia. 4.2 Tree Stem CH4 Fluxes By Site and Species Measurements of stem CH4 fluxes were conducted on conifer trees from three different sites in the Montane Spruce (MS), Engelmann Spruce – Subalpine Fir (ESSF), and Coastal Western Hemlock (CWH) biogeoclimatic zones in British Columbia. A complete list of trees CH4 fluxes measured at each site and from each tree is reported in Appendix B. A summary and analysis of the CH4 flux data are presented in this chapter. Table 4.1 shows the mean, maximum, and minimum tree stem CH4 fluxes for each site and conifer species. Minimum detectable flux (MDF) was calculated for each tree stem CH4 flux measurement to evaluate whether the flux (both positive or negative) was above the analytical threshold for detection. The MDF method used is specific to high-frequency laser-based analyzers (Christiansen et al., 2015). MDF values are reported in Appendix B. 4.2.1 Montane Spruce (MS) Methane flux measurements from 61 trees within 20 quadrants in the Montane Spruce (MS) site are presented in Table 4.1 and appendix B. The mean CH4 flux from trees at this site was 0.0063 ± 0.0587 µmol m-2 h-1 with a range of -0.2063 to 0.2387 µmol m-2 h-1 (Table 4.1). Picea engelmanii (n = 23 trees) and Abies lasicarpa (n = 37) species exhibited mean CH4 emissions of 0.0139 ± 0.0620 and 0.0021 ± 0.0567 µmol m-2 h-1 respectively, whereas the one Thuja plicata tree exhibited mean uptake of CH4 with a flux of -0.0230 ± 0.0246 µmol m-2 h-1 (Table 4.1). Overall, nine trees (14.8% of trees) exhibited CH4 emission, and four trees (6.6% of trees) 47 exhibited CH4 uptake (Table 4.2). However, 48 trees (78.7% of trees) had both emissions and uptake occurring simultaneously on different parts of the tree stem. From the entire set of flux measurements (n=236) conducted on 61 trees at the MS site 117 (49.6%) deployments detected CH4 emission and 103 (43.6%) showed CH4 uptake. Sixteen of the 236 CH4 flux measurements were below the MDF and are reported as 0 µmol m-2 h-1. 4.2.2 Engelmann Spruce – Subalpine Fir (ESSF) At the Engelmann Spruce – Subalpine fir (ESSF) site, the 30 trees studied in seven quadrats had a mean CH4 flux of -0.0335 ± 0.0570 µmol m-2 h-1 (Table 4.1). The mean flux from all three species was negative, corresponding to net uptake of CH4 on tree stems at this site. Only one tree showed positive CH4 emissions compared to 19 trees that exhibited CH4 uptake (Table 4.2). Nine trees (31% of trees) showed both emissions and uptake of CH4 simultaneously on different parts of their stem. The CH4 fluxes from Picea engelmanii (n = 10) ranged from -0.1208 to 0.0562 µmol m-2 h-1 with a mean of -0.0328 ± 0.0410 µmol m-2 h-1. Abies lasicarpa (n = 7) CH4 fluxes ranged from -0.3399 to 0.0437 µmol m-2 h-1 and had a mean CH4 flux of -0.0414 ± 0.0928 µmol m-2 h-1. Thuja plicata (n = 13) also showed mean CH4 uptake (-0.0299 ± 0.0496 µmol m-2 h-1) ranging from -0.1362 to 0.0930 µmol m-2 h-1. Overall, the total set of individual measurements (n=69) conducted on 30 trees at the ESSF site had 23.2% (n=16) CH4 emissions and 73.9% (n=51) CH4 uptake. Only two of the 68 tree stem flux measurements were below the MDF. 4.2.3 Coastal Western Hemlock (CWH) Methane flux measurements were conducted on 35 trees in ten quadrats at the Coastal Western Hemlock (CWH) site. The mean CH4 flux for tree stems at this site was -0.00007 ± 0.0457 µmol m-2 h-1, ranging from -0.1242 to 0.1614 µmol m-2 h-1 (Table 4.1). Similar to the MS and ESSF sites, Thuja plicata (n = 1) exhibited CH4 uptake with a mean CH4 flux of -0.0459 µmol m-2 h-1. 48 Populus balsamifera (n = 2) at CWH was the only deciduous tree species present in quadrats at all three study sites. The two P. balsamifera trees were located in the same quadrat and had a mean CH4 flux of -0.0072 ± 0.0405 µmol m-2 h-1. Abies amabilis (n = 20) and Tsuga heterophylla (n = 12) had mean CH4 fluxes of -0.0010 ± 0.0463 and 0.0062 ± 0.0461 µmol m-2 h-1 respectively. Overall, nine trees (25% of trees) exhibited CH4 emissions, and 16 trees (44.4% of trees) exhibited CH4 uptake (Table 4.2). Eleven trees (41.3% of trees) showed both CH4 emission and uptake simultaneously on different parts of their stems. From the entire set of CH4 flux measurements (n=76), 31 (40.7%) showed CH4 emissions and 40 (52.6%) showed CH4 uptake. Five of the 76 tree stem measurements were below MDF and are reported as 0 µmol m-2 h-1. 49 Table 4.1 Summary of tree stem CH4 flux measurements. Values are reported in units of µmol CH4 m-2 h-1. Site Species Min Mean Max SD Number of trees MS -0.2063 0.0063 0.2387 0.0587 61 Picea engelmanii -0.2063 0.0139 0.2183 0.0620 23 Abies lasicarpa -0.1950 0.0021 0.2387 0.0567 37 Thuja plicata -0.0514 -0.0230 -0.0085 0.0246 1 ESSF -0.3399 -0.0335 0.0930 0.0570 30 Picea engelmanii -0.1208 -0.0328 0.0562 0.0410 10 Abies lasicarpa -0.3399 -0.0414 0.0437 0.0928 7 Thuja plicata -0.1362 -0.0299 0.0930 0.0496 13 CWH -0.1242 -0.00007 0.1614 0.0457 35 Abies amabilis -0.12418 -0.0010 0.1614 0.0463 20 Tsuga heterophylla -0.0494 0.0062 0.1609 0.0461 12 Thuja plicata -0.0673 -0.0459 -0.0245 0.0303 1 Populus balsamifera -0.0603 -0.0072 0.0339 0.0405 2 50 Table 4.2. Summary of CH4 uptake and emissions for trees and individual stem measurements at each study site ('no flux’ = flux below MDF). Totals are presented in the number and the percentage of the total for that site. Site Trees exhibiting CH4 emissions (n, %) Trees exhibiting CH4 uptake (n, %) Trees exhibiting CH4 uptake and emissions (n, %) Tree measurements exhibiting CH4 emissions (n, %) Tree measurements exhibiting CH4 uptake (n, %) Tree measurements exhibiting no flux (n, %) MS 9 (14.8%) 4 (6.6%) 48 (78.7%) 117 (49.6%) 103 (43.6%) 16 (6.8%) ESSF 1 (3.4%) 19 (65.5%) 9 (31.0%) 16 (23.2%) 51 (73.9%) 2 (2.9%) CWH 9 (25%) 16 (44.4%) 11 (30.6%) 31 (40.7%) 40 (52.6%) 5 (6.6%) 4.2.3 Methane Fluxes Between Sites. When comparing the CH4 fluxes from the tree stem measurements at each site, regardless of tree species, each site exhibited variation in the CH4 fluxes (Figure 4.1). The MS site is the only one that showed net CH4 emissions from tree stems, whereas the EESF and CWH sites showed net uptake of CH4 (Table 4.1). The EESF site exhibited more CH4 uptake on tree stems compared to the tree stem measurements at the other two sites (Table 4.1). 51 Figure 4.1. Box plot showing CH4 fluxes for tree stem measurements at each site. The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data and the dots represent data points. 4.2.4 Methane Flux Differences Between Tree Species. Methane stem fluxes for tree species were not significantly different from each other (p-value = 0.111), however, when looking at the statistical differences between species and site (Figure 4.2), Picea engelmanii was significantly different between EESF and MS sites, but the other tree species were not. The MS site had the largest range in values and the most fluxes measured. The range in values at each site shows trees both emit and uptake CH4 on stems leading to an approximately net zero flux. Abies lasicarpa exhibited the largest range in fluxes (Figure 4.2) from 0.2387 µmol m-2 h-1 at the MS site to -0.3399 µmol m-2 h-1 at the ESSF site (Table 4.1). Those values were the overall maximum and minimum tree stem flux values observed in this study. Thuja plicata was the only conifer species present at all three sites and exhibited primarily 52 CH4 uptake. There were no significant differences in CH4 fluxes for T. plicata between sites. Further, a linear mixed effects model revealed no statistical differences from species within sites (p-value = 0.565). Figure 4.2 Boxplot of the CH4 fluxes for each tree species presented by site (blue = CWH, yellow = ESSF, grey = MS). The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data. Overall, CH4 fluxes for all tree species in this study were very small in comparison to the published CH4 fluxes from deciduous trees (Table 4.3); however, measured values in this study were above minimum detectable fluxes (MDFs) calculated for each measurement. Only 23 chamber deployments yielded flux measurements that were less than the MDF. Tree stem fluxes from this study are similar to values reported for boreal upland Pinus sylvestris by Machacova et al. (2016), which to our knowledge is the only other published study to date that has investigated CH4 emissions from coniferous tree stems bark surface. 4.2.5 Methane Flux Relationships with Environmental Variables Environmental variables that were measured in situ are reported in Appendices C (air temperature, soil moisture and soil temperature) and F (tree stem diameter). Air temperature was 53 recorded at the time of the flux measurement and was significantly correlated with the CH4 flux (p-value = 1.59x10-6). Tree stem diameter was not significantly correlated with CH4 flux (p-value = 0.9662). Soil moisture and soil temperature were both not significantly correlated with the tree stem CH4 flux (MS soil moisture: p-value = 0.3772 note: soil moisture was only measured at the MS site and therefore the correlation of soil moisture and tree stem CH4 flux only applies to the MS site; Soil temperature: p-value = 0.5479; soil temperature was recorded at all sites). 4.3 Global Comparison of Tree Stem CH4 Fluxes Table 4.3 compares CH4 fluxes from this study to other published tree stem emissions in wetland and upland environments. The units for flux rates from this study have been converted to a mass basis to facilitate comparison with other studies. Methane fluxes measured in this study are much smaller than those reported previously for wetland environments and deciduous tree species (Table 4.3). Stem fluxes from wetland trees in both tropical and temperate environments ranged from 17.18 to 567.9 µmol m-2 h-1, and all studies were conducted on deciduous trees with the exception of Vainio et al. (2022) which also included wetland coniferous trees in Norway. The latter study reported the lowest tree stem CH4 flux value from existing published studies (Table 4.3). Methane fluxes from tree stem reported for upland forests ranged from 6.3 to 103.1 µmol m-2 h-1 for deciduous tree species. Data from Covey et al. (2012) are excluded due to differences in sampling methods used in that study. Machacova et al. (2016) was the only other study to our knowledge that investigated CH4 fluxes from coniferous tree stems from upland forests and reported flux values. The study investigated tree stem CH4 fluxes from Scots pine in southern Finland and reported an average flux value of 0.005 µmol m-2 h-1 from tree stems in dry plots. The flux value is comparable to the average tree stem CH4 fluxes observed in this study -0.032 ± 54 0.914 µmol m-2 h-1, and was also similar to the mean MDF of tree stem fluxes in this study (Mean MDF = 0.004 µmol m-2 h-1) Wang et al. (2021) investigated tree stem CH4 fluxes from upland conifer trees in a boreal forest in China and concluded that the fluxes were not substantial compared to emissions from other regional ecosystems. Most values measured in the study were below their MDF threshold of 10000 𝜇𝐿 𝐿−1 of CH4. The general conclusion of the study was that coniferous trees in upland forests exhibit very small CH4 fluxes compared to deciduous trees and trees located in wetland environments. The small variation in CH4 fluxes from these two studies (Machacova et al., 2016 and the current study) and different sites could be attributed to the different biogeoclimatic zones and their associated environmental variables, tree ages, tree species, and forest type (boreal vs temperate), microbial communities and other factors. A better understanding of the factors that drive CH4 fluxes in upland conifer trees could explain the variation observed between species and locations. Although these CH4 fluxes are small, upscaling the values to a landscape scale has the potential to yield a much larger CH4 sink or source that could be offsetting or contributing to the CH4 sink from upland forest soils. This topic will be explored further in Chapter 6. This study is the only one to date to report an average uptake of CH4 rather than emissions from the tree stems (Table 4.3). Some studies have observed low rates of CH4 uptake in both wetland and upland forests (e.g., Pitz et al., 2018; Vainio et al., 2022; Table 4.3). Additionally, Barba et al. (2021) observed tree stem CH4 uptake in 20% of deciduous trees in an upland forest. These studies were conducted primarily on deciduous trees, so the higher CH4 emission rates from the deciduous species typically will offset tree stem CH4 uptake. 