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Patterns in forest soil microbial community composition across a range of regional climates in Western.. Brockett, Beth 2008

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PATTERNS IN FOREST SOIL MICROBIAL COMMUNITY COMPOSITION ACROSS A RANGE OF REGIONAL CLIMATES IN WESTERN CANADA by  BETH BROCKETT BSc Hons., University of East Anglia, 2004  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in The Faculty of Graduate Studies (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (VANCOUVER) February, 2008  © Beth Brockett, 2008  ABSTRACT Soil microbial communities can be characterized by community structure and function (community composition) across a spectrum of spatial scales, and variation in soil microbial composition has been associated with a number of environmental gradients. This study investigates the structure and function of soil microbial communities under mature, undisturbed forested sites across a range of regional climates in British Columbia and Alberta, and also examines the variation in community composition within sites. Phospholipid fatty acid analysis was used to investigate the structure of soil microbial communities and total soil microbial biomass at each site. Extra-cellular enzyme assays established the functional potential of the soil microbial community at each site. Multivariate analysis of the data showed that the soil microbial communities under different forest types did significantly separate along the regional climate gradient by both community structure and function, despite high local variation in the communities. Soil moisture content and soil organic matter concentration consistently exhibited the strongest relationship with microbial community characteristics, although the functional and structural responses to the external drivers were different. Microbial community function and structure also changed with soil depth but not with time of sampling. Microbial community function was related to the regional annual average precipitation gradient. Most of the locations exhibited unique microbial community functional profiles in their soil layers; however the enzyme activities in the samples from the driest (Ponderosa Pine) and wettest (Mountain Hemlock) locations were notably different from each other and from those of the other locations, especially in the organic layers. The moist maritime-influenced Coastal Western Hemlock (CWH) forest exhibited microbial community structural characteristics which were unique from those of the other forest locations. The higher abundance of bacteria relative to fungi in the CWH forest soils may be related to the significantly higher available nitrogen concentrations at this site.  ii  TABLE OF CONTENTS Abstract ............................................................................................................................. ii Table of contents...............................................................................................................iii List of tables .....................................................................................................................vii List of figures .................................................................................................................... ix List of abbreviations ........................................................................................................xiv Acknowledgements ......................................................................................................... xv Dedication ...................................................................................................................... xvii 1. Introduction and literature review .................................................................................. 1 1.1. Rationale ................................................................................................................ 1 1.2. Literature review ..................................................................................................... 2 1.2.1. The role of microorganisms in forest ecosystems............................................ 2 1.2.2. Microbial community function and structure..................................................... 3 1.2.3. External drivers of microbial community function and structure....................... 4 1.2.4. Differential responses of microbial groups to external gradients ..................... 9 1.3. Introduction to the study ....................................................................................... 10 1.4. Specific hypotheses.............................................................................................. 11 2. Methodology................................................................................................................ 13 2.1. Location of study sites along a climate gradient ................................................... 13 2.2. Sampling design ................................................................................................... 18 2.3. Field measurements ............................................................................................. 19 iii  2.4. Laboratory sample analysis .................................................................................. 20 2.4.1. Microbial community structural analysis ........................................................ 21 2.4.2. Microbial community functional analysis ........................................................ 23 2.5. Statistical analysis ................................................................................................ 29 3. Results ........................................................................................................................ 33 3.1. Hypothesis one: Analysis of composite soil samples for microbial community function and structure provides the same results as analysis of individual soil samples. ..................................................................................................................................... 33 3.2. Hypothesis two: Soil microbial community structure and function are significantly different in spring and summer. ................................................................................... 34 3.3. Hypothesis three: It is possible to separate forest types along a regional climate gradient based on microbial community function and/or structure, despite high local microbial community variability. ................................................................................... 47 3.3.1. Multivariate analysis of functional data for a combination of all soil profile layers ....................................................................................................................... 47 3.3.2. Multivariate analysis of microbial functional data for individual soil layers ..... 48 3.3.3. Multivariate analysis of microbial structural data for a combination of all soil profile layers............................................................................................................. 52 3.3.4 Multivariate analysis of structural data for individual layers ............................ 54 3.3.5. Environmental characteristics of the PP, MH and CWH locations ................. 57 3.4. Hypothesis four: A set of measured environmental variables can be shown to significantly correlate with microbial community function and structure across a regional climate gradient. Post-hoc hypothesis: If accepted, I hypothesize that moisture is highly correlated with microbial community function and structure. .......... 66  iv  3.4.1 Correlations between microbial community function and structure and measured environmental variables .......................................................................... 66 3.4.2 Ordinations for microbial community data and measured environmental variables................................................................................................................... 70 3.5 Hypothesis five: Analysis of soil microbial structure and function will show separation of the mineral and organic layers............................................................... 87 3.5.1 Microbial functional community data ............................................................... 87 3.5.2. Microbial structural community data .............................................................. 98 4. Discussion ................................................................................................................. 109 4.1. Separating distinct forest types at a regional scale based on soil microbial community function and structure .......................................................................... 109 4.2. Forest types with distinct microbial community functional profiles .................. 109 4.3. Forest types with distinct microbial community structural profiles .................. 111 4.4. Differences in microbial community structure and function............................. 112 4.5. Correlations between soil microbial community function and structure and environmental site variables along the regional climate gradient .......................... 113 4.6. Correlations between components of the microbial communities ................... 117 4.7. Changes in microbial community function and structure with soil depth ......... 118 4.8. Changes in microbial community function and structure with season ............ 119 4.9. Sampling design recommendations ................................................................ 121 5. Conclusions............................................................................................................... 123 6. Further work .............................................................................................................. 124 7. References ................................................................................................................ 125 v  Appendices ................................................................................................................... 137 Appendix I. Non-metric Multidimensional Scaling (NMS) test statistics ................... 137 Appendix II. Paired MRPP test statistics .................................................................. 158 Appendix III. PRSTM Probe Raw Data ....................................................................... 163  vi  LIST OF TABLES Table 2.1. Study site characteristics …………………………………………………………15 Table 2.2. Break-down of sampling strategy ………………………………………………..18 Table 2.3. Measured site variables and sampling time ……………………………………20 Table 2.4. Signature PLFAs chosen to characterize microbial community structure ......23 Table 2.5. Hydrolytic enzyme assays chosen for this study ………………………………24 Table 3.1. Test statistics from a nonparametric MANOVA on structural microbial data 33 Table 3.2. Test statistics from a nonparametric MANOVA on functional microbial data 33 Table 3.3. Statistics for measured environmental variables in the organic layers at the IDF location …………………………………………………………………………………….45 Table 3.4. Statistics for measured environmental variables in the organic layers at the ESSF location ………………………………………………………………………………….45 Table 3.5. Statistics for measured environmental variables in the organic layers at the PP location ……………………………………………………………………………………..45 Table 3.6. Statistics for measured environmental variables in the organic layers at the BWBS location …………………………………………………………………………………46 Table 3.7. Statistics for measured environmental variables in the organic layers at the ICH location …………………………………………………………………………………….46 Table 3.8. Statistics for measured environmental variables in the organic layers at the MH location …………………………………………………………………………………….46 Table 3.9. Statistics for measured environmental variables in the organic layers at the CWH location …………………………………………………………………………………..47 Table 3.10. Pair-wise MRPP analysis of enzyme activities in all soil layers combined at each location …………………………………………………………………………………. .48 vii  Table 3.11. Pair-wise MRPP analysis of enzyme activities in the F layer at each location ……………………………………………………………………………………………………49 Table 3.12. Pair-wise MRPP analysis of enzyme activities in the H layer at each location…………………………………………………………………………………………. 51 Table 3.13. Pair-wise MRPP analysis of enzyme activities in the mineral soil at each location ………………………………………………………………………………………….52 Table 3.14. Pair-wise MRPP analysis of PLFA analysis results for each location; all soil layers combined  53  Table 3.15. Pair-wise MRPP analysis of PLFA analysis results for each location; F layer 55 Table 3.16. Pair-wise MRPP analysis of PLFA analysis results for each location; H layer…………………………………………………………………………………………….. 56 Table 3.17. Pair-wise MRPP analysis of PLFA analysis results for each location; mineral layer…………………………………………………………………………………………….. 57 Table 3.18. Tree species composition at the sampling sites………………………………59 Table 3.19. Spearman’s rank correlations between enzyme activities and measured environmental variables ……………………………………………………………………… 67 Table 3.20. Significant correlations between PLFA signatures and measured environmental variables…………………………………………………………………………………………68 Table 3.21. Significant correlations between PLFA signatures and enzyme activities...69 Table 3.22. Significant correlations between enzyme activities ………………………….69 Table 3.23. Pair-wise MRPP analysis of enzyme assay results for F, H, and mineral (M) layers……………………………………………………………………………………………. 87 Table 3.24. Pair-wise MRPP analysis on PLFA results for F, H, and mineral (M) soil layers …………………………………………………………………………………………… 98 viii  LIST OF FIGURES Figure 1.1. Visualization of the role of microbial communities in relation to biogeochemical processes in forest soils ……………………………………………………11 Figure 2.1. Map of the biogeoclimatic zones of British Columbia showing the seven study locations ………………………………………………………………………………….14 Figure 2.2. Sample plate outline for fluorimetric enzyme bioassay ………………………27 Figure 2.3. Soil buffer plate outline for fluorimetric enzyme bioassay ……………………27 Figure 2.4. Sample plate outline for colorimetric enzyme bioassay ………………………29 Figure 3.1. Mean phosphatase and sulfatase activities of all soil layers combined from the seven study locations……………………………………………………………………...37 Figure 3.2. Mean xylanase activity of all soil layers combined from the seven study locations………………………………………………………………………………………… 38 Figure 3.3. Mean phenoloxidase and peroxidase activities of all soil layers combined from the seven study locations ……………………………………………………………… 38 Figure 3.4. Mean arbuscular mycorrhizal and saprophytic fungi PLFA signature concentrations of all soil layers combined from the seven study locations………………39 Figure 3.5. Mean total fungi PLFA signature concentration of all soil layers combined from the seven study locations ……………………………………………………………… 40 Figure 3.6. Mean temperature (˚C) of organic layers from the seven study locations ….40 Figure 3.7. Mean water content (%) of organic layers from the seven study locations…41 Figure 3.8. Mean pH of organic layers from the seven study locations ………………….41 Figure 3.9. Mean C:N ratio of combined organic soil layers from the seven study locations ……………………………………………………………………………………….. 42 Figure 3.10. Mean C:N ratio of mineral soil from the seven study locations ……………42 ix  Figure 3.11. Mean total C concentration (%) of organic layers combined from the seven study locations…………………………………………………………………………………. 43 Figure 3.12. Mean total C concentration (%) of mineral soil from the seven study locations………………………………………………………………………………………… 43 Figure 3.13. Mean total N concentration (%) of organic layers combined from the seven study locations ………………………………………………………………………………… 44 Figure 3.14. Mean soil N concentration (%) of mineral soil from the seven study locations …………………………………………………………………………………. ………………..44 Figure 3.15. Available nitrogen in the organic layers at the seven study locations ……..60 Figure 3.16. Mean available P concentration in all soil layers combined at the seven study locations ………………………………………………………………………………… 61 Figure 3.17. Mean available Ca, Mg, and K concentrations in all soil layers combined at the seven study locations…………………………………………………………………….. 62 Figure 3.18. Mean available S concentration in all soil layers combined at the seven study locations ………………………………………………………………………………… 63 Figure 3.19. Mean available micronutrients (Fe, Mn, Zn, Cu, Bo) concentrations in all soil layers combined at the seven study locations ……………………………………………...64 Figure 3.20. Mean available Cu concentrations in all soil layers combined at the seven study locations ………………………………………………………………………………… 65 Figure 3.21. NMS ordination of microbial communities from all soil profile layers combined at the seven locations based on enzyme activity……………………………….71 Figure 3.22. NMS ordination of microbial communities from organic layers at the seven locations based on enzyme activity ………………………………………………………….73 Figure 3.23. NMS ordination of axes 2 and 3 showing microbial communities from the F layer at the seven locations based on enzyme activity …………………………………….75  x  Figure 3.24. NMS ordination of axes 1 and 2 showing microbial communities from the H layer at the seven locations based on enzyme activity …………………………………… 77 Figure 3.25. NMS ordination showing microbial communities from all layers at the seven locations based on PLFA signature microbial community groupings …………………….79 Figure 3.26. Mean total bacterial:total fungi PLFA signature ratios for the F layer at the seven locations ………………………………………………………………………………...80 Figure 3.27. Mean total bacterial:total fungi PLFA signature ratios for the H layer, at the seven locations ……………………………………………………………………………….. 80 Figure 3.28. NMS ordination showing microbial communities from the F layer at the seven locations based on PLFA signature microbial community groupings …………….82 Figure 3.29. NMS ordination showing microbial communities from the H layer at the seven locations based on PLFA signature microbial community groupings …………….84 Figure 3.30. Mean total fungal PLFA concentration (total divided by sample biomass) in the H layer at the seven locations ……………………………………………………………85 Figure 3.31. NMS ordination showing microbial communities from the mineral layer at the seven locations based on PLFA signatures …………………………………………… 86 Figure 3.32. Mean cellulase activity rates in each soil layer (spring samples) ………….88 Figure 3.33. Mean cellulase activity rates in each soil layer (summer samples) ……….88 Figure 3.34. Mean glucosidase activity rates in each soil layer (spring samples) ……...89 Figure 3.35. Mean glucosidase activity rates in each soil layer (summer samples) ……89 Figure 3.36. Mean xylanase activity rates in each soil layer (spring samples) …………90 Figure 3.37. Mean xylanase activity rates in each soil layer (summer samples) ………90 Figure 3.38. Mean NAG activity rates in each soil layer (spring samples) ……………...91 Figure 3.39. Mean NAG activity rates in each soil layer (summer samples) …………...91  xi  Figure 3.40. Mean urease activity rates in each soil layer (spring samples) …………...92 Figure 3.41 Mean urease activity rates in each soil layer (summer samples) …………92 Figure 3.42. Mean phosphatase activity rates in each soil layer (spring samples) …….93 Figure 3.43. Mean phosphatase activity rates in each soil layer (summer samples) ….93 Figure 3.44. Mean sulfatase activity rates in each soil layer (spring samples) …………94 Figure 3.45. Mean sulfatase activity rates in each soil layer (summer samples) ………94 Figure 3.46. Mean phenoloxidase activity rates in each soil layer (spring samples) …..95 Figure 3.47. Mean phenoloxidase activity rates in each soil layer (summer samples) ..95 Figure 3.48. Mean peroxidase activity rates in each soil layer (spring samples) ………96 Figure 3.49. Mean peroxidase activity rates in each soil layer (summer samples) ……96 Figure 3.50. NMS ordination of enzyme activities from all soil layers …………………..97 Figure 3.51. Mean total microbial biomass PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………………. 100 Figure 3.52. Mean total microbial biomass PLFA concentration in each soil layer at the seven locations (summer samples) ………………………………………………………..100 Figure 3.53. Mean total bacteria PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………………………. 101 Figure 3.54 Mean total bacteria PLFA concentration in the each soil layer at the seven locations (summer samples) …………………………………………………………. …….101 Figure 3.55. Mean Gram-positive bacteria PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………………. 102 Figure 3.56. Mean Gram-positive bacteria PLFA concentration in each soil layer at the seven locations (summer samples) ………………………………………………………..102  xii  Figure 3.57. Mean Gram-negative bacteria PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………………..103 Figure 3.58. Mean Gram-negative bacteria PLFA concentration in each soil layer at the seven locations (summer samples) ……………………………………………………….. 103 Figure 3.59. Mean actinobacteria PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………………………. 104 Figure 3.60. Mean actinobacteria PLFA concentration in each soil layer at the seven locations (summer samples) ……………………………………………………………….. 104 Figure 3.61. Mean total fungi PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………………………. 105 Figure 3.62. Mean total fungi PLFA concentration in each soil layer at the seven locations (summer samples) ……………………………………………………………….. 105 Figure 3.63. Mean arbuscular mycorrhizal fungi PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………….. 106 Figure 3.64. Mean arbuscular mycorrhizal fungi PLFA concentration in each soil layer at the seven locations (summer samples) …………………………………………………... 106 Figure 3.65. Mean saprophytic fungi PLFA concentration in each soil layer at the seven locations (spring samples) …………………………………………………………………. 107 Figure 3.66. Mean saprophytic fungi PLFA concentration in each soil layer at the seven locations (summer samples) ………………………………………………………………. 107 Figure 3.67. NMS ordination of PLFA data for all soil layers combined ……………….108  xiii  LIST OF ABBREVIATIONS BEC – Biogeoclimatic Ecosystem Classification BWBS – Boreal White and Black Spruce C - Carbon CWH – Coastal Western Hemlock DOPA - L-3, 4-dihydroxyphenylalanine ESSF – Englemann Spruce Subalpine Fir F – Fermentation H - Humic ICH – Interior Cedar Hemlock IDF – Interior Douglas Fir MANOVA – Multivariate Analysis Of Variance MH – Mountain Hemlock Min - Mineral MRPP – Multi-Response Permutation Procedure MUB - 4-methylumbelliferyl N - Nitrogen NMS – Non-metric Multi-dimensional Scaling PLFA – PhosphoLipid Fatty Acid PP – Ponderosa Pine SOM – Soil Organic Matter xiv  ACKNOWLEDGEMENTS This has been an interesting, challenging and fun academic and personal journey and I’d like to thank the people who have helped make it so. Dr Sue Grayston for being a patient and encouraging supervisor.  Dr Cindy Prescott and Dr Bill Mohn for being  supportive committee members – I always enjoyed our committee meetings! Dr Gary Bradfield for kindly giving up his time to help me with my statistics. Dr Les Lavkulich for helping me unravel the mystery of the sulfur concentrations!  Dr. Val LeMay for  answering many statistics questions so quickly and thoroughly. The Below Ground Ecosystems Group members and my office mates. Especially Richa Anand, Denise Brooks, Jocelyn Campbell, Shannon Daradick, Julie Deslippe, Rachelle Lalonde, Virginie Pointeau and Toktam Sajedi – many thanks for the information, advice, words of encouragement, pints of beer, cups of tea and for your help in the field and in the lab. Our lab manager Kate Delbel for her advice, unending patience and coffee breaks. Ron Chan and Cherry for their help in the lab, special thanks to Ron for his dedication and attention to detail. Per Bengston, Candis Staley and Alice Jang for their hard work and good humour in the field. The administration staff in the Faculty of Forestry who work so hard to help grad students. The janitorial staff for a sparkling building and the daily chats. Andre Arsenault, Mike Curran and Graeme Hope from the BC Ministry of Forests for providing me with information about my sites and showing me around the research forests. Jason Edwards and all at the University of Alberta’s EMEND research station for their hospitality and help with field work in Alberta. Ionut Aron for his help with field work at UBC’s Malcolm Knapp Research Forest. Diane Cyr and all at the Canadian Commonwealth Scholarship Program. NSERC for funding this research project. The IMAJO and Van Dusen Scholarship funds. My family and friends in the UK who have stayed in such good contact and helped me through the home-sick times. My fiancée Andrew Burwood who has supported and  xv  encouraged me all the way through this degree and who has made this Canadian experience such an amazing one.  xvi  DEDICATION  For Peter William Brockett and Sidney James Walker  xvii  1. INTRODUCTION AND LITERATURE REVIEW 1.1. Rationale A combined aboveground-belowground approach to community and ecosystem ecology is enhancing our understanding of the regulation and functional significance of biodiversity and of the environmental impacts of human-induced global change phenomena (Wardle et al., 2004). Established macro-ecological theory and observed biogeochemical processes are used as a basis for integrating the complex and relatively new field of soil belowground ecology with aboveground processes. Micro-organisms provide the link between these biogeochemical processes and the ecology of the soil system. Increasing interest in the ecology of microbial communities can be attributed in part to an understanding that these organisms have direct effects on ecosystem processes (Beare et al., 1995; Horner-Devine et al., 2004; Fierer and Jackson, 2006) and also to the development of novel molecular-based and biochemical techniques which allow us to characterize these communities with increasing clarity and rapidity (Kirk et al., 2004; Neufeld and Mohn, 2006). Soil microbial communities are of particular interest as the soil environment is extremely heterogeneous and a large number of ecological niches allow a diverse community of soil micro-organisms to persist (Standing and Killham, 2007). Cited differences between micro and macro organisms, such as rate of population growth, dispersal ability, abundance, diversity, the unique aspects of microorganisms’ biology and the relatively large scales of time and space over which most microorganisms are studied, do not necessarily prevent the application of existing ecological theory to microorganisms (Prosser et al., 2007). It is important to try to apply the new and growing tool-box of microbial techniques to tried-and-tested macroecological theory in belowground systems.  The challenge facing soil microbial  ecologists is to match the appropriate theoretical approach to the organism, system, scale and question of interest (Prosser et al., 2007).  1  1.2. Literature review 1.2.1. The role of microorganisms in forest ecosystems Micro-organisms1 play a key role in the processes which sustain forest ecosystems, such as litter decomposition, humification, and the mineralization of Carbon (C) and Nitrogen (N). Litter decomposition involves the combined action of the decomposer community, which is composed predominantly of microorganisms, in breaking down complex organic material of plant origin (detritus) (Swift et al., 1979). Some of the products from this breakdown process are utilized by the decomposer community for respiration and growth; this is termed secondary production (Swift et al., 1979). The mineralization of the organic material by microorganisms converts C and nutrients from an organic (non-plant available) to an inorganic (plant-available) form.  Competition  between microbes and plants for limiting nutrients is often intense (Prescott, 2005a). A proportion of the nutrients and C which have been mineralized are subsequently immobilized by incorporation into microbial biomass (Swift et al., 1979). Whether N, and other nutrients essential for plant growth, are immobilized or accumulate in the soil depends on the associated microorganisms’ requirement for growth (Paul and Clark, 1989; Prescott, 2005a). Eventually the decomposers die and their carcasses enter the detritus compartment and are acted upon by other decomposers; this ensures the recycling of carbon and nutrients within the system (Swift et al., 1979). Although microbially-bound nutrients are temporarily unavailable, such short-term sequestration can prevent longer-term sequestration within recalcitrant materials in the soil organic matter (SOM) (Prescott et al. 2000b). These more recalcitrant products are called humic substances or humus. Humic substances are dark-coloured amorphous colloidal products which can be formed from a number of different parent substances (Swift et al., 1979; Oxford University Press, 2004). Humification is the accumulation of these more resistant end-products via a variety of reactions taking place under natural conditions, either directly or indirectly through biological processes (Swift et al., 1979).  1  For the purpose of this thesis I will be referring to soil bacteria and fungi when I use the term ‘soil microorganisms’, and not to algae, viruses, and archaea which are also members of this group. 2  The recalcitrance of these substances means that they are not easily utilized for energy or growth products by soil microorganisms and their degradation requires a higher degree of functional specialization.  1.2.2. Microbial community function and structure Microbial community function can be inferred from rates of ecosystem processes such as litter decomposition and N mineralization, or directly measured as microbial respiration, nutrient and carbon assimilation and enzyme activity. Litter decomposition usually proceeds through a series of well-characterized stages involving a succession of decomposer communities with different degrees of enzymatic competence; the activities of the various functional groups are temporally and spatially separated from each other, operating at different depths in the soil profile and at different times. The activity of many of these extra-cellular soil enzymes can be measured with a high degree of accuracy (Nannipieri et al., 2002). Microbial biomass is the primary source of extracellular enzymes in soil (Tabatabai and Fu, 1992) and measuring the activity of these enzymes in relation to microbial biomass is one way of investigating the efficiency of the microbial community. Several processes, such as carbon mineralization, are carried out by a wide variety of different microorganisms and this functional redundancy or resilience is a feature of most soil systems (Nannipieri et al., 2002). The combination of functional redundancy in soil ecosystems, the huge biodiversity of the soil microbial community, and the lack of a consensus regarding a microbial species concept ensure a complex relationship between microbial community function and structure (or microbial community composition2).  