55 The number of trees exhibiting CH4 emissions and uptake from our study varied by site, with the ESSF site having the highest rate of tree stem CH4 uptake (79.7% of measurements), and CWH and MS having close to half of measurements showing uptake of CH4 (53.3% and 43.8% respectively; Table 4.2). At the MS site, 78.7% of trees showed both emissions and uptake occurring simultaneously, and the ESSF and CWH sites exhibited 31.0% and 30.6% (respectively) of trees experiencing uptake and emissions simultaneously. The larger proportion of trees showing emissions and uptake at the MS site may be due to bias as a result of the higher number of measurements, which was approximately twice that of the ESSF and CWH sites. Moreover, the collection of flux measurements at different heights from the whole tree survey may reveal heterogeneity on the barks surface as well, which could indicate preferential gas transport pathways, heterogeneity of heartwood rot, and or variation in microbial communities (Barba et al., 2019; Covey & Megonigal, 2019; Lenhart et al., 2012; Li et al., 2020). This complex heterogeneity of tree stem surface uptake and emissions of CH4 could result in net zero flux of CH4, however, it’s possible to miss areas of flux or uptake when surfaces are heterogeneous on a small scale. Using multiple measurements from a single tree stem appears to be necessary to accurately quantify the extent to which spatial variability of stem surfaces yields either a net positive or negative CH4 flux for individual trees. 56 Table 4.3. Comparison between the tree stem methane fluxes for this study and published studies. 57 4.4 Environmental Variables Driving Tree Stem CH4 Fluxes Studies conducted on upland forests have predicted soil moisture (Pitz & Megonigal, 2017), stem moisture (Barba et al., 2021; Wang et al., 2017), soil temperature (Li et al., 2020), tree species (Barba et al., 2019; Covey & Megonigal, 2019; Pitz & Megonigal, 2017), and the microbial communities in the tree stem and the soil (Covey et al., 2012; Li et al., 2020; Welch et al., 2019) to be key factors that affect tree stem CH4 emissions. Of the environmental variables measured, soil temperature (p-value = 0.5479) and soil moisture (p-value = 0.3772) were not correlated with tree stem fluxes within or between sites in this study. 4.4.1 Soil moisture Variations in soil moisture are expected to follow overall trends from the biogeoclimatic zones and all three sites were underlain by the same soil class. Since the CWH biogeoclimatic zone is one of the moistest areas of the province, the soil moisture at the CWH site would also likely be high relative to the other two sites. We expected that if the tree stem CH4 fluxes were driven by soil moisture, then there would be the highest CH4 emissions in the CWH site, and they would be different from the MS site. However, the fluxes from these two sites were similar to each other, and the ESSF site was different from the other two sites, since it was experiencing higher rates of CH4 uptake. We also found that the highest CH4 fluxes came from the MS site, and the CWH and ESSF sites exhibited more uptake than emissions from the tree stems. These findings suggest that the soil moisture does not explain the differences in CH4 fluxes in mature upland conifer forests. These findings contradict those from Pitz et al. (2018), who investigated CH4 fluxes from deciduous trees along a soil moisture gradient from upland forests, wetland forests, and transitional zone forests. They observed differences in the tree stem CH4 fluxes from these three 58 zones, which could be due to physiological adaptations observed in wetland trees which lead to CH4 emissions from soil-produced CH4. Our study was conducted on coniferous species on only upland soils. Differences in coniferous and deciduous trees could also explain the differences in the CH4 fluxes from these two studies. 4.4.2 Tree Stem Moisture Wang et al. (2017) predicted stem moisture was the main factor driving CH4 production and emissions in upland trees in both coniferous and deciduous species. Barba et al. (2021) also observed increasing CH4 emissions with increased stem water content in Hickory trees. Although we were not able to quantitatively measure the heartwood water content, observations were recorded regarding the stem moisture from the tree cores taken at each site. The tree cores from the CWH had the highest stem moisture and the MS had the lowest stem moisture, which followed assumptions relating to moisture gradients by biogeoclimatic zones. These findings differ from the conclusions from Wang et al. (2017) since the tree stem CH4 fluxes from the CWH were similar to the MS site. 4.4.3 Soil and Air Temperature Soil and air temperatures both have the potential to impact the tree stem CH4 fluxes since CH4 production and consumption are driven by microbial metabolic activity that occurs at an optimal temperature (Li et al., 2020a). Soil temperatures did not correlate with the tree stem CH4 fluxes in this study; however, this is likely due to the small ranges in temperature that existed during the study. Soil temperature is driven by solar radiation and lags air temperature increases and diurnal variations. All measurements in the study were conducted during daylight hours in the late summer and early fall, so differences associated with temperature would be less prevalent than if the CH4 fluxes were collected year-round. Unlike wetland environments where the CH4 is 59 produced in the soil, CH4 fluxes in upland environments are not expected to be affected by the soil temperature. Air temperature was correlated with the tree stem CH4 fluxes (p-value = 1.59x10-6) which agrees with other studies which found that the tree stem temperature was a controlling factor for the tree stem CH4 fluxes in deciduous tree species (Barba et al., 2021; Han et al., 2022). Although we did not exclusively measure tree stem temperature, it is possible that the small variations in CH4 fluxes could be attributed to the stem temperature, since there was small variation in air and soil temperature in each site over a condensed sampling period, but further testing would be required to confirm this. 4.5 Differences in Angiosperm and Gymnosperm Species Many studies have investigated deciduous species, which are mostly angiosperms (Table 4.3). Fewer studies have investigated the CH4 fluxes from the tree stems of coniferous species (Table 4.3; with the exception of Covey et al., 2012; Machacova et al., 2016; Vainio et al., 2022; Wang et al., 2021), which are gymnosperms. Of the published studies, there is a difference in tree stem CH4 fluxes between these two groups, which could be attributed to physiological differences in angiosperm and gymnosperm species. Aside from the reproductive differences between these two groups, there are also differences in the wood (Sperry et al., 2006). Gymnosperms are softwood trees, where angiosperms typically are hardwood trees, and these differences are related to wood density, pore spaces in the wood, cell morphology, strength and weight of wood, and their growth (Sperry et al., 2006). Gymnosperms have tracheid cells which are smaller than the long vessels created by angiosperm cells (Pitterman et al., 2006). The tracheid cells in gymnosperms (conifer trees) aid in conducting efficiency which favors water transport, protection against cavitation from freeze thaw cycles, and strength of the structure against implosion which allows trees to grow much larger in 60 comparison to angiosperm (Carriquí et al., 2020; Pittermann et al., 2006; Pittermann & Sperry, 2003, 2006; Sperry et al., 2006). These adaptations to environmental conditions allow for coniferous species to grow in nutrient and water restricted areas (Sperry et al., 2006), however, they affect gas diffusion through wood (Sorz & Hietz, 2006) and could explain the low CH4 fluxes in tree stems. Diffusion of oxygen was studied in hardwood and softwood trees in a laboratory and found that diffusion was lowest in conifers (Sorz & Hietz, 2006). 4.6 Coniferous Species-Specific Differences The differences in tree stem CH4 fluxes between species were not significant overall but small variation could still be observed. Thuja plicata, Picea engelmanii, and Abies lasicarpa were present in multiple sites, and there was variation in the CH4 fluxes between sites for the same species. This indicates that the changes in CH4 fluxes are likely driven by environmental variables unique to the sites rather than species. Picea engelmanii was significantly different between sites (MS and ESSF), however, the overall tree stem CH4 fluxes from all species were significantly different between these two sites, which indicates the differences are attributed to the environment. The differences within species are assumed to be minimal since all trees within a site were on similar terrain (Barba et al., 2021), but any small variation between species at each site could be attributed to differences in microbial communities, bark thickness, bark roughness, tree stem diameter and other physiological differences (Barba et al., 2019; Covey & Megonigal, 2019; Gorgolewski et al., 2022). The variation in tree stem CH4 fluxes within a site could be attributed to physiological differences between species, and therefore the variation in CH4 fluxes is likely driven by both the environment and species differences. A strong correlation was not present between tree stem diameter and CH4 fluxes within or between species. The absence of a relationship may be due to the bias introduce by selecting 61 mature forest sites and only measuring trees over 20 cm in diameter; however, the lack of correlation associated with the limited number of small tree stem diameters agreed with the findings of Barba et al. (2021), which also reported a similar absence of correlation. Populus balsamifera was the only deciduous tree species investigated in this study. Based on physiological differences between coniferous and deciduous trees, CH4 fluxes were expected to be higher from P. balsamifera; however, negligible emissions were detected. As noted in section 4.4.1, other studies of deciduous upland trees have reported significantly higher CH4 stem fluxes; however, the sample size for P. balsamifera was n=2 in this study and sampling of more individual trees would be needed to confirm the current findings of low emissions. The two P. balsamifera trees exhibited minor evidence of heartwood rot based on tree cores that were collected, and in general, appeared to be in a healthy condition. 4.7 Summary Heterogeneity in tree stem CH4 fluxes was observed between conifer trees located in each site from the three different biogeoclimatic zones in British Columbia and between conifer tree species. Methane fluxes were small from the stems of upland conifer trees investigated in this study, but there were patterns of CH4 emission and uptake between the sites. These differences can be partly attributed to environmental variables that differ between sites, and physiological differences between the tree species; however, the relationship between emission rates and environmental variables seem less prevalent than in wetland environments where trees develop stem adaptations to enhance root aeration in response to soil conditions. Tree surfaces at the study sites commonly both emitted and consumed CH4 simultaneously on different parts of the stems indicating that factors that control tree stem CH4 fluxes in upland conifer species likely vary on fine spatial scales. Environmental variables may influence emission processes on an 62 individual basis, but more likely is that fine scale heterogeneity in stem structures is controlling the observed differences in low rates of CH4 uptake and emission on these tree stems. 63 5.0 Results and Discussion on Conks, Bark Anomalies, and Heartwood Rot in Upland Conifer Tree Stems 5.1 Background Individual trees can experience different processes based on their environmental conditions, microbiome, age, condition, and other factors. Therefore, understanding how tree stem CH4 fluxes differ on an individual scale is crucial in understanding and predicting how tree stem CH4 fluxes occur on whole forest or landscape scales. Processes involved in CH4 gas production in upland forests are driven through a chain of biotic decay processes. In the heartwood of living trees, fungi are usually the first step in this process, as they break down lignin and cellulose into substrates suitable for consumption by fermentive bacteria and methanogenic archaea (Barba et al., 2019; Beckmann et al., 2011; Covey & Megonigal, 2019; Hietala et al., 2015; Jeffrey et al., 2021; Mukhin & Voronin, 2007). Fungal species that infect heartwood have the potential to enhance CH4 production by breaking down the heartwood of living trees. Trees that are infected by fungal pathogens commonly also are inhabited by conks, which are the fungal fruiting bodies of the saprotrophic fungi. Conks are porous structures that connect to the heartwood of the tree and potentially have the ability to act as a venting structure for CH4 produced within trees; however, this possibility has not been investigated to date to our knowledge. Additionally, fungal spores spread via wind and enter tree stems through wounds and other areas of imperfections on the bark surface. There is potential that these wounds also can be an emission pathway for internally produced CH4. Jeffrey et al. (2021) reported that bark anomalies were hotspots for CH4 emissions from deciduous wetland tree species. Furthermore, Gorgolewski et al. (2022) observed that major and minor stem wounds 64 were areas of CH4 emission in upland deciduous trees and speculated that fungus could be another potential factor in upland tree stem CH4 fluxes. This chapter aims to (i) evaluate whether conks formed as a result of heart rot fungus are a potential venting structure for internally produced CH4 in trees, and (ii) identify potential relationships between tree stem CH4 fluxes and the presence of wounds and other bark anomalies, the health status of trees, and spatial patterns on the bark surface. 