This relationship is receiving increasing amounts of interest, partly  because the last decade has seen the rapid development of phylogenetic and other molecular-based techniques which give an unprecedented view of the structural diversities of such communities. Previously microbial ecologists had to rely on culturedependent techniques in order to characterize microbial community structure. Culturebased methods still play a role in physiological and functional studies but are less useful  2  For the purpose of this thesis microbial community composition will refer to microbial community  function and structure. 3  for measuring ecological abundance and diversity, as it is possible to culture only a relatively small number of micro-organisms in the laboratory (Kirk et al., 2004). There are now a wealth of techniques which can be employed to characterize both the function and structure of microbial communities and these novel techniques have expanded the range of ecological questions that can now be addressed (Neufeld and Mohn, 2006). Those studies which integrate both functional and structural measures of microbial community characterization with measures of soil environmental processes and ecosystem functioning are termed ‘polyphasic’ (Torsvik and Øvreås, 2007; Thies, 2007).  1.2.3. External drivers of microbial community function and structure “Mineralization and immobilization of inorganic nutrients by microbes, the complexing of nutrients into soil organic matter (SOM), and the relationship of these processes to factors such as litter chemistry, climate and endogenous site characteristics inter-relate to form a complex system which dictates the availability of nutrients at a particular site” (Binkley and Hart, 1989) (Figure 1.1). Climate Microbes have been shown to exhibit temperature and moisture respiration optima and therefore we expect climate (at a regional scale) to have an observable effect on microbial community composition or rates of processes controlled by microorganisms. Temperature directly affects the rates of physiological reactions and has many indirect effects on soil biological activity through temperature-induced changes to other aspects of the soil physiochemical environment (Killham, 1994; Voroney, 2007). The direct effect of solar radiation on soil microbial communities is mediated by diurnal and seasonal effects as well as factors such as vegetation status and composition, moisture and soil depth. In most soils with a mesophilic microbial community, there is an approximate doubling of microbial activity with each 10 ˚C rise in temperature between 0˚C and 30-35 ˚C; this is called the “Q10 effect” (Killham, 1994). Soil temperature often co-varies with other factors that affect microbial community composition such as lignin content, C chemistry, toughness, and initial nutrient content of the litter (Prescott, 2005a). Soil water directly affects the growth and activity of soil microorganisms and mediates its effects through the supply of nutrients to the organisms in question, the soil aeration status, the osmotic pressure, and the pH of the soil solution (Paul and Clark, 1989; 4  Killham, 1994). Where water is non-limiting, biological activity may depend primarily on temperature, but as soils dry, moisture is more controlling of biological processes than is temperature (Voroney, 2007).  These two environmental influences do not affect  microbial activity in linear fashion but display complex, non-linear, inter-related effects that likely reflect the individual responses of the various microorganisms and their associated enzymatic systems (Voroney, 2007). In forests of British Columbia (B.C.), moisture is the factor most highly correlated with litter decomposition rates (Prescott et al., 2004) and can therefore be expected to be correlated with microbial community function and perhaps with microbial community structure.  There is large variation in available moisture in B.C. forests, due to the  Province’s size, maritime influences and varied topography. Prescott et al. (2004) found a significant negative correlation between pine needle litter decomposition and potential evapo-transpiration and a positive correlation with precipitation. The wettest zones in B.C. had the greatest mass loss and driest had the least mass loss. At high latitudes, climate is thought to play a larger role in nutrient availability and longterm ecosystem productivity than in other ecosystems (Prescott et al., 2000b). High levels of mor humus accumulation in northern sites is due to climatic limitations on microbial community activity (Prescott et al. 2000b). The SOM provides a long-term nutrient pool for the ecosystem, but immobilizes large amount of available nutrients by complexing them within recalcitrant compounds. Some studies have observed direct correlations between microbial community composition and regional climate gradients.  Hackl et al. (2005) investigated the  influence of regional climate on microbial community characteristics in native European forests. The authors studied zonal forests, where the vegetation communities reflect regional climate, and also azonal forests, which exhibit extreme site conditions and therefore altered vegetation communities from those predicted by the climate gradient. Using Phospholipid Fatty Acid (PLFA) analysis to characterize the microbial community structure and a number of techniques for assessing microbial biomass, they found that the microbial communities in the zonal forests were similar to each other and were strongly influenced by a gradient of mean annual temperature. Soil water availability was found to correlate with microbial community structure in the azonal forests.  5  Plant litter and root exudates Climate is the main factor determining the composition of any ecosystem’s vegetation climax community. The successional stage and the more detailed composition of the vegetation at a site scale are influenced by the disturbance history and site characteristics.  The composition of vegetation at a site in turn influences the soil  environmental conditions and so the habitat for soil microorganisms. Plants alter the soil environment by releasing root exudates and litter and by taking up available nutrients and water. The chemical composition of the exudates and litter has differential effects on the various components of the microbial community, depending on the microorganism’s functional niche and associated environmental preferences. Different tree species produce litter and root exudates of varying chemical composition and so provide a range of carbon sources for heterotrophic microorganisms (Priha et al., 2001). Litter chemistry is correlated with early rates of litter decay (Prescott, 1996; Prescott, 2005a) and litter from different species often exhibit different initial decay rates (Prescott et al., 2000a). This is because each chemical fraction will have an associated decomposer community and the labile or leachable fractions are quickly degraded (Prescott, 2005a). Once the litter has been humified it is a much poorer substrate for decomposing micro-organisms; it is relatively low in carbohydrates and therefore low in available C for microbial energy requirements and so will be decomposed at a much slower rate (Prescott, 2005a). However, litter decomposition rates from different tree species eventually converge (Prescott et al., 2000a, Prescott et al., 2003). How long it takes for this asymptote to be reached is influenced by the activities of soil macrofauna (Prescott, 2005b), climatic variables, litter quality, and especially by the availability of labile C to microbial communities (Prescott, 2005a). The relationship between ecosystem processes and plant species composition has been investigated in a number of studies, but the findings are often contradictory and appear to be context-dependent.  Welke et al. (2005) measured the influence of stand  composition on nutrient chemistry in pure and mixed stands of Douglas-fir and paper birch in the Interior Cedar Hemlock Biogeoclimatic Ecosystem Classification (BEC) zone of B.C. They found significantly more N mineralization under pure birch stands than under Douglas-fir, with mixed-woods having an intermediate value. Concentrations of forest floor total N, exchangeable potassium and magnesium and pH were also 6  consistently higher. The authors related this effect to the higher nutrient concentrations of birch foliar litter.  However, Thomas and Prescott (2000) found litter chemical  characteristics to be poor predictors of N mineralization in a laboratory experiment using litter from three tree species. They found that N mineralization was positively correlated with forest floor total N concentration. Jerabkova et al. (2006) found consistently higher pH and associated higher extractable P under deciduous compared to mixed-wood and coniferous stands in the boreal forests of northern Alberta, but could not relate this directly to tree species composition. Other studies have related tree species composition directly to that of the soil microbial community. Priha et al. (2001) observed a tree species effect on microbial biomass and C mineralization across adjacent pine, spruce and birch forests. modified by differences in site fertility.  This effect was  When soil chemical parameters were held  constant Lejon et al. (2005) found that microbial C biomass as a percentage of total organic C was lowest under Douglas-fir compared to Norway spruce and native forest (dominated by oak and beech) and that the community profile, as characterized by genetic profiling, was unique under the different forest types. As the differences in soil pH, C:N ratio, and total organic C and N across the forest types were negligible, the authors concluded that tree species (variance in litter quality and root exudates) was the main influence on the composition of the microbial community. However, in pure stands of four tree species on northern Vancouver Island, Grayston and Prescott (2005) found that forest floor layer (fermentation vs. humus layer) had the greatest overall effect on microbial community structure, followed by site, and tree species had the least effect. Other site variables The biogeochemical process or mechanism of interest (and therefore the scale of investigation) drives the choice of site variables to be measured in any abovegroundbelowground study (Prosser et al., 2007).  Patterns in soil microbial community  composition have been identified at a range of scales. Grayston et al. (2001) observed that vegetation type and site were the main factors influencing spatial variation in soil microbial carbon and microbial respiration in temperate grassland ecosystems at a scale of metres. Stevenson et al. (2004) found that the ability of soil microbial communities to catabolise a range of substrates depended on land-use type, so patterns in community function could be observed at a scale of kilometres. Bengston et al. (2007) found spatial 7  patterns in microbial biomass, nutrient availability, and soil moisture content were autocorrelated at scales up to 1 km. They hypothesized that observed over-lapping spatial patterns in the forest floor and mineral layers were related directly to the hydrological processes in the soil or indirectly to soil moisture effect on nutrient availability. In soil ecosystems there is usually high heterogeneity of resources and variation in microclimate over small distances.  However, a number of studies have found  correlations at a site scale between one or more site variables and microbial community composition. Trofymow (1998) found that variations in endogenous site characteristics affected soil microbial community composition in coastal temperate rain forests in B.C.; forest floor microbial biomass, basal respiration and substrate induced respiration were significantly correlated with soil C concentration and soil moisture in the forest floor. Decker et al. (1999) found that soil microbial community activity, measured using potential enzyme activity as a functional index, increased with increasing nutrient availability and with decreasing organic matter content in a mature oak woodland. In a study on Vancouver Island Leckie et al. (2004) found no statistical differences between composited samples and un-composited samples for microbial biomass estimates, PLFA biomarker concentration values, and other forest floor measurements (e.g. pH). They concluded that composite sampling within a site is likely to be suitable for characterizing microbial communities despite high site heterogeneity. Resource availability and microclimate also vary with depth in the soil profile, and soil microbial communities can be expected to change accordingly. Soil Organic Matter (SOM) concentration, nutrient availability, soil temperature, and moisture are just some of the variables which change with depth.  In an study which manipulated the  temperature of different soil layers and controlled for other physiochemical factors, Blume et al. (2002) found the surface soil horizons had significantly higher microbial activity (measured using 3H-acetate incorporation into phospholipids) than the subsurface horizons and that shifts in microbial community function in each layer were dependent on the incubation temperature. Jörgensen et al. (2002) found, at soil depths of 0-140 cm, microbial biomass C and N, concentrations of the adenylates adenosine triphosphate (ATP), adenosine diphosphate (ADP) and adenosine monophosphate (AMP), and the basal respiration rate all declined significantly with depth.  8  Carbon availability and the proportion of C from plant-derived sources (as opposed to SOM-derived sources) decline with soil depth (Fierer et al., 2003; Kramer and Gleixner, 2008).  Kramer and Gleixner (2008) found that Gram-positive bacteria preferentially  utilize different SOM-derived-C and Gram-negative bacteria preferentially utilize plantderived-C.  These results agree with those of Fierer et al. (2003) who found that  abundance of Gram-negative bacteria (measured with PLFA analysis) declined with depth along with total microbial biomass and abundances of fungi, and protozoa, whereas, Gram-positive bacteria and actinobacteria tended to increase in proportional abundance with increasing soil depth.  Differential responses of microbial groups to  other external gradients are explored in the next section.  1.2.4. Differential responses of microbial groups to external gradients Different components of the microbial community respond to climate and site gradients in different ways; for example there is high variation in fungal-to-bacterial biomass ratios in forested ecosystems along resource and microclimate gradients. The influence of nutrient availability on forest floor microbial community structure was demonstrated in forests on northern Vancouver Island (Leckie et al. 2004): structure,  characterized  with  PLFA  analysis  and  microbial community  Denaturing  Gradient  Gel  Electrophoresis (DGGE), differed among forest types; fungal PLFA signatures were more abundant in nutrient-poor cedar-hemlock forests and bacterial PLFAs were proportionately more abundant in richer hemlock-amabilis fir forests.  Grayston and  Prescott (2005), using similar analysis techniques, also found fungal biomass to be higher on nutrient-poor sites than nutrient-rich sites on southern Vancouver Island. Acidity has also been shown to differentially affect fungal and bacterial community composition in forest soils.  Högberg et al. (2007) examined changes in microbial  biomass and shifts in the relative proportions of different groups of soil microorganisms across a natural pH and N-supply gradient in a Fennoscandian boreal forest.  The  microbial community structure (characterized by PLFA analysis) changed along the natural biochemical gradients, with fungal biomass increasing with decreasing pH and increasing C:N ratio, and bacteria showing the opposite trend. They suggested that the fungal community was better able to utilize recalcitrant C sources, acclimatize to nutrient-poor conditions and tolerate/compete in lower pH conditions than bacteria.  9  Microbial communities can be variously described based on phylogenetic characteristics, functional traits, guild, habitat preference, growth strategy and by many other classifications. It is essential to understand how these community characteristics relate to observed macro-ecological and biogeochemical processes and at what scale these relationships manifest themselves in order to link observed aboveground processes to the belowground ecosystem (Hodkinson and Wookey, 1999; Neufeld and Mohn, 2006; Kandeler, 2007).  1.3. Introduction to the study This study investigates the shifts in microbial community function and structure (community composition) along a regional climate gradient, and the relationship between community composition, measured site variables and regional climate (Figure 1.1). It is part of a larger study investigating the relationships among regional climate, site factors, tree species, soil organisms, and nutrient cycling processes. A variety of forest types with distinct regional climates were selected based on the provincial Biogeoclimatic Ecosystem Classification (BEC) system (Pojar et al., 1986), key site factors were characterized, and forest floor and mineral soil samples were collected from each site. Phospholipid fatty acid analysis was used to investigate the structure of soil microbial communities and total soil microbial biomass, and extra-cellular enzyme assays established the functional potential of the soil microbial community at each site. The results from this study will be used to explore relationships and derive hypotheses regarding the interactions between regional climatic variables, site endogenous factors and soil microorganisms.  10  Figure 1.1. Visualization of the role of microbial communities in relation to biogeochemical processes in forest soils.  1.4. Specific hypotheses Financial- and time-limitations often require researchers to reduce the number of samples collected and analyzed, and compositing the replicate samples is one way of achieving this. It is important that the variation in the individual samples is not obscured by this practice. Hypothesis one: Analysis of composite soil samples for microbial community function and structure provides the same results as analysis of individual soil samples.  11  This study covers the change in microbial communities and environmental factors over one field season. Changes in microbial community characteristics have been observed over annual time scales so samples were taken twice during the field season to try and capture some of this variation. Hypothesis two: Soil microbial community structure and function are significantly different in spring and summer. Despite local-scale heterogeneity of site characteristics and associated high variability in microbial community composition, previous studies suggest that it is possible to detect variation in microbial community composition at a regional scale due to variation in climate.  Hypothesis three: It is possible to separate forest types along a regional  climate gradient based on microbial community function and/or structure. Hypothesis four: A set of measured environmental variables can be shown to significantly correlate with microbial community function and structure across a regional climate gradient. Prescott et al. (2004a) found that the available moisture influenced litter decomposition at the same study sites. Post-hoc hypothesis: If Hypothesis four is not rejected; moisture is highly correlated with microbial community function and structure. Changes in soil depth are associated with variations in resource availability and microclimate.  Such variations would be expected to influence microbial community  composition and therefore organic soil layers should be analyzed separately from mineral layers. Hypothesis five: Analysis of soil microbial structure and function will show separation of the mineral layers from the organic layers.  12  2. METHODOLOGY 2.1. Location of study sites along a climate gradient Seven study locations were chosen from those sampled by Prescott et al. (2004) (Figure 2.1 and Table 2.1)3. The Biogeoclimatic Ecosystem Classification (BEC) system (Pojar et al., 1986) was used to define these locations along a regional climatic gradient. The British Columbia Ministry of Forest’s BEC system is widely used in B.C. as a common framework for understanding terrestrial landscape ecology in the Province. The BEC system characterizes and describes the major forest and range ecosystems in B.C. as influenced by regional climate and based on the principals of climax and succession and ecological equivalence. The broad units (zones) are divided into subzones, variants, and phases based on topographic and edaphic influences. Zonal sites are those which are representative of the regional climate. Three zonal site replicates, approximately 1 km apart4, were chosen at each location. The ClimateBC web-based program (Wang et al., 2006) was used to establish climate variables for each of my locations in conjunction with BEC zone climate data from the Environment Canada website (http://climate.weatheroffice.ec.gc.ca), Meidinger and Pojar (1991); Hope et al. (2003), Kishchuk (2004), and Prescott et al. (2004). ClimateBC is a visual basic program which calculates seasonal and annual climate variables for specific locations based on latitude, longitude and elevation (elevation is an optional input) (Wang et al., 2006).  The ClimateBC program coverage includes my study  locations in British Columbia and Alberta.  3  The EMEND site was not part of this study but Jerabkova et al. (2006) studied litter decomposition at this site. 4  Except in the Ponderosa Pine location, where the distances were smaller due to the size of the ecological reserve, and at the Mountain Hemlock location, because of topographical constraints. 13  Figure 2.1. Map of the biogeoclimatic zones of British Columbia showing the seven study locations. Map based on an original from the British Columbia Ministry of Forests, 1995.  14  Table 2.1. Study site characteristics. Name BEC zone Subzone Elevation (m) of sample sites Malcolm Knapp Research Forest Sicamous Research Forest Mount Seven Research Forest EMEND  Cypress Park  Coastal Western Hemlock (CWH) Englemann Spruce – Sub-alpine Fir (ESSF) Interior Cedar Hemlock (ICH) Boreal White and Black Spruce (BWBS) Mountain Hemlock (MH)  vm1 (very wet, maritime) wc2 (wet, cold)  240  Latitude (degrees) of sample sites 49.22  1700  50.50  119.55  -2.0 to 2.0 (1.2)  400 to 2200 (930)  mk1 (moist, cool)  1200  51.17  116.56  2.0 to 8.7  500 to 1200  cold and dry5  677-880  56.44 to 56.51  118.19 to 118.27  –2.9 to 2.0  330 to 570  mm (moist, maritime)  Approx. 1500  49.23  123.15  0 to 5  2916  Longitude (degrees) of sample sites 122.34  Mean annual temperature (degrees centigrade) 5.2 to 10.5  Mean annual precipitation (mm) 2787  Opax Mt Interior xh (xeric, 1100 50.49 120.28 1.6 to 9.5 379 (Mud Douglas hot) Lake) Fir (IDF) Skihist Ponderosa xh (xeric, 175 50.22 121.51 4.8 to 10 390 Ecological Pine (PP) hot) Reserve Information from BC Ministry of Forests website (http://www.for.gov.bc.ca/hfd/pubs/Docs/Rr/Rr24.htm); Environment Canada website (http://climate.weatheroffice.ec.gc.ca); Meidinger and Pojar (1991); Hope et al. (2003); Kishchuk (2004); and Prescott et al. (2004), and ClimateBC (Wang et al., 2006).  5  This site is in Alberta, so no BEC subzone has been assigned. 15  The chosen sites are mature forests which have not been exposed to any direct anthropogenic disturbance in recent history. The Ecosystem Management Emulating Natural Disturbance (EMEND) site is located approximately 90 km northwest of the town of Peace River in northern Alberta. The ecosystem is equivalent to those classified in the Boreal White and Black Spruce (BWBS) BEC zone in B.C. Elevation ranges from 677 to 880 m above sea level and latitude from 56.44 to 56.51 degrees.  Soils are primarily Gray Luvisols with minor  occurrences of Brunisols, Gleysols, and Solonetzic soils derived from similar glaciolacustrine and glacial-till parent material is containing few coarse fragments (Kishchuk 2004). The site is cold (mean annual temperature is -0.3 ˚C, with mean January and July temperatures of –18.8 and 14.6 °C, respectively) and dry (mean annual precipitation is 433 mm) (Jerabkova et al., 2006). Soils are well-drained and exhibit little pedogenic variation across sites (Jerabkova et al., 2006). The dominant tree species at the study sites were white spruce (Picea glauca) and Populus species. The under-storey was not formally surveyed, but contained pricky rose (Rosa acicularis), Wood’s rose (Rosa woodsii), saskatoon (Amelancier alnifolia), and various mosses and lichens. Sicamous Research Forest is a silvicultural systems trial in the Engelmann SpruceSubalpine Fir (ESSF) zone, in subzone wc2 (Pojar et al., 1986).  Elevation is  approximately 1700 m above sea level and latitude is 50.50 degrees. The ESSF is relatively wet and cold compared to other BEC zones in B.C. with approximately 930mm mean annual precipitation and a mean annual temperature of 1.2 ˚C (Prescott et al., 2003). The soils are derived mostly from morainal deposits laid down during the last glacial period. Soils are primarily Humo-Ferric Podzols with a discontinuous Ae layer and a Hemihumimor humus form (Hollstedt and Vyse, 1997). The soil texture varies, but is predominantly a sandy loam with 25–40 % coarse fragment content (Hollstedt and Vyse, 1997). The underlying bedrock is primarily granitic gneiss (Hollstedt and Vyse, 1997). The dominant tree species at the study sites were sub-alpine fir (Abies lasiocarpa) and Engelmann spruce (Picea engelmannii). The under-storey was not formally surveyed, but contained rhododendron species, Vaccinium ovalifolium, and Valariana species. Opax Mountain (Mud Lake) is part of the Opax Mountain Silvicultural Systems Trial in the dry Interior Douglas Fir (IDF) zone near Kamloops, BC. The site is xeric and hot (xh) 16  relative to other sites in the IDF zone (Pojar et al., 1986). Elevation is approximately 1100 m above sea level and latitude is 50.49 degrees. Soils are loam and sandy loam textured Orthic Gray Luvisols and Orthic Eutric Brunisols, with a range in average forest floor thickness of 2.5–4.0 cm and a Hemimor humus form (Hope et al., 2003). The dominant tree species at the study sites was interior Douglas-fir (Pseudotsuga menziesii var. glauca).  The under-storey was not formally surveyed, but was observed to be  sparse containing some grasses. Malcolm Knapp Research Forest is in the Coastal Western Hemlock (CWH) vm1 (very wet maritime) subzone (Pojar et al., 1986). Elevation is approximately 240 m above sea level and latitude is 49.22 degrees. Mean monthly temperatures for the coldest and warmest months are 1.4˚C and 16.8˚C. Mean annual precipitation is 2140 mm (Klinka and Krajina, 1986). The soils are Orthic and Sombric Humo-Ferric Podzols of gravelly loamy sand over a glaciofluvial blanket over glacial marine deposits (Carter and Lowe, 1986). The dominant tree species at the study sites were Douglas-fir (Pseudotsuga menziesii var. menziesii) and western redcedar (Thuja plicata). The under-storey was not formally surveyed, but contained many species of shrubs and forbs at one site, including devil’s club (Oplopanax horribilus), and salal (Gaultheria shallon) and mainly ferns and mosses at the other two sites. Mount Seven is located in the Interior Cedar Hemlock (ICH) zone, in the mk1 (moist and cool) subzone (Pojar et al., 1986) near the town of Golden, B.C. The dominant tree species at the study sites were interior Douglas-fir (Pseudotsuga menziesii var. glauca) and Engelmann spruce (Picea engelmannii). Elevation is approximately 1200 m above sea level (its high elevation accounts for the lack of Thuja plicata and Tsuga heterophylla (Mike Curran, personal communication)). Latitude is 51.17 degrees. Surface soils are predominantly silt loam and loam textured over calcareous parent material with a high pH (Quesnel and Curran, 2000). The under-storey was not formally surveyed. Cypress Park is in the Mountain Hemlock (MH) BEC zone. The total average yearly rainfall from 1954 to 1990 for nearby Hollyburn Ridge is 2115.4 mm. Mean annual temperature ranges from 0-5 ˚C. The highest daily maximum temperature occurs in July and August. The coldest of the daily minimum temperatures occurs in December and January (CEAA, 2006). Elevation is approximately 1500 m above sea level and latitude is 49.23 degrees. The dominant tree species at the study sites were mountain hemlock 17  (Tsuga mertensiana) and yellow-cedar (Chamaecyparis nootkatensis).  The under-  storey was not formally surveyed, but contained Vaccinium and moss species. Skihist Ecological Reserve is in the Ponderosa Pine (PP) xh (xeric and hot) BEC subzone (Pojar et al., 1986). Elevation is approximately 175 m above sea level and latitude is 50.22 degrees.  The dominant tree species was ponderosa pine (Pinus  ponderosa) with some interior Douglas-fir (Pseudotsuga menziesii var. glauca).  There  was little to no under-storey present, except for Pinus ponderosa seedlings.  2.2. Sampling design Five random soil sub-samples were taken from the fermentation (F) layer, the humic (H) layer and from the first 10 cm of the mineral (min) layer (horizon A) from each of three 10-m2 site replicates in each BEC zone location (Table 2.2). Sampling was carried out twice in 2006 – once during the spring flush and once in mid-summer. The sampling times took into account the seasonal phenology of the seven forest sites; sites situated at higher elevations and latitudes were sampled later in the spring than those at lower altitudes and latitudes. Site-specific literature was used to identify approximate dates for the initial top-soil thaw at applicable sites, and the first sampling was timed for just after the thaw (if applicable).  Each location was sampled again 60 days after the first  sampling (within 1 or 2 days). Sub-samples of the individual layers were composited for each site. Some of the sub-samples from the spring sampling at the ICH location were kept separate and used to test the validity of compositing samples for microbial analysis (see Methodology section).  Table 2.2. Break-down of sampling strategy. Hierarchical level Number of samples BEC zones/ locations 7 Site replicates for each location 3 Sub-samples at each site replicate 5 - composited Soil layers at each sub-sample location 3 Sampling times 2 Total samples 126 (+ 15 not composited)  18  2.3. Field measurements Slope aspect, slope angle, and soil temperature at 10-cm depth were recorded at each site (soil temperature was recorded at both sampling times). The species of any mature trees which had canopy overhanging the sampling area and their distance from the sampling points were recorded (a more detailed vegetation and soil diagnostic analysis and measurements of litter inputs to the sites will be carried out by Ali Araghir in 2008 as part of a related project). A number of climate variables were estimated for each location using the ClimateBC web-based program (Wang et al., 2006). These values were compared to values in the literature and to those from the Environment Canada website. Mean annual temperature (MAT) (°C), mean annual precipitation (MAP) (mm) and actual evapo-transpiration (AET) rates were calculated for each location.  MAT and MAP are the main variables  describing a regional climate gradient, and AET is thought to be a good predictor of soil moisture influence on litter decomposition processes across a range of climates (Prescott, 2005a).  The other variables calculated were mean warmest month  temperature (MWMT) (°C), mean coldest month temperature (MCMT) (°C), temperature difference between MWMT and MCMT (continentality) (°C), mean annual summer (May to Sept.) precipitation (mm), annual heat:moisture index (MAT+10)/(MAP/1000)), and summer heat:moisture index ((MWMT)/(MSP/1000)). Five replicate sets of PRSTM available-nutrient probes6 were incubated in the F, H, and mineral soil layers for 2 months (60 days +/- 1 or 2 days) at each sampling site. After incubation the probes were thoroughly cleaned in distilled and de-ionized water and sent to Western Ag’s laboratory for analysis. PRSTM nutrient probes provide information on available concentrations of a range of nutrients (NO3+, NH4-, Ca2+, Mg2+, K+, H2PO4-, Fe3+, Mn2+, Cu2+, Zn2+, B3+, SO42-, Pb2+, Al3+).  The plastic-cased probes contain a  charged ion-exchange membrane window (10-cm2 surface area).  One set of four  sample replicate cation probes are positively charged and one set of four sample replicate anion probes are negatively charged. Inorganic (available) ions in the soil adsorb to the membrane.  6  Soil water content and microbial activity play a role in  Manufactured by Western Ag. Innovations, Saskatoon, SK. 19  adsorption of ions to the probes (Qian and Schoenau, 2002). After incubation Western Ag dissolve the membranes from the four replicate probes and the concentration of ions is determined for each sample. The NO3--N and NH4+-N contents within the PRS™probe  eluate  are  analysed  (www.westernag.ca/innov/).  colourimetrically  using  an  autoanalyzer  All remaining nutrient ion contents in the eluate are  measured using inductively-coupled plasma spectrometry (www.westernag.ca/innov/). The units are in micro-grams of adsorbed ions per 10cm2 of the membrane window per burial period (days).  Table 2.3. Measured site variables and sampling time. Site variable Spring sampling Summer sampling Particle size (% sand, silt, clay) x %C x x %N x x Available nutrients (PRSTM probes) incubated for whole period Soil temperature x x Soil moisture x x Slope angle x Slope aspect x Dominant tree species x Distance of sample from nearest tree x pH x x  2.4. Laboratory sample analysis All soil samples were stored on ice in the field and then at 4 ºC in the laboratory for a short time until storage preparation.  The soils were sieved through a 2-mm mesh  immediately on return to the laboratory. They were then composited, except for some of the ICH spring samples which were kept aside as individual samples, and divided into two portions. One portion was stored at -20 ºC in preparation for chemical analysis and enzyme assays and the other portion was freeze-dried in preparation for phospholipid fatty acid (PLFA) analysis. Samples for each of the three site replicates were composited by layer for chemical and physical analysis in the laboratory. The validity of compositing the five samples from each site replicate was tested by statistically comparing the values for microbial  20  community characteristics in individual sub-samples taken from the ICH spring sample with those from composited samples. Total soil C and N concentration was determined by dry combustion-CO2 determination and then by analysis in a Leco CHN2000 analyzer. Soil pH was recorded with a pH meter after the soil sample was vortexed in de-ionized water for 30 seconds and allowed to settle for 1 hour (adapted from Hendershot et al., 1993). Gravimetric moisture content was measured by weighing soil samples before and after oven-drying at 105 ºC for 48 hours. Particle size analysis (% sand, silt, clay) was performed using a hydrometer method (after Sheldrick and Wang, 1993).  2.4.1. Microbial community structural analysis The biomass and structure of the forest floor microbial communities were assessed by analyzing the ester-linked PLFA composition of the F, H, and mineral layer samples. Phospholipids are essential components of cell membranes which are rapidly degraded after cell death. Profiling of phospholipid fatty acids can be used to monitor overall changes in subsets of the microbial community using signature biomarkers (Kandeler, 2007).  Individual PLFA signature biomarkers relate to an identified group of  microorganisms (Table 2.4).  PLFA provides an accurate picture of the relative  proportions of each microbial component in the sample (Allison et al., 2007).  The  resolution of the technique is fairly low and provides no information on the actual species present, but authors note its ability to discriminate between samples which may have community differences too subtle for other techniques to identify (Grayston et al., 2004; Leckie et al., 2004a). Briefly, lipids were extracted from 0.5 - 1.5 g samples of mineral soil and forest floor using the procedure described by Bligh and Dyer (1959) and Frostegård et al. (1991). The  separated  fatty  acid  methyl-esters  were  identified  and  quantified  by  chromatographic retention time and mass spectral comparison on an Agilent 6890N GC with an Agilent 5973N mass selective detector. The column was a HP5 MS:30m with a 250μm i.d., and 0.25 μm film thickness. The peaks were identified using a standard qualitative bacterial acid methyl ester mix (Supelco; Sigma Canada, Mississauga, Ontario, Canada) that ranged from C11 to C20, and by referring to the template in Knief et al. (2003). 21  Fatty acids are designated as the ratio of the total number of C atoms to the number of double bonds, followed by the position of the double bond from the methyl end of the molecule. The prefixes “a” and “i” refer to anteiso- and isobranching. A 10Me indicates a methyl group on the tenth C atom from the methyl end of the molecule. Cyclopropyl fatty acids are indicated by the prefix “cy” (Pennanen et al., 1999). The abundance of individual fatty acid methyl-esters in each sample was expressed as nmol PLFA g-1 dry forest floor or mineral soil and also as nmol % of total sample biomass.  PLFA provides information on the ratios of various microbial community  components (e.g. Gram-positive to Gram-negative bacteria) and measurements of abundance including total soil microbial biomass (Leckie et al., 2004b). One of the major advantages of using PLFA to characterize soil microbial community structure is that it provides an accurate picture of the viable cells present at the time of sampling (Kandeler, 2007).  To avoid recording fatty acid signature 18:2ω6 from plant cells  (especially concentrated in plant roots) in the samples (fatty acid signature18:2ω6 was used in this study to record presence of fungal biomass) the soil was sieved and visible roots and other plant material were removed (Bardgett and McAlister, 1999).  22  Table 2.4. Signature PLFAs chosen to characterize microbial community structure. Fatty acid Organism Reference i15:0 Gram-positive bacteria Bååth et al. 1992 a15:0 Gram-positive bacteria Zelles 1999 15:0 total bacteria Federle 1986, Frostegard et al. 1993 i16:1_7c Gram-negative bacteria Zogg et al. 1997 15:0_6m/10Me16:0 actinobacteria Allison et al. 2007, Bååth et al. 1992, Coleman et al. 1993, i16:0 Gram-positive bacteria Frostegård et al. 1993, Zelles 1999 16:1_9c Gram-positive bacteria Fritze et al. 2000 16:1_7 Gram-positive bacteria Frostegård et al. 1993, Zelles, 1999 16:1_5c arbuscular mycorrhizal Federle 1986, Frostegard et al. 1993, fungi Zogg et al. 1997 16:0 Common i17:1_8c Gram-negative bacteria Zogg et al. 1997 16:0_6m/10Me17:0 actinobacteria Federle 1986, Frostegard et al. 1993 i17:0 Gram-positive bacteria Bååth et al. 1992 a17:0 Gram-positive bacteria Bååth et al. 1992 cy17:0 Gram-negative bacteria Bååth et al. 1992 17:0 total bacteria Zogg et al. 1997 17:0_7m/10Me18:0 actinobacteria Federle 1986, Frostegard et al. 1993 18:2_6,9 saprophytic fungi Federle 1986, Frostegard et al. 1993 18:1_9c Gram-positive bacteria Allison et al. 2007, Federle 1986, bacteria & fungi Frostegard et al. 1993, Zak et al. 1996 18:1_7 Gram-negative bacteria Frostegard et al. 1993, Zelles, 1999 18:1_5c Gram-negative bacteria Zogg et al. 1997 18:0 Gram-positive bacteria Zogg et al. 1997 18:1_7c7m/10Me19:1_7c total microbial biomass 18:0_8m/10Me19:0 actinobacteria Zelles 1999 cy19:0 Gram-negative bacteria Federle 1986, Frostegard et al. 1993 19:0 internal standard  2.4.2. Microbial community functional analysis Litter decomposition usually proceeds through a series of stages involving a succession of decomposer communities with different degrees of enzymatic competence.  The  activities of the various functional groups are temporally and spatially separated from each other, operating at different depths in the soil profile and at different times. Enzyme bioassays were used to obtain potential activity rates of specific extra-cellular enzymes in the soil samples when incubated with a synthetic substrate at a consistent pH and temperature. Over 100 enzymes have been identified in soil and there is likely to be more than one enzyme acting within each decomposition stage, therefore a representative suite of  23  enzyme assays was chosen to try to cover a suite of identified degradative processes (Table 2.5).  Table 2.5. Hydrolytic enzyme assays chosen for this study. Name(s) of enzyme  Assay substrate  Natural substrate group Organic molecules containing P  Acid phosphatase/ phosphomonoesterase  4-MUBphosphate  Cellobiohydrolase  4-MUBbeta-Dcellobiosid e  Cellulose and other carbohydra te polymers  Beta-1,4-glucosidase  4-MUBbeta-Dglucoside  Cellulose and other carbohydra te polymers  Reaction details  Class of enzyme  Product(s) of interest  Mineralization of principal sources of organic P in litter under acidic conditions – hydrolyzes organic phosphoric mono-esters and di-esters. Activity greatest under conditions which favor N mineralization – strongly correlated with rate of release of both inorganic N and P to the soil solution. Activity often closely related to fungal presence. Catalyzes hydrolysis of 1,4-b-Dglucosidic linkages in cellulose and cellotetraose. Cleaves successive disaccharide units (cellobiose) in the 2nd stage of cellulose degradation. Activity correlates well with fungal presence. Degrades structural components with little N or P. Activity measured by the MUB technique is mainly fungal in origin. Third and final enzyme (ratelimiting) in chain which breaks down labile cellulose (cellobiose) into glucose. Catalyzes the hydrolysis of terminal 1,4-linked b-D-glucose residues from b-D-glucosides, including short-chain cellulose oligomers. Degrades structural components containing little N or P. Highest activity early in decomposition of litter. Produced by fungi and bacteria.  Repressible  Inorganic P  Adaptive  Low molecular mass C compounds  Adaptive  Low molecular mass C compounds  24  Name(s) of enzyme  Assay substrate  Natural substrate group Cellulose and other carbohydra te polymers  Beta-1,4-xylosidase  4-MUBbeta-Dxyloside  Beta-1,4-Nacetylglucosaminidase (NAG)  4-MUB- Nacetylbeta-Dglucosami nide  Chitin  Phenol oxidase  L-3,4Dihydroxyp henylalnin e  Lignin  Peroxidase  L-3,4Dihydroxyp henylalnin e  Lignin  Aryl sulfatase  4-MUBarylsulfatase  Organic molecules containing S  Reaction details  Class of enzyme  Product(s) of interest  Involved in C transformation. Degrades xylooligomers (short xylan7 chains) into xylose. Degrades structural components with little N or P. Both fungi and bacteria produce this enzyme. Second enzyme in chain of three. Catalyzes the hydrolysis of terminal 1,4-linked Nacetylbeta- D-glucosaminide residues in 8 chitooligosaccharides . Hydrolysis of principle sources of organic N in litter. Chitin found in fungal cell walls. Mainly produced by fungi. Also known as polyphenol oxidase or laccase. One of a suite of enzymes degrading 9 lignin. Oxidizes benzenediols to 10 semiquinones . White rot fungi is a major producer of phenol oxidase. Requires co-enzymes. One of a suite of enzymes degrading lignin. Catalyzes oxidation reactions via the reduction of H2O2. It is considered to be used by soil microorganisms as a lignolytic enzyme because it can degrade molecules which do not have a precisely repeated structure. Basidiomycetes are a major producer of peroxidase. Does not require co-enzymes. S mineralization from organic compounds. Have a stabilized,  Adaptive  Low molecular mass C compounds  Constitutive  Low molecular mass C- and Nrich compounds  Simpler compounds derived from recalcitrant polymers Simpler compounds derived from recalcitrant polymers  Repressible  Inorganic S  extracellular, organomineralbound component.  7 Xylans are b-1,4-linked polymers of xylopyranose - a plant structural polymer less tightly associated with plant cell walls than cellulose. 8  Chitin-derived oligomers  9  Benzenediols, or dihydroxybenzenes, are aromatic C compounds in which two hydroxyl groups are substituted onto a benzene ring. 10  A semiquinone is a free radical resulting from the removal of one hydrogen atom with its electron during the process of dehydrogenation of a hydroquinone to quinone. 25  Name(s) of enzyme  Assay substrate  Urease  Urea  Natural substrate group Urea  Reaction details  Class of enzyme  Product(s) of interest  Degrades urea. Routinely produced by cells. Always extracellular. Hydrolyzes urea into CO2 and NH3.  Constitutive and repressible  N containing compounds  From Miller et al. (1998); Møller et al. (1999); Decker et al. (1999); Burns and Dick (2002); Saiya Cork et al. (2002); Andersson et al. (2004); Stursova (2006); Killham and Prosser (2007); Weintraub et al. (2007).  The microplate enzyme bioassay methods of Marx et al. (2001) and Sinsabaugh et al. (2000; 2003) were used as a basis for developing a fluorimetric enzyme assay protocol modified for our laboratory.  The determination of enzyme activity using 4-  methylumbelliferyl (MUB) substrates is a highly sensitive technique (Kjøller and Struwe, 2002). For the fluorimetric enzyme bioassays, 0.1-g of soil (from the F, H, or mineral layer) was ground in a pestle and mortar, from frozen, for 1 min. Fifty mL of 50-mM sodium acetate (pH 5) was added to buffer each sample, along with approximately thirty sterile glass beads. The buffered conditions standardize the method and stabilize the fluorescent intensity of the 4-MUB, which is highly dependent on pH (Marx et al., 2001).  The  solution was shaken on high for 1 hour in a shaker, and then another 50-ml of buffer was added. A 10-μM concentration of 4-MUB standard solution was prepared and kept at -20˚C (for up to a fortnight) until needed. One hundred millilitres of the 4-MUB synthetic substrates (200-μM) were prepared in sterile water and kept until needed (for up to a week, except for 4-MUB-phosphate which was prepared fresh for each assay). Ninety six-well black microplates were prepared as outlined in Figures 2.2 and 2.3, with 16 replicates (16 wells) for each soil sample. A quenched standard, an optical abiotic control, and a substrate control were included with each set of plates. One set of plates was used for each substrate.  26  Enzyme name and plate replicate # Std 200ul buffer + 50ul 4-MUB std (on each sample plate) Sub 200ul buffer + 50ul sub (on each sample plate) S1 200ul soil suspension + 50ul sub S1 200ul soil suspension + 50ul sub S2 “” S2 “” S3 “” S3 “” … … … …. … …. …. …. Figure 2.2. Sample plate outline for fluorimetric enzyme bioassay. Standard (Std), Sample (S), Substrate (Sub).  Soil buffer plate replicate # Std 200ul buffer + 50ul 4-MUB std (on each SB plate) Q1 200ul soil suspension + 50ul MUB std SB1 200ul soil suspension + 50ul buffer Q2 “” SB2 “” Q3 “” SB3 “” …. …. …. …. …. ….. … …. BB 250ul buffer (on each SB plate) Figure 2.3. Soil buffer plate outline for fluorimetric enzyme bioassay. Standard (Std), Quench (Q), Soil Buffer (SB), Background Buffer (BB).  27  The plates were placed in the dark at 20 ˚C in an incubator for different periods of time, as outlined in below, according to calibration curves obtained before the analysis. o  Phosphatase: 2 hours  o  Β-glucosidase: 3 hours  o  NAG: 3 hours  o  Sulfatase: 3 hours  o  Xylosidase: 4 hours  o  Galactosidase: 5 hours  o  Cellobiohydrolase: 7 hours  At the end of the incubation a 20-μl aliquot of 0.5-M sodium hydroxide was immediately added to each well to alkalinize the solutions for optimum fluorescence readings (Marx et al., 2001).  The plates were then read in a CytofluorTM II plate reader using the  Cytofluor software program. Excitation was set at 360/40 nm, emission at 460/40-nm, gain at 50, mixing for 5 seconds on a “Costar” plate-type setting. Potential activity was calculated as nmol of substrate converted per hour per gram of sample and also as nmol of substrate converted per hour per gram dry-weight of sample. If the calculated value was negative it was assumed to be a zero-activity reading. For the colorimetric enzyme bioassays 0.5-g of soil (from the F, H , or mineral layer) was ground in a pestle and mortar, from frozen, for 1 minute.  Fifty millilitres of 50-mM  sodium acetate buffer (pH 5) was added to each sample in a 250-ml conical flask along with approximately 30 sterile glass beads. The buffer ensures standardized conditions. The solution was shaken on high for 1 hour in a shaker, and then another 50-ml of buffer was added. A 25-mM L-3, 4-dihydroxyphenylalanine (DOPA) solution was prepared in 50-mM acetate buffer (pH 5.0) and kept at -4˚C in the dark (for up to 24 hours) until needed. Ninety six-well clear microplates were prepared as outlined in Figure 2.4 with 16 wellreplicates for each soil sample. A DOPA standard, an optical abiotic control, and a substrate control were included with each set of plates. One set of plates was used for 28  each substrate. For peroxidase assays only 10-µl of 0.3 % H2O2 was added to the substrate and sample wells after 50-µl of DOPA was added.  Enzyme name and plate # Sub 200ul buffer + 50ul DOPA SB1 200ul soil suspension + 50ul buffer S1 200ul soil suspension + 50ul DOPA S1 “” SB2 “” S2 “” S2 “” …. …. …. …. … …. BB 250ul buffer Figure 2.4. Sample plate outline for colorimetric enzyme bioassay. Sample (S), Substrate (Sub), Background Buffer (BB).  The plates were placed in the dark at 20˚C in an incubator for 5 hours before taking readings for the peroxidase activity and for 18 hours before taking readings for the phenoloxidase activity. The plates were read in a Spectra Max 340 plate reader using the Softmax Pro software program. Wavelength was set to 460-nm with the “automix option” on. Potential activity was calculated as nmol of substrate converted per hour per gram of sample and also as nmol of substrate converted per hour per dry weight gram of sample.  If the calculated value was negative it was assumed to be a zero-activity  reading.  Peroxidase values include phenol oxidase activity:  To obtain peroxidase  activity alone the phenol oxidase activity was subtracted from the initial peroxidase activity.  2.5. Statistical analysis Data were tested for normality using a Kruskal Wallis test (Statistica, version 6) and by visually examining the data.  The microbial community datasets and site variables  dataset were multivariate non-normal.  Transformations were tried, but failed to  normalize the data, therefore all data was left untransformed and was analyzed using nonparametric techniques.  29  All analyses (except those employed in investigating hypothesis two) combined the microbial community data from both sampling times (refer to the results section 3.2). Tests were considered significant at p ≤ 0.05. Enzyme activity on a dry-weight soil basis and PLFA signature concentrations divided by sample biomass and reported as ratios were found to provide consistent and reproducible results and so were used for all multivariate analyses. Multivariate statistical techniques have been shown in many similar studies to improve the discriminatory power of techniques such as PLFA and enzyme bioassays (for example Bååth et al., 1992; Leckie et al., 2004a; Ritz et al., 2004) and as an alternative approach to single indexes (Kandeler et al., 1996).  Multivariate statistical analysis  methods were used for all analyses. A nonparametric Multiple Analysis of Variance (MANOVA) (“PerMANOVA” PC ORD, version 5, 1999) (mixed model - one fixed effect and one nested) was used to test hypothesis one. Traditional multivariate analysis of variance (MANOVA) is generally inappropriate for analysis of ecological communities. Nonparametric MANOVA has no assumptions of linearity or multivariate normality and sums of squares are calculated directly from the distances among data points, rather than the distances from the data points to the mean (Anderson, 2001).  Nonparametric MANOVA does assume  independence of sample units and similar dispersions among sample units (McCune and Grace, 2002). Multi-Response Permutation Procedure (MRPP) analysis and paired MRPP analysis (PC-ORD for Windows, McCune and Mefford, version 5, 1999) were used to test hypotheses two, three and five. MRPP and paired MRPP have been used in a similar and successful way by Stark et al. (2006) in their analysis of forest seed banks. MRPP is similar to Canonical Variates Analysis but it does not require the same assumptions of data normality to be satisfied. MRPP tests the hypothesis of no difference between two or more groups of entities (using within-group homogeneity to test separation). Groups are identified a priori (either time of sampling, location, or soil layer) and there is a choice of distance measurements. The Sørensen distance measurement was chosen for all analyses, where appropriate, as it has been shown to consistently distinguish  30  ecologically distinct groups (McCune and Grace, 2002; Stark et al., 2006). The effectsize (A11) varies between plus one and minus one. ‘A-maximium’ equals one when all items are identical within groups. ‘A’ equals zero when heterogeneity within groups equals expectation by chance. ‘A’ less than zero has more heterogeneity within groups than expected by chance. According to McCune and Grace (2002), when dealing with ecological data, an ‘A’ value greater than 0.3 indicates “very high” separation of groups (i.e. very high within-group homogeneity) and an ‘A’ value greater than 0.1 indicates “high” separation of groups. MRPP and paired MRPP share the same assumptions as a nonparametric MANOVA, along with the assumption that the distance measure chosen is appropriate to the data set to be tested and that the variables measured are weighted appropriately for the ecological question posed. Paired Multi-Response Permutation Procedure (paired MRPP) (PC-ORD for Windows, McCune and Mefford, version 5, 1999) can be used in a similar way to a nonparametric MANOVA. The Ponderosa Pine location had no H layer, so the sampling design was unbalanced, and unlike nonparametric MANOVA, paired MRPP does not require a balanced design. Paired MRPP was used to examine whether combinations of pairs of locations (BEC zones) and soil layers were significantly different from each other based on microbial community characteristics.  The p value was adjusted (Bonferroni’s  correction) depending on the number of pair combinations. A Mann Whitney U test (a nonparametric t-test) (Statistica, version 6) was used to test for significant differences in enzyme activities, PLFA concentrations, and environmental variables between spring and summer sampling time, for individual locations. Spearman’s rank correlations (Statistica, version 6) were used to determine the significance and strength of any relationships between microbial community variables and measured environmental variables. Ordination techniques provide a graphical representation of data, which can aid in interpretation and analysis. In this study, Non-metric Multidimensional Scaling (NMS) (Mather, 1976; Kruskal, 1964) (PC-ORD for Windows, McCune and Mefford, version 5,  11  A = 1 - (observed delta/expected delta). 31  1999) was used to visualize microbial community function and structure data (the primary matrix), investigate which components of the microbial communities were mainly influencing the final ordination solution, and establish the dimensionality of the data set based on stress and stability measurements.  The communities were grouped by  location, sampling time and soil layer. Measured environmental variables were used as the secondary matrix. NMS is a very robust ordination technique and is recommended for ecological data sets. It does not have assumptions of data multivariate normality nor linearity among variables (McCune and Grace, 2002). NMS uses ranked distances and offers a choice of distance measure.  “NMS is the most generally effective ordination method for ecological  community data and should be the method of choice” (McCune and Grace, 2002). The microbial community functional and structural datasets were separately plotted on ndimensions; the number of dimensions is chosen to minimize stress in the ordination. Distance between two points is inversely proportional to the similarity value for a given pair, such that points positioned close together are more similar than points plotted further apart.  The Sørensen distance measure was used with a random starting  configuration.  Pearson and Kendall correlations (r and tau values) between the  ordination axes and the environmental variables were calculated.  The measured  environmental variables were plotted on the ordination if their correlation with the ordination axes had an associated r2 value greater than 0.3. The dimensionality of the dataset was assessed by referring to the minimum stress and instability of the final solution. Fifty real data runs and fifty randomized data runs were used, and Monte Carlo randomization test result probability values were reported. The stability criterion was 0.00001. All functional ordinations were orientated by the soil moisture (%) vector (in NMS the axes are orthogonal to each other).  32  3. RESULTS The results from each hypothesis are presented separately.  3.1. Hypothesis one: Analysis of composite soil samples for microbial community function and structure provides the same results as analysis of individual soil samples. There was no significant difference in microbial community structure (PLFA signature concentration) (Table 3.1) or function (enzyme activities) (Table 3.2) between the individual soil samples and the composite samples from the same site replicates at the ICH location (spring sampling time). The results suggest that composite soil sampling was successful in reproducing the same results for microbial community structural and functional analysis as were produced by five individual soil samples. The hypothesis is accepted; composite sampling was appropriate for the scale and methods employed in this study.  Table 3.1. Test statistics from a nonparametric MANOVA on structural microbial data. Source df SS MS F p Sampling type 1 0.04 0.04 0.13 0.9 Layer 4 1.36 0.34 34.51 0 Residual 12 0.12 1 Total 17 1.53  Table 3.2. Test statistics from a nonparametric MANOVA on functional microbial data. Source df SS MS F p Sampling type 1 0.09 0.09 0.22 0.9 Layer 4 1.58 0.39 10.29 0.001 Residual 12 0.46 0.38 Total 17 2.12  33  3.2. Hypothesis two: Soil microbial community structure and function are significantly different in spring and summer. Microbial community function (T=-1.24, A=0.0052, p=0.11, n=116) and structure (T=0.24, A=0, p=0.29, n=118) were not significantly different between spring and summer sampling periods.  The results indicate that there was no difference in microbial  community function and structure between the spring and summer samples, therefore the hypothesis is rejected.  It was appropriate to combine the microbial community  composition results from spring and summer samples. However, when sampling times were compared for specific enzyme activities and individual PLFA signatures, there were some statistically significant differences in microbial community function and structure between spring and summer sampling times (all locations) when all the soil profile layers were combined. The enzymes activities for aryl sulfatase (T=-3.01, A=0.02, p=0.03, n=116) (Figure 3.1), acid phosphatase (T=2.59, A=0.01, p=0.03, n=116) (Figure 3.1), xylanase (T=-2.44, A=0.01, p=0.03, n=116) (Figure 3.2), and phenoloxidase (T=-3.24, A=0.02, p=0.01, n=116) (Figure 3.3) were significantly higher in the summer sample compared to the spring sample, although, the effect-sizes (A values) were consistently low (≤0.02). Phosphatase activity was significantly higher in the summer samples from the ESSF location (U=12, z=-2.31, p=0.021, n=17) (Figure 3.1). Sulfatase activity was significantly higher in the summer samples from the BWBS (U=3, z=-3.18, p=0.001, n=17) and ICH (U=3, z=-3.31, p=0.001, n=17) locations (Figure 3.1). Xylanase activity was significantly higher in the summer samples from the MH location (U=4, z=-2.91, p=0.004, n=17) (Figure 3.2). Phenoloxidase activity was significantly higher in the summer samples from the IDF (U=0, z=-3.464, p=0.001, n=17) and CWH locations (U=3, z=-3.311, p=0.001, n=17) (Figure 3.3). Arbuscular fungi concentration (T=-7.83, A=0.051, p=0.00011, n=118) was significantly lower in the summer samples compared to the spring samples, although, the effect-sizes (A values) were consistently low (≤0.03). (Figure 3.4), saprophytic fungal concentration (T=-7.93, A=0.046, p=0.00017, n=118) (Figure 3.4), and total fungi concentration (T=34  4.592, A=0.0299, p=0.0045, n=118) (Figure 3.5) were significantly higher in the summer samples compared to the spring samples, although, the effect-sizes (A values) were consistently low (≤0.03). Arbuscular mycorrhizal fungi concentration was significantly lower in the summer samples in the PP location (U=0, z=-2.88, p=0.004, n=12) and the BWBS location (U=0, z=3.57, p=0, n=18) (Figure 3.4). Saprophytic fungi concentration was significantly higher in the summer samples in the BWBS (U=0, z=-2.25, p=0.024, n=18), the ICH (U=11, z=2.61, p=0.009, n=18), and the MH (U=11, z=-2.21, p=0.027, n=16) locations (Figure 3.4). Total fungi concentration was significantly higher in the summer samples in the ICH location (U=13, z=-2.43, p=0.015, n=18) and the MH (U=10, z=-2.31, p=0.021, n=16) locations (Figure 3.5). Statistical tests on a selection of environmental variables for the ESSF, BWBS, ICH, MH and CWH locations in the organic soil layers (F and H layers) are presented in Tables 3.3 – 3.9. Soil temperature was significantly higher in the summer for all the locations (Figure 3.6). Soil water (%) was significantly lower in the summer samples compared to the spring samples in the CWH location only and significantly higher in the summer compared to the spring in the PP, BWBS, and ICH locations (Figure 3.7). Soil pH was significantly lower in the summer samples from the MH location (Figure 3.8). In the organic soil layers the C:N ratios12 were very similar in the spring and summer samples for all locations. The PP and MH locations had the highest ratios (Figure 3.9). In the mineral layer of the PP location the C:N ratio increased between the spring and summer sampling times (Figure 3.10). The C:N ratio of mineral soil at the BWBS, ICH, and MH locations decreased from the spring to summer samples (Figure 3.10). The MH location had the highest C:N ratio in the mineral layer from the spring samples (Figure 3.10). The PP and MH locations had the highest C:N ratios in the mineral layer from the summer samples (Figure 3.10). In the organic soil layers of the ESSF location the concentration of total soil C decreased (by about 10%) between the spring and the summer samples (Figure 3.11).  