5.2 Results Data from the whole tree survey is included in Appendix D. The minimum detectable flux (MDF) was calculated for all tree stem CH4 flux values in this section and presented in Appendix B. The MDF was calculated to evaluate the significance of CH4 fluxes determined from each chamber deployment. The MDF calculation employed is specific to high-frequency laser-based analyzers as discussed in section 3.5.1 (Christiansen et al., 2015). 5.2.1 Heartwood Rot The trees that were measured in the quadrat surveys were cored at breast height to determine if there was heart rot present beneath the stem area where the flux chamber was placed during measurements. Tree cores were categorized as containing rot or no rot. If a tree core showed signs of heartwood decay, staining or discoloration, the tree was considered to have heartwood rot. Forty-nine trees in total showed evidence of heartwood rot (Table 5.1). The Engelmann Spruce – Subalpine Fir (ESSF) site had the highest percentage of trees with evidence of heartwood rot (56.7%) followed by the Coastal Western Hemlock (CWH) site (42.9%). The Montane Spruce (MS) site had the lowest percent of trees with evidence of heartwood rot (27.8%; Table 5.1). 65 Table 5.1 Trees from the quadrat surveys showing evidence of heartwood rot. Site Number of trees with core rot % of trees with core rot MS 17 27.8 ESSF 17 56.7 CWH 15 42.9 Figure 5.1 displays tree stem CH4 fluxes for each study site grouped by trees that did and did not exhibit heartwood rot in core samples. There was no statistically significant difference in CH4 fluxes between trees that have and did not have evidence of heartwood rot (p- value = 0.634) nor was there a statically significant difference in emissions when trees were grouped by species (p-value = 0.432). 66 Figure 5.1 Boxplot of CH4 fluxes for tree stems that showed, and did not show, evidence of heartwood rot separated by site (blue = CWH, yellow = ESSF, grey = MS). The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data. Two trees from the CWH site that had evidence of heartwood rot exhibited CH4 fluxes after the tree was cored. Immediately following coring, the hole in the tree stem was covered for 10 minutes to allow the tree stem to cool after frictional heating during coring and for internal gases to re-equilibrate. The hole was then uncovered, and the chamber was placed overtop for an additional five-minute flux measurement. The first tree (Q2T2) had CH4 uptake on the bark surface before coring, but after coring exhibited high rates of CH4 emission from the hole that penetrated to heartwood. The second tree (T2T3) initially had CH4 emissions from the bark surface and subsequently lower rates of CH4 flux from heartwood after coring (Table 5.2). These two trees had wet heartwood and were expected to contain a high concentration of CH4 internally similar to reports by Barba et al. (2019), Covey et al. (2012) and Wang et al. (2021). 67 Table 5.2 Tree CH4 flux for CWH Q2 trees before and after tree cores were collected. All units are in µmol m-2 h-1. Tree Chamber placement Flux (µmol m-2 hr-1) Q2T2 Bark Surface -0.007 Hole to heartwood 0.161 Q2T3 Bark Surface 0.047 Hole to heartwood 0.011 5.2.2 Presence of Conks on tree Stems Methane fluxes were measured from two additional trees at the ESSF site that had conks but that were not within the defined quadrats. The trees infected with conks were investigated for CH4 fluxes following the standard procedure on areas of the tree with no conks at breast height or within 10 cm of conks, and then additional flux measurements were taken over the conks. Conks were present at the ESSF site only on Picea engelmannii species and only living trees infected with conks were studied. Methane fluxes over the conks ranged from -0.0597 to 0.0065 µmol m-2 h-1 with a mean flux rate of 0.0129 ± 0.0403 µmol m-2 h-1 (Table 5.3). Methane fluxes from trees that contained conks, but measured on areas of clear bark, ranged from -0.0740 to -0.0337 µmol m-2 h-1 with a mean CH4 flux rate of -0.0366 ± 0.0278 µmol m-2 h-1 (Table 5.3). All trees infected with conks exhibited CH4 uptake on clear bark, but CH4 flux from areas containing conks showed both emission and uptake. All flux measurements conducted on trees infected with conks, including measurements made over conks, were above minimum detectable fluxes (MDFs). Bark anomalies include conks, cracks, knots, and major and minor damage. There were no statistically significant differences between CH4 fluxes measured over bark anomalies compared 68 to clear bark (p-value = 0.650). Only one knot on the tree bark was identified and measured as part of this study and it exhibited CH4 emissions that were significantly different statistically than from cracks (p-value = 0.0456) and wounds (p-value = 0.0192); however, the small sample size for CH4 emissions from knots does not allow for a conclusive finding that knots emit CH4 at a higher rate more generally than other types of bark anomalies. Table 5.3 Summary of CH4 fluxes measured from Picea engelmannii infected with conks at the EESF site. All units are in µmol m-2 h-1. Chamber placement Min Mean Max SD Conks -0.060 0.007 0.056 0.040 Uninfected bark -0.074 -0.034 0.013 0.028 5.2.3 Whole Tree Survey The whole tree survey detected both emissions and uptake of CH4 on the tree stem with no distinct spatial pattern (Figure 5.2). There no axial (i.e., height) trends in CH4 flux were present, which is the typical pattern for wetland trees transporting soil-produced CH4 to the atmosphere (Barba et al., 2019; Jeffrey et al., 2021; Pangala et al., 2015). There also are no patterns radially that suggest CH4 fluxes are occurring preferentially from any given stem region. The mean CH4 flux from the 20 measurments on the tree stem was 0.0021 ± 0.0246 µmol m-2 h-1 and ranged from -0.0420 to 0.0534 µmol m-2 h-1. Interpolation of the flux values suggests a possible relationship between CH4 emission and bark anomalies, in particular, from cracks and wounds (Figure 5.2); however, the sampling was preliminary and further study is needed to confirm conclusively that bark anomalies of this type are consistent sources of CH4 flux. 69 Figure 5.2 Tree CH4 fluxes from the whole tree survey interpolated on the tree surface. White dots indicate bark anomalies where branches previously existed. Black dots indicate the location of cracks and wounds in the bark. Warmer colors indicate areas of CH4 emission and cooler colors indicate areas of CH4 uptake. The axial bar shows how CH4 flux rates change with height. 5.2.4 Tree Condition The overall tree condition was used as an indicator for tree health. There was no statistically significant difference in CH4 fluxes between trees that were alive and trees that were dead (p-value = 0.439). Trees that were alive were separated further into two groups: “alive” or “unhealthy” for additional analysis. Trees that were grouped as “alive” had no observable wounds on the bark, their foliage was plentiful and green, and the tree foliage and stems in general did not show signs of distress. Trees that were classified as “unhealthy” had bark wounds and/or exhibited signs of distress, such as excessive sap running, and/or had sparse foliage. Within the quadrat surveys, 12 of the trees were dead (six from the MS site, four from the ESSF site, and two from the CWH site), 42 of the trees were considered unhealthy (21 from the MS 70 site, 16 from the ESSF site, five from the CWH site), and the remaining trees (n=72) were grouped into the ‘alive’ category. There were no significant differences in the CH4 fluxes between these three categories (p-value = 0.663). 5.3 Methanogenesis in the Heartwood of Tree Stems In upland forests, CH4 emitted from the stems of trees rooted in well-drained aerobic soil typically originates in heartwood (Barba, 2019; Covey, 2012; Wang et al., 2016, 2021). Heartwood rot occurs when endophytes, usually fungi, break down lignin and cellulose into lower molecular weight substrates that can be metabolized under oxygen-poor conditions by fermentive bacteria and methanogenic archaea (Aarti et al., 2015; Barba, 2019; Barba et al., 2021; Covey & Megonigal, 2019; Griffin & Carson, 2018; Li et al., 2020a; Sieber, 2007; Tirsit, 2020; Zeikus & Henning, 1975). Saprotrophic fungi have been reported to produce CH4 independently of archaea in oxic environments (Lenhart et al., 2012b); however, the extent to which such organisms contribute to methanogenesis in tree stems has not been investigated to date. Although heartwood rot indicates the presence of fungi, and possibly communities of fermentive bacteria and methanogenic archaea (Covey & Megonigal, 2019), the occurrence of heartwood rot in 42.2% of trees in this study did not correlate significantly with CH4 stem emissions. Results from this study are consistent with previous reports that heartwood rot in both coniferous and deciduous tree species do not favor higher rates of CH4 flux (Barba et al., 2021; Covey, 2012). Low rates of CH4 emission from tree stems occur regardless of tree health status in upland forests. Additionally, the presence of heartwood rot did not correlate directly to moisture conditions. Site ESSF had the highest percentage of trees with heartwood rot, but trees at the CWH site had the highest long-term exposure to high levels of moisture based upon typical conditions in the CWH biogeoclimatic zone. 71 Variations in the internal microflora of trees may account for differences in stem CH4 fluxes. A study of microbial communities in tree stems reported that Methanobacterium was a dominant microorganism in the heartwood of some tree species but not others (Li et al., 2020a). Trees inhabited by Methanobacterium contained high concentrations of CH4. In contrast, trees where the microorganism was absence had little to no CH4 in heartwood (Li et al., 2020a). The study also determined that phenolic compounds and other acetone-soluble extractives suppress CH4 production when present in heartwood of upland trees by inhibiting production of secondary metabolites necessary for archaeal production of CH4 (Li et al., 2020b). The presence of other common microorganisms in tree stems, including other methanogenic archaea species, has been reported in deciduous trees (Putkinen et al., 2021b; Zeikus & Henning, 1975). Their absence in coniferous trees could be a factor in the low CH4 emissions observed from coniferous trees at the MS, ESSF and CWH sites and other studies (Machacova et al., 2016). A high concentration of CH4 in the heartwood of upland conifer trees was reported by Covey (2012) although the extent to which core bit heating may have contributed to thermal methanogenesis in that study is uncertain. Internal CH4 concentrations from the two trees cored in this study showed elevated concentrations in only one after a period of time was permitted for wood cooling and re-equilibration of internal stem gases. The difference in gas composition between the two trees may reflect differences in microbial community composition similar to the study by Li et al. (2020a). Microbial communities differ between living and dead trees, and studies have shown higher CH4 emissions from living trees compared to dead trees (Covey & Megonigal, 2019; Warner et al., 2017). The greater production and emission of methane from living trees is thought to occur because labile non-structural photosynthates are a key substrate for methanogens and those 72 compounds typically decline in concentration after tree death. Warner et al. (2017) also found that emissions from living trees to be much higher than any potential uptake from decaying fallen trees, however, their study was conducted on deciduous species. Warner et al. (2017) also observed both uptake and emission of CH4 from woody debris from dead trees, which could be attributed to variation in aerobic and anaerobic fungi (Lenhart et al., 2012b; Mukhin & Voronin, 2007). The current study only investigated standing trees in different health conditions and detected variation in CH4 fluxes that did not correlate significantly with tree condition regardless of whether the tree was dead or alive. 