The  12  C:N ratios, total soil C concentration, and total soil N concentration were not tested for significant differences between locations as there were no site replicates for these analyses. 35  concentration of total soil C decreased (by about 5%) in the ICH location between the spring and summer samples (Figure 3.11). The highest concentration of total soil C in the spring samples were from the ESSF, BWBS, ICH, and MH locations (Figure 3.11). The highest concentration of total soil C in the summer samples was from the MH location and the lowest concentration was from the CWH location (Figure 3.11). In the mineral layer the concentration of total soil C in the PP location increased between the spring and summer sampling times (Figure 3.12). The concentration of total soil C in the BWBS, MH, and CWH locations decreased between the spring and summer sampling times (Figure 3.12). In the organic soil layers of the IDF and PP locations the concentration of total soil N increased between the spring and summer sampling times (Figure 3.13).  The  concentration of total soil N decreased between the spring and summer sampling times in the ESSF and ICH locations (Figure 3.13). In both the spring and summer samples the IDF and PP locations had lower concentrations of total soil N than the other locations. In the mineral soil layer of the ESSF and PP location the concentration of total soil N increased between spring and summer sampling times (Figure 3.14).  The  concentration of total soil N decreased in the MH and CWH locations between the spring and summer sampling times (Figure 3.14). The MH and CWH locations had the highest spring concentration of total soil N and the PP location had the lowest (Figure 3.14). The IDF, PP, and ICH locations had the lowest summer concentration of total soil N (Figure 3.14).  36  Figure 3.1. Mean phosphatase and sulfatase activities (nmol of substrate converted per hour per gram of sample) of all soil layers combined from the seven study locations. Spring and summer sampling times are shown. Different letters indicate a significant difference between spring and summer samples. Each value is the mean of 9 samples; error bars represent the standard error of the mean.  37  Figure 3.2. Mean xylanase activity (nmol of substrate converted per hour per gram of sample) of all soil layers combined from the seven study locations. Spring and summer sampling times are shown. Different letters indicate a significant difference between spring and summer samples. Each value is the mean of 9 samples; error bars represent the standard error of the mean.  Figure 3.3. Mean phenoloxidase and peroxidase activities (nmol of substrate converted per hour per gram of sample) of all soil layers combined from the seven study locations. Spring and summer sampling times are shown. Different letters indicate a significant difference between spring and summer samples. Each value is the mean of 9 samples; error bars represent the standard error of the mean.  38  Figure 3.4. Mean arbuscular mycorrhizal and saprophytic fungi PLFA signature concentrations, relativized by total PLFA signature of all soil layers combined from the seven study locations. Spring and summer sampling times are shown; different letters indicate a significant difference between spring and summer samples. Each value is the mean of 9 samples; error bars represent +/- standard error of the mean.  39  Figure 3.5. Mean total fungi PLFA signature concentration, relativized by total PLFA signature of all soil layers combined from the seven study locations. Spring and summer sampling times are shown. Different letters indicate a significant difference in the means between spring and summer samples. Each value is the mean of 9 samples; error bars represent +/- standard error of the mean.  Figure 3.6. Mean temperature (˚C) of organic layers from the seven study locations, with standard error bars. All pairs of locations were significantly different from each other, except for IDF vs. ICH, IDF vs. MH, IDF vs. CWH, ESSF vs. BWBS, ESSF vs. MH, BWBS vs. MH, and ICH vs. MH, when p=0.05/7=0.007 (n=78). Different letters indicate significant differences between spring and summer samples. See Appendix 8.2 for test statistics. 40  Figure 3.7. Mean water content (%) of organic layers from the seven study locations, with standard error bars. All pairs of locations were significantly different from each other, except for ESSF vs. BWBS, ESSF vs. CWH, BWBS vs. ICH, BWBS vs. CWH, and ICH vs. CWH, when p=0.05/7=0.007 (n=78). Different letters indicate significant differences between spring and summer samples. See Appendix 8.2 for test statistics.  Figure 3.8. Mean pH of organic layers from the seven study locations, with standard error bars. All locations were significantly different from each other, except for IDF vs. PP, IDF vs. ICH, ESSF vs. CWH, and PP vs. BWBS, when p=0.05/7=0.007 (n=78). See Appendix 8.2 for test statistics. 41  Figure 3.9. Mean C:N ratio of combined organic soil layers from the seven study locations.  Figure 3.10. Mean C:N ratio of mineral soil from the seven study locations.  42  Figure 3.11. Mean total C concentration (%) of organic layers combined from the seven study locations.  Figure 3.12. Mean total C concentration (%) of mineral soil from the seven study locations. 43  Figure 3.13. Mean total N concentration (%) of organic layers combined from the seven study locations.  Figure 3.14. Mean soil N concentration (%) of mineral soil from the seven study locations. 44  Table 3.3. Statistics for measured environmental variables in the organic location. p values ≤ 0.05 are in bold. Environmental Spring Summer U Z p variable mean mean Soil temperature 9.7˚C 13.1˚C 0 -2.88 0.004 Soil water 30% 24.82% 12 0.96 0.337 pH 6.2 6 8 1.27 0.201 C:N 25.5 26.2 Total C 14.7% 23.2% concentration Total N 0.6% 0.9% concentration  layers at the IDF n 12 12 11  Table 3.4. Statistics for measured environmental variables in the organic layers at the ESSF location. p values ≤ 0.05 are in bold. Environmental Spring Summer U Z p n variable mean mean Soil temperature 5.57˚C 8.1˚C 0 -2.88 12 0.004 Soil water 59.23% 56.08% 12 0.96 0.34 12 pH 5.46 5.18 6 1.64 0.1 11 C:N 29.1 27.8 Total C 39.7% 28.8% concentration Total N 1.4% 1% concentration  Table 3.5. Statistics for measured environmental variables in the organic location. p values ≤ 0.05 are in bold. Environmental Spring Summer U Z p variable mean mean Soil temperature 14.63˚C 21.23˚C 0 -1.96 0.05 Soil water 4.51% 13.0% 0 -1.96 0.05 pH 5.9 5.77 3 0.65 0.513 C:N 39.8 38.3 Total C 20.4% 30% concentration Total N 0.5% 0.8% concentration  layers at the PP n 6 6 6  45  Table 3.6. Statistics for measured environmental variables in the organic layers at the BWBS location. p values ≤ 0.05 are in bold. Environmental Spring Summer U Z p n variable mean mean Soil temperature 3˚C 9˚C 0 -2.88 12 0.004 Soil water 38.44% 57.16% 3 -2.4 12 0.016 pH 5.69 5.62 17 0.16 0.873 12 C:N 26.7 26.8 Total C 31.8% 31.2% concentration Total N 1.2% 1.2% concentration  Table 3.7. Statistics for measured environmental variables in the organic location. p values ≤ 0.05 are in bold. Environmental Spring Summer U Z p variable mean mean Soil temperature 9.7˚C 10.53˚C 8 -1.6 0.109 Soil water 33.49% 47.39% 2 -2.56 0.01 pH 6.52 6.63 17 -0.16 0.87 C:N 27.9 30.2 Total C 39.2% 34.8% concentration Total N 1.4% 1.2% concentration  layers at the ICH  Table 3.8. Statistics for measured environmental variables in the organic location. p values ≤ 0.05 are in bold. Environmental Spring Summer U Z p variable mean mean Soil temperature 6.1˚C 11.67˚C 0 -2.88 0.004 Soil water 69.14% 63.17% 6 1.92 0.055 pH 4.54 4.27 3 -2.4 0.016 C:N 41.9 41.4 Total C 47.2% 47.9% concentration Total N 1.1% 1.2% concentration  layers at the MH  n 12 12 12  n 12 12 12  46  Table 3.9. Statistics for measured environmental variables in the organic layers at the CWH location. p values ≤ 0.05 are in bold. Environmental Spring Summer U Z p n variable mean mean Soil temperature 10.93˚C 14.07˚C 0 -2.88 12 0.0039 Soil water 59.6% 47% 5 2.08 12 0.0374 pH 5.06 5.12 16 -0.24 0.8102 12 C:N 25.5 24.5 Total C 22.3% 23% concentration Total N 0.9% 0.9% concentration  3.3. Hypothesis three: It is possible to separate forest types along a regional climate gradient based on microbial community function and/or structure, despite high local microbial community variability. The soil layers were first analyzed together and then separately. Different layers have different chemical and physical characteristics (section 3.5) and therefore can be expected to have different microbial community characteristics.  The microbial  community profile of all the soil layers combined may resemble one of the layers or a mixture of all layers, depending how the much the community profile is influenced by the chemical and physical characteristics of each layer.  3.3.1. Multivariate analysis of functional data for a combination of all soil profile layers MRPP analysis showed high significant overall separation of the seven locations when all the soil profile layers were combined (p=0, A=0.18, n=116).  The microbial  community, as characterized by the functional profile, was significantly different at each of the seven locations studied; therefore the hypothesis is accepted. Pair-wise MRPP analysis (Table 3.10) indicated that the enzyme activities in soil samples from the Ponderosa Pine (PP) location were significantly different from all other locations, except for the Boreal White and Black Spruce (BWBS) location. Microbial community function (enzyme activities) was most different between the PP and Mountain Hemlock (MH) locations. Enzyme activities in soil samples from the MH sites were also significantly different from all other locations, except for at the Engelmann Spruce Sub47  alpine Fir (ESSF) location. Enzyme activities in the ESSF and the Interior Douglas Fir (IDF) locations were also significantly different from each other. Table 3.10. Pair-wise MRPP analysis of enzyme activities in all soil layers combined) at each location (raw values, dry-weight basis).  A>0.1 - high for ecological data - high within-group homogeneity (9 significant pair-wise comparisons) A>0.3 very high for ecological data - very high within-group homogeneity (1 significant pair-wise comparison) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  3.3.2. Multivariate analysis of microbial functional data for individual soil layers F layer The MRPP analysis indicated that enzyme activities in the F-layer samples significantly differed between locations (p=0, A=0.38, n=42). Pair-wise MRPP analysis (Table 3.11) showed that the enzyme activities in F-layer samples from the PP location were significantly different from those at all other locations. The enzyme activities in the F layer at the MH location were significantly different than 48  those of the F layer at the IDF, PP, and the Interior Cedar Hemlock (ICH) locations, with a corresponding effect-size (A) greater than 0.3. The enzyme activities in the F layer at the MH location were significantly different from those at the Coastal Western Hemlock (CWH) and BWBS locations (A > 0.1). There were no significant differences between the enzyme activities of the F layer at the MH and ESSF locations.  The enzyme  activities in the F layer at the ESSF location were significantly different from those at the IDF and ICH locations (A > 0.1) and the F-layer enzyme activities at the ICH location were also significantly different from those at the BWBS and CWH locations (A > 0.1).  Table 3.11. Pair-wise MRPP analysis of enzyme activities in the F layer at each location (raw values, dry-weight basis).  A>0.1 - high for ecological data - high within-group homogeneity (6 significant pair-wise comparisons) A>0.3 very high for ecological data - very high within-group homogeneity (8 significant pair-wise comparisons) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  49  H layer Enzyme activities in samples from the H layer13 were significantly different between locations (p=0, A=0.37, n=34). Pair-wise MRPP analysis showed a similar pattern of significant differences in enzyme activities between locations in the H layer compared to the patterns of significant differences in all soil layers combined and in the F layer (Table 3.12). Enzyme activities were significantly different in the H layer at the IDF location compared to those at the ESSF location, and were significantly different at the ICH location compared to the MH location (A > 0.1). There were significant differences in the H-layer enzyme activities of the ESSF and BWBS locations, the BWBS and ICH locations, and the ICH and MH locations (A > 0.3).  There were also significant differences in the H-layer enzyme  activities of the ESSF and ICH locations, the BWBS and MH locations, and the CWH and ESSF, ICH, and MH locations (A > 0.1).  13  The PP location had no H layer so the PP location was not included in this analysis. 50  Table 3.12. Pair-wise MRPP analysis of enzyme activities in the H layer at each location (raw values, dry weight basis).  A>0.1 - high for ecological data - high within-group homogeneity (5 significant pair-wise comparisons) A>0.3 very high for ecological data - very high within-group homogeneity (6 significant pair-wise comparisons) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  Mineral layer Enzyme activities in samples from the mineral layer significantly differed between locations (p=0, A=0.37, n=40). Pair-wise MRPP analysis (Table 3.13) showed that enzyme activities were significantly different in the mineral layer at the MH location compared to the PP, BWBS, and ICH locations (A > 0.3). The mineral-layer enzyme activities at the BWBS location were significantly different from those at the PP and ESSF locations (A > 0.1). The minerallayer enzyme activities at the ESSF location were also significantly different from those at the ICH location (A > 0.1). The mineral-layer enzyme activities at the IDF and CWH locations were not significantly different from those at any other locations.  51  Table 3.13. Pair-wise MRPP analysis of enzyme activities in the mineral soil at each location (raw values, dry weight basis).  A>0.1 - high for ecological data - high within-group homogeneity (2 significant pair-wise comparisons) A>0.3 very high for ecological data - very high within-group homogeneity (4 significant pair-wise comparisons) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  3.3.3. Multivariate analysis of microbial structural data for a combination of all soil profile layers MRPP analysis showed significant differences in concentrations of PLFA signature molecules (microbial community structure) between locations when all soil layers were combined (p=0, A=0.18, n=118). The microbial community, as characterized by the structural profile, was significantly different at each of the seven locations studied; therefore the hypothesis is accepted. The pattern of significant differences between locations based on the microbial community structural data was different from that based on the microbial community functional data (Table 3.14). Pair-wise MRPP analysis showed that microbial community structure at the CWH location was significantly different from the community structure at 52  all other locations, and particularly when compared to that at the ESSF location (A > 0.3). The microbial community structure at the ESSF location was also significantly different from those at the BWBS and ICH locations (A > 0.1). The microbial community structure at the BWBS location was also significantly different from that at the ICH location (A > 0.1). In contrast to the functional analysis, microbial community structure at the PP location was not significantly different from those of any other location.  Table 3.14. Pair-wise MRPP analysis of PLFA analysis results for each location; all soil layers combined.  A>0.1 - high for ecological data - high within-group homogeneity (9 significant pair-wise comparisons) A>0.3 very high for ecological data - very high within-group homogeneity (1 significant pair-wise comparison) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  53  3.3.4 Multivariate analysis of structural data for individual layers F layer MRPP analysis showed that microbial community structure in the F layer was significantly different between locations (p=0 A=0.25, n=42). Pair-wise MRPP analysis (Table 3.15) showed that the microbial community structure in the F layer at the CWH location was significantly different from those at the IDF, PP and ICH locations (A > 0.3) and the microbial community structure in the F layer at the CWH location was also significantly different from those at the BWBS and MH locations (A > 0.1). The microbial community structure in the F layer at the PP location was also significantly different from that at the ESSF location (A > 0.3) and the microbial community structure in the F layer at the ESSF location was also significantly different from those at the IDF and MH locations (A > 0.1).  54  Table 3.15. Pair-wise MRPP analysis of PLFA analysis results for each location; F layer.  A>0.1 - high for ecological data - high within-group homogeneity (4 significant pair-wise comparisons) A>0.3 very high for ecological data - very high within-group homogeneity (5 significant pair-wise comparisons) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  H layer The microbial community structure in the H layer significantly differed among locations (p=0 A=0.37, n=35). Pair-wise MRPP analysis (Table 3.16) indicated that microbial community structure in the H layer at the CWH location was significantly different from those at the IDF, ESSF, MH and ICH locations (A > 0.3). The microbial community structure in the H layer at the CWH location was also significantly different from that at the BWBS location (A > 0.1). The microbial community structure in the H layer at the IDF location was significantly different from that at the ESSF location (A > 0.3).  55  Table 3.16. Pair-wise MRPP analysis of PLFA analysis results for each location; H layer.  A>0.1 - high for ecological data - high within-group homogeneity (1 significant pair-wise comparison) A>0.3 very high for ecological data - very high within-group homogeneity (5 significant pair-wise comparisons) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  Mineral layer The microbial community structure in the mineral layer significantly differed among locations (p=0.0028, A=0.14, n=41). Pair-wise MRPP analysis on the microbial community structural data in the mineral soil (Table 3.17) showed a very different pattern of significant differences when compared to the patterns shown in all soil layers combined, the F layer, and the H layer. Microbial community structure in the mineral layer significantly differed between the PP and IDF locations only (A > 0.1).  56  Table 3.17. Pair-wise MRPP analysis of PLFA analysis results for each location; mineral layer.  A>0.1 - high for ecological data - high within-group homogeneity (1 significant pair-wise comparison) A>0.3 very high for ecological data - very high within-group homogeneity (0 significant pair-wise comparisons) Pair-wise comparisons p sig ≤ 0.0024 (alpha 0.05/21). Significant values are shown in bold. *significant to 0.1/21 = 0.0048 (Bonferroni’s correction)  3.3.5. Environmental characteristics of the PP, MH and CWH locations When the environmental variables at the different locations were analyzed, the PP, MH and CWH locations were found to have some unique environmental characteristics: The soil temperature (˚C) was highest in the organic layers from the PP location for both sampling times and the soil temperatures at the PP and MH locations were significantly different (Figure 3.6). Soil water content of the organic layer was highest at the MH location and lowest at the PP location  The water content of the organic layers at the PP and MH locations were  significantly different (Figure 3.7). The soil pH in the organic layers from the CWH location was significantly lower than in all other locations, except for the MH and ESSF locations (Figure 3.8). 57  The C:N ratio in the organic layers from the PP and MH locations was higher than all in other locations (Figure 3.9). The mineral layer of the PP and MH locations had the highest C:N ratios in the summer and the MH location had the highest ratio in the spring (Figure 3.10). The C concentration was highest in the MH location in both the organic and mineral soil layers (from spring and summer samples), and the C concentration in the CWH location was the lowest (summer samples) (Figures 3.11 and 3.12). The PP location had one of the lowest concentrations of total soil N of all the locations, in the organic layers from both the spring and summer samples (Figure 3.13).  In the  mineral layer the highest concentrations of N in the spring samples were in the MH and CWH locations and the lowest concentrations were in the PP location samples. The lowest summer mineral soil N concentration was in the IDF and PP locations (Figure 3.14). There were significantly higher available N concentrations in the CWH location than in the other six locations (Figure 3.15). The tree species composition at the PP site was 90.5% ponderosa pine (Pinus ponderosa) and 9.5% Douglas fir (Pseudotsuga menziesii) (Table 3.18).  The tree  species composition at the MH location was 83.3% western hemlock (Tsuga heterophylla) and 16.7% yellow cedar (Chamaecyparis nootkatensis) (Table 3.18). The tree species composition at the CWH site was 64.6% P. menziesii, 22.9% western redcedar (Thuja plicata), and 12.5% big leaf maple (Acer macrophyllum) (Table 3.18).  58  Picea mariana %  Abies lasiocarpa %  Thuja plicata %  Populus species %  Pinus ponderosa %  Tsuga heterophylla %  Acer macrophyllum %  100  0  0  0  0  0  0  0  0  0  0  ESSF  0  12  0  0  88  0  0  0  0  0  0  PP  9.5  0  0  0  0  0  0  90.5  0  0  0  BWBS  0  0  76.1  8.7  0  0  15.2  0  0  0  0  ICH  59.6  40.4  0  0  0  0  0  0  0  0  0  MH  0  0  0  0  0  0  0  0  83.3  0  16.7  CWH  64.6  0  0  0  0  22.9  0  0  0  12.5  0  Additional information from Volney, 2007.  59  nootkatensis %  Picea glauca %  IDF  Chamaecyparis  Picea engelmannii %  Pseudotsuga menziesii %  Table 3.18. Tree species composition at the sampling sites.  Figure 3.15. Available nitrogen (μg per 10cm2 ion exchange membrane per burial period in days) in the organic layers at the seven study locations. Each value is the mean of 4 probes (with standard error bars. Different letters indicate significant differences. Asterisks indicate significant differences between the BWBS and MH locations (n=39). See Appendix 8.2 for test statistics.  The seven study locations exhibited unique available nutrient concentration profiles and there were a number of significant differences in the concentrations of available nutrients when different locations were compared (see Figures 3.15 to 3.20) (see Appendix 8.3 for the complete PRSTM probe data set). As well as differences in available N (mentioned above), available P concentrations were highest at the IDF and BWBS locations and lowest at the ICH, MH, and CWH locations (Figure 3.16). Available Ca concentration was highest at the CWH and ICH and lowest at the IDF, ESSF, and PP (Figure 3.17). Available Mg concentration was highest at the IDF and lowest at the ESSF locations (Figure 3.17). Available K concentration was highest at the IDF and lowest at the CWH locations (Figure 3.17). Available S concentration was far higher at the BWBS location than at the other locations (Figure 3.18). Available Fe concentration was highest at the CWH and BWBS locations and lowest at the IDF and MH locations (Figure 3.19).  60  Available Mn concentration was highest at the MH and ESSF locations and lowest at the IDF and ICH locations (Figure 3.19). Available Zn concentration was highest at the MH location and lowest at the ICH location (Figure 3.19). Available Bo concentration was highest at the ICH location and lowest at the ESSF location (Figure 3.19). Available Cu concentration was highest at the SK and lowest at the BWBS and ICH locations (Figure 3.20).  Figure 3.16. Mean available P concentration (μg per 10cm2 ion exchange membrane per burial period in days) in all soil layers combined at the seven study locations, with standard error bars. There were significant differences between the IDF vs. ESSF, IDF vs. SK, IDF vs. ICH, IDF vs. MH, IDF vs. CWH, ESSF vs. BWBS, ESSF vs. MH, ESSF vs. CWH, SK vs. ICH, SK vs. MH, SK vs. CWH, BWBS vs. ICH, BWBS vs. MH, and BWBS vs. CWH locations (n=63). See Appendix 8.2 for test statistics.  61  Figure 3.17. Mean available Ca, Mg, and K concentrations (μg per 10cm2 ion exchange membrane per burial period in days) in all soil layers combined at the seven study locations, with standard error bars. There were significant differences between the IDF vs. BWBS, IDF vs. ICH, IDF vs. MH, IDF vs. CWH, ESSF vs. BWBS, ESSF vs. ICH, SK vs. BWBS, SK vs. ICH, BWBS vs. MH, BWBS vs. CWH, and ICH vs. MH locations when p=0.05/7=0.007 (n=63). See Appendix 8.2 for test statistics.  62  Figure 3.18. Mean available S concentration (μg per 10cm2 ion exchange membrane per burial period in days) in all soil layers combined at the seven study locations, with standard error bars. There were significant differences between the IDF vs. BWBS, IDF vs. MH, IDF vs. CWH, ESSF vs. BWBS, ESSF vs. MH, ESSF vs. CWH, SK vs. BWBS, SK vs. MH, SK vs. CWH, BWBS vs. ICH, BWBS vs. ICH, BWBS vs. MH, BWBS vs. CWH locations when p=0.05/7=0.007 (n=63). See Appendix 8.2 for test statistics.  63  Figure 3.19. Mean available micronutrients (Fe, Mn, Zn, Cu, Bo) concentrations (μg per 10cm2 ion exchange membrane per burial period in days) in all soil layers combined at the seven study locations, with standard error bars. There were significant differences between the IDF vs. ESSF, IDF vs. SK, IDF vs. BWBS, IDF vs. MH, IDF vs. CWH, ESSF vs. SK, ESSF vs. BWBS, ESSF vs. ICH, SK vs. MH, BWBS vs. ICH, BWBS vs. MH, ICH vs. MH, ICH vs. CWH, and MH vs. CWH locations when p=0.05/7=0.007 (n=63). See Appendix 8.2 for test statistics.  64  Figure 3.20. Mean available Cu concentrations (μg per 10cm2 ion exchange membrane per burial period in days) in all soil layers combined at the seven study locations, with standard error bars (n=63). See Appendix 8.2 for test statistics.  65  3.4. Hypothesis four: A set of measured environmental variables can be shown to significantly correlate with microbial community function and structure across a regional climate gradient. hypothesis:  Post-hoc  If accepted, I hypothesize that moisture is highly  correlated with microbial community function and structure. Mean annual precipitation and mean annual temperature at each location was approximated from information from a literature search, from climate station data and from the ClimateBC software program (Table 2.1).  Where a range of values was  presented, the median of the range was used to create a regional annual precipitation gradient and annual temperature gradient based on the ranked values for each location14: The moisture gradient, from driest to wettest was: PP < BWBS < IDF < ICH < ESSF < CWH < MH The temperature gradient, from coldest to warmest was: BWBS < ESSF < MH < ICH = IDF < CWH = PP Other gradients were constructed using the ClimateBC software (Wang et al., 2006) (Section 2.3), but there were no clear relationships between these gradients and the soil microbial community structure and function, so these gradients are not presented. Measured environmental variables included in the analyses can be found in Table 2.3.  3.4.1 Correlations between microbial community function and structure and measured environmental variables Spearman’s rank correlations between microbial community structure (PLFA) and function (enzyme assays) and measured environmental variables (for both sampling times) are presented in Tables 3.19 to 3.22. Significant correlation values (r2) greater than 0.4 are presented in bold.  14  The gradients presented are site-specific and are not indicative of all BEC zone sites. 66  Significant correlations between enzyme activities and measured environmental variables Soil moisture (%) was significantly negatively correlated with the activities of enzymes which degrade lignocellulase, along with the chitin-degrading enzyme beta-1,4-Nacetylglucosaminidase (NAG), and the labile C-degrading enzyme beta-1,4-glucosidase (glucosidase) (Table 3.19). All forms of available N were significantly negatively correlated with NAG activity; total available N and available NH4+ were significantly negatively correlated with peroxidase activity; and available NH4+ was significantly negatively correlated with glucosidase activity (Table 3.19). Other measured environmental variables which significantly correlated with enzyme activity were:  Percentage sand negatively with NAG activity; total C concentration  negatively with peroxidase and phenol oxidase activity, and positively with acid phosphatase (phosphatase) activity; total N concentration negatively with peroxidase, phenol oxidase, and phosphatase activity; C:N ratio negatively with phenol oxidase and aryl sulfatase (sulfatase) activity;  pH  positively with glucosidase,  NAG,  cellobiohydrolase (cellulase), and beta-1,4-xylosidase (xylanase) activity (Table 3.19). The hypothesis and post-hoc hypothesis are accepted.  Table 3.19. Spearman’s rank correlations between enzyme activities and measured environmental variables (n=105). Significant correlations (> 0.4) are in bold.  % soil water Total N NO3 N NH4N % sand %C %N C:N pH  Phenoloxidase  Peroxidase  Urease  Beta-1,4glucosidase  Cellobiohy -drolase  Beta-1,4xylosidase  Arylsulfatase  -0.35  Beta-1,4-Nacetylglucosamini -dase -0.51  -0.35  Acidphosphat ase 0.13  -0.5  -0.72  -0.24  -0.49  -0.34  -0.23  -0.44  -0.31  -0.39  -0.27  0.2  -0.49  -0.08  -0.07  -0.18  -0.36  -0.28  -0.26  -0.23  -0.13  -0.4  -0.01  -0.15  -0.24  -0.46  -0.23  -0.45  -0.31  0.25  -0.52  -0.17  0.02  -0.1  -0.16  -0.11  -0.33  -0.29  -0.12  -0.47  -0.09  -0.27  -0.57  -0.54  -0.7 -0.73  -0.08 -0.06  -0.06 0.07  0.08 0.15  -0.06 0  -0.02 0.09  -0.3 -0.24  0.5 -0.51  -0.5 0.33  -0.25 0.2  0.03 0.3  -0.08 0.73  -0.03 0.59  -0.1 0.41  -0.01 0.65  -0.44 0.32  0.33 0.17  67  Significant correlations between PLFA signatures and measured environmental variables Soil moisture was significantly positively correlated with total microbial biomass, and with all bacterial PLFA signatures except those indicative of Gram-negative bacteria (Table 3.20). Total soil C and N concentration were significantly positively correlated with total fungi and saprophytic fungi (Table 3.20). The hypothesis and post-hoc hypothesis are accepted.  Table 3.20. Significant correlations between PLFA signatures and measured environmental variables (n=119). Significant correlations (> 0.4) are in bold.  