5.4 Microbial Communities on the Bark Surface of Tree Stems Methane fluxes on the bark surface of upland trees are heterogeneous and potentially impacted by a variety of factors, making flux estimation unpredictable. The whole tree survey (section 5.1.3) detected both emission and uptake of CH4 occurring simultaneously (Figure 5.2). Radial heterogeneity of CH4 emissions is expected for upland trees where CH4 is produced in the heartwood. Emission of CH4 will be favored in areas where gas is transported though pockets of heartwood rot and varying tissue types (sapwood or hardwood; Barba, 2019; Barba et al., 2021; Covey, 2012; Covey & Megonigal, 2019; Wang et al., 2016). The lack of axial emission patterns, where emission rates decrease with stem height, indicates that trees are likely not transporting CH4 produced in soil (Barba et al., 2021), which is common in wetland environments (Barba, 2019; Jeffrey et al., 2021; Moldaschl et al., 2021; Pangala et al., 2013; Terazawa et al., 2015). However, it is possible CH4 uptake occurs concurrently on stem surfaces, masking possible emission patterns by consuming a portion of the CH4 flux (Barba et al., 2021). Axial tree stem emissions patterns are distinct under wetland conditions (e.g., Jeffrey et al., 73 2021) which makes it unlikely that the emission pattern could be completely masked by CH4 uptake. Microbial communities that live on bark surfaces are unique and diverse, and could play a role in tree stem fluxes that occur independently of the heartwood microbiome (Bižić et al., 2020; Lenhart et al., 2012b; Lyu & Lu, 2018). Jeffery et al. (2021) studied methane-oxidizing bacteria, which cause CH4 uptake on bark surfaces in lowland tree species and reported a strong correlation between their abundance and rates of CH4 uptake. They found that bark dwelling methane-oxidizing bacteria consumed approximately 36% of CH4 emissions from the tree stems of Melaleuca quinquenervia. Bark-dwelling methane-oxidizing bacteria are likely to also exist on upland conifer species, reducing potential rates of CH4 emission. They are the primary means by which CH4 concentration in closed chambers was likely to have decreased below ambient levels in forest air in this study. Alternately, resin on conifer stems also could decrease potential emission rates or possibly, yield negative flux rates. Spruce resin, even in small qualities, has been shown to absorb CH4; however, the mechanism behind the uptake is unclear (Machacova et al., 2020). Resin was present on the surface of many trees in this study but could not be quantified. Its capacity for absorbing CH4 could help to explain some of the observed heterogeneity in CH4 fluxes on bark surfaces. 5.5 Spatial Heterogeneity of CH4 Fluxes on the Bark Surface With the potential for methanotrophy and methanogenesis to occur on the bark surface simultaneously (Jeffrey et al., 2021; Lenhart et al., 2012; Lyu & Lu, 2018; Machacova et al., 2020), the CH4 fluxes are spatially heterogenous with little to no discernable pattern as shown in Figure 5.2. There is also no clear emission pattern associated with the bark surface spatially, however, there does appear to be higher emissions from areas where cracks occur in the bark 74 surface, which agrees with other reports to date (Gorgolewski et al., 2022; Jeffrey et al., 2021). Although the tree in this analysis did not have wounds or major damage as observed in Gorgolewski et al. (2022), bark anomalies, such as knots or cracks, were still assumed to be potential transport pathways for release of internally produced CH4 since they are areas of the stem containing weaker wood (Gorgolewski et al., 2022; Szewczyk et al., 2017). There is still potential for methanogenesis to occur in heartwood, but the CH4 is either being consumed by methanotrophy or is not in proximity to bark anomalies that enable escape from the tree stem. Trees that had wounds and prominent bark anomalies did not exhibit higher rates of CH4 emission as predicted from the results of Gorgolewski et al. (2022) which were based on a study of CH4 emissions from upland deciduous trees. Since both studies (Gorgolewski et al., 2022 and the current study) were conducted in upland forests on well-drained soil, the trees did not have hypertrophied lenticels, aerenchyma tissue, and adventitious roots, which occur under wet soil conditions (Pangala et al., 2013; Rusch & Rennenberg, 1998). These physiological adaptions are key structures for gas transport and emissions from wetland trees, and in their absence, gas transport and emissions in upland tree species have been predicted to come from areas of heartwood rot and damage or anomalies on the bark surface (Barba, 2019; Gorgolewski et al., 2022). This discrepancy also could be explained by potential differences in microbial communities and physiological difference between angiosperm and gymnosperm species (Covey & Megonigal, 2019). 5.6 Conks: Connection between heartwood, bark surface, and atmosphere Conks are the fruiting bodies of fungal pathogens and are external signs of heartwood rot. Phellinus pini is one of the most common heartwood rot fungus that infect living conifers and has the potential to inhabit all species investigated in this study (Glaeser and Smith, 2016; 75 Government of Canada, 2013). The role of conks, including those produced by P. pini, is to spread spores by wind and other mechanisms that enter trees through wounds, knots, and twig stubs (Szewczyk et al., 2017). All sites included in this study contained trees in a variety of health conditions, including trees that had wounds susceptible to infection by fungal spores. Conks only appear on trees that have an extreme level of infection and that have experienced heartwood rot for a long period of time. All sites had mature trees which would have potential to exhibit heartwood rot, but the presence of conks would depend on environmental conditions that favor growth of fungal pathogens and the duration of infection time. Since fungal spores from these particular fungal species are spread via wind, trees with conks present are usually in proximity to one another, which was observed at the ESSF site. Various fungal species were observed at each site on the ground or on dead, decaying trees; however, ESSF was the only site where conks were present on living trees. All conks included in this study were evaluated on their visual and behavioral attributes and were identified as P. pini. Wood decay from this fungus occurs in pockets, separated by sound wood (Glaeser and Smith, 2016), which contributes to spatial flux heterogeneity at the bark surface. Additionally, the presence of a P. pini conk indicates a small decay column around the conk (Glaeser and Smith, 2016; Wallis et al., 1980). It is possible that CH4 produced through metabolic activity related to P. pini. is not transported from pockets of rot and therefore is not emitted into the atmosphere. Cavities in the wood are produced only in cases of advanced decay and take a long time to form (Glaeser and Smith, 2016). Most of the tree cores collected that showed signs of heartwood rot were mainly stains and wood discolorations. Only a few cores had loss of wood or distinct cavities evident in the tree core. 76 Fungal features on the bark surface, such as conks, were predicted to be a potential pathway for CH4 emissions (Gorgolewski et al., 2022; Mukhin & Voronin, 2007). Due to their porous structures and direct connection between the heartwood and the atmosphere, they could act as a potential conduit for gas exchange. However, CH4 fluxes measured from the conks were not distinct from emission rates collected on undamaged bark in close proximity to the conks. Additionally, CH4 fluxes on trees that had conks (both over conks and on the surrounding bark), were similar to CH4 fluxes from trees that did not have conks, signs of fungal infection, or heartwood rot. It is possible that CH4 produced is not transported or diffused through the conk. Any potential CH4 emissions also could be consumed before it is transported to the conk. Conifers also have the ability to ‘compartmentalize’ which is a strategy to resist the spread of infection. A feature of this process is the production of resin (Blanchette & Biggs, 2013; Smith, 2006; Smith et al., 2016). The resin created as a defense mechanism also could acting as a potential sink for CH4 emissions from the tree stem (Machacova et al., 2020). 5.7 Summary Heterogeneity on bark surfaces and processes that create the heterogeneity likely exist on a microbial community level. Trees that were observed to be with and without heartwood rot in the vicinity of flux measurements had variable flux patterns. High spatial variability in CH4 fluxes was measured on bark surfaces, and trees that were alive and dead exhibited variations in the CH4 fluxes. Conks did not serve as venting structures on the trees investigated, however, further study is needed on a greater variety of tree and conk species to conclusively eliminate the possibility that conks facilitate emission of CH4 produced in the stems of trees experiencing heart wood rot. The presence of CH4 uptake and emission on bark surfaces indicates the likelihood that both methanogens and methanotrophs are present in and on tree stems, which complicates 77 characterization of mechanisms and the full potential for trees to emit internally produced CH4. Knowledge of the species and abundances of microorganisms that exist on bark surfaces and in the heartwood of trees may further enhance understanding of the potential for upland conifer trees to contribute to the global CH4 cycle. 78 6.0 Results and Discussion on the Net Methane Exchange in Upland Conifer Forests 6.1 Background High affinity methanotrophic bacteria capable of acquiring energy and carbon from trace concentrations (<2 ppmv) of methane inhabit soil in upland forests and consume atmospheric CH4 that diffuses into the subsurface (Dlugokencky et al., 2011). However, upland soils are not a ubiquitous sink for CH4 and can produce and emit CH4 during periods when soil moisture is excessive, which leads to occluded pores and oxygen-depletion. The extent to which tree stems may emit or consume CH4 in these environments further increases uncertainty about the net balance of CH4 fluxes in mature upland conifer forests. Tree stem and soil CH4 fluxes are generally measured from a small area, and when scaled to forest or landscape levels, total emissions or consumption can be significant. In particular, small CH4 fluxes from tree stems can be considerable when scaled to the equivalent surface area of cylinders (e.g., 2prh where r and h are tree radius and height, respectively) compared to the area of ground occupied by a tree (2pr). Small fluxes per m2 from a tree stem have the potential to offset or contribute to the soil CH4 sink in upland forests. Accurate quantification of CH4 budgets in upland forests requires accounting for tree stem CH4 fluxes to determine their magnitude relative to soil uptake of atmospheric CH4. In addition to better characterization of current net CH4 emissions of CH4 from upland forest, accurately assessing the contribution of tree stem emissions relative to the soil sink for atmospheric CH4 is important for predicting the effects of changes in atmosphere-biosphere exchange of CH4 in these globally significant ecosystems as a result of climate change or changes in forestry 79 management practices. This chapter reports analysis of tree stem CH4 fluxes and rates of CH4 uptake by soil in the sites, situated in different biogeoclimatic zones investigated in British Columbia. 6.2 Soil CH4 Fluxes Soil CH4 fluxes were measured in 13 of 20 quadrats at the Montane Spruce (MS) site, four of seven quadrats at the Engelmann Spruce – Subalpine Fir (ESSF) site, and four of ten quadrats at the Coastal Western Hemlock (CWH) site (Table 3.1). Detailed quadrat maps for all sites showing the location of soil flux measurements are presented in Appendix 1. The soil in all quadrats studied exhibit CH4 uptake (i.e., negative CH4 fluxes; Table 6.1). All CH4 fluxes measured were greater than minimum detectable flux (MDF) values (Appendix B). The MDF was calculated for each individual flux measurement to evaluate whether the flux rate was above the analytical threshold for detection. The mean CH4 flux for the MS site was -2.7592 ± 0.9593 µmol m-2 h-1 and ranged from -4.7290 to -0.0013 µmol m-2 h-1. The EESF site had a mean CH4 flux of -2.4220 ± 0.5731 µmol m-2 h-1 and ranged from -3.4180 to -1.3420 µmol m-2 h-1. The CWH site had a mean CH4 flux of -2.2451 ± 0.8708 µmol m-2 h-1 and ranged from -3.5662 to -0.9113 µmol m-2 h-1 (Table 6.1). Table 6.1 Summary of soil CH4 flux measurements. All units are in µmol m-2 h-1. Site Min Mean Max SD MS -4.729 -2.759 -0.002 0.959 ESSF -3.418 -2.422 -1.342 0.573 CWH -3.566 -2.245 -0.911 0.871 80 6.3 Soil Temperature and Soil Moisture Soil moisture and soil temperature data were recorded in each quadrat at the MS, ESSF and CWH sites and are listed in Appendix C. Soil temperature at the MS site ranged from 1.7 to 9.9°C, averaging 5.6 ± 2.6°C and were measured during September and October 2022. Soil temperature was measured at the ESSF and CWH sites during July 2023 and consequently, are higher than the values at MS and more similar to one another, averaging 11.5 ± 0.5 and 13.6 ± 1.8°C, respectively. Soil temperature at the ESSF site ranged from 11.0 to 12.4°C and at the CWH site from 11.2 to 15.5°C. Figure 6.1 shows rates of CH4 uptake by soil in the subset of quadrats where soil fluxes were measured. Regression analysis showed that soil temperature correlates with soil CH4 uptake rates (p-value = 0.0034). Soil moisture was recorded only at the MS site in the quadrats where soil CH4 fluxes were measured. Soil moisture does not highly correlate with CH4 flux at the site (p–value = 0.2991). 81 Figure 6.1 Soil CH4 uptake and soil temperature for each quadrat at the (a) MS, (b) ESSF, and (c) CWH sites. Methane uptake (µmol m-2 h-1) rates are shown in blue and soil temperature (°C) is plotted in orange. The two data types are plotted on a common y-axis, which was possibly by representing soil uptake rates for methane as positive fluxes into the soil. Note that the y-axis scale for MS data differs from ESSF and CWH to accommodate the higher soil temperatures in July. The box shows the interquartile range and the line within the box is the mean values. Lines extending from the box represent the first and last quartiles of data. 6.4 Methane Flux Comparison and Scaling Soil exhibited ubiquitous uptake of atmospheric CH4 at all sites whereas tree stems showed both positive and negative CH4 emissions. Rates of CH4 uptake by soil were much larger than tree fluxes at all three sites (Figure 6.2; p-value = <2.0 x10-16). The average tree CH4 flux was -0.009 ± 0.054 µmol m-2 h-1 with a range of -0.049 to 0.239 µmol m-2 h-1 and the average soil CH4 flux was -2.475 ± 0.801 µmol m-2 h-1 and a range of -3.418 to -0.002 µmol m-2 h-1. 82 Figure 6.2 Boxplot showing CH4 fluxes for trees and soils at the three study sites (blue = CWH, yellow = ESSF, grey = MS). The box shows the interquartile range with the line contained within displaying the mean. The lines extending from the box represent the first and last quartiles of data. Methane flux rates for each tree stem were scaled to represent the entire tree truck based on the stem diameter at breast height and a conservative stem height of 15 m. All tree stem fluxes, diameters and scaled flux rates for the three study sites are listed in Appendix E. The tree stem CH4 emissions rates are a representative value for each tree; however, there is a bias introduced in the estimates that results from measuring CH4 flux at only one height and by measuring the flux rates from a surface on the tree stem which does not contain shoots or foliage. Branches and foliage of trees have the potential to emit or take up atmospheric CH4 as well (Gorgolewski et al., 2023b) and were not investigated in this study because of accessibility issues and challenges deploying and fitting flux chambers at significant heights on trees. 83 Rates of CH4 uptake by soil also were scaled for each quadrat based on the percentage of exposed soil, which was determined using the individual quadrat maps from Appendix A. Appendix F contains a summary of the percentage of exposed soil in each quadrat, which was estimated by subtracting the area of ground covered by obstructions that includes fallen trees, boulders, and streams. These forms of ground cover impede or eliminate gas exchange between soil and the atmosphere, limiting consumption of atmospheric CH4 by soil methanotrophs. Appendix F also outlines unit conversions and calculations used to determine the representative soil CH4 flux for each quadrat. Quadrats in which soil CH4 fluxes were not measured used an uptake rate that was the average of soil CH4 uptake fluxes for the entire site. The average CH4 uptake rate was then scaled for the individual quadrat (values also included in Appendix F). Net CH4 flux within each quadrat was then calculated by subtracting total tree fluxes from the total soil uptake of atmospheric CH4 (Table 6.2). 84 Table 6.2 Scaled tree and soil CH4 fluxes for each quadrat and site. The net CH4 flux per quadrat is the soil flux minus the tree flux. All units are in µmol · h-1. Site Quadrat Total tree flux for quadrat (µmol · h-1) Soil flux for quadrat (µmol · h-1) Net flux per quadrat (µmol · h-1) MS 1 1.0 -248.3 -247.2 2 -0.2 -155.6 -155.9 3 -1.4 -195.9 -197.3 4 2.7 -301.8 -299.2 5 0.2 -209.9 -209.7 6 -0.2 -208.1 -208.3 7 4.2 -166.7 -162.5 8 1.2 -167.9 -166.7 9 -1.0 -242.0 -243.0 10 0.0 -95.7 -95.7 11 0.5 -195.9 -195.4 12 0.6 -80.5 -79.9 13 0.1 -167.9 -167.8 14 1.1 -112.6 -111.5 15 -2.0 -221.0 -223.0 16 2.4 -105.1 -102.7 17 2.0 -195.9 -193.9 18 0.3 -207.8 -207.5 19 0.4 -193.8 -193.5 20 -0.1 -139.9 -140.1 ESSF 1 -4.0 -224.4 -228.4 2 -5.7 -252.8 -258.5 3 -2.5 -79.7 -82.2 4 -3.7 -104.7 -108.4 5 -2.1 -156.1 -158.2 6 -1.1 -152.9 -154.0 7 -1.8 -178.4 -180.2 CWH 1 0.1 -200.7 -200.6 2 -0.6 -189.5 -190.1 3 -2.3 -111.5 -113.8 4 -1.7 -222.3 -224.0 5 0.1 -180.6 -180.6 6 2.9 -168.6 -165.7 7 -1.7 -156.6 -158.2 8 -0.6 -163.5 -164.0 9 1.7 -202.5 -200.9 10 0.1 -130.8 -130.7 85 6.5 Rates of Soil Uptake of Atmospheric CH4 The locations where soil flux chambers were deployed in quadrats in this study were areas of even ground that allowed the chamber edge to seal to the soil. Selection of these areas may have introduced an unavoidable bias in the flux data that were collected. However, the MS, ESSF and CWH sites all had gentle slopes and similar topography, and the effect of the introduced bias should have been comparable for the three locations. Variation in the measured CH4 fluxes should be caused primarily by differences in environmental variables between the three sites. All soils investigated in this study exhibited uptake of atmospheric CH4 as expected from other reports to date of well-drained upland soil (e.g., Brachmann et al., 2020; Dlugokencky et al., 2011; Feng et al., 2022; Le Mer & Roger, 2001). Notably, the measured rates of CH4 uptake were comparable to values reported in a study of Brunisol soils in British Columbia conducted by Brachmann et al. (2020) (Table 6.3). Branchmann et al. (2020), reported a mean flux from all sites to be -58 µg m-2 h-1, which is similar to those reported in the current study. The study locations in the Branchmann et al. (2020) were at higher elevations than those in the current study and were experiencing warmer soil temperatures which could account for the higher uptake rates at some of their sites. Notably, the low elevation site was experiencing CH4 emissions, which was not observed in any of the sites of the current study (Branchmann et al, 2020). 86 Table 6.3 Comparison between the soil CH4 fluxes measured in this study and reported by Brachmann et al. (2020) for other sites in British Columbia. Note the change in flux units from other tables and figures that were made to facilitate the comparison. Study Flux (µg m-2 h-1) Elevation (cm) Soil Temperature (°C) Air Temperature (°C) Soil Order Brachmann et al. (2020) Alpine -112 2100 20.9 ± 0.6 22.6 ± 0.8 Brunisol high elevation - 33 2100 24.2 ± 1.0 24.2 ± 0.9 Brunisol mid elevation -155 2050 27.1 ± 0.8 24.9 ± 0.5 Brunisol low elevation 1 2000 27.1 ± 0.5 24.7 ± 0.4 Brunisol Current Study MS -44.2 ± 15.4 1370 5.6 ± 2.6 8.4 ± 5.5 Brunisol ESSF -39.0 ± 9.2 1480 11.6 ± 0.5 20.4 ± 2.7 Brunisol CWH -32.0 ± 14.0 220 13.6 ± 1.8 18 ± 2.4 Brunisol Uptake of atmospheric CH4 can be offset by periods of CH4 emission that occur when soil becomes oxygen-depleted due to saturation of pore spaces with water, which creates anoxic micro-environments that are favorable for methanogenesis. Soils at the three study sites had similar CH4 uptake rates, but the mean rate at the wettest site (CWH) was more positive than at the MS and ESSF sites, possibly reflecting the impact of higher long-term moisture content in soil. Notably, even at a lower temperature the well-drained soil at the MS site exhibited the highest uptake rate for atmospheric CH4. However, a well-defined relationship between soil moisture and CH4 uptake rates could not be established at the MS site due to the short temporal duration of sampling and the small range in moisture variation that occurred during that time period. In contrast, soil temperature varied more widely, and was measured at all three sites, which yielded a significant correlation with rates of soil uptake of atmospheric CH4 (Feng et al., 2022). 87 6.6 Scaling Methane for the Whole Ecosystem On an areal basis (e.g., per m2), rates of soil uptake of CH4 were an order of magnitude higher than tree stem CH4 fluxes in the three study sites (Figure 6.2). However, the total area of tree stem surface can vary widely within forests of different ages and climate zones. Thus, small emissions from trees have the potential to enhance or offset uptake of CH4 by soil in forested uplands. At the three sites investigated in British Columbia, the net effect on CH4 uptake in upland quadrats was both positive and negative because of variations in tree stem CH4 fluxes. Trees that exhibited CH4 uptake contributed further to the forest soil sink for atmospheric CH4 in the quadrat (Table 6.2). Methane emissions from tree stems caused a small decrease in net CH4 exchange in the quadrats. However, overall the three upland conifer forests were net sinks for atmospheric CH4 because of the significantly higher CH4 oxidation rates that existed in the well-drained Brunisols. 6.7 Spatial Interpolation and Net Ecosystem CH4 Flux Spatial interpolation of the scaled tree and soil CH4 fluxes revealed that the magnitude of CH4 uptake exhibits spatial variation, but that it does not appear to be impacted by the topography at the three sites (Figure 6.3). Each of the sites is characterized by topography having a modest slope, which contributes to well-drained soil conditions. Variations in CH4 fluxes from soil and tree stems do not appear to correlate with elevation change at the sites. The observed differences in fluxes measured in the quadrats are likely caused by small-scale changes in moisture and temperature that result from heterogeneity in ground cover. The changes cannot be generalized from the existing data set, but point to a need for fine scale measurements of CH4 fluxes accompanied by detailed measurements of environmental factors known to impact CH4 production and oxidation. Development of process-based models of biosphere-atmosphere of 88 trace gases are capable of estimating net ecosystem exchange gases such as CH4, but require refinement to include landscape heterogeneity to attain a high level of accuracy in predicting ecosystem response to perturbations such as climate change and land management practices. This study and analysis included only CH4 fluxes from tree stems and soil, and there is scope still for a more complete representation of the net CH4 fluxes in upland forests. For example, woody debris and fallen trees, tree branches/shoots and foliage, and other woody plants and shrubs all have the potential to contribute to net CH4 fluxes in forests (Carmichael et al., 2014; Gorgolewski et al., 2023b; Warner et al., 2017). Further complexity also can be added by considering how CH4 fluxes change temporally on both diurnal and seasonal time scales (Barba et al., 2021). 89 Figure 6.3 Panels a to c show the topography at the three study sites with the black square outlining the 100 x 100 m area that contained the study quadrats. Contour maps to the right of each topographic map show the spatial interpolation of the scaled methane fluxes for each site, which is the net sum of soil methane uptake and tree stem fluxes. The contour maps were created using an inverse distance weighted interpolation to estimate how CH4 uptake varied throughout the landscape. Darker areas represent the lowest methane uptake rates and lighter areas have the highest CH4 uptake rates. Each panel has an individual graded scale for CH4 uptake rates. The direction of the north area varies because the topographic and interpolated maps are oriented with the site access road on the south side. 90 6.8 Summary Mature upland forests in three sites that were situated in different biogeoclimatic zones in British Columbia were determined to be a net sink for atmospheric CH4. Upland soils exhibited a strong capacity for bacteria oxidation of CH4 at atmospheric mixing ratios (~1.9 ppmv) at rates that were an order of magnitude higher per m2 than fluxes from the stems of upland trees. Coniferous trees exhibited both emission and uptake of CH4 on stems, which either offset or increased net rates of atmospheric CH4 uptake in the uplands quadrats. Tree stems contribute to heterogeneity in biosphere-atmosphere exchange of CH4 in upland forests, but CH4 fluxes in the three sites investigated in British Columbia were dominated by soil uptake of atmospheric CH4. 