Aspect (degrees) Slope (degrees) Soil temperature (˚C) % sand % silt % clay % soil water %C %N C:N pH  Total microbial biomass 0.2  Grampositive bacteria 0.2  Gramnegative bacteria 0.22  Actinobacteria  Total bacteria  Saprophytic fungi 0.19  Arbuscular mycorrhizal fungi 0.34  0.17  0.2  0.06  0.04  0.05  -0.02  -0.04  0.05  0.01  0.09 -0.06 -0.11 0.42 0.39 0.37 0.18 -0.2  0.09 -0.06 -0.13 0.42 0.38 0.36 0.14 -0.19  0.09 -0.07 -0.1 0.36 0.31 0.28 0.15 -0.22  Total fungi 0.25  0.05  0.1  0.013  0.12  0.07  0.02  0.11  0.05  0.1  0.13 -0.15 -0.12 0.48 0.36 0.31 0.12 -0.29  0.01 -0.07 -0.11 0.42 0.38 0.35 0.17 -0.21  -0.05 0.1 -0.04 0.27 0.4 0.41 0.25 -0.04  0.07 -0.02 -0.15 0.38 0.33 0.33 0.1 -0.15  0.02 0.06 -0.08 0.32 0.42 0.42 0.23 -0.08  Significant correlations between PLFA signatures and enzyme activities Phosphatase activity was significantly positively correlated with all PLFA signatures except those of actinobacteria, arbuscular mycorrhizal fungi, and saprophytic fungi (Table 3.21).  Xylanase activity was significantly positively correlated with all PLFA  signatures (Table 3.21). Cellulase activity was significantly positively correlated with all PLFA signatures, except those indicative of actinobacteria (Table 3.21). Glucosidase activity was significantly positively correlated with total microbial biomass and total bacteria (Table 3.21).  The lignocellulase-degrading enzyme, peroxidase, was  significantly negatively correlated with all PLFA signatures, except total microbial biomass (Table 3.21).  68  Table 3.21. Significant correlations between PLFA signatures and enzyme activities (n=105). Significant correlations (> 0.4) are in bold.  Phenol oxidase Peroxidase Urease Beta-1,4-glucosidase Cellobiohydrolase Beta-1,4-xylosidase Beta-1,4-Nacetylglucosaminidase Aryl-sulfatase Acid-phosphatase  Total microbial biomass 0.01 -0.34 -0.13 0.48 0.51 0.5 0.45  Grampositive bacteria -0.08 -0.47 0.05 0.31 0.46 0.53 0.28  Gramnegative bacteria -0.06 -0.43 0.06 0.25 0.4 0.52 0.23  Actinobacteria  Total Bacteria  Saprophytic fungi -0.25 -0.47 0.14 0.26 0.45 0.49 0.29  Arbuscular mycorrhizal fungi -0.07 -0.53 0.19 0.21 0.4 0.45 0.19  -0.09 -0.47 0.07 0.2 0.36 0.52 0.15  0.01 -0.4 -0.07 0.43 0.5 0.54 0.4  0.19 0.65  0.17 0.44  0.18 0.4  0.2 0.35  0.2 0.6  Total fungi -0.2 -0.5 0.16 0.29 0.48 0.52 0.31  0.1 0.35  0.11 0.33  0.12 0.41  Significant correlations between enzyme activities Phosphatase activity was significantly positively correlated with the activities of the labile C-degrading enzymes glucosidase, cellulose, xylanase, the chitin-degrading enzyme NAGase, and was significantly negatively correlated with peroxidase activity (Table 3.22).  Sulfatase activity was significantly positively correlated with xylanase activity  (Table 3.22). NAGase activity was significantly positively correlated with the activities of labile C-degrading enzymes (glucosidase, cellulase and xylanase) (Table 3.22). The activities of labile C-degrading enzymes were all significantly positively correlated with each other (Table 3.22), and cellulase and xylanase activities were also significantly and negatively correlated with peroxidase activity (Table 3.22).  Table 3.22. Significant correlations between enzyme activities (n=105). Significant correlations (> 0.4) are in bold.  Phenol oxidase Peroxidase Urease Beta-1,4glucosidase Cellobiohydrol -ase Beta-1,4xylosidase Beta-1,4-Nacetylglucosa minidase Aryl-sulfatase Acidphosphatase  Phenol oxidase  Peroxi dase  Urease  Beta-1,4glucosidase  Cellobioh -ydrolase  Beta-1,4xylosidase -0.09  Beta-1,4-Nacetylglucos -aminidase -0.01  1  0.24  -0.23  0.09  0.01  1  -0.2 1  -0.27 -0.07 1  Arylsulfatase 0.35  Acidphosphat-ase -0.01  -0.41 0.12 0.86  -0.45 0.1 0.6  -0.29 0 0.87  -0.1 -0.16 0.39  -0.61 -0.15 0.63  1  0.75  0.84  0.39  0.65  1  0.59  0.46  0.57  1  0.33  0.74  1  0.2 1  69  3.4.2 Ordinations for microbial community data and measured environmental variables Microbial functional data NMS ordination of microbial functional data from all soil profile layers combined NMS discriminated microbial communities from the different locations based on their enzyme activities (Figure 3.21) (see Appendix 8.1 for test statistics). When the plot is orientated using the soil moisture (%) vector, axes 1 and 2 accounted for 63% and 26% respectively of the variation in the distance matrix. Communities from all three soil layers at the PP location clustered together closely, as did the mineral soil communities from the ICH and ESSF locations (identified in Figure 3.21). Microbial communities from the other locations clustered together more loosely (except the CWH location which did not cluster) (Figure 3.21). Soil moisture, soil N concentration, and soil C concentration were strongly correlated (r2 ≥ 0.4) with axis 1 (r2 = 68%, 55%, and 53% respectively). There were no strong correlations between the measured environmental variables and axis 2 (r2 ≥ 0.4). When orientated by the soil moisture vector, the first NMS axis separated locations along an average precipitation gradient. Microbial communities from the drier locations (PP and IDF and BWBS to some degree) place on the left side of the ordination (although the spring F-layer samples from the IDF location were placed further to the right), along with the mineral layers of ESSF and ICH. Microbial communities from the wetter locations ESSF (organic soil layers) and ICH (organic soil layers) (and MH to some degree) place on the right side of the ordination. The data points representing phenol oxidase and peroxidase activities were plotted at a distance from the other enzyme activities. Variation in peroxidase activity was mainly responsible for the separation of the data points along axis 1 (80% of the variance in the data explained). Variation in phosphatase and NAG activities were mainly responsible for the separation of the data points along axis 2 (45% and 41% of the variance in the data explained respectively).  70  Figure 3.21. NMS ordination of microbial communities from all soil profile layers combined at the seven locations based on enzyme activity (n=116). The axes are orientated by the soil moisture vector. Red arrows indicate IDF F-layer spring samples. Large arrow indicates mean annual precipitation gradient from left to right, driest to wettest, for reference (the CWH location is not shown as the data points do not cluster).  71  NMS ordination of microbial functional data from the organic layers When considering soil microbial communities only from the organic (F and H) soil layers at the seven locations, NMS again clearly discriminated microbial communities from the different locations based on their enzyme activities (Figure 3.22) (see Appendix 8.1 for test statistics). When the plot is orientated using the soil moisture vector, axes 1, 2 and 3 accounted for 22%, 52% and 17% respectively of the variation in the distance matrix. Microbial communities from the F layer at the PP location clustered together closely. Microbial communities from the other locations clustered together more loosely. Soil water content was strongly correlated with axis 2 (r2 = 61%). concentrations were correlated with axis 1 (both r2 = 40%).  Soil N and C  There were no strong  correlations between the measured environmental variables and axis 3 (r2 ≥ 0.4). When orientated by the soil water content vector, the first NMS axis seemed to separate locations along an annual average precipitation gradient. This pattern is more obvious than in the ordination of all soil layers combined (Figure 3.21). Microbial communities from the drier locations (PP and IDF) placed on the left side of the ordination (although the spring F-layer samples from the IDF location were placed further to the right). Microbial communities from the wetter locations (ESSF and MH) placed on the right side of the ordination. Microbial communities from the locations which are ranked in the middle of the average annual precipitation gradient (BWBS, CWH and ICH) placed in the middle of the ordination plot. The data points representing phenol oxidase and peroxidase activity were plotted at a distance from the other enzyme activities. Variation in phosphatase activity was mainly responsible for separation of the data points along axis 1 (53% of the variance in the data explained). Variation in peroxidase activity was mainly responsible for separation of the data points along axis 2 (50% of the variance in the data explained). There were no strong correlations between the enzyme activities and axis 3.  72  Figure 3.22. NMS ordination of microbial communities from organic layers at the seven locations based on enzyme activity (n=76). The axes are orientated by the soil moisture vector. Red arrows indicate IDF F-layer spring samples. Large arrow indicates mean annual precipitation gradient from left to right, driest to wettest.  73  NMS ordination of microbial functional data from the F layer Ordinations for the organic layers (F and H) are also presented individually, as different results are observed for each layer. When considering soil microbial communities only from the F layer at the seven locations, NMS again discriminated microbial communities from the different locations based on their enzyme activities (Figure 3.23) (see Appendix 8.1 for test statistics). When the plot was orientated using the soil moisture vector, axes 1, 2 and 3 accounted for 56%, 8% and 25% respectively of the variation in the distance matrix. Microbial communities from the F layer at the PP location clustered together closely, as did microbial communities from the IDF location.  Microbial communities from the  summer and spring sampling times at the IDF location clustered separately. Microbial communities from the other locations clustered together more loosely, especially from the CWH location (Figure 3.8 and Figure 3.9). 2  correlated with axis 1 (r = 58%).  Soil moisture content was strongly  There were no strong correlations between the  measured environmental variables and axes 2 and 3 (r2 ≥ 0.4). When orientated by the soil water content vector, the first NMS axis seemed to separate locations along an annual average precipitation gradient. The first NMS axis separated the enzyme activities of the PP location and the summer samples of the IDF location to the left of the other locations and the MH and ESSF location clusters placed slightly to the right of the other location clusters. The data points representing phenoloxidase and peroxidase activities were plotted at a distance from the other enzyme activities. Variation in peroxidase activity was mainly responsible the separation of data points along axis 1 (69% of the variance in the data explained). Phosphatase activity was mainly responsible the separation of data points along axis 2 (68% of the variance in the data explained). Phenoloxidase and sulfatase activity mainly responsible the separation of data points along axis 3 (55% and 50% of the variance in the data explained respectively).  74  Figure 3.23. NMS ordination of axes 2 and 3 showing microbial communities from the F layer at the seven locations based on enzyme activity (n=42). The axes are orientated by the soil moisture vector. The brown arrow indicates CWH spring sample from site 3. Large arrow indicates mean annual precipitation gradient from left to right, driest to wettest.  75  NMS ordination of microbial functional data from the H layer When considering soil microbial communities only from the H layer at the six locations15, NMS again discriminated microbial communities from the different locations based on their enzyme activities (Figure 3.24) (see Appendix 8.1 for test statistics). When the plot was orientated using the soil water content vector, axes 1 and 2 accounted for 56% and 35% respectively of the variation in the distance matrix. Nitrogen concentration was correlated with axis 1 (r2 = 44%). Soil water content was correlated with axis 2 (r2 = 58%). As with the previous microbial community function ordinations, when orientated by the soil water content vector, the first NMS axis seemed to separate locations along an annual average precipitation gradient.  Microbial communities from the drier (IDF)  location were clustered on the left side of the ordination. Microbial communities from the wetter locations (ESSF and MH) place on the right side of the ordination. Microbial communities from the locations which are ranked in the middle of the average annual precipitation gradient (BWBS, CWH and ICH) place in the middle of the ordination plot. The lack of an H layer for the PP location makes this gradient less obvious. The data points representing phenoloxidase and peroxidase activities were plotted at a distance from the other enzyme activities. Peroxidase activity was mainly responsible the separation of data points along axis 1 (68% of the variance in the data explained) and axis 2 (46% of the variance in the data explained). Glucosidase activity was mainly responsible the separation of data points along axis 2 (44% of the variance in the data explained).  15  The PP location had no H layer. 76  Figure 3.24. NMS ordination of axes 1 and 2 showing microbial communities from the H layer at the seven locations based on enzyme activity (n=34). PP does not have an H layer and is therefore not represented in this ordination. The axes are orientated by the soil water content vector. The brown arrow indicates CWH spring sample from site 2. Large arrow indicates mean annual precipitation gradient from left to right, driest to wettest..  77  NMS ordination of microbial functional data from the mineral layer When considering soil microbial communities only from the mineral soil at the seven locations, NMS did not discriminate microbial communities from the different locations based on their enzyme activities. There was only one dimension to the ordination plot (see Appendix 8.1 for test statistics). Peroxidase activity was mainly responsible the separation of data points along the one axis (69% of the variance in the data explained), but there were no environmental variables which correlated with the single axis (r2 ≥ 0.4) (soil water had an r2 value of 0.35). Microbial structural data NMS ordination of structural data from all soil profile layers combined The NMS of the PLFA data for all soil layers combined did not discriminate the microbial communities from the different locations very well, relative to the discrimination of the enzyme activity data (Figure 3.25) (see Appendix 8.1 for test statistics).  However,  microbial communities from the CWH location clustered distinctly, except for one sample from the mineral soil which is indicated by an arrow. Axes 1 and 2 of the ordination plot accounted for 26% and 71%, respectively, of the variation in the distance matrix. None of the environmental variables were strongly correlated with the ordination axes (r2 ≥ 0.4).  The total bacterial:saprophytic fungal, total bacterial:total fungal, and total  bacterial:arbuscular fungal PLFA ratios were plotted at a distance from the other PLFA signature ratios, near to the CWH community cluster. Figures 3.26 and 3.27 show that the total bacterial:total fungal ratios for the organic layers at the CWH location were the highest or are among the highest of all seven locations. The total bacterial:saprophytic fungal and the total bacterial:total fungal ratios were strongly correlated with axis 1 (70% and 51% of the variance in the data explained respectively).  The total  bacterial:arbuscular mycorrhizal fungal ratio was strongly correlated with axis 2 (56% of the variance in the data explained).  78  Figure 3.25. NMS ordination showing microbial communities from all layers at the seven locations based on PLFA signature microbial community groupings (n=118). The CWH location microbial community data points are circled and the spring sample from site outlier is indicated by an arrow.  79  Figure 3.26. Mean total bacterial:total fungi PLFA signature ratios for the F layer at the seven locations, with standard error bars (n=42).  Figure 3.27. Mean total bacterial:total fungi PLFA signature ratios for the H layer, at the seven locations, with standard error bars (n=35). 80  NMS ordination of structural data from the F layer As for all soil layers combined, the NMS of the PLFA data from the F layer could not discriminate the microbial communities from the different locations very well, relative to the discrimination of the enzyme activity data (Figure 3.28) (see Appendix 8.1 for test statistics). The F-layer ordination plot is very similar to the ordination plot of all soil layers combined.  Only microbial communities from the CWH location clustered  distinctly. Axes 1 and 2 of the ordination plot accounted for 77% and 19% respectively of the variation in the distance matrix. None of the environmental variables were strongly correlated with the ordination axes (r2 ≥ 0.4).  The total bacterial:saprophytic fungal, total bacterial:arbuscular mycorrhizal  fungal, and total bacterial:total fungal PLFA ratios were plotted at a distance from the other PLFA signature ratios. Figure 3.26 shows that the total bacterial:total fungal ratios for the F layers at the CWH location are among the highest of all seven locations. Variation in total bacterial:total fungal, total fungal:total microbial biomass ratios, and total bacterial:saprophytic fungal ratios were mainly responsible for separation of the data points along axis 1 (49%, 46%, and 45% of the variance in the data explained respectively). Variation in total bacterial:arbuscular mycorrhizal fungal ratios was mainly responsible for separation of data points along axis 2 (77% of the variance in the data explained).  81  Figure 3.28. NMS ordination showing microbial communities from the F layer at the seven locations based on PLFA signature microbial community groupings (n=42). The CWH location data points are circled.  82  NMS ordination of structural data from the H layer Unlike the previous NMS ordinations on microbial community structure data, the NMS of the PLFA data from the H layer discriminated the microbial communities from the different locations (Figure 3.29) (see Appendix 8.1 for test statistics).  Microbial  communities from the CWH location clustered on the right side of the ordination plot, away from the other locations. The CWH locations had significantly higher available total N, NO3-N and to a lesser extent NH4-N than any of the other locations (Figure 3.15). The microbial communities at the CWH location had low saprophytic and total fungal biomass compared to the other locations (Figure 3.30). When orientated by the available total N vector, axes 1 and 2 of the ordination plot accounted for 85% and 13% respectively of the variation in the distance matrix. Available total N and available nitrate were correlated with axis 2 (both r2 = 42%). None of the environmental variables were strongly correlated with the first ordination axis (r2 ≥ 0.4).  Variation in the total bacterial:arbuscular mycorrhizal fungal, total fungal:total  microbial biomass, total bacterial: total fungal, and total bacterial:saprophytic fungal ratios were mainly responsible for the separation of the data points along axis 1 (78%, 50%, 47%, and 43% of the variance in the data explained respectively).  The total  bacterial:saprophytic fungal and total bacterial:total fungal ratios were strongly correlated with axis 2 (both 73% of the variance in the data explained).  83  Figure 3.29. NMS ordination showing microbial communities from the H layer at the seven locations based on PLFA signature microbial community groupings (n=35).  84  Figure 3.30. Mean total fungal PLFA concentration (total divided by sample biomass) in the H layer at the seven locations, with standard error bars (n=35).  NMS ordination of structural data from the mineral layer As with the F layer and all soil layers combined, the NMS of the PLFA data from the mineral layer could not discriminate the microbial communities from the different locations very well relative to the discrimination of the enzyme activity data (Figure 3.31) (see Appendix 8.1 for test statistics). Axes 1 and 2 of the ordination plot accounted for 90% and 8% respectively of the variation in the distance matrix. None of the environmental variables were strongly correlated with the ordination axes (r2 ≥ 0.4). The Gram-positive bacterial:Gram-negative bacterial, total bacterial:arbuscular mycorrhizal fungal, total bacterial:total fungal, and total bacterial:saprophytic fungal ratios were mainly responsible for the separation of the data points along axis 1 (57%, 85  46%, 46%, and 46% of the variance in the data explained respectively).  The total  bacterial:arbuscular mycorrhizal fungal ratio was also mainly responsible for the separation of the data points along axis 2 (45% of the variance in the data explained). The total bacterial:arbuscular mycorrhizal fungal ratio was plotted at a distance from the other PLFA signature ratios.  Figure 3.31. NMS ordination showing microbial communities from the mineral layer at the seven locations based on PLFA signatures (n=41).  86  3.5 Hypothesis five: Analysis of soil microbial structure and function will show separation of the mineral and organic layers 3.5.1 Microbial functional community data Multivariate analysis of functional data from all soil layers combined MRPP analysis showed high significant overall separation of the three layers (alpha 0.05) (p=0 A=0.184, n=116). The enzyme activity profile of the microbial communities from the F and H layers at all seven locations was significantly different from that of the microbial communities in the mineral soil (Table 3.23). The hypothesis is accepted.  Table 3.23. Pair-wise MRPP analysis of enzyme assay results for F, H, and mineral (M) layers.  Pair-wise comparisons p significant ≤ 0.0167 (alpha 0.05/3 - Bonferroni’s correction). Significant values are shown in bold.  Patterns in individual enzyme activities down the soil profile Labile C-mineralizing enzymes (cellulase, xylanase, and glucosidase), NAG, urease, phosphatase, and sulfatase all showed a decrease in activity down the soil profile in both spring and summer (Figures 3.32 to 3.45).  However, recalcitrant C-mineralizing  enzymes (phenoloxidase and peroxidase) showed an increase in activity down the soil profile, except for phenoloxidase activity in the spring sample (Figures 3.46 to 3.49). 87  Figure 3.32. Mean cellulase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.33. Mean cellulase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error.  88  Figure 3.34. Mean glucosidase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.35. Mean glucosidase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error. 89  Figure 3.36. Mean xylanase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.37. Mean xylanase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error.  90  Figure 3.38. Mean NAG activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.39. Mean NAG activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error.  91  Figure 3.40. Mean urease activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.41 Mean urease activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error. 92  Figure 3.42. Mean phosphatase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.43. Mean phosphatase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error. 93  Figure 3.44. Mean sulfatase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  94  Figure 3.45. Mean sulfatase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error.  Figure 3.46. Mean phenoloxidase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.47. Mean phenoloxidase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error. 95  Figure 3.48. Mean peroxidase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (spring samples) with bars showing standard error.  Figure 3.49. Mean peroxidase activity rates (nmol of substrate converted per hour per gram of sample) in each soil layer (summer samples) with bars showing standard error.  96  NMS ordination of functional data from all soil layers NMS of the enzyme activity data enabled visualization of the discrimination of the microbial communities from the different soil layers (Figure 3.50). Microbial communities from the mineral soil clustered together, with the exception of two samples indicated by arrows (IDF summer sample site 3 and ESSF summer sample site 2). Soil moisture content was correlated with axis 1 (r2 = 31%).  The data points  representing phenol oxidase and peroxidase activities were plotted at a distance from the other enzyme activities. Variation in peroxidase activity was mainly responsible for the separation of the data points along axis 1 (80% of the variance in the data explained). Variation in phosphatase and NAG activities were mainly responsible for the separation of the data points along axis 2 (45% and 41% of the variance in the data explained respectively).  Figure 3.50. NMS ordination of enzyme activities from all soil layers (n=116). The axes are orientated by soil moisture. 97  3.5.2. Microbial structural community data Multivariate analysis of structural data from all soil layers combined MRPP analysis showed high significant separation of the three layers (alpha 0.05) (p=0 A=0.184, n=118). Microbial community structure in the F and H layers at all seven locations was significantly different from the structure of the microbial communities in the mineral soil, based on PLFA profiles (Table 3.24). The hypothesis is accepted.  Table 3.24. Pair-wise MRPP analysis on PLFA results for F, H, and mineral (M) soil layers.  Pair-wise comparisons p significant ≤ 0.0167 (alpha 0.05/3 – Bonferroni’s correction). Significant values are shown in bold.  98  Patterns in microbial community structure down the soil profile There was less overall pattern down the soil profile for the PLFA data. Total microbial biomass (all PLFA signatures combined) showed some pattern in the spring with the highest concentrations in the H layer and the lowest in the mineral layer (Figure 3.51). However, there was no discernable pattern in the summer samples (Figure 3.52). The was no discernable pattern in total bacterial PLFA concentration in the spring samples, but in the summer samples the mineral layers had consistently low concentrations and the H layer had the highest concentrations (Figures 3.53 and 3.54). The concentrations of PLFA characteristic of Gram-positive bacteria had no discernable pattern in either the spring or summer samples (Figures 3.55 and 3.56).  The  concentrations of PLFA characteristic of Gram-negative bacteria was highest in the H layer in both the spring and summer samples (Figures 3.57 and 3.58). There was no discernable pattern in the concentrations of PLFA characteristic of actinobacteria down the soil profile in the spring samples, but in the summer samples the concentration in the mineral soil was consistently high (except for the CWH location sample) (Figures 3.59 and 3.60). The concentrations of PLFA characteristic of fungi were highest in the organic layers (especially in the F layer) in the spring and summer samples (Figures 3.61 and 3.62). The concentration of PLFA characteristic of arbuscular mycorrhizal fungi had no discernable pattern down the soil profile in the spring and summer samples (Figures 3.63 and 3.64). The concentrations of PLFA characteristic of saprophytic fungi was highest in the F layer for the spring and summer samples (except for the MH location) (Figures 3.65 and 3.66).  99  Figure 3.51. Mean total microbial biomass PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples), with standard error bars (n=60).  Figure 3.52. Mean total microbial biomass PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (summer samples), with standard error bars (n=58). 100  Figure 3.53. Mean total bacteria PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples), with standard error bars (n=60).  Figure 3.54 Mean total bacteria PLFA concentration (total divided by sample biomass) in the each soil layer at the seven locations (summer samples) with standard error bars (n=58). 101  Figure 3.55. Mean Gram-positive bacteria PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples) with standard error bars (n=60).  Figure 3.56. Mean Gram-positive bacteria PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (summer samples) with standard error bars (n=58).  102  Figure 3.57. Mean Gram-negative bacteria PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples) with standard error bars (n=60).  Figure 3.58. Mean Gram-negative bacteria PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (summer samples) with standard error bars (n=58). 103  Figure 3.59. Mean actinobacteria PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples) with standard error bars (n=58).  Figure 3.60. Mean actinobacteria PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (summer samples) with standard error bars (n=58). 104  Figure 3.61. Mean total fungi PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples) with standard error bars (n=60).  Figure 3.62. Mean total fungi PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (summer samples) with standard error bars (n=58). 105  Figure 3.63. Mean arbuscular mycorrhizal fungi PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples) with standard error bars (n=60).  Figure 3.64. Mean arbuscular mycorrhizal fungi PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (summer samples) with standard error bars (n=58). 106  Figure 3.65. Mean saprophytic fungi PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (spring samples) with standard error bars (n=60).  Figure 3.66. Mean saprophytic fungi PLFA concentration (total divided by sample biomass) in each soil layer at the seven locations (summer samples) with standard error bars (n=58). 107  NMS ordination of structural data from all soil layers NMS of the PLFA data could not discriminate the microbial communities from the different soil layers (Figure 3.67).  Figure 3.67. NMS ordination of PLFA data for all soil layers combined (n=118).  108  4. DISCUSSION 4.1. Separating distinct forest types at a regional scale based on soil microbial community function and structure The variation in microbial community function and structure between the locations was enough to significantly separate them, despite the expectation that the microbial communities would exhibit high functional and structural diversity at the site level. Microbial communities have several nested levels of organization (Nemergut et al., 2005) and characterizations of community distribution will be influenced by the choice of taxonomic or functional resolution.  