91 7.0 Conclusions 7.1 Conclusions Tree stem CH4 fluxes from mature upland conifer trees in three sites in British Columbia were small and heterogeneous. Both positive and negative fluxes were detected from trees at the sites, which were situated in three different biogeoclimatic zones. Variations in CH4 fluxes between individual trees from the same species at the same site indicated that stem fluxes are likely controlled by biological factors rather than environmental variables. Physiological traits within the same species should vary only slightly between trees of similar age and health; however, the stem diameter, presence of heartwood rot from tree cores, and the overall condition by appearance did not correlate significantly with tree stem fluxes. Environmental variables did not vary greatly at individual sites during the brief study period. Therefore, the variations of CH4 fluxes on a single tree stem, or between trees from the same species, can be attributed primarily to biological factors. Variations in CH4 fluxes between different species at the same site are best explained by physiological differences. In contrast, variations in tree stem CH4 fluxes from the same species at different sites are likely to result from differences in environmental variables. Tree species were similar at the MS and ESSF sites, but the latter site contained a higher percentage of trees exhibiting CH4 uptake rather than emissions. The difference likely resulted from lower moisture levels in soil and tree stems, which reflected the conditions of the specific forest environment and its biogeoclimatic zone. Variations in tree stem CH4 fluxes were most prominent in the ESSF site and were different from MS and CWH sites. The ESSF site was the only one that contained living trees with abundant 92 conks, indicating long-term fungal infection. A greater prevalence of fungal infection was predicted to cause higher CH4 emission rates from tree stems, bark anomalies, and the conks themselves; however, the site had the highest rates of tree stem CH4 uptake, which is speculated to have been caused by a greater abundance of methanotrophic bacteria. It is unclear why higher rates of fungal infection and tree decay would have resulted in a higher capacity for high affinity methanotrophy. The CWH and MS sites were predicted to have different CH4 fluxes due to differences in moisture between the sites and their associated biogeoclimatic zones; however, the tree stem CH4 emissions from these sites were similar, despite the species differences. There were however differences in the rates of soil CH4 uptake rates for the two sites, which likely is due to the moisture difference between the two biogeoclimatic zones. Environmental variables that impact production and consumption of CH4 are likely the same for soil and trees since they impact the metabolic activity of microorganisms responsible for anaerobic decay in both settings. Differences in CH4 fluxes could be attributed to the diversity of microbial communities present in and gas diffusivity supplying CH4 uptake in both soil and tree stems. Tree stem CH4 fluxes were very small compared to rates of soil uptake of atmospheric CH4 and in all three sites and all the sites were determined to be net CH4 sinks after scaling tree and soil emissions. In conclusion, upland coniferous forests are heterogeneous with respect to CH4 fluxes from tree stems and soil. Although CH4 fluxes from mature upland coniferous trees are small compared to emission rates reported to date for lowland deciduous trees and wetland trees, they have the potential to both offset or enhance CH4 uptake from upland forest soils. This study demonstrated that fungal infection of conifers leading to heart wood rot and the formation of conks does not 93 result in significant production or emission of CH4 from upland forests. Methane budgets in well-drained upland forests are dominated by soil uptake of atmospheric CH4 with a lesser role for tree stems in both uptake and emission of biological CH4. 7.2 Limitations The limitations of this study mostly included the limited ability to measure all biological and environmental variables required to fully understand the complex nature of CH4 fluxes in upland conifer forest ecosystems. Since the CH4 fluxes are driven by microbial communities, identifying the microorganisms in each tree and soil sample would have complimented the data, however, it would be a novel dataset that would not be highly transferable to understanding other ecosystems and was therefore not included in this study. Additionally, flux measurements from the branches and foliage of trees would be beneficial in understanding the full tree CH4 flux dynamics. Each site was in a mature and developed forest and the branches on the trees were not accessible and could therefore not be examined in this study. The use of multiple mature conifer forest sites from three biogeoclimatic zones enhanced the certainty of results by showing the consistency of low flux rates from upland conifer trees regardless of the moisture and species variations. The scaling and application of this research would be limited to mature conifer forests with similar biogeoclimatic zones and environmental parameters. These CH4 fluxes are also only indicative of dry summer climates and seasonal variation would occur at these sites when microbial metabolic activity decreases with cool winter temperatures. Temporal studies would also be valuable to understand the diurnal variability of tree stem fluxes in conifer trees. Further complications are introduced by anthropogenic activity. 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An obligate anaerobe isolated from wetwood of living trees. Antonie van Leeuwenhoek, 41(1), 543–552. https://doi.org/10.1007/BF02565096 107 Appendices Appendix A: Quadrat maps for each 10 m x 10 m quadrat from all three sites. Each map is oriented with the access road along the south side. These are detailed maps of each quadrat drawn approximately to scale. Maps for the Montane Spruce (MS) site are first from quadrats 1-20, followed by Engelmann Spruce – Subalpine Fir (ESSF) site from quadrats 1-7, and lastly the maps for the Coastal Western Hemlock Site (CWH) quadrats 1-10. Appendix B: Methane Fluxes and their minimum detectable flux for each tree stem and soil measurement. Q_T_: Q= Quadrat number, T= tree number in QX. This table contains all tree stem methane flux measurements in two units, their minimum detectable flux value, and r2 value for all sites. Measurements are identified by site, quadrat number, and tree number. Each tree contains multiple measurements. Appendix C: Environmental variables for each quadrat and tree. This table contains the measurements recorded for soil temperature, soil moisture, air temperature and tree stem diameter at each quadrat and for each tree in the survey. Appendix D: Methane Fluxes from the Whole Tree Survey This table contains the 20 flux values for each measurement taken on the stem of tree 2 from quadrat 6 at the Montane Spruce (MS) site for the whole tree survey. The aspect/ direction of the tree stem at the location of each flux measurement is also recorded. Appendix E: Tree stem methane fluxes scaled to tree surface area. A fixed and conservative height of 15 m was used for all mature trees in the flux calculations. This table contains the site, quadrat number, stem diameter, stem circumference, and calculated surface area of the tree stem (assuming a fixed height of 15 m) for each tree stem flux measurement. This data was used to calculate a scaled total methane flux per tree which is also listed in the table. Appendix F: Soil methane fluxes scaled to quadrat area. This table contains the averaged soil methane fluxes for each site and quadrat. It also contains the estimated percentage and area of covered ground (covered by fallen trees, boulder, and other obstructions). This data was used to calculate an estimated soi flux per quadrat which is also listed in the table and was used for scaling the methane fluxes. 108 Appendix A: Quadrat maps for each 10 m x 10 m quadrat from all three sites. Each map is oriented with the access road along the south side. Appendix A: Quadrat maps for each quadrat from all three sites. Q1 Q2 Q3 Q5 Q4 109 Q6 Q10 Q9 Q8 Q7 110 Q15 Q14 Q13 Q12 Q11 111 Q16 Q17 Q18 Q19 Q20 112 Q1 Q2 Q3 Q4 Q5 113 Q6 Q7 114 Q1 Q2 Q3 Q4 Q5 115 Q6 Q7 Q8 Q9 Q10 116 Appendix B: Methane Fluxes and their minimum detectable flux for each tree stem and soil measurement. Q_T_: Q= Quadrat number, T= tree number in QX. Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Tree Q1T1 1.66E-04 1.03E-02 3.13E-03 0.06 MS Tree Q1T1 2.00E-05 1.24E-03 2.68E-03 0.00 MS Tree Q1T1 2.99E-04 1.86E-02 4.14E-03 0.15 MS Tree Q1T1 -6.60E-05 -4.11E-03 4.02E-03 0.03 MS Tree Q2T1 -4.90E-05 -3.03E-03 4.76E-03 0.00 MS Tree Q2T1 -2.82E-04 2.27E-02 2.83E-03 0.20 MS Tree Q2T1 2.27E-03 1.41E-01 3.88E-03 0.87 MS Tree Q2T1 3.41E-04 2.13E-02 4.31E-03 0.14 MS Tree Q2T2 -9.00E-05 -5.63E-03 3.94E-03 0.01 MS Tree Q2T2 -3.58E-04 -2.23E-02 4.08E-03 0.13 MS Tree Q2T2 -2.94E-04 -1.83E-02 4.49E-03 0.12 MS Tree Q2T2 5.50E-05 3.42E-03 4.08E-03 0.00 MS Tree Q2T3 -6.20E-05 -3.84E-03 4.13E-03 0.00 MS Tree Q2T3 7.40E-05 4.61E-03 3.73E-03 0.01 MS Tree Q2T3 7.90E-05 4.93E-03 5.66E-03 0.00 MS Tree Q2T3 -4.00E-05 -2.47E-03 9.08E-03 0.00 MS Tree Q2T4 1.86E-04 1.16E-02 5.42E-03 0.05 MS Tree Q2T4 -2.70E-04 -1.68E-02 3.18E-03 0.18 MS Tree Q2T4 5.00E-06 3.25E-04 3.89E-03 0.00 MS Tree Q2T4 9.50E-05 5.92E-03 4.19E-03 0.00 MS Tree Q3T1 3.01E-04 1.88E-02 4.03E-03 0.14 MS Tree Q3T1 -2.80E-04 -1.75E-02 3.22E-03 0.14 MS Tree Q3T1 -2.98E-04 -1.86E-02 2.82E-03 0.18 MS Tree Q3T1 2.59E-04 1.62E-02 3.99E-03 0.13 MS Tree Q3T2 1.63E-04 1.02E-02 3.84E-03 0.03 MS Tree Q3T2 -3.46E-04 -2.16E-02 4.30E-03 0.18 MS Tree Q3T2 2.30E-05 1.41E-03 3.98E-03 0.00 MS Tree Q3T2 -2.86E-04 -1.78E-02 4.81E-03 0.10 MS Tree Q3T3 -1.27E-03 -7.90E-02 4.26E-03 0.70 MS Tree Q3T3 -6.28E-04 -3.92E-02 4.37E-03 0.43 MS Tree Q3T3 -4.92E-04 -3.06E-02 4.22E-03 0.30 MS Tree Q3T3 5.47E-04 3.41E-02 3.98E-03 0.29 MS Tree Q3T4 8.50E-05 5.27E-03 4.58E-03 0.00 MS Tree Q3T4 -2.96E-04 -1.85E-02 3.98E-03 0.21 MS Tree Q3T4 -1.68E-04 -1.05E-02 4.09E-03 0.05 MS Tree Q3T4 -3.33E-04 -2.08E-02 4.09E-03 0.18 MS Tree Q3T5 -1.17E-03 -7.27E-02 3.90E-03 0.68 MS Tree Q3T5 -1.95E-04 -1.22E-02 3.96E-03 0.04 117 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Tree Q3T5 -1.17E-03 -7.27E-02 4.07E-03 0.27 MS Tree Q3T5 2.17E-04 1.35E-02 5.12E-03 0.08 MS Tree Q4T1 1.26E-03 7.85E-02 3.40E-03 0.65 MS Tree Q4T1 3.83E-03 2.39E-01 2.85E-03 0.96 MS Tree Q4T1 7.49E-04 4.67E-02 3.72E-03 0.44 MS Tree Q4T1 2.43E-03 1.52E-01 5.93E-03 0.82 MS Tree Q5T1 8.51E-04 5.31E-02 2.86E-03 0.70 MS Tree Q5T1 1.26E-03 7.82E-02 4.43E-03 0.74 MS Tree Q5T1 -1.55E-03 -9.66E-02 8.12E-03 0.66 MS Tree Q5T1 3.90E-04 2.43E-02 4.15E-03 0.22 MS Tree Q5T2 7.80E-05 4.83E-03 3.88E-03 0.01 MS Tree Q5T2 1.96E-04 1.22E-02 4.60E-03 0.06 MS Tree Q5T2 -3.18E-04 -1.98E-02 4.13E-03 0.17 MS Tree Q5T2 -2.52E-04 -1.57E-02 4.11E-03 0.13 MS Tree Q6T1 5.67E-04 3.54E-02 2.72E-03 0.55 MS Tree Q6T1 5.83E-04 3.64E-02 4.11E-03 0.40 MS Tree Q6T1 -8.71E-04 -5.43E-02 2.82E-03 0.63 MS Tree Q6T1 1.83E-04 1.14E-02 3.97E-03 0.06 MS Tree Q6T2 7.19E-04 4.48E-02 3.99E-03 0.50 MS Tree Q6T2 4.00E-04 2.49E-02 4.01E-03 0.22 MS Tree Q6T2 9.00E-06 5.45E-04 4.89E-03 0.00 MS Tree Q6T2 1.20E-05 7.60E-04 4.09E-03 0.00 MS Tree Q6T3 -9.54E-04 -5.95E-02 3.50E-03 0.61 MS Tree Q6T3 -3.90E-05 -2.42E-03 4.65E-03 0.00 MS Tree Q6T3 5.50E-04 3.43E-02 4.07E-03 0.34 MS Tree Q6T3 2.30E-04 1.43E-02 3.97E-03 0.09 MS Tree Q6T4 -5.00E-06 -3.00E-04 5.24E-03 0.00 MS Tree Q6T4 3.50E-05 2.21E-03 3.38E-03 0.00 MS Tree Q6T4 -7.07E-04 -4.41E-02 3.68E-03 0.48 MS Tree Q6T4 -4.58E-04 -2.86E-02 5.09E-03 0.27 MS Tree Q6T5 1.61E-03 1.00E-01 4.89E-03 0.83 MS Tree Q6T5 3.65E-04 2.28E-02 4.07E-03 0.22 MS Tree Q6T5 1.90E-04 1.19E-02 4.78E-03 0.06 MS Tree Q6T5 -5.08E-04 -3.17E-02 4.15E-03 0.20 MS Tree Q6T6 8.29E-04 5.17E-02 4.09E-03 0.62 MS Tree Q6T6 -9.10E-05 -5.69E-03 4.03E-03 0.02 MS Tree Q6T6 3.04E-04 1.89E-02 3.97E-03 0.17 MS Tree Q6T6 1.50E-04 9.38E-03 4.76E-03 0.00 MS Tree Q7T1 5.00E-05 3.13E-03 4.29E-03 0.01 MS Tree Q7T1 -2.82E-04 -1.75E-02 3.60E-03 0.14 118 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Tree Q7T1 -5.61E-04 -3.50E-02 3.84E-03 0.37 MS Tree Q7T2 -9.96E-04 -6.21E-02 5.87E-03 0.79 MS Tree Q7T2 3.66E-04 2.28E-02 4.35E-03 0.20 MS Tree Q7T2 -6.65E-04 -4.14E-02 2.99E-03 0.50 MS Tree Q7T2 -6.57E-04 -4.09E-02 2.72E-03 0.54 MS Tree Q7T3 5.55E-04 3.46E-02 4.55E-03 0.22 MS Tree Q7T3 1.78E-03 1.11E-01 7.06E-03 0.78 MS Tree Q7T3 1.59E-04 9.92E-03 3.83E-03 0.01 MS Tree Q7T3 -3.95E-04 -2.46E-02 3.98E-03 0.22 MS Tree Q7T4 -3.13E-03 -1.95E-01 4.04E-03 0.90 MS Tree Q7T4 2.39E-03 1.49E-01 3.98E-03 0.87 MS Tree Q7T4 2.31E-04 1.44E-02 3.61E-03 0.08 MS Tree Q7T4 6.71E-04 4.18E-02 3.95E-03 0.39 MS Tree Q7T5 -3.69E-04 -2.30E-02 3.99E-03 0.15 MS Tree Q7T5 1.93E-04 1.21E-02 3.99E-03 0.06 MS Tree Q7T5 1.43E-04 8.92E-03 3.97E-03 0.03 MS Tree Q7T5 -3.73E-04 -2.33E-02 4.42E-03 0.17 MS Tree Q8T1 5.76E-04 3.59E-02 3.99E-03 0.29 MS Tree Q8T1 3.48E-04 2.17E-02 2.31E-03 0.20 MS Tree Q8T1 1.87E-04 1.17E-02 3.24E-03 0.05 MS Tree Q8T1 -2.22E-04 -1.39E-02 3.66E-03 0.05 MS Tree Q8T2 1.84E-04 1.15E-02 4.01E-03 0.05 MS Tree Q8T2 -2.54E-04 -1.58E-02 4.69E-03 0.07 MS Tree Q8T2 2.06E-04 1.28E-02 3.89E-03 0.06 MS Tree Q9T1 6.16E-04 3.84E-02 3.81E-03 0.35 MS Tree Q9T1 8.40E-05 5.26E-03 2.71E-03 0.01 MS Tree Q9T1 -8.60E-05 -5.33E-03 3.79E-03 0.01 MS Tree Q9T1 -1.02E-03 -6.36E-02 3.89E-03 0.49 MS Tree Q9T2 -8.95E-04 -5.58E-02 3.81E-03 0.52 MS Tree Q9T2 9.47E-04 5.90E-02 2.89E-03 0.63 MS Tree Q9T2 -4.26E-04 -2.66E-02 3.78E-03 0.27 MS Tree Q9T2 -5.23E-04 -3.26E-02 3.72E-03 0.37 MS Tree Q9T3 1.61E-04 1.00E-02 4.77E-03 0.03 MS Tree Q9T3 -3.78E-04 -2.36E-02 5.89E-03 0.13 MS Tree Q9T3 -1.82E-03 -1.13E-01 5.85E-03 0.80 MS Tree Q9T3 -1.70E-03 -1.06E-01 3.93E-03 0.79 MS Tree Q9T4 1.53E-03 9.55E-02 5.85E-03 0.68 MS Tree Q9T4 -1.63E-03 -1.01E-01 4.44E-03 0.71 MS Tree Q9T4 -9.01E-04 -5.61E-02 4.56E-03 0.54 119 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Tree Q9T4 1.34E-03 8.34E-02 4.59E-03 0.82 MS Tree Q10T1 -9.00E-05 -5.62E-03 4.46E-03 0.01 MS Tree Q10T1 1.08E-03 6.76E-02 5.10E-03 0.55 MS Tree Q10T1 -3.51E-04 -2.18E-02 4.82E-03 0.22 MS Tree Q10T1 -1.24E-04 -7.71E-03 4.61E-03 0.02 MS Tree Q10T2 -3.48E-04 -2.17E-02 3.20E-03 0.20 MS Tree Q10T2 3.73E-04 2.33E-02 4.63E-03 0.17 MS Tree Q10T2 1.79E-04 1.12E-02 4.46E-03 0.04 MS Tree Q10T2 -1.04E-04 -6.46E-03 4.37E-03 0.02 MS Tree Q10T3 1.79E-03 1.11E-01 3.32E-03 0.86 MS Tree Q10T3 -7.37E-04 -4.59E-02 3.91E-03 0.47 MS Tree Q10T3 -1.21E-03 -7.54E-02 3.99E-03 0.68 MS Tree Q10T3 -2.18E-04 -1.36E-02 4.05E-03 0.08 MS Tree Q10T4 -2.42E-04 -1.51E-02 2.97E-03 0.08 MS Tree Q10T4 -3.59E-04 -2.24E-02 3.34E-03 0.04 MS Tree Q10T4 -1.85E-04 -1.15E-02 4.05E-03 0.04 MS Tree Q10T4 1.84E-04 1.15E-02 3.27E-03 0.08 MS Tree Q11T1 -1.22E-04 -7.60E-03 3.80E-03 0.02 MS Tree Q11T1 5.06E-04 3.15E-02 3.14E-03 0.42 MS Tree Q11T1 6.41E-04 3.99E-02 4.03E-03 0.43 MS Tree Q11T1 4.56E-04 2.85E-02 3.54E-03 0.28 MS Tree Q12T1 -2.64E-04 -1.65E-02 3.97E-03 0.12 MS Tree Q12T1 1.87E-03 1.16E-01 4.49E-03 0.83 MS Tree Q12T1 -2.96E-04 -1.85E-02 3.97E-03 0.16 MS Tree Q12T1 -5.54E-04 -3.45E-02 3.31E-03 0.44 MS Tree Q12T2 8.30E-05 5.18E-03 4.09E-03 0.01 MS Tree Q12T2 -1.18E-03 -7.34E-02 5.23E-03 0.68 MS Tree Q12T2 -1.88E-03 -1.17E-01 3.28E-03 0.89 MS Tree Q12T2 -2.48E-04 -1.55E-02 4.40E-03 0.10 MS Tree Q12T3 3.05E-03 1.90E-01 4.38E-03 0.92 MS Tree Q12T3 8.54E-04 5.32E-02 4.52E-03 0.50 MS Tree Q12T3 3.38E-04 2.11E-02 5.56E-03 0.14 MS Tree Q12T3 7.63E-04 4.76E-02 4.40E-03 0.54 MS Tree Q12T4 -1.44E-04 -8.96E-03 2.77E-03 0.03 MS Tree Q12T4 -8.25E-04 -5.14E-02 3.99E-03 0.39 MS Tree Q12T4 -1.37E-04 -8.53E-03 3.80E-03 0.02 MS Tree Q13T1 2.40E-03 1.50E-01 3.08E-03 0.92 MS Tree Q13T1 -4.93E-04 -3.07E-02 5.30E-03 0.17 MS Tree Q13T1 -9.01E-04 -5.62E-02 4.94E-03 0.50 120 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Tree Q13T1 -1.85E-04 -1.16E-02 4.24E-03 0.05 MS Tree Q13T2 3.84E-04 2.39E-02 3.89E-03 0.20 MS Tree Q13T2 1.05E-04 6.52E-03 4.15E-03 0.02 MS Tree Q13T2 -9.87E-04 -6.16E-02 3.62E-03 0.67 MS Tree Q13T2 -3.20E-05 -1.97E-03 3.42E-03 0.00 MS Tree Q14T1 1.84E-03 1.15E-01 4.10E-03 0.80 MS Tree Q14T1 3.83E-04 2.39E-02 3.38E-03 0.28 MS Tree Q14T1 5.40E-04 3.37E-02 3.03E-03 0.30 MS Tree Q14T1 3.92E-04 2.44E-02 3.78E-03 0.17 MS Tree Q14T2 -4.60E-04 -2.87E-02 3.94E-03 0.26 MS Tree Q14T2 1.65E-03 1.03E-01 4.23E-03 0.82 MS Tree Q14T2 4.64E-04 2.89E-02 2.95E-03 0.27 MS Tree Q14T2 -8.40E-05 -5.25E-03 3.87E-03 0.01 MS Tree Q14T3 5.27E-04 3.28E-02 4.18E-03 0.36 MS Tree Q14T3 2.24E-04 1.39E-02 4.67E-03 0.04 MS Tree Q14T3 -1.94E-04 -1.21E-02 3.61E-03 0.08 MS Tree Q14T3 -4.24E-04 -2.64E-02 4.08E-03 0.28 MS Tree Q14T4 3.50E-03 2.18E-01 4.02E-03 0.87 MS Tree Q14T4 -2.04E-04 -1.27E-02 7.01E-03 0.05 MS Tree Q14T4 8.53E-04 5.32E-02 3.50E-03 0.61 MS Tree Q14T4 1.10E-04 6.84E-03 2.58E-03 0.02 MS Tree Q14T5 -1.11E-03 -6.89E-02 4.57E-03 0.40 MS Tree Q14T5 -1.93E-04 -1.20E-02 6.77E-03 0.05 MS Tree Q14T5 -2.25E-04 -1.40E-02 4.14E-03 0.05 MS Tree Q14T5 -4.16E-04 -2.59E-02 4.45E-03 0.20 MS Tree Q15T1 -5.83E-04 -3.63E-02 3.10E-03 0.22 MS Tree Q15T1 -1.06E-03 -6.60E-02 4.35E-03 0.59 MS Tree Q15T1 -6.60E-04 -4.11E-02 3.64E-03 0.27 MS Tree Q15T2 6.52E-04 4.06E-02 4.03E-03 0.30 MS Tree Q15T2 -1.29E-03 -8.01E-02 3.95E-03 0.58 MS Tree Q15T2 -1.64E-03 -1.03E-01 4.74E-03 0.79 MS Tree Q15T3 8.50E-05 5.27E-03 3.36E-03 0.00 MS Tree Q15T3 8.86E-04 5.52E-02 4.30E-03 0.22 MS Tree Q15T3 -2.44E-03 -1.52E-01 4.61E-03 0.75 MS Tree Q15T3 -8.90E-04 -5.55E-02 4.47E-03 0.48 MS Tree Q15T4 2.45E-03 1.53E-01 3.99E-03 0.70 MS Tree Q15T4 -3.31E-03 -2.06E-01 3.82E-03 0.87 MS Tree Q15T4 4.63E-04 2.89E-02 4.90E-03 0.11 MS Tree Q15T4 -1.24E-04 -7.72E-03 4.90E-03 0.00 121 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Tree Q16T1 -3.84E-04 -2.39E-02 4.11E-03 0.21 MS Tree Q16T1 -6.44E-04 -4.02E-02 2.36E-02 0.32 MS Tree Q16T1 2.12E-03 1.32E-01 3.00E-03 0.83 MS Tree Q16T1 2.02E-03 1.26E-01 4.63E-03 0.86 MS Tree Q16T2 5.23E-04 3.26E-02 6.57E-03 0.26 MS Tree Q16T2 2.41E-03 1.50E-01 4.26E-03 0.89 MS Tree Q16T2 2.53E-04 1.58E-02 4.15E-03 0.05 MS Tree Q16T2 6.50E-04 4.05E-02 4.88E-03 0.38 MS Tree Q16T3 2.57E-04 1.60E-02 5.41E-03 0.09 MS Tree Q16T3 8.20E-05 5.09E-03 3.96E-03 0.01 MS Tree Q16T3 -2.15E-04 -1.34E-02 4.60E-03 0.06 MS Tree Q16T3 3.59E-04 2.24E-02 4.72E-03 0.23 MS Tree Q17T1 2.25E-03 1.40E-01 4.17E-03 0.91 MS Tree Q17T1 5.08E-04 3.16E-02 3.35E-03 0.29 MS Tree Q17T1 3.60E-04 2.24E-02 4.01E-03 0.22 MS Tree Q17T1 1.07E-03 6.65E-02 4.88E-03 0.64 MS Tree Q17T2 1.34E-03 8.35E-02 3.78E-03 0.78 MS Tree Q17T2 5.93E-04 3.70E-02 4.53E-03 0.37 MS Tree Q17T2 5.33E-04 3.32E-02 4.19E-03 0.32 MS Tree Q17T2 8.59E-04 5.36E-02 5.26E-03 0.58 MS Tree Q18T1 -3.11E-04 -1.94E-02 5.14E-03 0.10 MS Tree Q18T1 3.56E-04 2.22E-02 4.08E-03 0.21 MS Tree Q18T1 -2.17E-04 -1.36E-02 5.85E-03 0.05 MS Tree Q18T1 4.56E-04 2.85E-02 3.98E-03 0.26 MS Tree Q18T2 3.41E-04 2.13E-02 5.11E-03 0.15 MS Tree Q18T2 2.25E-04 1.40E-02 4.23E-03 0.07 MS Tree Q18T2 -5.51E-04 -3.44E-02 3.66E-03 0.29 MS Tree Q18T2 -6.13E-04 -3.82E-02 4.05E-03 0.35 MS Tree Q19T1 -3.77E-04 -2.35E-02 4.69E-03 0.18 MS Tree Q19T1 1.81E-03 1.13E-01 4.87E-03 0.71 MS Tree Q19T1 5.49E-04 3.42E-02 3.94E-03 0.32 MS Tree Q19T1 -1.10E-04 -6.87E-03 3.90E-03 0.02 MS Tree Q19T2 2.40E-05 1.47E-03 4.03E-03 0.00 MS Tree Q19T2 2.26E-04 1.41E-02 4.87E-03 0.06 MS Tree Q19T2 -1.09E-04 -6.81E-03 4.23E-03 0.01 MS Tree Q19T2 -5.57E-04 -3.47E-02 4.55E-03 0.30 MS Tree Q20T1 3.45E-04 2.15E-02 5.48E-03 0.15 MS Tree Q20T1 -5.89E-04 -3.67E-02 5.03E-03 0.34 MS Tree Q20T1 2.73E-04 1.70E-02 4.13E-03 0.11 122 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Tree Q20T1 6.24E-04 3.89E-02 4.05E-03 0.27 CWH Tree Q1T1 -3.11E-04 -1.94E-02 3.93E-03 0.12 CWH Tree Q1T1 3.42E-04 2.13E-02 3.93E-03 0.17 CWH Tree Q2T1 -2.45E-04 -1.53E-02 3.93E-03 0.08 CWH Tree Q2T1 -8.41E-04 -5.24E-02 3.93E-03 0.50 CWH Tree Q2T1 -1.24E-03 -7.73E-02 3.93E-03 0.69 CWH Tree Q2T2 1.84E-04 1.15E-02 3.93E-03 0.05 CWH Tree Q2T2 -1.11E-04 -6.91E-03 3.93E-03 0.02 CWH Tree Q2T2 2.59E-03 1.61E-01 3.93E-03 0.90 CWH Tree Q2T3 1.17E-04 7.28E-03 3.93E-03 0.02 CWH Tree Q2T3 7.61E-04 4.74E-02 3.93E-03 0.51 CWH Tree Q2T3 1.76E-04 1.10E-02 3.93E-03 0.04 CWH Tree Q3T1 -1.08E-03 -6.73E-02 3.93E-03 0.63 CWH Tree Q3T1 -3.93E-04 -2.45E-02 3.93E-03 0.19 CWH Tree Q3T2 4.98E-04 3.10E-02 3.93E-03 0.26 CWH Tree Q3T2 -3.54E-04 -2.20E-02 3.93E-03 0.17 CWH Tree Q3T3 -9.75E-04 -6.08E-02 3.93E-03 0.58 CWH Tree Q3T3 -5.99E-04 -3.74E-02 3.93E-03 0.32 CWH Tree Q4T1 -4.84E-04 -3.02E-02 3.93E-03 0.24 CWH Tree Q4T1 -9.72E-04 -6.06E-02 3.93E-03 0.56 CWH Tree Q4T2 6.10E-05 3.80E-03 3.93E-03 0.00 CWH Tree Q4T2 -7.40E-04 -4.61E-02 3.93E-03 0.40 CWH Tree Q4T3 -1.03E-04 -6.44E-03 3.93E-03 0.64 CWH Tree Q4T3 -1.03E-04 -6.44E-03 3.93E-03 0.01 CWH Tree Q4T4 -6.03E-04 -3.76E-02 3.93E-03 0.32 CWH Tree Q4T4 -2.45E-04 -1.53E-02 3.93E-03 0.08 CWH Tree Q5T1 1.02E-03 6.35E-02 3.93E-03 0.55 CWH Tree Q5T1 -1.99E-03 -1.24E-01 3.93E-03 0.83 CWH Tree Q5T2 5.57E-04 3.47E-02 3.93E-03 0.27 CWH Tree Q5T2 -7.00E-05 -4.34E-03 3.93E-03 0.00 CWH Tree Q5T3 1.10E-04 6.86E-03 3.93E-03 0.02 CWH Tree Q5T3 4.22E-04 2.63E-02 3.93E-03 0.23 CWH Tree Q5T4 -1.90E-05 -1.16E-03 3.93E-03 0.00 CWH Tree Q5T4 7.30E-05 4.58E-03 3.93E-03 0.00 CWH Tree Q6T1 7.30E-05 4.56E-03 3.93E-03 0.00 CWH Tree Q6T1 1.54E-04 9.59E-03 3.93E-03 0.01 CWH Tree Q6T2 1.53E-03 9.54E-02 3.93E-03 0.52 CWH Tree Q6T2 1.81E-04 1.13E-02 3.93E-03 0.01 CWH Tree Q6T3 3.03E-04 1.89E-02 3.93E-03 0.07 123 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 CWH Tree Q6T3 1.06E-03 6.61E-02 3.93E-03 0.56 CWH Tree Q6T3 1.36E-04 8.49E-03 3.93E-03 0.01 CWH Tree Q6T4 4.81E-04 3.00E-02 3.93E-03 0.19 CWH Tree Q6T4 1.21E-03 7.51E-02 3.93E-03 0.58 CWH Tree Q6T5 -1.79E-04 -1.12E-02 3.93E-03 0.03 CWH Tree Q6T5 3.23E-04 2.01E-02 3.93E-03 0.11 CWH Tree Q7T1 5.46E-04 3.41E-02 3.93E-03 0.24 CWH Tree Q7T1 3.80E-05 2.39E-03 3.93E-03 0.00 CWH Tree Q7T2 -2.67E-04 -1.67E-02 3.93E-03 0.10 CWH Tree Q7T2 -2.30E-04 -1.44E-02 3.93E-03 0.07 CWH Tree Q7T3 -7.92E-04 -4.94E-02 3.93E-03 0.50 CWH Tree Q7T3 -8.00E-06 -4.76E-04 3.93E-03 0.00 CWH Tree Q7T4 3.46E-04 2.16E-02 3.93E-03 0.17 CWH Tree Q7T4 -2.95E-04 -1.84E-02 3.93E-03 0.14 CWH Tree Q7T5 -6.33E-04 -3.94E-02 3.93E-03 0.39 CWH Tree Q7T5 -5.07E-04 -3.16E-02 3.93E-03 0.35 CWH Tree Q7T6 -4.90E-04 -3.05E-02 3.93E-03 0.30 CWH Tree Q7T6 -9.70E-05 -6.04E-03 3.93E-03 0.02 CWH Tree Q8T1 -7.92E-04 -4.94E-02 3.93E-03 0.43 CWH Tree Q8T1 2.40E-04 1.50E-02 3.93E-03 0.08 CWH Tree Q8T2 -3.49E-04 -2.18E-02 3.93E-03 0.14 CWH Tree Q8T2 3.90E-05 2.41E-03 3.93E-03 0.00 CWH Tree Q8T3 4.23E-04 2.64E-02 3.93E-03 0.19 CWH Tree Q8T3 -5.37E-04 -3.35E-02 3.93E-03 0.34 CWH Tree Q8T4 -1.28E-04 -7.98E-03 3.93E-03 0.03 CWH Tree Q8T4 -1.58E-04 -9.82E-03 3.93E-03 0.03 CWH Tree Q8T4 2.61E-04 5.69E-02 3.93E-03 0.05 CWH Tree Q9T1 -3.79E-04 -2.36E-02 3.93E-03 0.18 CWH Tree Q9T1 -4.73E-04 -2.95E-02 3.93E-03 0.27 CWH Tree Q9T2 -2.13E-04 -1.33E-02 3.93E-03 0.07 CWH Tree Q9T2 -5.96E-04 -3.72E-02 3.93E-03 0.36 CWH Tree Q9T3 8.61E-04 5.37E-02 3.93E-03 0.41 CWH Tree Q9T3 2.58E-03 1.61E-01 3.93E-03 0.67 CWH Tree Q10T1 6.05E-04 4.17E-02 3.93E-03 0.10 CWH Tree Q10T1 -2.31E-04 -1.44E-02 3.93E-03 0.05 CWH Tree Q10T2 1.95E-04 1.21E-02 3.93E-03 0.02 CWH Tree Q10T2 5.43E-04 3.39E-02 3.93E-03 0.24 CWH Tree Q10T2 -9.67E-04 -6.03E-02 3.93E-03 0.52 ESSF Tree Q1T1 -2.19E-03 -1.36E-01 3.99E-03 0.85 124 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 ESSF Tree Q1T1 4.55E-04 2.84E-02 3.99E-03 0.29 ESSF Tree Q1T2 -9.57E-04 -5.97E-02 3.99E-03 0.67 ESSF Tree Q1T2 2.15E-04 1.34E-02 3.99E-03 0.09 ESSF Tree Q1T3 1.15E-03 7.15E-02 3.99E-03 0.67 ESSF Tree Q1T3 -1.26E-03 -7.85E-02 3.99E-03 0.76 ESSF Tree Q1T4 -1.39E-03 -8.68E-02 3.99E-03 0.79 ESSF Tree Q1T4 -8.50E-05 -5.28E-03 3.99E-03 0.01 ESSF Tree Q1T5 -8.01E-04 -5.00E-02 3.99E-03 0.57 ESSF Tree Q1T5 -7.64E-04 -4.76E-02 3.99E-03 0.52 ESSF Tree Q2T1 3.97E-04 2.47E-02 3.94E-03 0.08 ESSF Tree Q2T1 7.01E-04 4.37E-02 3.94E-03 0.45 ESSF Tree Q2T2 3.03E-04 1.89E-02 3.94E-03 0.17 ESSF Tree Q2T2 -5.45E-03 -3.40E-01 3.94E-03 0.94 ESSF Tree Q2T3 -1.32E-03 -8.26E-02 3.94E-03 0.82 ESSF Tree Q2T3 -1.04E-03 -6.51E-02 3.94E-03 0.73 ESSF Tree Q2T4 1.49E-03 9.30E-02 3.94E-03 0.72 ESSF Tree Q2T4 -7.20E-04 -4.49E-02 3.94E-03 0.36 ESSF Tree Q2T5 -1.54E-03 -9.62E-02 3.94E-03 0.85 ESSF Tree Q2T5 -9.76E-04 -6.08E-02 3.94E-03 0.72 ESSF Tree Q3T1 -5.24E-04 -3.27E-02 3.89E-03 0.22 ESSF Tree Q3T1 2.30E-04 1.43E-02 3.89E-03 0.09 ESSF Tree Q3T2 -1.07E-03 -6.65E-02 3.89E-03 0.73 ESSF Tree Q3T2 -1.94E-03 -1.21E-01 3.89E-03 0.88 ESSF Tree Q3T3 -3.73E-04 -2.33E-02 3.89E-03 0.28 ESSF Tree Q3T3 -2.00E-04 -1.25E-02 3.89E-03 0.07 ESSF Tree Q4T1 -9.55E-04 -5.95E-02 3.88E-03 0.65 ESSF Tree Q4T1C -7.06E-04 -4.40E-02 3.88E-03 0.53 ESSF Tree Q4T2 -9.69E-04 -6.04E-02 3.88E-03 0.65 ESSF Tree Q4T2 -1.05E-03 -6.54E-02 3.88E-03 0.73 ESSF Tree Q4T3 5.05E-04 3.15E-02 3.88E-03 0.38 ESSF Tree Q4T3 -2.02E-04 -4.40E-02 3.88E-03 0.05 ESSF Tree Q4T3 -1.39E-03 -8.69E-02 3.88E-03 0.71 ESSF Tree Q4T3 -7.60E-05 -4.76E-03 3.88E-03 0.01 ESSF Tree Q4T3 -9.40E-04 -5.86E-02 3.88E-03 0.54 ESSF Tree Q4T4 1.42E-04 8.83E-03 3.88E-03 0.03 ESSF Tree Q4T4 -2.30E-04 -1.43E-02 3.88E-03 0.10 ESSF Tree Q4T5 -9.92E-04 -6.18E-02 3.88E-03 0.71 ESSF Tree Q4T5 -8.60E-05 -5.38E-03 3.88E-03 0.01 ESSF Tree Q5T1 0.00E+00 1.10E-05 3.95E-03 0.00 125 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 ESSF Tree Q5T1 -6.74E-04 -4.20E-02 3.95E-03 0.53 ESSF Tree Q5T2 -4.78E-04 -2.98E-02 3.95E-03 0.28 ESSF Tree Q5T2 -3.06E-04 -1.91E-02 3.95E-03 0.18 ESSF Tree Q5T3 -5.05E-04 -3.15E-02 3.95E-03 0.36 ESSF Tree Q5T3 -5.46E-04 -3.40E-02 3.95E-03 0.41 ESSF Tree Q5T4 -7.69E-04 -4.80E-02 3.95E-03 0.62 ESSF Tree Q5T4 -1.16E-03 -7.25E-02 3.95E-03 0.76 ESSF Tree Q5T5 -7.10E-04 -4.42E-02 3.95E-03 0.47 ESSF Tree Q5T5 -1.19E-03 -7.40E-02 3.95E-03 0.78 ESSF Tree Q5T5 2.58E-04 5.62E-02 3.95E-03 0.08 ESSF Tree Q5T5 1.97E-04 4.29E-02 3.95E-03 0.04 ESSF Tree Q6T1 2.78E-04 4.96E-03 1.12E-03 0.17 ESSF Tree Q6T1 -1.20E-03 -2.14E-02 1.12E-03 0.76 ESSF Tree Q6T2 -4.83E-04 -3.01E-02 3.93E-03 0.38 ESSF Tree Q6T2 -7.41E-04 -4.62E-02 3.93E-03 0.56 ESSF Tree Q6T3 -6.76E-04 -4.21E-02 3.93E-03 0.48 ESSF Tree Q6T3 4.80E-05 3.02E-03 3.93E-03 0.00 ESSF Tree Q7T1 -3.64E-04 -2.27E-02 3.91E-03 0.25 ESSF Tree Q7T1 2.90E-04 1.81E-02 3.91E-03 0.18 ESSF Tree Q7T2 -4.69E-04 -2.92E-02 3.91E-03 0.33 ESSF Tree Q7T2 -5.55E-04 -3.46E-02 3.91E-03 0.40 ESSF Tree Q7T3 -6.98E-04 -4.35E-02 3.91E-03 0.50 ESSF Tree Q7T3 -4.32E-04 -2.69E-02 3.91E-03 0.33 ESSF Tree Q7T4 -1.08E-03 -6.73E-02 3.91E-03 0.70 ESSF Tree Q7T4 -2.12E-04 -1.32E-02 3.91E-03 0.08 ESSF Tree QET1Conk 2.07E-04 1.29E-02 3.91E-03 0.10 ESSF Tree QET1Clear -5.87E-04 -3.66E-02 3.91E-03 0.30 ESSF Tree QET2Conk 9.30E-05 5.80E-03 4.04E-03 0.02 ESSF Tree QET2Clear -4.98E-04 -3.10E-02 4.04E-03 0.36 MS Soil Q1 -5.30E-02 -3.32E+00 2.44E-02 1.00 MS Soil Q1 -5.60E-02 -3.50E+00 2.44E-02 0.99 MS Soil Q1 -5.70E-02 -3.56E+00 2.44E-02 0.99 MS Soil Q1 -6.00E-02 -3.72E+00 2.42E-02 0.99 MS Soil Q1 -5.80E-02 -3.63E+00 2.42E-02 0.99 MS Soil Q2 -4.50E-02 -2.81E+00 2.42E-02 0.99 MS Soil Q2 -4.60E-02 -2.89E+00 2.42E-02 1.00 MS Soil Q2 -5.40E-02 -3.36E+00 2.42E-02 1.00 MS Soil Q2 -5.40E-02 -3.39E+00 2.41E-02 1.00 MS Soil Q2 -5.00E-02 -3.12E+00 2.41E-02 1.00 126 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Soil Q4 -5.60E-02 -3.49E+00 2.41E-02 1.00 MS Soil Q4 -6.60E-02 -4.13E+00 2.41E-02 1.00 MS Soil Q4 -6.60E-02 -4.09E+00 2.41E-02 1.00 MS Soil Q4 -7.60E-02 -4.73E+00 2.44E-02 0.99 MS Soil Q4 -5.90E-02 -3.69E+00 2.44E-02 1.00 MS Soil Q6 -5.90E-02 -3.69E+00 2.44E-02 1.00 MS Soil Q6 -6.50E-02 -4.05E+00 2.44E-02 1.00 MS Soil Q6 -6.90E-02 -4.31E+00 2.44E-02 1.00 MS Soil Q6 -7.10E-02 -4.41E+00 2.44E-02 0.99 MS Soil Q6 -7.00E-02 -4.35E+00 2.44E-02 0.99 MS Soil Q7 -5.10E-02 -3.17E+00 2.44E-02 1.00 MS Soil Q7 -5.40E-02 -3.37E+00 2.44E-02 0.99 MS Soil Q7 -5.10E-02 -3.20E+00 2.44E-02 0.99 MS Soil Q7 -5.40E-02 -3.36E+00 2.49E-02 1.00 MS Soil Q7 -5.70E-02 -3.57E+00 2.49E-02 1.00 MS Soil Q9 -4.60E-02 -2.84E+00 2.49E-02 1.00 MS Soil Q9 -4.90E-02 -3.06E+00 2.49E-02 0.99 MS Soil Q9 -5.10E-02 -3.20E+00 2.49E-02 0.99 MS Soil Q9 -4.70E-02 -2.94E+00 2.49E-02 1.00 MS Soil Q9 -5.00E-02 -3.09E+00 2.49E-02 0.99 MS Soil Q10 -2.50E-02 -1.53E+00 2.49E-02 1.00 MS Soil Q10 -2.60E-02 -1.61E+00 2.49E-02 1.00 MS Soil Q10 -2.70E-02 -1.66E+00 2.49E-02 0.99 MS Soil Q10 -2.50E-02 -1.58E+00 2.49E-02 0.99 MS Soil Q10 -2.50E-02 -1.59E+00 2.49E-02 1.00 MS Soil Q12 -2.70E-02 -1.69E+00 2.49E-02 1.00 MS Soil Q12 -3.10E-02 -1.92E+00 2.49E-02 0.99 MS Soil Q12 -2.90E-02 -1.82E+00 2.49E-02 0.99 MS Soil Q12 -2.70E-02 -1.67E+00 2.50E-02 0.99 MS Soil Q12 -2.90E-02 -1.83E+00 2.50E-02 0.99 MS Soil Q14 -3.10E-02 -1.92E+00 2.50E-02 1.00 MS Soil Q14 -3.20E-02 -1.99E+00 2.50E-02 1.00 MS Soil Q14 -3.10E-02 -1.93E+00 2.50E-02 0.99 MS Soil Q14 -2.70E-02 -1.66E+00 2.50E-02 0.99 MS Soil Q14 -3.00E-02 -1.89E+00 2.50E-02 1.00 MS Soil Q15 -4.60E-02 -2.85E+00 2.50E-02 1.00 MS Soil Q15 -5.10E-02 -3.16E+00 2.50E-02 0.99 MS Soil Q15 -5.10E-02 -3.20E+00 2.50E-02 0.99 MS Soil Q15 -5.30E-02 -3.28E+00 2.50E-02 1.00 127 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 MS Soil Q15 -5.30E-02 -3.29E+00 2.50E-02 1.00 MS Soil Q16 -2.20E-02 -1.35E+00 2.50E-02 1.00 MS Soil Q16 -2.20E-02 -1.36E+00 2.50E-02 1.00 MS Soil Q16 -2.30E-02 -1.43E+00 2.50E-02 1.00 MS Soil Q16 -2.30E-02 -1.44E+00 2.56E-02 0.99 MS Soil Q16 -2.30E-02 -1.43E+00 2.56E-02 1.00 MS Soil Q18 -4.10E-02 -2.55E+00 2.56E-02 0.99 MS Soil Q18 -4.60E-02 -2.85E+00 2.56E-02 0.99 MS Soil Q18 -4.70E-02 -2.95E+00 2.56E-02 0.99 MS Soil Q18 -4.50E-02 -2.83E+00 2.55E-02 1.00 MS Soil Q18 -4.30E-02 -2.67E+00 2.55E-02 0.99 MS Soil Q19 -3.90E-02 -2.41E+00 2.55E-02 1.00 MS Soil Q19 -4.10E-02 -2.58E+00 2.55E-02 1.00 MS Soil Q19 -4.40E-02 -2.74E+00 2.55E-02 0.99 MS Soil Q19 -4.20E-02 -2.64E+00 2.55E-02 1.00 MS Soil Q19 -4.10E-02 -2.55E+00 2.55E-02 0.99 CWH Soil Q1 -5.00E-02 -3.10E+00 2.43E-02 1.00 CWH Soil Q1 -5.00E-02 -3.10E+00 2.43E-02 1.00 CWH Soil Q1 -4.80E-02 -2.99E+00 2.43E-02 1.00 CWH Soil Q1 -4.70E-02 -2.95E+00 2.43E-02 1.00 CWH Soil Q1 -4.50E-02 -2.82E+00 2.43E-02 0.99 CWH Soil Q2 -5.50E-02 -3.45E+00 2.45E-02 1.00 CWH Soil Q2 -5.70E-02 -3.57E+00 2.45E-02 0.99 CWH Soil Q2 -5.30E-02 -3.31E+00 2.45E-02 0.99 CWH Soil Q2 -5.30E-02 -3.31E+00 2.45E-02 1.00 CWH Soil Q2 -5.20E-02 -3.22E+00 2.45E-02 1.00 CWH Soil Q3 -1.90E-02 -1.19E+00 2.45E-02 1.00 CWH Soil Q3 -1.90E-02 -1.18E+00 2.45E-02 1.00 CWH Soil Q3 -1.50E-02 -9.11E-01 2.45E-02 0.99 CWH Soil Q3 -1.90E-02 -1.18E+00 2.45E-02 0.99 CWH Soil Q3 -2.00E-02 -1.23E+00 2.45E-02 0.99 CWH Soil Q4 -2.80E-02 -1.74E+00 2.42E-02 0.99 CWH Soil Q4 -2.70E-02 -1.67E+00 2.42E-02 0.99 CWH Soil Q4 -2.80E-02 -1.75E+00 2.42E-02 1.00 CWH Soil Q4 -2.90E-02 -1.83E+00 2.42E-02 0.99 CWH Soil Q4 -1.70E-02 -1.06E+00 2.42E-02 0.96 CWH Soil Q6 -3.20E-02 -1.99E+00 2.42E-02 0.98 CWH Soil Q6 -3.40E-02 -2.14E+00 2.42E-02 0.99 CWH Soil Q6 -3.20E-02 -1.98E+00 2.42E-02 0.99 128 Site Sample Tree Flux (mg·m-2 ·hr-1) Flux (µmol·m-2 ·hr-1) MDFSE (µmol·m-2· hr-1) R2 CWH Soil Q6 -3.10E-02 -1.92E+00 2.42E-02 1.00 CWH Soil Q6 -3.50E-02 -2.17E+00 2.42E-02 0.99 ESSF Soil Q1 -3.60E-02 -2.24E+00 2.46E-02 0.89 ESSF Soil Q1 -4.00E-02 -2.52E+00 2.46E-02 0.96 ESSF Soil Q1 -5.20E-02 -3.27E+00 2.46E-02 0.99 ESSF Soil Q1 -5.50E-02 -3.42E+00 2.46E-02 0.99 ESSF Soil Q1 -5.40E-02 -3.37E+00 2.46E-02 1.00 ESSF Soil Q5 -3.60E-02 -2.30E+00 2.45E-02 0.99 ESSF Soil Q5 -3.90E-02 -2.51E+00 2.45E-02 0.99 ESSF Soil Q5 -3.70E-02 -2.35E+00 2.45E-02 0.99 ESSF Soil Q5 -3.60E-02 -2.32E+00 2.45E-02 0.99 ESSF Soil Q5 -3.40E-02 -2.19E+00 2.45E-02 0.99 ESSF Soil Q6 -4.30E-02 -2.70E+00 2.44E-02 0.99 ESSF Soil Q6 -4.10E-02 -2.57E+00 2.44E-02 0.93 ESSF Soil Q6 -4.30E-02 -2.67E+00 2.44E-02 0.98 ESSF Soil Q6 -4.50E-02 -2.80E+00 2.44E-02 0.99 ESSF Soil Q6 -4.40E-02 -2.76E+00 2.44E-02 0.97 ESSF Soil Q7 -2.60E-02 -1.65E+00 2.45E-02 0.97 ESSF Soil Q7 -2.20E-02 -1.34E+00 2.45E-02 0.95 ESSF Soil Q7 -2.40E-02 -1.51E+00 2.45E-02 0.96 ESSF Soil Q7 -2.80E-02 -1.78E+00 2.45E-02 0.99 ESSF Soil Q7 -3.00E-02 -1.90E+00 2.45E-02 1.00 129 Appendix C: Environmental variables for each quadrat and tree. Site Quadrat Soil Temperature (°C) Soil Moisture (m3m-3) Tree Air Temperature (°C) Diameter (cm) MS Q1 8.2 6.6 1 6.8 44.6 Q2 1 6.8 42.1 7.5 4.4 2 6.8 43.0 3 10.1 32.3 4 10.1 22.9 Q3 NA NA 1 10.1 28.8 2 9.7 36.7 3 9.7 42.5 4 9.7 26.0 5 9.7 31.6 Q4 8.2 8.1 1 7.2 44.1 Q5 1 7.2 39.2 2 7.2 44.3 Q6 9.0 3.2 1 11.5 23.9 2 11.5 45.5 3 11.5 34.3 4 11.5 34.9 5 11.5 55.0 6 11.5 39.9 Q7 9.4 6.1 1 13.9 50.0 2 13.9 26.8 3 13.2 24.6 4 13.2 32.4 5 17.0 30.8 Q8 NA NA 1 17.0 56.1 2 17.0 22.3 Q9 3.5 7.4 1 20.3 37.8 2 20.3 32.0 3 20.3 27.9 4 20.3 39.3 Q10 2.0 7.8 1 14.3 39.8 2 14.3 22.6 3 14.3 27.2 4 14.3 27.0 Q11 1.7 7.6 1 14.3 44.8 Q12 NA NA 1 18.5 23.0 2 18.5 20.4 3 18.5 33.7 130 Site Quadrat Soil Temperature (°C) Soil Moisture (m3m-3) Tree Air Temperature (°C) Diameter (cm) 4 18.5 30.2 1 18.5 31.6 2 18.5 22.0 Q14 3.5 7.3 1 9.2 31.8 2 9.2 20.5 3 9.2 30.8 4 9.2 20.7 5 9.2 43.6 Q15 2.8 7.4 1 15.6 40.2 2 15.6 24.8 3 15.6 28.6 4 15.6 26.7 Q16 5.8 7.5 1 4.2 52.0 2 4.2 22.7 3 4.2 45.8 Q17 NA NA 1 10.3 31.8 2 10.3 45.5 Q18 6.2 6.1 1 -1.0 21.0 2 -1.0 37.6 Q19 5.3 1.8 1 0.3 26.4 2 0.3 37.1 Q20 1 0.5 55.0 ESSF Q1 11.0 NA 1 15.4 54.1 2 15.4 55.6 3 15.4 48.6 4 15.4 45.2 5 15.4 40.4 Q2 NA NA 1 19.6 34.0 2 19.6 31.5 3 19.6 59.5 4 19.6 50.6 5 19.6 63.0 Q3 NA NA 1 23.0 27.0 2 23.0 46.1 3 23.0 40.1 Q4 NA NA 1 24.1 55.3 2 24.1 37.8 3 24.1 31.8 4 24.1 51.5 5 24.1 42.0 131 Site Quadrat Soil Temperature (°C) Soil Moisture (m3m-3) Tree Air Temperature (°C) Diameter (cm) Q5 11.3 NA 1 18.6 34.7 2 18.6 49.0 3 18.6 28.0 4 18.6 25.1 5 18.6 38.2 Q6 12.4 NA 1 20.4 39.1 2 20.4 28.3 3 20.4 43.9 Q7 11.6 NA 1 21.8 29.3 2 21.8 36.6 3 21.8 38.8 4 21.8 32.1 CWH Q1 14.2 NA 1 19.9 48.0 Q2 11.2 NA 1 19.9 34.0 2 19.9 35.0 3 19.9 34.5 Q3 11.8 NA 1 20.6 65.0 2 20.6 47.0 3 20.6 43.0 Q4 15.5 NA 1 22.3 39.0 2 22.3 26.0 3 22.3 32.3 4 22.3 38.0 Q5 NA NA 1 15.5 32.8 2 15.5 43.2 3 15.5 26.1 4 15.5 35.9 Q6 15.4 NA 1 14.9 33.7 2 15.6 66.1 3 15.6 35.0 4 15.6 35.0 5 15.6 23.2 Q7 NA NA 1 17.4 24.2 2 17.4 28.9 3 17.4 64.9 4 17.4 22.9 5 17.4 58.2 6 17.4 41.3 Q8 NA NA 1 18.6 59.5 2 18.6 57.2 132 Site Quadrat Soil Temperature (°C) Soil Moisture (m3m-3) Tree Air Temperature (°C) Diameter (cm) 3 18.6 47.4 4 18.6 28.3 Q9 NA NA 1 18.6 29.3 2 18.6 40.7 3 18.6 65.5 Q10 NA NA 1 16.9 58.2 2 16.9 22.9 *NA is where no data was collected 133 Appendix D: Methane Fluxes from the Whole Tree Survey. Number Aspect Flux (µmol·m-2·hr-1) 1 S -0.042 2 S -0.033 3 SE 0.019 4 S -0.009 5 S 0.027 6 SE 0.053 7 E 0.011 8 E -0.012 9 E 0.009 10 NE 0.017 11 NE -0.009 12 NE 0.044 13 NW -0.032 14 N 0.010 15 N 0.009 16 NW 0.027 17 W -0.016 18 W -0.016 19 NW -0.013 20 NW -0.003 134 Appendix E: Tree stem methane fluxes scaled to tree surface area. A fixed and conservative height of 15 m was used for all mature trees in the flux calculations. Site Tree Average Flux (µmol·m-2·hr-1) Stem diameter (m) Circumference (m) Surface area of tree stem (m2) Total Flux for the tree surface area (µmol·hr-1) MS Q1T1 0.01 0.45 1.40 21.02 0.13 Q2T1 0.05 0.42 1.32 19.82 0.92 Q2T2 -0.01 0.43 1.35 20.26 -0.23 Q2T3 0.00 0.32 1.01 15.22 0.02 Q2T4 0.00 0.23 0.72 10.79 0.00 Q3T1 0.00 0.29 0.90 13.57 0.00 Q3T2 -0.01 0.37 1.15 17.29 -0.13 Q3T3 -0.03 0.43 1.34 20.03 -0.57 Q3T4 -0.01 0.26 0.82 12.25 -0.14 Q3T5 -0.04 0.32 0.99 14.89 -0.54 Q4T1 0.13 0.44 1.39 20.78 2.68 Q5T1 0.01 0.39 1.23 18.47 0.27 Q5T2 0.00 0.44 1.39 20.88 -0.10 Q6T1 0.01 0.24 0.75 11.26 0.08 Q6T2 0.02 0.46 1.43 21.44 0.37 Q6T3 0.00 0.34 1.08 16.16 -0.04 Q6T4 -0.10 0.35 1.10 16.45 -1.71 Q6T5 0.03 0.58 1.82 27.33 0.71 Q6T6 0.02 0.40 1.25 18.80 0.35 Q7T1 0.18 0.50 1.57 23.56 4.24 Q7T2 -0.03 0.27 0.84 12.63 -0.38 Q7T3 0.03 0.25 0.77 11.59 0.38 Q7T4 0.00 0.32 1.02 15.27 0.04 Q7T5 -0.01 0.31 0.97 14.51 -0.09 Q8T1 0.01 1.76 5.53 82.94 1.15 Q8T2 0.00 0.22 0.70 10.51 0.03 Q9T1 -0.01 0.38 1.19 17.81 -0.11 Q9T2 -0.01 0.32 1.01 15.08 -0.21 Q9T3 -0.06 0.28 0.88 13.15 -0.76 Q9T4 0.01 0.39 1.23 18.52 0.10 Q10T1 0.01 0.40 1.25 18.75 0.15 Q10T2 0.00 0.23 0.71 10.65 0.02 Q10T3 -0.01 0.27 0.85 12.82 -0.07 Q10T4 -0.01 0.27 0.85 12.72 -0.12 Q11T1 0.02 0.45 1.41 21.11 0.49 Q12T1 0.01 0.23 0.72 10.84 0.13 Q12T2 -0.05 0.20 0.64 9.61 -0.48 135 Site Tree Average Flux (µmol·m-2·hr-1) Stem diameter (m) Circumference (m) Surface area of tree stem (m2) Total Flux for the tree surface area (µmol·hr-1) Q12T3 0.08 0.34 1.06 15.88 1.24 Q12T4 -0.02 0.30 0.95 14.23 -0.33 Q13T1 0.01 0.32 0.99 14.89 0.19 Q13T2 -0.01 0.22 0.69 10.37 -0.08 Q14T1 0.05 0.32 1.00 14.99 0.74 Q14T2 0.02 0.21 0.64 9.66 0.24 Q14T3 0.00 0.31 0.97 14.51 0.03 Q14T4 0.06 0.21 0.65 9.75 0.57 Q14T5 -0.02 0.44 1.37 20.55 -0.45 Q15T1 -0.04 0.40 1.26 18.94 -0.81 Q15T2 -0.03 0.25 0.78 11.69 -0.31 Q15T3 -0.05 0.25 0.78 11.69 -0.57 Q15T4 -0.02 0.29 0.90 13.48 -0.27 Q16T1 0.02 0.27 0.84 12.58 0.31 Q16T2 0.08 0.52 1.63 24.50 1.99 Q16T3 0.01 0.23 0.71 10.70 0.13 Q17T1 0.05 0.46 1.44 21.58 1.17 Q17T2 0.06 0.32 1.00 14.99 0.83 Q18T1 0.01 0.46 1.43 21.44 0.23 Q18T2 0.01 0.21 0.66 9.90 0.07 Q19T1 0.02 0.38 1.18 17.72 0.38 Q19T2 0.00 0.26 0.83 12.44 0.00 Q20T1 -0.01 0.37 1.17 17.48 -0.14 ESSF Q1T1A -0.05 0.54 1.70 25.51 -1.38 Q1T2A -0.02 0.56 1.75 26.26 -0.61 Q1T3A 0.00 0.49 1.53 22.96 -0.08 Q1T4A -0.05 0.45 1.42 21.31 -0.98 Q1T5A -0.05 0.40 1.27 19.06 -0.93 Q2T1A 0.03 0.34 1.07 16.06 0.55 Q2T2A -0.16 0.32 0.99 14.86 -2.38 Q2T3A -0.07 0.60 1.87 28.06 -2.07 Q2T4A 0.02 0.51 1.59 23.86 0.57 Q2T5A -0.08 0.63 1.98 29.71 -2.33 Q3T1A -0.01 0.27 0.85 12.76 -0.12 Q3T2A -0.09 0.46 1.45 21.76 -2.04 Q3T3A -0.02 0.40 1.26 18.91 -0.34 Q4T1A -0.05 0.55 1.74 26.11 -1.35 Q4T2A -0.06 0.38 1.19 17.86 -1.12 136 Site Tree Average Flux (µmol·m-2·hr-1) Stem diameter (m) Circumference (m) Surface area of tree stem (m2) Total Flux for the tree surface area (µmol·hr-1) Q4T3A -0.03 0.32 1.00 15.01 -0.49 Q4T4A 0.00 0.52 1.62 24.31 -0.07 Q4T5A -0.03 0.42 1.32 19.81 -0.67 Q5T1A -0.02 0.35 1.09 16.36 -0.34 Q5T2A -0.02 0.49 1.54 23.11 -0.56 Q5T3A -0.03 0.28 0.88 13.21 -0.43 Q5T4A -0.06 0.25 0.79 11.86 -0.71 Q5T5A 0.00 0.38 1.20 18.01 -0.09 Q6T1A -0.01 0.39 1.23 18.46 -0.15 Q6T2A -0.04 0.28 0.89 13.36 -0.51 Q6T3A -0.02 0.44 1.38 20.71 -0.44 Q7T1A 0.00 0.29 0.92 13.81 -0.03 Q7T2A -0.03 0.37 1.15 17.26 -0.55 Q7T3A -0.04 0.39 1.22 18.31 -0.64 Q7T4A -0.04 0.32 1.01 15.16 -0.61 CWH Q1T1 0.00 0.48 1.51 22.62 0.07 Q2T1 -0.06 0.34 1.07 16.02 -1.04 Q2T2 0.00 0.35 1.10 16.49 0.04 Q2T3 0.03 0.35 1.08 16.26 0.44 Q3T1 -0.05 0.65 2.04 30.63 -1.41 Q3T2 0.00 0.47 1.48 22.15 0.10 Q3T3 -0.05 0.43 1.35 20.26 -0.99 Q4T1 -0.05 0.39 1.23 18.38 -0.83 Q4T2 -0.02 0.26 0.82 12.25 -0.28 Q4T3 -0.01 0.32 1.01 15.20 -0.10 Q4T4 -0.03 0.38 1.19 17.91 -0.47 Q5T1 -0.03 0.33 1.03 15.46 -0.47 Q5T2 0.02 0.43 1.36 20.41 0.31 Q5T3 0.02 0.26 0.82 12.31 0.20 Q5T4 0.00 0.36 1.13 16.96 0.04 Q6T1 0.01 0.34 1.06 15.91 0.11 Q6T2 0.05 0.66 2.08 31.21 1.67 Q6T3 0.03 0.35 1.10 16.51 0.51 Q6T4 0.05 0.23 0.73 10.96 0.58 Q6T5 0.00 0.24 0.76 11.41 0.05 Q7T1 0.02 0.29 0.91 13.66 0.23 Q7T2 -0.02 0.65 2.04 30.61 -0.47 Q7T3 -0.02 0.23 0.72 10.81 -0.27 Q7T4 0.00 0.58 1.83 27.46 0.04 137 Site Tree Average Flux (µmol·m-2·hr-1) Stem diameter (m) Circumference (m) Surface area of tree stem (m2) Total Flux for the tree surface area (µmol·hr-1) Q7T5 -0.04 0.41 1.30 19.51 -0.69 Q7T6 -0.02 0.60 1.87 28.06 -0.51 Q8T1 -0.02 0.57 1.80 27.01 -0.46 Q8T2 -0.01 0.47 1.49 22.36 -0.24 Q8T3 0.00 0.28 0.89 13.36 -0.05 Q8T4 0.01 0.29 0.92 13.81 0.18 Q9T1 -0.03 0.41 1.28 19.21 -0.51 Q9T2 -0.03 0.66 2.06 30.91 -0.78 Q9T3 0.11 0.58 1.83 27.46 2.95 Q10T1 0.01 0.23 0.72 10.81 0.15 Q10T2 0.00 0.30 0.93 13.96 -0.07 138 Appendix F: Soil methane fluxes scaled to quadrat area. * Quadrats that are highlighted in green did not have flux measurements taken so the average of all soil measurements for that site was used. All quadrats have an area of 100 m2. Site Quadrat Flux (µmol·m-2·hr-1) Cover (%) Cover (m2) Area of exposed soil (m2) Soil Flux for quadrat (µmol·hr-1) MS Q1 -3.547 30 30 70 -248.27 Q2 -3.113 50 50 50 -155.63 Q3 -2.798 30 30 70 -195.88 Q4 -4.024 25 25 75 -301.83 Q5 -2.798 25 25 75 -209.87 Q6 -4.162 50 50 50 -208.10 Q7 -3.334 50 50 50 -166.69 Q8 -2.798 40 40 60 -167.90 Q9 -3.025 20 20 80 -242.01 Q10 -1.595 40 40 60 -95.68 Q11 -2.798 30 30 70 -195.88 Q12 -1.788 55 55 45 -80.45 Q13 -2.798 40 40 60 -167.90 Q14 -1.877 40 40 60 -112.61 Q15 -3.157 30 30 70 -221.00 Q16 -1.402 25 25 75 -105.12 Q17 -2.798 30 30 70 -195.88 Q18 -2.771 25 25 75 -207.81 Q19 -2.586 25 25 75 -193.83 Q20 -2.798 50 50 50 -139.91 CWH Q1 -2.992 15 25 75 -224.37 Q2 -3.371 15 25 75 -252.80 Q3 -1.138 30 30 70 -79.67 Q4 -1.610 25 35 65 -104.66 Q5 -2.230 20 30 70 -156.10 Q6 -2.039 25 25 75 -152.89 Q7 -2.230 25 20 80 -178.38 Q8 -2.230 10 10 90 -200.68 Q9 -2.230 15 15 85 -189.53 Q10 -2.230 50 50 50 -111.49 ESSF Q1 -2.964 25 25 75 -222.30 Q2 -2.409 25 25 75 -180.63 Q3 -2.409 30 30 70 -168.59 Q4 -2.409 35 35 65 -156.55 Q5 -2.335 30 30 70 -163.46 Q6 -2.700 25 25 75 -202.50 Q7 -1.635 20 20 80 -130.77
Thesis/Dissertation
eng
Earth and Environmental Sciences
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Hornibrook, Edward
Methane fluxes in mature upland conifer forests in three biogeoclimatic zones in southern British Columbia
Text