In soil environments, micro-site variability and a  complex set of inter-dependences can often eclipse patterns explained by broader-scale environmental heterogeneity. However, this study indicates that patterns in forest soil microbial community structure and function, measured by enzyme assays and PLFA analysis, can be discerned at a regional scale. Other studies have also identified distinct microbial community changes at a regional scale: Leckie et al. (2004a) found that bacterial and fungal biomass, measured using PLFA analysis and Ribosomal Intergenic Spacer Analysis (RISA), differed between two adjacent forest sites in British Columbia which exhibited different N availability and processing rates; and Decker et al. (1999) identified significant variations in forest soil enzyme activity at a regional scale (as well as at a water-shed and individual-tree scale) in mixed oak forests in the USA.  4.2. Forest types with distinct microbial community functional profiles Most of the locations exhibited unique microbial community functional profiles in their soil layers; however the enzyme activities in the samples from the PP and MH locations were notably different from each other and from those of the other locations, especially in the organic layers. The PP location was functionally distinct from the other locations due to the relatively low phosphatase activities in all layers and the relatively high peroxidase activities and relatively low phenoloxidase activities in its organic layers. As phosphatase activity has 109  been shown to be linked to P availability (McGill and Cole, 1981; Olander and Vitousek, 2000; Allison et al., 2007) it could be that the concentration of available P in the soil at the PP location was high enough to repress production of phosphatase.  The high  peroxidase activities may be explained by the high soil C:N ratio in the PP location relative to the other locations. There was very little litter on the forest floor of the PP forest16. The litter that was present mainly consisted of pine needles (due to the lack of under-storey vegetation) which have high concentrations of phenols and other recalcitrant chemicals (Hackl et al., 2005) and would be decomposed by a suite of oxidizing enzymes, probably including peroxidase. The peroxidase enzyme degrades lignocellulose and other recalcitrant compounds with high C:N ratio by catalyzing oxidation reactions via the reduction of H2O2 (Gianfreda and Bollag, 2002). Peroxidase is an important control of litter breakdown in the F layer (Grandy et al., 2007), although the PP location also had high activity in the mineral layer. The peroxidase enzyme is produced by plants and basidiomycetes (Finlay, 2007; García-Garrado et al., 2002; Gianfreda and Bollag, 2002) and the PP location had relatively high concentrations of the PLFA signatures indicative of saprophytic fungi in both the F and mineral layers. As phenol oxidase also degrades recalcitrant material, it is counter-intuitive that phenol oxidase activity was low whilst peroxidase activity was high. A possible reason for the high peroxidase activity relative to phenol oxidase activity at the PP location is that plants have been shown to respond to stress by producing enzymes which neutralize active oxygen species (García-Garrado et al., 2002) and the water-stressed soil environment of the PP location (soil water was significantly lower in both spring and summer than all of the other locations and soil temperature was higher) may have induced the production of peroxidase by the trees. Microbial community function in the organic samples from the MH location was distinguished from those of other locations by relatively high phosphatase activities and relatively low glucosidase, NAG, and sulfatase activities (in the organic layers only). Available P in the MH samples was the lowest of all the locations. McGill and Cole (1981) suggest that phosphatase activity is responsive to P availability, as do the results  16  The removal of litter by ants is one hypothesis for the lack of litter at these sites (C. Prescott, personal communication). 110  of subsequent studies (Allison et al., 2007; Olander and Vitousek, 2000), and the low P availability likely explains the high phosphatase activities. The low glucosidase, NAG, and sulfatase activities in the organic layers may be related to the significantly low soil pH; Spiers et al. (1999) found that soil acidification was the main cause of a decrease in aryl-sulfatase activity in a contaminated soil. In the same experiment, acid phosphatase activity was not found to be affected by the increased acidity, so the low pH at the MH location would not be expected to decrease phosphatase activity. It is also possible that the low soil temperatures (especially in spring) reduced enzyme activities by decreasing the rates of physiological reactions (Voroney, 2007) or that anaerobic soil conditions caused the low glucosidase, NAG, and sulfatase activities. The MH location had significantly higher soil water content than the other locations and McLatchey and Roddy (1998) found beta-glucosidase activity (among other enzyme activities) decreased with decreasing redox potential in experimentally-manipulated wetland soils. Unfortunately no redox measurements were taken at the study locations so it is only possible to hypothesize that the MH location would exhibit some of the lowest soil redox potentials of all the locations. However, if soil temperature and redox potential were affecting the activities of glucosidase, NAG, and sulfatase it is likely that phosphatase activity would be affected too. The PP and MH locations have significantly different soil moisture and soil temperature values for their organic soil layers. It is notable that these locations, the driest and the wettest, are the ones which appear to have the most unique microbial community function of the seven locations. The enzyme activities of the organic layers in the IDF and ESSF locations were also significantly different from each other and the IDF zone is relatively dry compared to the ESSF zone (the ESSF zone’s mean annual precipitation is approximately double that of the IDF zone (Meidinger and Pojar, 1991)).  4.3. Forest types with distinct microbial community structural profiles The only location to exhibit a unique microbial community structure consistently in all soil layers was the CWH location. The CWH location had low saprophytic fungal biomass and a high total bacterial-to-fungal biomass ratio compared to the other locations. Douglas-fir (Pseudotsuga menziesii) is the dominant tree species at the CWH location (sixty percent of the trees surveyed) and P. menziesii stands are often associated with 111  high available N concentration, along with high net rates of N mineralization (Prescott and Vesterdal, 2005). The significantly high available N and low C:N ratio in the CWH location soil samples would be conducive to the proliferation of bacterial biomass over fungal biomass (Swift et al., 1979) and this finding agrees with other studies where relative soil fungal biomass has decreased with increased N availability (Myers et al., 2001; Grayston and Prescott, 2005; Högberg et al., 2007; Boyle et al., 2008). The presence of western redcedar (Thuja plicata) only at the CWH sites (21% of the trees surveyed) further explains the high total bacterial-to-fungal biomass ratio, as forest floors under T. plicata tend to exhibit high bacterial biomass, often due to high basecation and pH levels (Prescott et al., 2000; Leckie et al., 2004a; Prescott and Vesterdal, 2005). Although the site average soil pH at the CWH location in spring and summer was not high relative to the other locations (approximately 5.0), local increases in pH under the canopy of T. plicata trees may have increased the bacterial biomass. The relatively high arbuscular mycorrhizal fungi biomass, compared to saprophytic and total fungal biomass, can also be explained by the presence of T. plicata, as T. plicata is unusual in its symbiosis with arbuscular mycorrhizal fungi (Smith and Read, 1997). All the other tree species in this study form ectomycorrhizal symbioses. T. plicata is often associated with higher concentrations of nitrate relative to ammonium (Prescott and Vesterdal, 2005) and nitrate concentration was far higher than ammonium concentration in soil samples from the CWH location.  Myrold and Posavatz (2007)  suggest that bacteria dominate the nitrate assimilation pathway and Boyle et al., (2008) found this to be true in N-limited forest soils. Bacteria are relatively more abundant in the nitrate-rich environment of the CWH location but this location does not appear to be N-limited.  4.4. Differences in microbial community structure and function Despite successfully discriminating regionally-distinct locations based on both soil microbial community function and structure, the results of this study indicate that the functional and structural characteristics of the microbial community do not respond to changes in regional climate in the same way. The locations identified as having unique microbial community functional characteristics are not the same as the ones exhibiting unique structural characteristics, and the soil samples from each location cluster 112  together when based on enzyme activity data, but not when based on PLFA data (except for loose clustering in the H layer). It is difficult to define the relationship between microbial community structural and functional diversity (Kirk et al., 2004; Standing and Killham, 2007).  Microbial  communities exhibit a fecundity of function or ecological functional redundancy, which Folke et al. (2004) and Neufeld and Mohn (2006) suggest confers a degree of ecological resilience. Functional redundancy may explain the differential response of the functional and the structural aspects of the microbial community composition to a regional climate gradient in this study. Other studies have also noted a discord between different components of microbial community function and structure along various spatial and resource gradients. At a regional scale in Mediterranean oak forests, enzyme activities and respiration rates were unrelated to spatial shifts in microbial biomass (Waldrop and Firestone 2006). Potential function of Danish forest soil microbial communities, measured by enzyme bioassays and Community Level Physiological Profiles (CLPP), was negatively correlated with estimations of bacterial abundance (Winding and Hendriksen 2007). Williams and Rice (2007) could not fully explain the change in C processing along a water-stress gradient by referring to the shifts in microbial community structure, as measured by PLFA analysis. They suggested that a change in microbial C substrate-utilization efficiency may have occurred with a change in the ratio of fungal and actinobacterial biomass relative to bacterial biomass, but that more studies are needed to elucidate the link between microbial community-level structure and community function and processes.  4.5. Correlations between soil microbial community function and structure and environmental site variables along the regional climate gradient The regional mean annual precipitation gradient appears to influence the function of the soil microbial communities at the study locations.  This observation is supported by  significant negative correlations of soil moisture with enzymes which degrade lignocellulase, chitin and cellulase. Soil moisture was also consistently highly correlated with the variation in microbial community function in the ordination plots. Prescott et al. (2004) found that average annual precipitation, potential evapo-transpiration and actual 113  evapo-transpiration were the climate variables most highly correlated with litter decomposition in these forests and their results are partly explained, at the microbial community scale, by these findings. It was not possible to directly link the regional mean annual precipitation gradient with the structure of the microbial communities based on the multivariate ordinations; however, soil moisture was significantly positively correlated with total microbial biomass and with total bacterial biomass (apart from Gram-negative bacterial biomass). Soil moisture has been reported to significantly influence microbial community function and structure (and indirect measurements of these factors) in other studies. In a study of soil microbial community structure in zonal and azonal forest sites along a regional climate gradient, Hackl et al. (2004) discovered that PLFA patterns were compositionally distinct among forests with different hydrological regimes and microbial activity was limited by soil water content in the drier sites. Frey et al. (1999) found that soil moisture had a positive effect on fungal biomass but no effect on bacterial biomass in agricultural systems. They suggest that this effect was indirect and that the relationship was a product of the effect of water potential on factors such as pH, aeration, nutrient availability and microbivory.  In a laboratory incubation study which investigated the  effects of climate on litter decomposition and nutrient cycling, Van Meeteren et al. (2007) found that both soil moisture and temperature had a large effect on microbial P immobilization and a significant, but less pronounced, effect on microbial respiration, qCO2, net P and N mineralization rates, nitrification, and C and N immobilization. Bengston et al. (2007) found available soil moisture to be auto-correlated with nutrient availability, microbial biomass, and microbial activity in a coniferous forest on Vancouver Island, B.C. They also found microbial activity to increase during rainfall events. Soil moisture has also been shown to indirectly influence the effect of soil arthropod activity on N cycling (Persson, 1989) and further studies investigating the interaction between microorganisms and soil animals will enhance our understanding of the influence of the microbial community on biogeochemical processes. Tree species were not identified as having direct influence on microbial community function or structure at any of the locations.  Other studies have found correlations  between vegetation and microbial community function and/or structure (e.g. Grayston  114  and Prescott, 2005; Höberg et al., 2008), but these studies were focused at smaller scales, and as Wardle et al. (2004) point out, the effects of plant composition on decomposer communities appear to be context and scale-dependent. The observed effects of average annual precipitation on soil microbial function and structure in this study may in fact be indirect - mediated through the effect of moisture on dominant tree species. The use of the BEC zone system to identify research sites implicitly accepts the relationship between dominant tree species composition and regional climate. As soil C and N concentrations exhibited similar patterns in relative concentration at each location and behaved similarly in relation to the microbial community structure function, the results for C and N concentrations were used together as a proxy for total soil organic matter (SOM). SOM concentration was positively correlated with total fungal biomass and with saprophytic fungal biomass.  It is therefore surprising that SOM  concentration was negatively correlated with the activity of lignocellulose-degrading enzymes, which are produced by saprophytic fungi (Swift et al., 1979). Similarly, it is also surprising that the C:N ratio was negatively correlated with phenol-oxidase activity. These apparent anomalies may be partly or fully explained by the increase in phenoloxidase and peroxidase activities down the soil profile. The organic layers had a higher concentration of SOM and this is where most enzyme activity occurs.  Recalcitrant  material is more likely to persist in the soil and become leached down the profile where it is degraded by enzymes such as phenol oxidase and peroxidase. Soil Organic Matter (SOM) concentration was strongly correlated with the ordination axes which explained most of the variation in microbial community function in the organic layers, and this finding is supported by the results of a study by Hackl et al. (2005) who found that the size of the soil microbial biomass in Eastern European forest stands was tightly coupled with the SOM concentration. The same authors found that soil moisture influenced overall microbial activity, which supports the findings of this study whereby both soil moisture and SOM were found to be important drivers of the soil microbial community. Except for positive correlations with the activities of enzymes which degrade labile C and chitin, soil pH was not one of the main explanatory variables for the patterns in microbial community function and structure in this study. A number of studies have identified soil pH as the primary, or one of the primary, environmental variables driving soil microbial 115  function and/or structure, such as Fierer and Jackson (2006), Hackl et al. (2005), and Högberg et al. (2007); however, these studies have either investigated a different aspect of microbial community composition or have focused their research at a scale which is different to the one investigated in this study. For example, Fierer and Jackson (2006) observed that the diversity and richness of soil bacterial communities at a global scale could largely be explained by variations in soil pH by investigating the phylogenetic diversity of soil bacteria, and Hackl et al. (2005) and Högberg et al. (2007) observed soil pH to have an influence on microbial PLFA composition over distances of a few kilometeres. It is not correct to state that, based on the findings of this study, soil pH does not have a major effect on soil microbial communities at a regional scale, but it can be stated that this study indicates soil pH plays a minor role, when compared to soil moisture and SOM, in influencing microbial community function and structure at these locations at a regional scale. These findings could be explored further by including sites with a larger range of soil pH values into the study or by experimentally manipulating the soil pH at these sites under controlled conditions. All forms of available N (nitrate, ammonium, and their sum) were negatively correlated with the chitin-degrading enzyme NAG.  NAG hydrolyzes chitin in litter (Kjøller and  Struwe, 2007). Sinsabaugh et al. (1993) suggest NAG activity is linked to available N in some forests; therefore it is congruent that low concentrations of soil N would induce increased NAG activity. Olander and Vitousek (2000) also found that the activity of Nmineralizing enzymes was negatively correlated with the availability of inorganic N. Phosphatase activity was negatively correlated with total soil N and significantly positively correlated with NAG activity. These results agree with Trasar-Cepeda et al. (1998) who found that phosphatase activity was highest under conditions which favor Nmineralization. Phosphatase activity was also significantly positively correlated with the activities of the labile C-degrading enzymes and with soil total C concentration. This indicates that phosphatase mineralization is coupled to respiration of C by soil microorganisms.  This finding challenges the conceptual model of McGill and Cole  (1981) which hypothesizes that P is mineralized independently of C. Phosphatase was not significantly negatively correlated with available P as may be expected from the results of other studies (e.g. Olander and Vitousek, 2002; Sinsabaugh et al., 1993). This may be due to an underestimation of available P in the samples due 116  to the limited mobility of P in soils (Plante, 2007) (the PRS probes require the movement of ions in solution across the membrane in order for the ions to adsorb) or possibly because P cycling in the study systems is very tight (mycorrhizal fungi may “short circuit” the conventional decomposer pathways with direct recycling of organic nutrients to plant hosts (van Elsas et al., 2007)). Criquet et al. (2004) also failed to observe a negative feed-back system in their study on water-extractable P concentrations and Pmineralization. They suggest that some other substrate limitation may have complicated the cycle.  4.6. Correlations between components of the microbial communities Phosphatase activity was significantly positively correlated with all PLFA signatures except for actinobacteria, arbuscular mycorrhizal fungi, and saprophytic fungi. It is not surprising that phosphatase is correlated with so many of the PLFA signatures as P is an element essential to life and phosphatase is produced by 70-80 % of the microbial population (Plante, 2007). Phosphorus plays both a structural and functional role in virtually all organisms, is found in many cell components, and plays an important role in storing and transferring biochemically useful energy (Plante, 2007).  However, it is  surprising that phosphatase activity is not correlated with the arbuscular mycorrhizal biomass. Not only is phosphatase activity often correlated with fungal presence, but mycorrhizal fungi play a major role in mineralizing P for plant uptake (Smith and Read, 1997; Finlay, 2007). Phenoloxidase and peroxidase activities were visually separate from the activities of the other enzymes on the ordination graphics and peroxidase activity was negatively correlated with the activities of phosphatase, cellulase, and xylanase. These results can be explained by the different behaviours of the two groups of enzymes; the increase in peroxidase and phenol oxidase activity and the decrease in the activities of phosphatase, cellulase, and xylanase down the soil profile. The activities of cellulase, xylanase, glucosidase and NAG all significantly positively correlated with each other. All of these enzymes play a role in mineralizing C from simple organic compounds (Nannipieri et al., 2007) and would therefore be expected to be produced under similar environmental conditions.  117  The positive correlations of PLFA signatures with the activities of the enzymes xylanase (with all PLFA signatures), cellulase (with all PLFA signatures, except for actinobacteria), and glucosidase (with total microbial biomass and total bacterial biomass) suggest that these enzymes are closely tied to the living biomass of soil.  The lignocellulase-  degrading enzyme peroxidase was significantly negatively correlated with all PLFA signatures, except for total microbial biomass. Again, this may be explained by the pattern of peroxidase activity with depth in the soil profile; higher activity in the mineral soil corresponds to low abundance of microorganisms (Paul and Clark, 1989).  4.7. Changes in microbial community function and structure with soil depth The enzyme activities of the microbial communities in the organic layers were significantly different from those in the mineral layers. This finding was confirmed by the multivariate ordination plot, where the mineral layers were clearly discriminated from the organic layers. Soil microorganisms typically decline in biomass and number with depth in a soil profile with a concomitant decline in SOM concentration (Paul and Clark, 1989; Fierer et al., 2003).  This decline in microbial biomass and number is a function of the variable  availability of nutrients, energy, and the vertical diversity of pedogenic factors (Agnelli et al., 2004). Kramer and Gleixner (2008), who investigated the influence of C3 and C4 plants on microbial communities in soil depth profiles, found that SOM- and plantderived-C was utilized as a microbial energy source in different ways, depending on the type of microorganism studied; Gram-negative bacteria utilize proportionately more plant-derived C than SOM-derived-C when compared to Gram-positive bacteria. The differential use of C-resources down a soil profile by various components of the microbial community suggests that the various microbial community structural groups would respond differently to environmental heterogeneity. This would contribute to the decoupling of microbial function and structural responses to external gradients, as observed in this study. The activities of NAG, phosphatase, sulfatase, urease, and the labile C-mineralizing enzymes decreased with depth, whereas the activity of the enzymes which degrade more complex materials (phenoloxidase and peroxidase) increased with depth (except 118  for the phenoloxidase summer samples)17.  Daradick (2007) also described this  phenomenon in her work in CWH forest sites on Vancouver Island. The increased recalcitrance of the ligno-cellulytic material ensures a longer retention time in the soil compared to the more labile compounds, and it is thought that the recalcitrant material leaches into the lower layers of the soil profile, where it is acted upon by phenoloxidase and peroxidase (Daradick, 2007). The structural composition of the microbial communities in the organic layers was also significantly different from that of the mineral layer. Leckie et al. (2004a) and Grayston and Prescott (2005) also found microbial communities were discriminated by forest floor layer. In both studies the forest floor tended to be a better discriminator of soil microbial community samples than either forest type or site.  4.8. Changes in microbial community function and structure with season Microbial community structure and function did not significantly differ between the two sampling times (spring and summer) in this study. Despite expectations that seasonal changes in temperature and precipitation would significantly affect the function and structure of the microbial communities in this study, as soil microbial communities possess the metabolic and genetic capability to adapt to changing environmental conditions on very short time scales (Schmidt et al., 2007), this finding is not inconsistent with other studies.  Boerner et al. (2005) did not find any significant differences in  activities of acid-phosphatase, α-glucosidase, phenoloxidase, or NAG between spring and summer samples in soils from burned and unburned Quercus-dominated forests in the USA. Blume et al. (2002) found that the size of the microbial biomass in both the surface and the subsurface soils of agricultural land was not significantly affected by seasonal variation. When my results were analyzed by individual enzyme activity, some significant differences in community function between spring and summer samples were observed,  17  The relative difference in activity between the lignocellulase-degrading enzymes and the other enzymes may be under-represented, as the activities in this study were recorded relative to soil weight and the mineral soil is expected to have a higher bulk density than the organic layers. 119  despite the overall lack of seasonal effect on soil microbial structure and function. Activity rates of all enzymes were typically higher in the summer and the activities of phosphatase, sulfatase, xylanase and phenoloxidase were significantly higher in some locations in the summer samples.  Increased summer soil temperatures would be  expected to directly and positively influence microbial physiology and respiration rates, and so enzyme production (Standing and Killham, 2007). A change in temperature would also indirectly affect nutrient and substrate diffusion (Standing and Killham, 2007), which may have had a positive effect on enzyme activity. Low soil water availability may have limited spring enzyme activity at the drier (PP and BWBS) locations but increased in summer with a significant increase in soil moisture (Frey et al., 1999; Voroney, 2007). However, seasonal changes in enzyme activity are unlikely to be purely controlled by soil temperature and moisture and other possible mechanisms are discussed below. Phosphatase activity in the samples from the ESSF location was significantly higher in summer samples than in spring samples. Phosphatase activity has been shown to be linked to P availability (Sinsabaugh et al., 1993; Olander and Vitousek, 2002) and there may have been a decrease in available P in the summer after the spring ‘flush’ of vegetative growth and a subsequent increase in phosphatase activity. Unfortunately, nutrient availability was only measured once so it is not possible to link this hypothesis to changes in P availability. However, P availability was relatively low in the ESSF location compared to the other sites, which could indicate that P may become limiting under conditions of increased vegetative and microbial growth. Sulfatase activity in the samples from the BWBS location was significantly higher in summer samples than in spring samples. The BWBS exhibits very high S availability as the Alcan (Grey Wooded Solod) soils exhibit some minor solonetzic features and contain sulfites in the C horizon (L. Lavkulich, personal communication; Pawluk and Bayrock, 1969). These solonetzic soils are in the advanced stages of development; they were previously highly saline, but the sodium salts have been removed down the profile during pedogenic development (L. Lavkulich, personal communication). The principal salt is sodium sulfate (with some samples containing magnesium sulfate) (L. Lavkulich, personal communication; Pawluk and Bayrock, 1969). Groundwater recharge produces gypsum (calcium sulfate) crystals and so increases available S concentrations (L. Lavkulich, personal communication; Pawluk and Bayrock, 1969). At the time of spring sampling, the soil at the BWBS location was still frozen at depths of approximately 10 to 120  20 cm from the surface. It is possible that the luxury uptake of S by the vegetation and the lack of groundwater recharge due to the frozen soil had depleted the S levels to an extent that sulfatase activity was induced when the soil thawed. Xylanase activity was significantly higher in the summer samples from the MH location. Soil pH was found to be significantly lower in the summer samples from this location. The effect of soil pH on the various components of the microbial community is complex (Standing and Killham, 2007).  The distribution and activity of soil microbes with  variations in soil pH are not simply determined by physiological pH preference (Nannipieri et al., 2002). Many organisms can tolerate pH conditions that are far from their optimum (Nannipieri et al., 2002). However, fungi can be highly competitive under considerable acidity (Nannipieri et al., 2002). Fungi are a major producer of xylanase (Kjøller and Struwe, 2002) and the significant decrease in pH with a concomitant increase in saprophytic and total fungal biomass in the summer sample may show increased competitiveness of xylanase-producing fungi over the other components of the microbial community. Phenol oxidase activity at the IDF location was significantly higher in the summer samples than in the spring samples. In fact, phenoloxidase activity levels in the F-layer summer samples from the IDF were orders of magnitude higher than the activities in the rest of the samples.  Phenoloxidase is one of a suite of enzymes which degrades  material with a high C:N ratio (Swift et al., 1979) and it is therefore unsurprising that the C:N ratio was higher in the summer samples from the IDF location compared to the spring samples. It is also interesting that the extremely high total microbial biomass (relative to the other samples) in the F-layer summer sample from the IDF location was associated with extremely high phenol oxidase activity, but was not associated with an unusually large saprophytic biomass. This discrepancy indicates that either some of the saprophytic fungal biomass may not be accounted for by the PLFA signatures chosen for this study or that this enzyme is also produced by other organisms such as actinobacteria (Falcon et al., 1995).  4.9. Sampling design recommendations The similarity among composite and individual samples is consistent with the findings of Leckie et al. (2004a) who used phylogenetic and PLFA analyses to characterize soil 121  microbial communities in CWH forests on Vancouver Island.  Composite samples  appear to provide a representative picture of microbial communities in these forests and therefore their use is recommended when using enzyme bioassay and PLFA analysis techniques to characterize the microbial community function and structure of forest soils. The differences in microbial community function and structure between soil layers indicate that organic layers must be analyzed separately from mineral layers. Whilst it is interesting to look at the effect of seasonal change on microbial community composition, there were no overall differences in the microbial community function and structure between the spring and summer samples. There were interesting shifts in some of the individual components of the microbial community between the two sampling times, but as there was no seasonal replication no recommendations can be made with regard to sampling design.  122  5. CONCLUSIONS o  Forest types could be discriminated at a regional scale based on the attributes of soil microbial community function and structure.  o  Soil microbial community function and structure were correlated with moisture availability, and microbial community function appears to be influenced by annual average precipitation along a regional climate gradient. The observed effects of average annual precipitation on soil microbial function and structure in this study may be indirectly mediated through the effect of moisture on dominant tree species.  o  Most of the locations exhibited unique microbial community functional profiles in their soil layers; however the enzyme activities in the samples from the driest (Ponderosa Pine) and wettest (Mountain Hemlock) locations were notably different from each other and from those of the other locations, especially in the organic layers.  o  The moist maritime-influenced Coastal Western Hemlock (CWH) forest exhibited microbial community structural characteristics which were unique from those of the other forest locations. The higher abundance of bacteria relative to fungi in the CWH forest soils may be related to the significantly higher available nitrogen concentrations at this site.  o  Soil Organic Matter (SOM) concentration also influenced soil microbial community function and structure at a regional scale.  o  Patterns in microbial community function and structure differed in response to external climate and environmental variables.  o  Microbial community function and structure also changed with soil depth but not with time of sampling.  123  6. FURTHER WORK o  Archaea have been shown to be important components of the soil microbial community (Nicol and Schelper, 2006). It would be interesting to include data on soil archaeal lipid concentrations (phospholipid ether lipid analysis) in the multivariate models and ordinations.  o  The measurement of redox potentials at each site would help to examine the link between microbial community composition and soil moisture.  o  Correlations of individual tree species and under-storey vegetation species composition with microbial community measurements would enhance our knowledge of site-level shifts in microbial community composition and potential drivers of these shifts and the effects on litter decomposition.  o  Experimental manipulation of soil moisture, SOM and pH would allow testing of new hypotheses regarding regional and site-level drivers of microbial community composition and to examine the relationship between these variables.  o  Collecting data on soil macro- and meso-fauna and the shifts in their community composition with regional and site-level variables could be useful in completing the picture of the belowground ecosystem and its influence on nutrient-cycling processes.  124  7. 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The number of iterations for the final solution was 78. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance. 44.2980690................................................................................ . . . . .* . . . . * . . ** . . * . . * . . * . . . . * . . . . . . * . STRESS . . . . . * . . . . ** * . . . . . . * * . . * . . * * * ** . . ***** . . ****************************************************. . . . . . . . . 0.0000000................................................................................ 10 20 30 40 50 60 70 80 ITERATION NUMBER  Figure 8.1. Stress plot for enzyme data.  Table 8.1. Correlations between variation in the data and ordination axes.  Axis 1 2  R Squared Increment Cumulative .874 .874 .105 .979  137  NMS ordination of functional data for a combination of all soil profile layers A 2-dimensional solution was recommended.  The final stress was 13.57409 (‘fair’  according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002).  The final  instability value of 0.00001 and the stability plot suggest that a stable solution was found. The number of iterations for the final solution was 49.  The proportion of variance  represented by axis 1, based on the r2 value between distance in the ordination space and distance in the original space, is 0.629. The r2 value for axis 2 is 0.255; therefore the cumulative proportion of variance in the dataset represented by the final 2dimensional solution is 0.884. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance.  47.1099129................................................... . . . . .* . . ******** . . **** . . ** . . . . * . . . . * . . . . * * . . * . . . STRESS . . . . . ** . . . . *** . . ** . . **********************. . . . . . . . . . . . . . . . . . . 0.0000000................................................... 10 20 30 40 50  ITERATION NUMBER  Figure 8.2. Stress plot for enzyme data.  Table 8.2. Correlations between variation in the data and ordination axes. R Squared Axis 1 2  Increment .629 .255  Cumulative .629 .884  138  Table 8.3. Correlations between enzyme activities and the ordination axes. Axis: r Cellulas Xylans NAG Phosphat Glucosid Sulfatas Phenolox Peroxid Urease  .067 .069 .082 .492 .027 .058 -.286 -.896 -.134  1 r-sq .005 .005 .007 .242 .001 .003 .082 .803 .018  tau .137 .138 .076 .413 .064 -.088 -.311 -.832 -.050  r -.563 -.573 -.641 -.670 -.593 -.460 -.158 .346 -.136  2 r-sq .317 .328 .411 .449 .351 .211 .025 .120 .018  tau -.566 -.477 -.574 -.572 -.536 -.219 .025 .271 -.069  Table 8.4. Correlations between measured environmental variables and ordination axes. Axis: r TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  .088 .070 .213 .036 -.295 -.028 -.152 -.303 .312 -.114 .316 -.265 -.143 .247 .217 .116 -.311 .105 -.108 -.034 .822 .728 .742 .258 -.305  1 r-sq .008 .005 .046 .001 .087 .001 .023 .092 .098 .013 .100 .070 .021 .061 .047 .014 .097 .011 .012 .001 .676 .530 .551 .066 .093  tau .294 .246 .299 .039 -.185 -.005 -.130 -.280 .246 -.042 .204 -.187 -.003 .114 .139 .098 -.212 .071 -.081 .041 .611 .535 .513 .271 -.210  r .109 .110 .043 -.106 .047 -.367 -.216 .248 .083 .115 .204 .018 .123 .184 .145 -.108 -.019 .256 -.291 -.040 -.154 -.481 -.549 -.054 -.245  2 r-sq .012 .012 .002 .011 .002 .135 .047 .062 .007 .013 .042 .000 .015 .034 .021 .012 .000 .065 .085 .002 .024 .231 .302 .003 .060  tau -.029 -.004 -.042 -.075 .040 -.323 -.209 .229 .052 .135 .142 .007 .140 .188 .099 -.045 -.010 .192 -.194 -.127 -.131 -.373 -.417 -.167 -.138  139  NMS ordination of functional data for the organic layers (F and H18) A 3-dimensional solution was recommended.  The final stress was 8.99840 (‘good’  according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002).  The final  instability value of 0.00001 and the stability plot suggest a stable solution was found. The number of iterations for the final solution was 67.  The proportion of variance  represented by axis 1, based on the r2 value between distance in the ordination space and distance in the original space, is 0.507. The r2 value for axis 2 is 0.193 and for axis 3 is 0.214; therefore the cumulative proportion of variance in the dataset represented by the final 3-dimensional solution is 0.914. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance. 38.7250557..................................................................... . . . . .* . . . . * . . **** . . *** . . ** . . * . . * . . * . . * . . * . . * . STRESS . * . . * . . ** . . * . . ** . . ** . . **** . . ******* . . ******************************. . . . . . . . . . . . . . . 0.0000000..................................................................... 10 20 30 40 50 60  ITERATION NUMBER  Figure 8.3. Stress plot for enzyme data.  18  The PP location exhibits no H layer. 140  Table 8.5. Correlations between variation in the data and ordination axes.  Axis 1 2 3  R Squared Increment Cumulative .220 .220 .521 .741 .174 .915  Table 8.6. Correlations between enzyme activities and the ordination axes. Axis: r Cellulas Xylans NAG Phosphat Glucosid Sulfatas Phenolox Peroxid Urease  -.203 -.071 -.286 -.729 -.236 .153 .258 .552 .022  1 r-sq .041 .005 .082 .532 .056 .023 .067 .305 .000  tau -.102 -.097 -.156 -.574 -.077 .211 .240 .517 .033  r .614 .608 .600 .116 .604 .339 .515 .707 .325  2 r-sq .377 .370 .360 .013 .364 .115 .265 .499 .106  tau  r  .526 .475 .541 .061 .541 .368 .276 .534 .113  -.315 -.109 -.202 -.148 -.194 -.615 -.410 .513 .220  3 r-sq .099 .012 .041 .022 .038 .379 .168 .263 .048  tau -.220 -.149 -.145 -.180 -.263 -.275 -.314 .432 .086  Table 8.7. Correlations between measured environmental variables and ordination axes. Axis: r TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  .252 .255 .098 -.178 .335 .047 .232 .407 -.227 .262 .018 .100 .217 -.070 -.317 -.363 .264 .234 -.259 -.044 -.338 -.630 -.629 -.234 .054  1 r-sq .064 .065 .010 .032 .113 .002 .054 .166 .052 .069 .000 .010 .047 .005 .101 .132 .070 .055 .067 .002 .114 .397 .396 .055 .003  tau -.006 .051 -.058 -.152 .207 .023 .164 .325 -.106 .239 .090 .046 .203 .109 -.231 -.270 .228 .145 -.162 -.130 -.208 -.459 -.455 -.262 .066  r -.153 -.132 -.292 -.067 .276 .484 .348 .092 -.456 .195 -.459 .205 -.009 -.404 -.460 .003 .502 -.232 .304 -.028 -.780 -.364 -.318 -.105 .518  2 r-sq .023 .017 .085 .004 .076 .234 .121 .008 .208 .038 .210 .042 .000 .164 .211 .000 .252 .054 .092 .001 .609 .132 .101 .011 .268  tau -.245 -.195 -.279 .009 .128 .330 .308 .137 -.334 -.047 -.329 .185 -.123 -.320 -.275 -.028 .298 -.175 .232 .008 -.573 -.270 -.186 -.124 .399  r -.138 -.111 -.349 .015 .003 -.043 -.028 .136 .108 .425 .291 .128 .195 .224 .141 .070 -.011 -.006 -.094 .154 -.099 -.010 -.305 .432 -.212  3 r-sq .019 .012 .122 .000 .000 .002 .001 .018 .012 .180 .085 .016 .038 .050 .020 .005 .000 .000 .009 .024 .010 .000 .093 .187 .045  tau -.176 -.163 -.176 -.015 .017 -.050 .011 .243 .095 .296 .235 .077 .265 .150 .026 -.003 -.062 .042 -.131 .006 .001 -.012 -.233 .196 -.143  141  NMS ordination of microbial functional data for the F layer A 3-dimensional solution was recommended.  The final stress was 8.40868 (‘good’  according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002).  The final  instability value of 0.00001 and the stability plot suggest a stable solution was found. The number of iterations for the final solution was 74.  The proportion of variance  represented by axis 1, based on the r2 value between distance in the ordination space and distance in the original space, is 0.155. The r2 value for axis 2 is 0.303 and for axis 3 is 0.44; therefore the cumulative proportion of variance in the dataset represented by the final 3-dimensional solution is 0.898. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance.  37.2830429............................................................................ . . . . .* . . . . *** . . ****** . . ** . . * . . * . . . . * . . * . . * . . * . STRESS . ** . . * . . ** . . * . . ******** . . ****** . . **** . . *** * ** . . ** * ***********************. . . . . . . . . . . . . . . 0.0000000............................................................................ 10 20 30 40 50 60 70 ITERATION NUMBER  Figure 8.4. Stress plot for enzyme data.  Table 8.8. Correlations between variation in the data and ordination axes.  Axis 1 2 3  R Squared Increment Cumulative .560 .560 .084 .644 .254 .898 142  Table 8.9. Correlations between enzyme activities and the ordination axes. Axis: r Cellulas Xylans NAG Phosphat Glucosid Sulfatas Phenolox Peroxid Urease  -.475 -.587 -.524 -.023 -.510 -.051 -.337 -.832 -.425  1 r-sq .225 .344 .275 .001 .260 .003 .114 .692 .180  tau -.426 -.438 -.477 -.066 -.433 -.187 -.076 -.736 -.167  r -.279 -.170 -.303 -.822 -.317 .001 .009 .480 .073  2 r-sq .078 .029 .092 .676 .101 .000 .000 .230 .005  tau -.151 -.161 -.178 -.719 -.092 .020 -.124 .331 .125  r .614 .418 .391 -.009 .421 .709 .742 .067 .018  3 r-sq .377 .175 .153 .000 .177 .503 .551 .004 .000  tau .489 .405 .366 .001 .559 .591 .647 .053 .011  Table 8.10. Correlations between measured environmental variables and ordination axes. Axis: r TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  .280 .241 .471 .134 -.160 -.511 -.294 -.163 .359 -.510 .324 -.273 .077 .274 .285 -.299 -.550 .187 -.322 .129 .762 .309 .471 -.344 -.414  1 r-sq .078 .058 .222 .018 .026 .262 .087 .027 .129 .260 .105 .074 .006 .075 .081 .089 .302 .035 .103 .017 .581 .095 .222 .118 .171  tau .343 .282 .372 -.014 -.099 -.362 -.320 -.268 .207 -.158 .240 -.245 .000 .227 .188 -.099 -.304 .184 -.227 .080 .477 .244 .180 -.199 -.328  r .216 .216 .141 -.260 .104 .174 .263 .387 -.051 .467 .119 .068 .227 -.067 -.258 -.176 .245 .271 -.276 -.091 -.206 -.458 -.511 .101 .028  2 r-sq .046 .047 .020 .067 .011 .030 .069 .150 .003 .219 .014 .005 .052 .004 .067 .031 .060 .073 .076 .008 .042 .209 .261 .010 .001  tau .024 .061 .000 -.235 .066 .146 .212 .315 .071 .328 .198 .024 .240 .114 -.188 -.179 .194 .165 -.168 -.189 -.110 -.311 -.327 .018 .040  r .121 .111 .156 -.149 .315 .292 .256 .096 -.437 -.182 -.463 -.020 -.256 -.377 -.513 -.147 .355 -.042 .188 -.199 -.392 -.457 -.083 -.526 .436  3 r-sq .015 .012 .024 .022 .100 .085 .066 .009 .191 .033 .214 .000 .065 .142 .263 .022 .126 .002 .035 .040 .154 .209 .007 .277 .190  tau .031 .002 .059 -.040 .125 .205 .186 .125 -.289 -.116 -.365 .031 -.190 -.207 -.294 -.075 .323 -.031 .178 -.083 -.338 -.401 -.085 -.430 .410  NMS ordination of microbial functional data for the H layer A 2-dimensional solution was recommended.  The final stress was 11.17956 (‘fair’  according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002).  The final  instability value of 0 and the stability plot suggest a stable solution was found. The number of iterations for the final solution was 131.  The proportion of variance  143  represented by axis 1, based on the r2 value between distance in the ordination space and distance in the original space, is 0.558. The r2 value for axis 2 is 0.349; therefore the cumulative proportion of variance in the dataset represented by the final 2dimensional solution is 0.907. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance. 46.0422440...................................................................................................... . . . . .* . . . . * . . * . . . . * . . . . ** . . * . . . . ** . . . STRESS . ** . . . . ** . . **** . . ************************************************************************ * . . *** . . ** * . . *** *. . . . . . . . . . . . . . . . . 0.0000000...................................................................................................... 10 20 30 40 50 60 70 80 90 100 ITERATION NUMBER  46.0422440................................. . . . . . . . . . . . . . . . . . . . . . . . . . . . . STRESS . . . . . . . . . . . . . . .*******************************. . . . . . . . . . . . . . . . . 0.0000000................................. 110 120 130  ITERATION NUMBER  Figure 8.5. Stress plot for enzyme data.  144  Table 8.11. Correlations between variation in the data and ordination axes.  Axis 1 2  R Squared Increment Cumulative .558 .558 .349 .907  Table 8.12. Correlations between enzyme activities and the ordination axes. Axis: r Cellulas Xylans NAG Phosphat Glucosid Sulfatas Phenolox Peroxid Urease  .137 .043 .196 .518 .169 -.029 -.349 -.826 -.166  1 r-sq .019 .002 .039 .269 .029 .001 .122 .683 .027  tau -.029 .062 -.039 .482 .005 -.234 -.455 -.712 -.108  r -.567 -.545 -.622 -.174 -.661 -.539 -.567 -.675 -.063  2 r-sq .322 .297 .387 .030 .437 .290 .321 .456 .004  tau -.590 -.472 -.613 -.086 -.543 -.519 -.250 -.506 .018  145  Table 8.13. Correlations between measured environmental variables and ordination axes. Axis: r TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  -.189 -.190 -.040 .171 -.374 .093 -.343 -.342 .225 -.125 -.015 .021 -.171 -.003 .387 .580 -.175 -.096 .223 -.168 .453 .607 .662 .371 -.034  1 r-sq .036 .036 .002 .029 .140 .009 .118 .117 .051 .016 .000 .000 .029 .000 .150 .337 .031 .009 .050 .028 .206 .369 .439 .137 .001  tau .128 .056 .169 .111 -.278 .019 -.124 -.220 .165 -.167 -.046 .066 -.182 -.037 .339 .378 -.171 -.048 .116 .007 .358 .518 .528 .424 -.124  r .079 .069 .211 -.074 -.429 -.427 -.360 .061 .550 .291 .473 -.253 .053 .441 .466 .242 -.370 .398 -.353 -.205 .762 .476 .399 .393 -.601  2 r-sq .006 .005 .044 .005 .184 .182 .130 .004 .302 .085 .224 .064 .003 .195 .217 .059 .137 .159 .124 .042 .581 .226 .159 .154 .362  tau .196 .138 .264 -.053 -.189 -.295 -.206 .073 .390 .243 .356 -.254 .138 .351 .216 .161 -.263 .287 -.267 -.192 .543 .258 .234 .261 -.407  NMS ordination of microbial functional data for the mineral layer A 1-dimensional solution was recommended.  The final stress was 9.90937 (‘good’  according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002).  The final  instability value of 0.00001 and the stability plot (see Appendix ) suggest a stable solution was found.  The number of iterations for the final solution was 66.  The  proportion of variance represented by axis 1, based on the r2 value between distance in the ordination space and distance in the original space, is 0.944. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance.  146  60.4881744.................................................................... . . . . .* . . * . . . . * . . . . . . * * . . * . . * . . * . . * . . . STRESS . . . . . ** . . . . ** * . . * . . * . . * . . . . * * * . . **********************************************. . . . . . . . . . . 0.0000000.................................................................... 10 20 30 40 50 60 ITERATION NUMBER  Figure 8.6. Stress plot for enzyme data.  Table 8.14. Correlations between variation in the data and ordination axes.  Axis 1  R Squared Increment Cumulative .944 .944  Table 8.15. Correlations between enzyme activities and the ordination axes. Axis: r  1 r-sq  tau  r  2 r-sq  tau  r  3 r-sq  tau Cellulas Xylans NAG Phosphat Glucosid Sulfatas Phenolox Peroxid Urease  .080 -.085 .264 -.286 .220 .300 .543 .831 .106  .006 .007 .070 .082 .048 .090 .295 .691 .011  .050 -.067 .179 -.117 .251 .115 .335 .916 .142  147  Table 8.16. Correlations between measured environmental variables and ordination axes. Axis: r  1 r-sq  tau  r  2 r-sq  tau  r  3 r-sq  tau TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  -.241 -.242 -.109 .097 .321 .171 .513 .129 -.070 -.337 -.408 .178 .150 -.359 -.323 -.451 .012 -.344 .183 .360 -.594 -.455 -.474 -.296 .382  .058 .059 .012 .009 .103 .029 .263 .017 .005 .113 .167 .032 .022 .129 .104 .204 .000 .118 .034 .129 .353 .207 .224 .088 .146  -.281 -.267 -.278 .093 .205 .096 .328 .153 -.182 -.296 -.349 .221 .035 -.215 -.145 -.367 -.049 -.296 .214 .286 -.405 -.296 -.241 -.191 .231  NMS ordination of structural data for a combination of all soil profile layers A 2-dimensional solution was recommended, see Figure 3.10. The final stress was 7.84652 (‘good’ according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002). The final instability value of 0.00001 and the stability plot suggest a stable solution was found.  The number of iterations for the final solution was 122.  The proportion of  variance represented by axis 1, based on the r2 value between distance in the ordination space and distance in the original space, is 0.26. The r2 value for axis 2 is 0.705; therefore the cumulative proportion of variance in the dataset represented by the final 2dimensional solution is 0.966. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance.  148  . .* . . ** . . ** . . * . . * . . ** . . * . . * . . . . * . . * . . * . STRESS . * . . * . . * . . * . . ** . . ** . . ** . . **** . . ******************************* * * . . **** *********** ** ** ** * . . * * ** *********** **. . . . . . . . . . . 0.0000000...................................................................................................... 10 20 30 40 50 60 70 80 90 100 ITERATION NUMBER  46.4902420........................ . . . . . . . . . . . . . . . . . . . . . . . . . . . . STRESS . . . . . . . . . . . . . . . . . . .** ** . . * *****************. . .  Figure 8.7. Stress plot for PLFA data. Table 8.17. Correlations between variation in the data and ordination axes.  Axis 1 2  R Squared Increment Cumulative .260 .260 .705 .966  149  Table 8.18. Correlations between PLFA concentrations and the ordination axes. Axis: r Bac:SapF B-ac:sap Bac:ArbF B-ac:arb Bac:TotF B-act:tf Gpos:Gng Bac:Tbio B-a:tbio TF:Tbio Gps:Tbio Gng:Tbio Act:Tbio  -.836 -.831 -.045 -.034 -.713 -.702 .099 -.061 -.054 .320 -.009 -.111 -.101  1 r-sq .699 .690 .002 .001 .508 .493 .010 .004 .003 .103 .000 .012 .010  tau -.842 -.835 -.196 -.181 -.824 -.811 .138 -.126 -.122 .246 -.100 -.141 -.205  r -.327 -.329 -.750 -.745 -.474 -.486 .475 .232 .221 .372 .339 -.016 .282  2 r-sq .107 .108 .563 .555 .225 .236 .225 .054 .049 .138 .115 .000 .079  tau -.324 -.324 -.825 -.822 -.410 -.423 .350 .016 .010 .239 .084 -.093 -.003  Table 8.19. Correlations between measured environmental variables and ordination axes. Axis: r  1 r-sq  tau  r  2 r-sq  tau  r  3 r-sq  tau TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  -.455 -.445 -.324 -.073 -.062 .427 .258 -.343 .042 -.083 -.189 -.193 -.052 -.269 -.101 .238 -.045 -.265 .328 .002 .056 .368 .361 .196 .241  .207 .198 .105 .005 .004 .182 .067 .118 .002 .007 .036 .037 .003 .072 .010 .057 .002 .070 .107 .000 .003 .135 .130 .038 .058  -.079 -.063 -.036 -.051 -.075 .316 .206 -.208 .052 -.139 -.144 -.126 -.263 -.261 -.058 .160 -.052 -.111 .164 -.017 .084 .302 .303 .270 .139  -.221 -.206 -.254 .187 .026 .074 .181 .003 -.067 -.326 -.206 .029 .196 -.263 .072 -.043 -.172 -.317 .202 .285 .060 -.017 .045 -.212 .138  .049 .042 .065 .035 .001 .005 .033 .000 .005 .106 .042 .001 .038 .069 .005 .002 .030 .100 .041 .081 .004 .000 .002 .045 .019  -.129 -.110 -.121 .121 -.024 .066 .067 .004 -.038 -.320 -.192 -.003 -.152 -.269 .097 .098 -.164 -.194 .191 .097 .067 .024 .097 -.012 .139  150  NMS ordination of structural data for the F layer A 2-dimensional solution was recommended, see Figure 3.11. The final stress was 8.83568 (‘good’ according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002).  The final instability value of 0.00001 and the plot suggest a stable solution was found. The number of iterations for the final solution was 66.  The proportion of variance  represented by axis 1, based on the r2 value between distance in the ordination space and distance in the original space, is 0.786. The r2 value for axis 2 is 0.185, therefore the cumulative proportion of variance in the dataset represented by the final 2dimensional solution is 0.953. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance.  46.9715042.................................................................... . . . . .* . . . . * . . * . . * . . . . * . . . . ** . . . . * . . . STRESS . * . . . . * . . * . . * . . ** . . * . . ** . . **** . . *********************************************. . . . . . . . . . . . . 0.0000000.................................................................... 10 20 30 40 50 60 ITERATION NUMBER  Figure 8.8. Stress plot for PLFA data.  151  Table 8.20. Correlations between variation in the data and ordination axes.  Axis 1 2  R Squared Increment Cumulative .768 .768 .185 .953  Table 8.21. Correlations between PLFA concentrations and the ordination axes. Axis: r Bac:SapF B-ac:sap Bac:ArbF B-ac:arb Bac:TotF B-act:tf Gpos:Gng Bac:Tbio B-a:tbio TF:Tbio Gps:Tbio Gng:Tbio Act:Tbio  .671 .668 .473 .455 .699 .697 -.200 -.332 -.332 -.675 -.327 -.265 -.271  1 r-sq .450 .446 .223 .207 .488 .486 .040 .110 .110 .456 .107 .070 .073  tau .509 .507 .507 .481 .709 .716 -.137 .058 .046 -.560 -.030 .149 .146  r .361 .357 -.880 -.882 -.102 -.112 .221 .371 .341 .328 .372 .217 .508  2 r-sq .130 .128 .774 .778 .010 .013 .049 .137 .116 .107 .138 .047 .258  tau .058 .060 -.777 -.802 -.040 -.047 .184 -.053 -.079 .035 .063 -.186 .058  152  Table 8.22. Correlations between measured environmental variables and ordination axes. Axis: r tau TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  .403 .381 .434 -.091 .070 -.370 -.345 .274 .094 .321 .364 -.017 .055 .466 -.039 -.016 .164 .406 -.364 -.211 .015 -.189 -.326 .123 -.366  1 r-sq .162 .145 .188 .008 .005 .137 .119 .075 .009 .103 .132 .000 .003 .217 .002 .000 .027 .165 .133 .044 .000 .036 .106 .015 .134  tau .193 .202 .207 -.066 .028 -.207 -.202 .231 .113 .390 .264 .047 .315 .437 -.005 -.029 .182 .219 -.268 -.125 -.079 -.178 -.347 -.063 -.177  r .066 .064 .053 .173 -.022 .118 .207 -.129 -.136 -.476 -.115 -.043 -.100 -.133 -.101 -.299 -.412 -.157 .055 .207 .390 .099 .336 -.362 .140  2 r-sq .004 .004 .003 .030 .001 .014 .043 .017 .018 .227 .013 .002 .010 .018 .010 .090 .170 .025 .003 .043 .152 .010 .113 .131 .020  tau  r  3 r-sq  .151 .118 .118 .071 -.038 .005 -.028 -.169 -.028 -.427 -.061 -.099 -.198 -.179 -.009 -.107 -.309 -.026 .050 .144 .246 .023 .249 -.235 .135  NMS ordination of structural data for the H layer A 2-dimensional solution was recommended, see Figure 3.12. The final stress was 4.26440 (‘good’ according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002). The final instability value of 0 and the plot suggest a stable solution was found. The number of iterations for the final solution was 96.  The proportion of variance  2  represented by axis 1, based on the r value between distance in the ordination space and distance in the original space, is 0.853. The r2 value for axis 2 is 0.129, therefore the cumulative proportion of variance in the dataset represented by the final 2dimensional solution is 0.982. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance.  153  45.6436501.................................................................................................. . . . . .* . . . . * . . * . . . . * . . . . ** . . * . . . . . . . STRESS . . . * . . . . * . . * . . ** . . ** . . *** . . *************************** * . . *********** . . ** * ** . . *** *** ** . . * * * * ***********************. . . . . . . 0.0000000.................................................................................................. 10 20 30 40 50 60 70 80 90 ITERATION NUMBER  Figure 8.9. Stress plot for PLFA data. Table 8.23. Correlations between variation in the data and ordination axes.  Axis 1 2  R Squared Increment Cumulative .853 .853 .129 .982  Table 8.24. Correlations between PLFA concentrations and the ordination axes. Axis: r  1 r-sq  tau  r  2 r-sq  tau  r  3 r-sq  tau Bac:SapF B-ac:sap Bac:ArbF B-ac:arb Bac:TotF B-act:tf Gpos:Gng Bac:Tbio B-a:tbio TF:Tbio Gps:Tbio Gng:Tbio Act:Tbio  .657 .661 .883 .879 .688 .702 -.360 -.180 -.154 -.706 -.394 .231 -.290  .432 .437 .781 .773 .474 .493 .130 .033 .024 .499 .155 .054 .084  .509 .503 .818 .805 .553 .590 -.277 .297 .314 -.321 .160 .345 .240  -.855 -.848 -.007 .020 -.856 -.847 -.222 -.328 -.306 .393 -.259 -.259 -.394  .730 .718 .000 .000 .733 .718 .049 .108 .094 .154 .067 .067 .155  -.765 -.771 -.092 -.079 -.721 -.684 -.045 -.324 -.321 .388 -.314 -.163 -.361  154  Table 8.25. Correlations between measured environmental variables and ordination axes. Axis: TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  r .451 .435 .471 -.167 -.075 -.378 -.287 .191 .001 .444 .219 .030 -.237 .280 -.030 -.156 .347 .519 -.402 -.359 -.220 -.206 -.283 -.058 -.070  1 r-sq .203 .189 .222 .028 .006 .143 .083 .037 .000 .197 .048 .001 .056 .078 .001 .024 .121 .270 .162 .129 .049 .042 .080 .003 .005  tau .164 .259 .123 -.055 .109 -.239 -.184 .102 -.034 .378 .205 .027 .130 .258 -.177 -.224 .327 .287 -.281 -.137 -.207 -.251 -.310 -.254 -.061  r -.649 -.644 -.330 -.055 .045 .558 .181 -.587 .141 -.402 -.053 -.246 -.087 -.083 -.137 .309 -.258 -.315 .351 .048 .128 .364 .270 .364 .109  2 r-sq .421 .415 .109 .003 .002 .311 .033 .345 .020 .161 .003 .060 .008 .007 .019 .096 .067 .099 .123 .002 .016 .132 .073 .132 .012  tau -.147 -.201 -.017 -.010 -.003 .167 .065 -.338 .147 -.187 -.072 -.113 -.160 -.092 .058 .336 -.141 -.058 .175 .086 .099 .334 .234 .455 .013  NMS ordination of structural data for the mineral layer A 2-dimensional solution was recommended, see Figure 3.13. The final stress was 4.54007 (‘good’ according to ‘Kruskal’s Rule of Thumb’ see McCune and Grace, 2002). The final instability value of 0.00001 and the plot suggest a stable solution was found. The number of iterations for the final solution was 52.  The proportion of variance  2  represented by axis 1, based on the r value between distance in the ordination space and distance in the original space, is 0.895. The r2 value for axis 2 is 0.08, therefore the cumulative proportion of variance in the dataset represented by the final 2-dimensional solution is 0.974. The probability that a similar final stress could have been obtained by chance is 0.0196; therefore (using an alpha of 0.05) it can be accepted that the solution could not have been obtained by chance.  155  47.4415359...................................................... . . . . .* . . . . *** . . ** . . * . . * . . . . . . * * . . . . . . . STRESS . ** . . . . . . . . . . . . * . . . . * . . *** * . . *** * . . *** . . * **************************. . . . . . . 0.0000000...................................................... 10 20 30 40 50 ITERATION NUMBER  Figure 8.10. Stress plot for PLFA data. Table 8.26. Correlations between variation in the data and ordination axes.  Axis 1 2  R Squared Increment Cumulative .895 .895 .080 .974  Table 8.27. Correlations between PLFA concentrations and the ordination axes. Axis: r Bac:SapF B-ac:sap Bac:ArbF B-ac:arb Bac:TotF B-act:tf Gpos:Gng Bac:Tbio B-a:tbio TF:Tbio Gps:Tbio Gng:Tbio Act:Tbio  .675 .678 .676 .672 .677 .690 -.754 -.268 -.256 -.434 -.456 .180 -.292  1 r-sq .455 .460 .457 .451 .459 .477 .569 .072 .066 .188 .208 .032 .085  tau .722 .720 .729 .741 .729 .741 -.580 -.093 -.098 -.444 -.232 .137 -.080  r -.218 -.212 .672 .670 -.255 -.239 -.360 .076 .092 .233 -.018 .317 -.015  2 r-sq .048 .045 .451 .448 .065 .057 .130 .006 .009 .054 .000 .100 .000  tau -.344 -.341 .190 .178 -.351 -.329 -.046 .041 .051 .280 .034 .178 .010  156  Table 8.28. Correlations between measured environmental variables and ordination axes. Axis: r TotalN NO3--N NH4+-N Ca Mg K P Fe Mn Cu Zn B S Pb Al Slopedeg Soiltemp %sand %silt %clay %water %Carbon %Nitrogen C:N pH  .243 .220 .322 -.168 -.023 .214 .018 -.212 .022 .053 .185 .011 -.225 .217 -.088 -.056 -.073 .202 -.107 -.213 .120 .233 .182 .186 -.136  1 r-sq .059 .048 .104 .028 .001 .046 .000 .045 .000 .003 .034 .000 .051 .047 .008 .003 .005 .041 .012 .046 .014 .054 .033 .034 .018  tau .204 .065 .226 -.149 -.060 .095 .000 -.189 .050 .102 .189 -.035 .050 .243 -.065 -.078 -.020 .153 -.099 -.059 .160 .243 .140 .063 -.143  r -.105 -.102 -.078 -.163 -.043 .294 -.003 -.181 -.097 .191 -.017 -.061 -.153 .001 -.143 .233 .059 .048 .146 -.296 -.117 .020 -.178 .213 .182  2 r-sq .011 .010 .006 .026 .002 .087 .000 .033 .009 .036 .000 .004 .024 .000 .020 .054 .003 .002 .021 .088 .014 .000 .032 .046 .033  tau -.112 -.178 -.073 -.072 .047 .256 .087 -.087 -.042 .058 -.107 .032 -.256 -.013 -.052 .159 -.076 -.032 .109 -.156 -.079 -.130 -.153 .095 .131  157  Appendix II. Paired MRPP test statistics Table 8.29. MRPP pair-wise comparisons for all available N ions and total N by location, organic layers. * indicates significant differences when p=0.05/7=0.007. Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T 0.46344082 0.01005922 -1.53896763 -0.15252436 -2.63738834 -5.61073772 -0.32423583 -3.49583275 -0.36484841 -0.68042049 -5.54807040 -0.36355213 0.60258412 -2.81342216 -3.82933318 -1.77204150 -5.42713031 -6.10057827 -3.00512178 -5.53858458 -6.03334427  A -0.02217072 -0.00072352 0.09824691 0.00803136 0.09311510 0.35667512 0.03445679 0.20898026 0.02396348 0.03910322 0.39163242 0.02990739 -0.05024699 0.22167102 0.36951201 0.08917520 0.36313075 0.36494899 0.17442864 0.35996605 0.49568968  p 0.59317082 0.42110927 0.07867390 0.32386581 0.01687117 0.00153424* 0.28461001 0.00897168 0.25199151 0.20467693 0.00147510* 0.28108859 0.67608781 0.01257628 0.00579687* 0.06095658 0.00155070* 0.00074799* 0.01414464 0.00123785* 0.00114018*  Table 8.30. MRPP pair-wise comparisons for all available N ions and total N by location, mineral layer. * indicates significant differences when p=0.05/7=0.007 (n=39). Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T -2.94509439 -2.93449404 -1.59868226 -2.30487651 -2.95830909 -2.60134669 -2.96908688 -2.54149168 -0.90583633 0.25508550 -1.23707965 -1.50010808 -2.90508016 -2.98393165 -2.75306588 -0.90260767 -2.77871227 -2.06554359 -2.08692329 -1.92625469 -1.62305170  A 0.51168387 0.77248731 0.11994001 0.31228083 0.62695527 0.43518742 0.68379995 0.24001426 0.06200527 -0.03617708 0.20499404 0.28333060 0.64076698 0.79859249 0.53609893 0.06325372 0.36167055 0.25870193 0.23210097 0.27939711 0.28509368  p 0.02180992 0.02191185 0.05973429 0.02859693 0.02174589 0.02455896 0.02167592 0.02472841 0.17575231 0.47493838 0.11147509 0.07598029 0.02207742 0.02158744 0.02321054 0.17269690 0.02296187 0.03765161 0.02906900 0.04151106 0.06280648 158  Table 8.31. MRPP pair-wise comparisons for all available P by location, all layers combined. * indicates significant differences when p=0.05/7=0.007 (n=63). Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T -6.04947773 -3.46748775 0.82931504 -9.71750750 -9.88865690 -8.67169476 -1.74975800 -4.60607517 -3.26956412 -5.46877141 -4.42594901 -2.84177295 -3.57683502 -4.72155607 -3.74223623 -7.07744608 -7.15333285 -6.31434766 -0.63669623 -1.18220628 0.36708833  A 0.24078419 0.15736363 -0.02850579 0.42846072 0.44688927 0.37577298 0.08826339 0.18193874 0.16576672 0.26791555 0.21690579 0.11641396 0.16077003 0.20040205 0.15805806 0.31495177 0.32772499 0.27217495 0.02936439 0.05619393 -0.01813584  p 0.00031727* 0.00679037* 0.81566659 0.00003042* 0.00002860* 0.00004865* 0.06156749 0.00231698* 0.01528942 0.00164665* 0.00384986* 0.01556306 0.00574226* 0.00107991* 0.00468983* 0.00034541* 0.00039290* 0.00061564* 0.19283400 0.11346160 0.52583018  Table 8.32. MRPP pair-wise comparisons for all available S by location, all layers combined. * indicates significant differences when p=0.05/7=0.007 (n=63). Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T 0.65795854 -1.99525649 -9.24429197 -1.77564214 -8.97590768 -9.24676324 -1.65496205 -8.67053957 -1.47326161 -6.87265718 -7.45881557 -5.20966348 -2.30609406 -3.81799452 -4.27798954 -9.38169726 -6.60199550 -7.02389202 -9.27725534 -9.50812118 0.54253254  A -0.03468210 0.11512873 0.38252170 0.07799126 0.43259950 0.49816041 0.09221348 0.34480258 0.06602788 0.32646975 0.39021390 0.19505555 0.14576724 0.19732516 0.22731652 0.42040164 0.26324985 0.28650141 0.50549659 0.58906006 -0.02639926  p 0.72116233 0.04646336 0.00002553* 0.06190107 0.00005400* 0.00006542* 0.06921756 0.00003307* 0.08559220 0.00031371* 0.00025286* 0.00062848* 0.03241722 0.00449256* 0.00239368* 0.00002901* 0.00027184* 0.00019476* 0.00005984* 0.00007529* 0.63468125  159  Table 8.33. MRPP pair-wise comparisons for all available K, Ca, and Mg by location, all layers combined. * indicates significant differences when p=0.05/7=0.007 (n=63). Compared 1 1 1 1 1 1 2 2 2 2 2 3 3 3 3 4 4 4 5 5 6  T vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs. vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  A -3.22081244 -2.37474733 -9.94352822 -9.78696100 -6.68070061 -5.12011061 -1.59450328 -7.87238097 -6.56272275 -1.57800811 -0.55417051 -4.72954421 -4.15090694 -2.47691558 -1.71923325 -0.36558316 -8.74125109 -3.74213767 -6.73821585 -3.13856516 -1.32625661  p 0.10182796 0.12341719 0.42194025 0.41973236 0.26122488 0.16775446 0.07944389 0.31068342 0.26582617 0.06583896 0.02132733 0.21910888 0.20550013 0.13434888 0.07675848 0.01147584 0.29177731 0.13650295 0.23629154 0.11744271 0.04422741  0.01236351 0.02945430 0.00003156* 0.00003778* 0.00051225* 0.00104451* 0.07432662 0.00015969* 0.00053307* 0.07528215 0.21654216 0.00133137* 0.00306630* 0.02627244 0.06397879 0.25941203 0.00004760* 0.00852828* 0.00035666* 0.01565805 0.10085158  Table 8.34. MRPP pair-wise comparisons for all available micronutrients by location, all layers combined. * indicates significant differences when p=0.05/7=0.007 (n=63). Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T -7.97004833 -3.78804278 -5.24863569 -2.64863688 -9.71120443 -7.28587279 -3.59377341 -4.35297290 -8.66000050 -2.50267790 -2.44307571 -1.66300140 -3.45719661 -5.30251830 -2.68147968 -4.01037384 -7.00290427 -1.55636723 -10.40885314 -7.59102794 -4.59828538  A 0.33950956 0.17099593 0.15355108 0.07300508 0.41678508 0.27644438 0.15639741 0.15595537 0.35484965 0.07920869 0.08289951 0.06840443 0.15453208 0.21139670 0.11001800 0.10453141 0.24929783 0.04467096 0.42725361 0.26545796 0.14601319  p 0.00016786* 0.00477395* 0.00132448* 0.02256177 0.00002993* 0.00024236* 0.00544085* 0.00393477* 0.00007460* 0.02665037 0.02917388 0.06877030 0.00752483 0.00042977* 0.01856582 0.00455282* 0.00019040* 0.07801214 0.00001554* 0.00012810* 0.00155943*  160  Table 8.35. MRPP pair-wise comparisons for soil water (%) by location. * indicates significant differences when p=0.05/7=0.007 (n=78). Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T -13.10993123 -7.50713791 -7.51784154 -5.80039134 -14.52340999 -11.44180294 -10.48457920 -2.47476233 -7.93130254 -5.57304992 -1.21247288 -8.88437152 -9.60005526 -10.77399248 -10.18988245 -1.23475571 -7.22226770 0.55563574 -11.71737197 -3.77079511 -5.72822333  A 0.52136707 0.39044806 0.26430277 0.18668659 0.60992963 0.39520748 0.61795687 0.07449771 0.31614141 0.17512927 0.03890384 0.46660482 0.50892222 0.65829350 0.54901406 0.04180286 0.23100420 -0.01623310 0.48024254 0.13328708 0.21158007  p 0.00000297* 0.00014163* 0.00026718* 0.00102887* 0.00000114* 0.00000726* 0.00001440* 0.03059808 0.00025792* 0.00193778* 0.11013066 0.00004571* 0.00002073* 0.00001181* 0.00001510* 0.10332148 0.00029211* 0.64336178 0.00000864* 0.00929258 0.00169192*  Table 8.36. MRPP pair-wise comparisons for soil temperature (˚C) by location. * indicates significant differences when p=0.05/7=0.007 (n=84). Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T -11.22797138 -9.09370846 -6.48963077 -1.91186768 -3.24565940 -0.89074260 -14.31577613 -2.50682671 -10.66691975 -2.30600520 -12.87630184 -11.09671373 -12.57373314 -9.89525237 -7.31302180 -5.43109055 -2.34439283 -8.11929812 -2.94201237 -6.11241964 -4.61027104  A 0.42819599 0.34898072 0.25980716 0.08264780 0.12879577 0.03908930 0.58202684 0.09954657 0.40420650 0.09933582 0.50568675 0.39915252 0.50879309 0.36014460 0.27117089 0.23407482 0.09145270 0.31612308 0.11943988 0.24318517 0.18378261  p 0.00000539* 0.00004310* 0.00047289* 0.05475198 0.01376498 0.14022146 0.00000090* 0.02935852 0.00001262* 0.03760627 0.00000197* 0.00000284* 0.00000378* 0.00001104* 0.00025111* 0.00167350* 0.03500551 0.00008827* 0.01848482 0.00108431* 0.00337784*  161  Table 8.37. MRPP pair-wise comparisons for soil pH by location. * indicates significant differences when p=0.05/7=0.007 (n=76). Compared 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 1 vs. 2 vs. 2 vs. 2 vs. 2 vs. 2 vs. 3 vs. 3 vs. 3 vs. 3 vs. 4 vs. 4 vs. 4 vs. 5 vs. 5 vs. 6 vs.  2 3 4 5 6 7 3 4 5 6 7 4 5 6 7 5 6 7 6 7 7  T -11.26042865 -1.69913803 -7.56192076 -3.84763747 -14.71116769 -10.94801352 -5.00867047 -4.42751607 -11.62395418 -13.09766777 -0.98812182 -0.32843233 -4.33747638 -10.98623655 -5.39889964 -9.68421484 -15.02978506 -6.76164071 -15.04093542 -12.05483979 -9.03394000  A 0.48120443 0.08902274 0.29044780 0.14284511 0.75822732 0.45269166 0.25655222 0.16036441 0.48373136 0.56409771 0.03560752 0.01626585 0.22964972 0.67913691 0.27399913 0.37591444 0.68438435 0.24364376 0.70607881 0.46707677 0.33914983  p 0.00001200* 0.06726816 0.00029172* 0.00812745 0.00000149* 0.00001903* 0.00206071* 0.00451572* 0.00000944* 0.00000309* 0.13619781 0.26396407 0.00491864* 0.00001182* 0.00179406* 0.00004522* 0.00000098* 0.00056078* 0.00000104* 0.00000648* 0.00007273*  162  Appendix III. PRSTM Probe Raw Data  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): IDF 1 4 IDF 2 4 IDF 3 4 IDF 4 4 IDF 5 4 IDF 6 4 IDF 7 4 IDF 8 4 IDF 9 4 IDF 10 4 IDF 11 4 IDF 12 4 IDF 13 4 IDF 14 4 IDF 15 4 IDF 16 3 IDF 17 4 IDF 18 4 IDF 19 4 IDF 20 3 IDF 21 4 IDF 22 4 IDF 23 4 IDF 24 IDF 25 4 IDF 26 4 IDF 27  F H M F H M F H M F H M F H M F H M F H M F H  Total N 2 6 <mdl <mdl 15 8 5 4 <mdl <mdl 7 2 2 3 <mdl <mdl 4 4 <mdl 9 12 4 8 2  NO3--N 2 <mdl <mdl <mdl 5 4 3 <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl 4 9 <mdl 4 <mdl  NH4+-N 2 6 <mdl <mdl 10 4 3 4 <mdl <mdl 7 2 2 3 <mdl <mdl 4 4 <mdl 5 3 4 4 2  Ca 2 714 787 848 513 860 631 979 877 594 803 1045 946 1524 1968 1474 559 747 617 525 648 647 443 573  Mg 4 239 264 321 186 285 249 303 380 277 295 316 378 452 638 392 164 194 184 140 165 229 116 168  K 4 885 403 418 614 870 795 558 471 404 646 439 370 411 232 256 691 654 191 414 297 335 611 354  P 0.2 37.3 14.7 17.4 49.8 31.4 20.3 40.1 23.4 13.4 48.0 34.0 15.6 83.9 56.7 44.9 42.6 23.4 9.3 32.7 15.5 28.4 30.3 11.3  F H  16 18  10 14  7 4  693 479  217 184  557 370  20.7 11.7  #Anion  Layer  4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4  163  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): IDF 28 4 IDF 29 4 IDF 30 IDF 31 2 IDF 32 4 IDF 33 IDF 34 4 IDF 35 IDF 36 IDF 37 4 IDF 38 4 IDF 39 IDF 40 3 IDF 41 4 IDF 42 IDF 43 4 IDF 44 4 IDF 45 ESSF 46 4 ESSF 47 ESSF 48 4 ESSF 49 4 ESSF 50 ESSF 51 4 ESSF 52 3 ESSF 53 ESSF 54 4 ESSF 55 4 ESSF 56 ESSF 57 4 ESSF 58 4 ESSF 59 ESSF 60 4  F H  Total N 2 14 <mdl  NO3--N 2 8 <mdl  NH4+-N 2 6 <mdl  Ca 2 568 420  Mg 4 207 206  K 4 330 163  P 0.2 18.9 9.8  2 3  F H  7 5  2 5  5 <mdl  750 859  430 537  332 249  24.2 15.8  4  F  7  <mdl  7  768  206  618  20.0  4 4  F H  17 11  13 11  5 <mdl  704 585  205 190  484 427  12.9 14.3  2 4  F H  23 13  10 8  14 6  535 749  168 269  565 428  22.4 13.3  4 4  F H  6 3  4 3  2 <mdl  827 608  360 236  368 355  19.6 7.9  4  F  7  4  3  758  129  157  5.6  4 4  M F  26 10  <mdl 3  26 7  297 286  56 58  279 125  9.9 2.7  4 3  M F  3 24  <mdl 12  3 12  124 800  33 111  144 101  2.4 46.5  4 4  M F  65 10  21 2  44 7  866 363  136 157  667 224  36.2 9.2  4 4  M F  5 19  <mdl 6  5 13  381 557  129 77  326 271  7.0 6.6  4  M  10  5  5  390  58  363  11.0  #Anion  Layer  4 4  164  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): ESSF 61 4 ESSF 62 4 ESSF 63 4 ESSF 64 4 ESSF 65 4 ESSF 66 4 ESSF 67 4 ESSF 68 4 ESSF 69 4 ESSF 70 4 ESSF 71 4 ESSF 72 4 ESSF 73 4 ESSF 74 4 ESSF 75 4 ESSF 76 4 ESSF 77 4 ESSF 78 4 ESSF 79 4 ESSF 80 4 ESSF 81 4 ESSF 82 4 ESSF 83 4 ESSF 84 4 ESSF 85 4 ESSF 86 4 ESSF 87 4 ESSF 88 4 ESSF 89 ESSF 90 4 PP 91 4 PP PP 93 4  #Anion  Layer  4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 5 4 4 4 4  F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M  4 4 4  Total N 2 9 10 9 13 12 10 29 19 9 8 8 8 10 9 8 9 7 12 5 7 4 3 3 6 6 6 6 3  NO3--N 2 5 5 3 8 6 4 12 7 3 4 2 4 5 5 4 4 4 6 2 5 <mdl <mdl <mdl 2 <mdl 3 <mdl <mdl  NH4+-N 2 4 5 6 5 7 6 17 13 6 4 6 4 5 4 4 4 3 7 3 2 4 3 3 4 6 4 6 3  Ca 2 655 474 377 670 919 598 913 677 377 787 667 738 1087 1037 917 1697 1239 1655 1565 1787 1397 1045 1096 1401 982 1007 1407 811  Mg 4 99 62 49 89 77 53 92 47 38 154 115 65 131 99 79 271 225 312 199 229 206 152 183 252 242 197 292 120  K 4 391 397 400 160 233 287 96 101 145 430 529 158 362 296 256 89 284 148 453 305 272 209 406 272 344 448 233 253  P 0.2 17.0 25.3 5.1 7.0 11.8 11.8 13.2 9.6 4.1 19.4 7.8 4.1 10.6 12.4 2.8 4.0 18.9 2.5 9.1 10.7 4.9 6.6 8.8 12.0 6.3 8.2 1.7 7.1  6 2  2 <mdl  4 2  814 999  179 148  341 278  2.5 7.6  <mdl  <mdl  <mdl  1129  182  212  10.8 165  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): PP 94 4 PP 95 PP 96 4 PP 97 4 PP 98 PP 99 4 PP 100 4 PP 101 PP 102 PP 103 4 PP 104 PP 105 PP 106 4 PP 107 PP 108 PP 109 4 PP 110 PP 111 PP 112 4 PP 113 PP 114 PP 115 3 PP 116 PP 117 PP 118 4 PP 119 PP 120 PP 121 4 PP 122 PP 123 PP 124 4 PP 125 PP 126  #Anion  Layer  4  F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M  4 4 4 4  4  4  4  4  4  4  4  4  Total N 2 10  NO3--N 2 4  NH4+-N 2 6  Ca 2 507  Mg 4 76  K 4 249  P 0.2 17.9  3 4  3 <mdl  <mdl 4  1065 881  188 162  194 314  7.4 23.5  <mdl 6  <mdl 3  <mdl 3  1026 715  198 95  395 283  6.3 14.5  5  3  2  578  106  312  10.8  7  3  4  606  129  533  8.2  10  4  6  596  136  628  14.7  10  5  5  688  148  497  15.4  7  3  4  747  131  614  26.7  10  5  5  961  273  878  18.9  7  4  3  586  187  550  26.3  7  3  4  504  125  473  24.9  166  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): PP 127 4 PP 128 PP 129 PP 130 4 PP 131 PP 132 PP 133 4 PP 134 PP 135 BWBS 136 4 BWBS 137 4 BWBS 138 4 BWBS 139 4 BWBS 140 4 BWBS 141 4 BWBS 142 4 BWBS 143 4 BWBS 144 4 BWBS 145 5 BWBS 146 4 BWBS 147 4 BWBS 148 4 BWBS 149 4 BWBS 150 4 BWBS 151 3 BWBS 152 4 BWBS 153 4 BWBS 154 4 BWBS 155 4 BWBS 156 4 BWBS 157 3 BWBS 158 4 BWBS 159 4  #Anion  Layer  4  F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M  4  4  3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 4 3 3 4 4  Total N 2 <mdl  NO3--N 2 <mdl  NH4+-N 2 <mdl  Ca 2 544  Mg 4 91  K 4 189  P 0.2 4.2  <mdl  <mdl  <mdl  478  89  330  5.9  3  <mdl  3  556  117  409  14.4  <mdl <mdl <mdl 3 <mdl <mdl <mdl <mdl <mdl 3 4 3 3 2 <mdl 4 7 7 7 5 4 7 3 <mdl  <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl 3 4 3 3 <mdl <mdl <mdl <mdl 5 4 2 <mdl 2 <mdl <mdl  <mdl <mdl <mdl 3 <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl 2 <mdl 4 7 2 4 3 4 5 3 <mdl  1843 2146 2654 1793 2048 2310 1390 1669 2668 1625 1602 2324 1837 2446 2770 1340 1419 1943 1901 2070 2135 2034 2116 1581  232 246 260 191 241 243 166 194 249 181 238 276 232 279 234 145 256 383 223 372 361 189 294 275  448 179 27 494 202 84 494 237 18 525 316 47 263 53 12 1040 459 98 308 143 72 725 359 355  23.4 7.4 1.0 31.9 39.0 5.0 51.1 5.3 2.7 29.5 17.3 4.1 15.1 6.5 0.8 31.8 20.8 4.2 41.4 10.5 9.0 128.7 95.3 69.7 167  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): BWBS 160 4 BWBS 161 4 BWBS 162 4 BWBS 163 4 BWBS 164 4 BWBS 165 4 BWBS 166 4 BWBS 167 BWBS 168 4 BWBS 169 4 BWBS 170 BWBS 171 4 BWBS 172 4 BWBS 173 BWBS 174 4 BWBS 175 4 BWBS 176 BWBS 177 4 BWBS 178 4 BWBS 179 BWBS 180 4 ICH 181 4 ICH 182 ICH 183 4 ICH 184 4 ICH 185 ICH 186 4 ICH 187 4 ICH 188 ICH 189 4 ICH 190 4 ICH 191 4 ICH 192 4  #Anion  Layer  4 4 4 4 4 4 4  F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M  4 4 4 4 4 4 4 4 4 4 4 4 4 3 2 4 4 4  Total N 2 11 <mdl 2 2 <mdl 5 <mdl  NO3--N 2 5 <mdl <mdl <mdl <mdl 5 <mdl  NH4+-N 2 5 <mdl 2 2 <mdl <mdl <mdl  Ca 2 1258 1474 1810 1436 1525 2160 1572  Mg 4 167 261 344 248 284 343 116  K 4 623 336 143 583 772 182 676  P 0.2 52.1 24.9 27.1 42.0 27.6 22.1 33.6  <mdl 7  <mdl <mdl  <mdl 7  1017 1312  172 155  212 758  7.9 22.1  3 <mdl  <mdl <mdl  3 <mdl  1747 1348  355 110  123 585  9.9 25.6  2 5  <mdl 5  2 <mdl  1986 886  319 58  164 606  20.6 7.8  3 18  3 18  <mdl <mdl  1741 1452  211 105  105 640  9.3 18.9  4 7  2 <mdl  2 7  1628 1040  167 164  301 485  19.7 5.5  6 4  <mdl <mdl  6 4  666 1925  158 206  317 309  1.6 10.5  4 4  <mdl <mdl  4 4  1589 1453  220 160  742 484  5.5 16.3  2 20 27 20  <mdl 12 22 17  2 8 6 3  1169 1481 1719 932  184 182 220 120  285 411 290 140  3.8 5.9 2.4 3.7 168  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): ICH 193 4 ICH 194 4 ICH 195 4 ICH 196 4 ICH 197 4 ICH 198 4 ICH 199 4 ICH 200 4 ICH 201 4 ICH 202 3 ICH 203 ICH 204 4 ICH 205 4 ICH 206 4 ICH 207 4 ICH 208 4 ICH 209 4 ICH 210 4 ICH 211 4 ICH 212 ICH 213 4 ICH 214 4 ICH 205 ICH 216 4 ICH 217 4 ICH 218 ICH 219 4 ICH 220 4 ICH 221 ICH 222 4 ICH 223 4 ICH 224 ICH 225 4  #Anion  Layer  4 4 4 4 4 4 4 4 4 4  F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M  2 4 3 5 4 4 4 4 4 4 4 4 4 4 3 4 4  Total N 2 3 3 3 4 5 5 4 5 3 4  NO3--N 2 <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl  NH4+-N 2 3 3 3 4 5 5 4 5 3 4  Ca 2 1123 2124 1290 1579 1615 591 2166 1197 2408 3024  Mg 4 127 205 159 185 306 106 213 184 168 138  K 4 436 503 360 548 318 596 223 256 117 321  P 0.2 6.7 9.2 4.9 6.1 2.8 1.9 5.4 2.7 2.0 3.9  <mdl 3 <mdl 3 7 2 12 3  <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl  <mdl 3 <mdl 3 7 2 12 3  1907 1596 1694 1319 1601 1614 1181 1493  84 155 141 181 145 153 189 130  170 645 383 299 324 313 264 348  2.4 7.9 4.2 1.6 12.3 5.5 5.0 12.6  2 3  <mdl <mdl  2 3  1530 2548  272 215  235 253  2.8 10.8  <mdl 4  <mdl 2  <mdl 2  2622 2358  330 243  83 268  6.5 9.2  6 3  4 <mdl  2 3  1874 1266  303 75  228 184  6.9 6.6  3 7  <mdl 2  3 5  2497 932  205 96  233 308  4.6 8.6  3  <mdl  3  1724  323  244  10.4 169  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): MH 226 4 MH 227 4 MH 228 4 MH 229 4 MH 230 4 MH 231 4 MH 232 4 MH 233 4 MH 234 4 MH 235 4 MH 236 4 MH 237 4 MH 238 4 MH 239 4 MH 240 4 MH 241 4 MH 242 4 MH 243 4 MH 244 4 MH 245 4 MH 246 MH 247 4 MH 248 4 MH 249 MH 250 4 MH 251 4 MH 252 MH 253 4 MH 254 4 MH 255 MH 256 4 MH 257 4 MH 258 4  #Anion  Layer  4 4 3 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4  F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M  4 4 4 4 4 4 4 4 4  Total N 2 9 15 16 8 6 10 8 16 7 16 11 10 14 8 10 10 6 13 10 14  NO3--N 2 <mdl 4 <mdl <mdl <mdl <mdl 2 9 <mdl 10 <mdl 2 <mdl <mdl 2 3 <mdl <mdl 3 8  NH4+-N 2 9 11 16 8 6 10 5 7 7 6 11 8 14 8 8 7 6 13 6 6  Ca 2 1360 1147 1414 1403 311 549 833 458 1264 1738 761 1773 892 813 1249 749 1023 925 1198 892  Mg 4 192 142 257 152 50 112 124 128 141 281 141 230 203 146 169 236 193 268 188 186  K 4 52 339 357 346 531 534 554 310 156 185 179 77 402 333 134 364 464 334 195 154  P 0.2 1.2 3.7 1.5 4.6 5.4 2.1 3.3 2.5 1.3 5.2 1.4 1.9 3.7 4.9 0.8 17.8 4.2 3.4 2.6 1.7  12 7  7 2  4 5  1773 1598  175 138  100 174  2.8 2.2  7 10  3 4  4 7  1109 455  272 362  182 194  8.0 1.8  8 5  3 <mdl  5 5  1305 1348  195 194  253 169  2.2 1.2  8 12 17  3 6 10  5 7 7  667 978 479  132 387 201  348 395 276  1.7 12.9 1.7 170  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): MH 259 3 MH 260 5 MH 261 4 MH 262 4 MH 263 3 MH 264 3 MH 265 4 MH 266 3 MH 267 1 MH 268 4 MH 269 4 MH 270 4 CWH 271 4 CWH 272 3 CWH 273 4 CWH 274 4 CWH 275 4 CWH 276 3 CWH 277 4 CWH 278 4 CWH 279 4 CWH 280 4 CWH 281 4 CWH 282 4 CWH 283 4 CWH 284 4 CWH 285 4 CWH 286 4 CWH 287 4 CWH 288 4 CWH 289 4 CWH 290 4 CWH 291 3  #Anion  Layer  4 4 4 4 4 4 4 4 0 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4  F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M F H M  Total N 2 17 6 9 9 7 10 7 11 N/A 7 5 8 203 156 187 255 347 320 230 147 190 170 215 212 204 51 184 175 43 27 43 32 18  NO3--N 2 4 <mdl <mdl 5 2 4 3 6 N/A 4 <mdl 3 161 120 126 249 341 306 170 138 173 100 182 169 183 41 125 125 17 20 22 24 9  NH4+-N 2 14 6 9 4 5 5 4 5 3 3 5 6 42 36 61 6 6 14 60 9 17 69 34 43 21 10 59 50 25 7 21 8 9  Ca 2 553 660 468 1574 1985 2347 1744 2178 1705 1020 1326 1074 1186 382 937 702 1678 1133 1378 833 871 1035 852 604 1598 1601 1215 796 548 531 420 1587 349  Mg 4 147 185 118 165 179 223 137 195 239 155 191 143 160 55 103 79 175 159 157 92 116 163 133 86 214 215 132 138 83 92 73 157 55  K 4 334 404 223 136 279 217 102 66 75 144 224 261 384 383 308 136 64 183 256 134 313 332 152 291 277 350 62 425 129 41 207 61 101  P 0.2 3.1 10.5 15.3 3.7 3.4 1.5 1.6 1.4 N/A 1.6 13.5 9.5 4.3 5.2 5.1 2.3 1.5 5.8 9.0 3.8 4.5 15.1 17.6 6.6 18.1 7.4 1.9 4.4 3.0 1.8 2.2 1.2 1.1 171  PRS™-Probe Supply Rate (μg/10cm2/burial period) Location Sample #Cation Method Detection Limits (mdl): CWH 292 4 CWH 293 4 CWH 294 4 CWH 295 4 CWH 296 4 CWH 297 4 CWH 298 4 CWH 299 4 CWH 300 4 CWH 301 4 CWH 302 4 CWH 303 4 CWH 304 4 CWH 305 4 CWH 306 4 CWH 307 4 CWH 308 4 CWH 309 4 CWH 310 4 CWH 311 4 CWH 312 4 CWH 313 4 CWH 314 4 CWH 315 4  #Anion  Layer  4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4 4  F H M F H M F H M F H M F H M F H M F H M F H M  Total N 2 43 18 24 28 19 10 24 15 10 332 444 214 264 248 308 207 240 321 274 460 380 261 229 301  NO3--N 2 23 11 17 10 8 5 15 9 7 326 439 211 244 246 303 201 235 317 261 456 370 244 224 297  NH4+-N 2 20 6 7 18 11 5 9 6 3 6 5 3 20 3 5 6 5 4 13 4 9 17 5 3  Ca 2 959 592 290 327 641 244 247 357 242 2360 2586 1752 1422 1914 1817 1836 2198 1913 1955 2032 1810 2028 1980 1970  Mg 4 208 225 58 81 152 31 75 103 52 285 262 217 179 204 203 212 210 221 270 287 286 219 195 219  K 4 166 201 122 249 153 106 347 276 221 80 53 120 218 132 148 170 116 124 143 47 62 109 112 61  P 0.2 5.7 1.0 0.9 2.3 1.5 0.6 2.5 1.1 1.4 6.2 1.4 1.4 11.9 3.2 2.3 3.6 1.9 1.0 3.5 0.9 1.3 7.8 1.3 1.2  172  Location Sample #Cation Method Detection Limits (mdl): IDF 1 4 IDF 2 4 IDF 3 4 IDF 4 4 IDF 5 4 IDF 6 4 IDF 7 4 IDF 8 4 IDF 9 4 IDF 10 4 IDF 11 4 IDF 12 4 IDF 13 4 IDF 14 4 IDF 15 4 IDF 16 3 IDF 17 4 IDF 18 4 IDF 19 4 IDF 20 3 IDF 21 4 IDF 22 4 IDF 23 4 IDF 24 IDF 25 4 IDF 26 4 IDF 27 IDF 28 4 IDF 29 4 IDF 30  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 2.8 7.1 0.4 1.2 4 H 3.0 2.9 0.3 0.8 4 M 2.8 1.7 0.3 0.9 4 F 1.8 9.0 <mdl 0.8 4 H 4.0 5.4 <mdl 1.0 4 M 1.6 7.7 <mdl 1.0 4 F 2.3 5.0 0.3 1.2 4 H 1.6 2.4 <mdl 0.8 4 M 3.0 1.0 <mdl 0.6 4 F 1.3 6.8 <mdl 1.2 4 H 1.4 2.6 <mdl 1.0 4 M 3.0 3.1 <mdl 1.1 4 F 4.7 2.8 <mdl 1.7 4 H 4.7 1.9 <mdl 1.5 4 M 5.9 1.5 <mdl 1.4 4 F 3.1 5.3 0.3 0.9 4 H 2.3 4.3 0.2 1.0 4 M 3.1 2.1 <mdl 0.8 4 F 2.4 4.1 <mdl 0.6 4 H 1.8 1.7 <mdl 0.4 4 M 1.0 3.4 <mdl 0.5 4 F 1.1 6.7 <mdl 0.6 4 H 3.1 4.8 <mdl 0.5  B 0.2 0.9 0.9 1.1 0.7 0.8 0.8 1.0 1.0 0.9 0.8 0.7 1.0 0.9 1.4 0.7 1.1 1.8 2.7 0.8 1.0 0.7 0.4 0.8  S 2 17 15 18 17 16 13 17 15 13 16 23 19 19 15 18 15 12 14 13 13 13 11 10  Pb 0.2 <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl 0.7 0.3 1.8 0.4 1.2 0.7 0.3 0.6  4 4  F H  1.9 1.0  4.8 1.1  <mdl <mdl  0.5 0.3  0.6 1.1  11 11  0.9 0.8  4 4  F H  1.7 0.8  4.0 3.5  <mdl <mdl  0.6 0.4  0.6 1.1  11 10  0.6 0.7  173  Location Sample #Cation Method Detection Limits (mdl): IDF 31 2 IDF 32 4 IDF 33 IDF 34 4 IDF 35 IDF 36 IDF 37 4 IDF 38 4 IDF 39 IDF 40 3 IDF 41 4 IDF 42 IDF 43 4 IDF 44 4 IDF 45 ESSF 46 4 ESSF 47 ESSF 48 4 ESSF 49 4 ESSF 50 ESSF 51 4 ESSF 52 3 ESSF 53 ESSF 54 4 ESSF 55 4 ESSF 56 ESSF 57 4 ESSF 58 4 ESSF 59 ESSF 60 4 ESSF 61 4 ESSF 62 4 ESSF 63 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 2 F 0.7 2.6 <mdl 0.5 3 H 5.3 2.3 <mdl 0.5  B 0.2 0.5 1.9  S 2 10 11  Pb 0.2 0.3 0.6  4  F  1.1  6.1  <mdl  0.9  0.7  11  0.3  4 4  F H  3.1 3.5  4.2 2.9  <mdl <mdl  0.8 0.7  0.9 0.5  10 12  0.4 2.9  2 4  F H  4.7 1.3  17.2 5.9  <mdl <mdl  0.9 0.8  0.7 1.1  12 10  0.3 0.5  4 4  F H  9.1 2.7  18.2 2.7  <mdl <mdl  0.6 0.5  0.7 1.3  13 7  0.6 1.1  4  F  2.9  49.5  <mdl  3.4  0.2  10  <mdl  4 4  M F  9.8 3.1  11.0 19.3  0.4 <mdl  1.6 1.2  0.9 0.3  15 10  9.9 <mdl  4 3  M F  13.2 1.6  2.7 41.3  <mdl <mdl  1.1 2.5  1.1 0.3  15 12  2.7 <mdl  4 4  M F  4.2 2.8  60.1 14.6  <mdl <mdl  4.2 1.4  0.3 0.3  26 10  1.5 <mdl  4 4  M F  10.7 2.2  7.1 33.6  <mdl <mdl  1.4 1.4  0.7 0.3  14 10  3.2 <mdl  4 4 4 4  M F H M  3.3 2.8 2.6 3.6  14.2 34.2 13.7 9.7  <mdl <mdl <mdl <mdl  1.7 1.8 1.4 1.3  <mdl 0.2 <mdl <mdl  16 13 24 14  <mdl <mdl <mdl 0.3 174  Location Sample #Cation Method Detection Limits (mdl): ESSF 64 4 ESSF 65 4 ESSF 66 4 ESSF 67 4 ESSF 68 4 ESSF 69 4 ESSF 70 4 ESSF 71 4 ESSF 72 4 ESSF 73 4 ESSF 74 4 ESSF 75 4 ESSF 76 4 ESSF 77 4 ESSF 78 4 ESSF 79 4 ESSF 80 4 ESSF 81 4 ESSF 82 4 ESSF 83 4 ESSF 84 4 ESSF 85 4 ESSF 86 4 ESSF 87 4 ESSF 88 4 ESSF 89 ESSF 90 4 PP 91 4 PP PP 93 4 PP 94 4 PP 95 PP 96 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 1.0 28.1 <mdl 1.6 4 H 2.0 38.2 <mdl 2.5 4 M 2.7 25.2 <mdl 2.9 4 F 1.1 36.6 <mdl 2.8 4 H 2.4 24.7 <mdl 5.1 4 M 3.0 11.1 <mdl 2.3 4 F 2.2 26.5 <mdl 1.3 4 H 2.1 19.6 <mdl 1.4 4 M 4.4 11.0 <mdl 1.7 4 F 1.6 40.1 <mdl 1.9 4 H 1.8 27.0 <mdl 2.1 4 M 1.7 12.3 <mdl 1.5 4 F 3.5 10.1 <mdl 0.9 4 H 4.5 9.0 <mdl 1.0 4 M 5.3 9.4 <mdl 1.2 4 F 1.4 5.0 <mdl 0.9 4 H 3.3 5.0 <mdl 0.9 4 M 4.5 3.3 <mdl 1.2 4 F 2.6 8.4 <mdl 1.4 4 H 2.6 6.7 <mdl 1.5 5 M 7.3 5.0 <mdl 1.2 4 F 2.8 39.4 <mdl 0.9 4 H 3.2 7.2 0.2 0.8 4 M 5.7 8.9 0.3 0.9 4 F 3.2 15.6 <mdl 0.8 H 4 M 11.0 12.6 <mdl 1.0 4 F 5.6 3.3 0.4 0.8 H 4 M 6.1 3.7 0.3 0.8 4 F 7.3 5.7 0.5 1.0 H 4 M 5.5 5.1 0.3 0.6  B 0.2 0.6 1.4 0.8 0.7 0.5 0.8 0.4 0.4 0.4 0.4 0.4 0.4 0.5 1.2 1.2 1.1 1.6 1.7 1.0 1.0 0.9 0.9 1.1 1.7 1.4  S 2 8 15 14 9 12 17 12 10 11 10 9 9 7 13 11 9 12 17 9 17 36 15 33 31 10  Pb 0.2 <mdl 1.0 1.3 0.4 1.2 0.7 <mdl 0.3 <mdl 0.4 0.4 0.3 <mdl 1.6 <mdl <mdl 0.2 <mdl <mdl 0.3 <mdl <mdl 0.3 0.3 <mdl  1.7 2.0  15 14  0.7 <mdl  1.2 0.9  13 20  <mdl <mdl  1.2  13  <mdl 175  Location Sample #Cation Method Detection Limits (mdl): PP 97 4 PP 98 PP 99 4 PP 100 4 PP 101 PP 102 PP 103 4 PP 104 PP 105 PP 106 4 PP 107 PP 108 PP 109 4 PP 110 PP 111 PP 112 4 PP 113 PP 114 PP 115 3 PP 116 PP 117 PP 118 4 PP 119 PP 120 PP 121 4 PP 122 PP 123 PP 124 4 PP 125 PP 126 PP 127 4 PP 128 PP 129  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 2.6 11.0 0.4 1.2 H 4 M 3.1 6.3 0.3 0.8 4 F 4.0 4.2 0.5 1.1 H M 4 F 6.8 6.9 0.3 1.1 H M 4 F 6.3 9.7 0.6 1.8 H M 4 F 12.3 19.2 0.9 3.0 H M 4 F 8.3 13.2 0.4 1.5 H M 4 F 4.6 7.0 0.7 1.4 H M 4 F 5.4 30.3 0.5 3.6 H M 4 F 7.7 17.2 0.9 2.8 H M 4 F 6.1 11.8 0.8 1.8 H M 4 F 4.3 0.5 0.9 0.4 H M  B 0.2 1.2  S 2 24  Pb 0.2 <mdl  0.6 0.6  13 19  <mdl <mdl  1.0  14  <mdl  0.9  16  0.2  0.6  20  0.8  1.1  22  0.4  0.9  19  0.7  0.9  19  0.5  1.2  21  0.5  1.2  29  0.5  1.2  12  0.7  176  Location Sample #Cation Method Detection Limits (mdl): PP 130 4 PP 131 PP 132 PP 133 4 PP 134 PP 135 BWBS 136 4 BWBS 137 4 BWBS 138 4 BWBS 139 4 BWBS 140 4 BWBS 141 4 BWBS 142 4 BWBS 143 4 BWBS 144 4 BWBS 145 5 BWBS 146 4 BWBS 147 4 BWBS 148 4 BWBS 149 4 BWBS 150 4 BWBS 151 3 BWBS 152 4 BWBS 153 4 BWBS 154 4 BWBS 155 4 BWBS 156 4 BWBS 157 3 BWBS 158 4 BWBS 159 4 BWBS 160 4 BWBS 161 4 BWBS 162 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 4.3 5.1 0.6 0.8 H M 4 F 3.9 9.0 1.0 2.0 H M 3 F 5.2 3.5 0.3 1.5 4 H 12.3 2.2 <mdl 0.6 4 M 8.8 0.5 <mdl 0.5 4 F 3.8 7.3 <mdl 1.3 4 H 7.7 5.2 <mdl 0.8 4 M 20.1 2.5 <mdl 0.5 4 F 1.5 1.0 <mdl 0.8 4 H 8.4 0.7 <mdl 0.5 4 M 9.5 <mdl <mdl 0.3 4 F 2.0 2.6 <mdl 1.3 4 H 4.4 3.1 <mdl 0.7 4 M 9.4 5.1 <mdl 0.5 4 F 5.1 0.5 <mdl 1.1 4 H 9.1 1.1 <mdl 0.4 4 M 3.6 0.5 <mdl 0.3 4 F 1.8 3.8 <mdl 1.7 4 H 2.4 7.9 <mdl 1.3 4 M 7.2 4.6 <mdl 1.1 3 F 4.0 4.1 <mdl 3.2 4 H 4.6 2.1 <mdl 1.5 3 M 4.5 1.3 <mdl 1.5 3 F 9.8 6.0 <mdl 4.4 4 H 14.0 14.4 <mdl 5.5 4 M 11.8 17.5 <mdl 4.9 4 F 2.7 9.0 <mdl 2.1 4 H 3.2 27.0 <mdl 3.2 4 M 5.2 14.5 <mdl 2.6  B 0.2 1.5  S 2 11  Pb 0.2 0.5  1.7  20  0.9  1.4 0.9 1.1 1.0 0.8 1.2 2.2 2.3 1.6 2.2 1.7 1.2 1.6 1.4 2.5 0.4 1.4 1.6 1.1 2.3 2.6 2.3 1.4 1.3 0.5 0.8 1.5  109 1151 1478 244 216 669 54 175 1114 29 43 157 232 1237 1659 60 53 63 11 15 30 105 94 43 19 72 57  0.3 <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl 0.3 <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl <mdl 0.3 177  Location Sample #Cation Method Detection Limits (mdl): BWBS 163 4 BWBS 164 4 BWBS 165 4 BWBS 166 4 BWBS 167 BWBS 168 4 BWBS 169 4 BWBS 170 BWBS 171 4 BWBS 172 4 BWBS 173 BWBS 174 4 BWBS 175 4 BWBS 176 BWBS 177 4 BWBS 178 4 BWBS 179 BWBS 180 4 ICH 181 4 ICH 182 ICH 183 4 ICH 184 4 ICH 185 ICH 186 4 ICH 187 4 ICH 188 ICH 189 4 ICH 190 4 ICH 191 4 ICH 192 4 ICH 193 4 ICH 194 4 ICH 195 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 3.0 3.6 <mdl 1.3 4 H 3.1 2.6 <mdl 1.2 4 M 14.6 6.4 <mdl 1.3 4 F 2.6 11.3 <mdl 1.4 H 4 M 6.1 28.3 <mdl 0.9 4 F 3.5 6.9 <mdl 1.0 H 4 M 6.4 68.5 <mdl 1.0 4 F 1.4 5.0 <mdl 0.9 H 4 M 15.0 73.7 <mdl 0.7 4 F 2.7 2.8 <mdl 0.5 H 4 M 5.7 34.8 <mdl 0.4 4 F 3.7 2.5 <mdl 1.0 H 4 M 7.4 22.8 <mdl 0.6 4 F 3.3 11.7 <mdl 0.7 H 4 M 15.7 15.0 <mdl 0.4 4 F 4.1 1.9 <mdl 0.4 H 4 M 3.1 1.5 <mdl 0.3 3 F 3.9 2.9 <mdl 0.6 H 2 M 11.1 6.9 <mdl 0.4 4 F 1.9 1.5 <mdl 0.4 4 H 3.4 0.7 <mdl 0.4 4 M 1.5 <mdl <mdl 0.3 4 F 3.9 2.4 <mdl 0.5 4 H 0.9 1.5 <mdl 0.3 4 M <mdl 1.2 <mdl 0.4  B 0.2 1.7 1.5 1.6 1.3  S 2 55 39 203 30  Pb 0.2 <mdl 0.4 0.3 <mdl  1.9 0.8  34 26  0.3 <mdl  1.2 1.8  35 25  <mdl <mdl  2.5 2.2  244 18  0.4 <mdl  2.1 1.0  28 25  0.3 <mdl  0.7 1.0  32 14  0.2 <mdl  2.0 1.4  15 13  0.6 <mdl  2.1 1.8  13 19  0.3 <mdl  2.8 1.9 2.1 2.7 1.9 1.8 3.6  20 12 17 16 12 12 14  0.6 <mdl <mdl 0.6 <mdl <mdl 0.3 178  Location Sample #Cation Method Detection Limits (mdl): ICH 196 4 ICH 197 4 ICH 198 4 ICH 199 4 ICH 200 4 ICH 201 4 ICH 202 3 ICH 203 ICH 204 4 ICH 205 4 ICH 206 4 ICH 207 4 ICH 208 4 ICH 209 4 ICH 210 4 ICH 211 4 ICH 212 ICH 213 4 ICH 214 4 ICH 205 ICH 216 4 ICH 217 4 ICH 218 ICH 219 4 ICH 220 4 ICH 221 ICH 222 4 ICH 223 4 ICH 224 ICH 225 4 MH 226 4 MH 227 4 MH 228 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 1.0 3.3 <mdl 0.5 4 H 1.0 10.9 <mdl 0.5 4 M 3.3 3.2 <mdl 0.4 4 F 3.6 2.3 <mdl 0.5 4 H 3.0 5.3 <mdl 0.5 4 M 5.0 1.9 <mdl 0.3 4 F 2.3 1.7 <mdl 0.3 H 2 M 4.8 1.2 <mdl 0.6 4 F 2.4 3.5 <mdl 0.5 3 H 3.9 2.6 <mdl 0.4 5 M 4.5 1.5 <mdl 0.3 4 F 2.1 2.4 <mdl 0.3 4 H 2.1 2.1 <mdl 0.3 4 M 3.9 3.5 <mdl 0.3 4 F 2.5 3.4 <mdl 0.6 H 4 M 4.0 5.0 <mdl 0.4 4 F 3.6 3.0 <mdl 0.7 H 4 M 4.6 1.8 <mdl 0.5 4 F 4.4 12.2 <mdl 0.3 H 4 M 6.6 10.5 <mdl 0.4 4 F 5.2 2.4 <mdl 0.4 H 3 M 7.2 5.2 <mdl 0.5 4 F 5.1 1.9 <mdl 0.4 H 4 M 6.1 7.9 <mdl 0.5 4 F 4.0 7.6 0.2 10.2 4 H 4.3 54.3 0.3 12.2 3 M 3.6 29.5 0.4 12.5  B 0.2 1.7 1.5 1.2 1.8 1.5 1.5 1.0  S 2 12 12 17 15 15 14 12  Pb 0.2 <mdl <mdl 0.8 <mdl <mdl <mdl <mdl  3.4 1.4 1.2 1.6 2.2 1.9 0.9 0.8  12 15 15 14 15 15 20 18  0.3 <mdl <mdl <mdl <mdl <mdl 0.4 <mdl  1.4 1.5  17 12  0.4 <mdl  2.2 1.2  25 17  0.6 <mdl  1.8 1.6  19 15  0.4 <mdl  1.8 0.7  12 12  0.9 <mdl  1.4 0.6 1.0 0.5  20 20 19 59  <mdl 1.3 4.2 11.6 179  Location Sample #Cation Method Detection Limits (mdl): MH 229 4 MH 230 4 MH 231 4 MH 232 4 MH 233 4 MH 234 4 MH 235 4 MH 236 4 MH 237 4 MH 238 4 MH 239 4 MH 240 4 MH 241 4 MH 242 4 MH 243 4 MH 244 4 MH 245 4 MH 246 MH 247 4 MH 248 4 MH 249 MH 250 4 MH 251 4 MH 252 MH 253 4 MH 254 4 MH 255 MH 256 4 MH 257 4 MH 258 4 MH 259 3 MH 260 5 MH 261 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 3.0 69.5 0.3 5.3 4 H 2.4 12.7 <mdl 2.1 4 M 2.8 24.9 <mdl 3.5 4 F 2.7 103.2 <mdl 2.8 4 H 2.9 18.2 0.2 5.1 4 M 2.4 11.0 <mdl 5.1 4 F 1.6 92.0 0.3 7.4 4 H 5.8 11.0 <mdl 4.7 4 M 3.4 5.1 <mdl 9.3 4 F 4.1 44.8 0.4 4.2 4 H 5.2 17.2 <mdl 6.3 4 M 6.2 7.0 <mdl 5.2 4 F 4.7 39.6 0.6 6.2 4 H 1.7 39.3 0.4 6.0 4 M 1.6 59.3 0.3 9.6 4 F 2.7 33.9 <mdl 5.4 4 H 1.2 7.9 0.2 9.3 M 4 F 2.5 33.8 <mdl 4.4 4 H 3.1 12.3 <mdl 4.3 M 4 F 3.2 33.5 0.5 6.9 4 H 7.5 10.8 0.3 8.3 M 4 F 1.6 58.4 0.3 5.2 4 H 2.2 41.5 <mdl 8.0 M 4 F 2.1 21.3 0.4 4.3 4 H 2.7 23.6 0.4 16.3 4 M 2.3 12.2 0.3 6.4 4 F 1.6 16.7 0.2 2.2 4 H 4.5 26.7 0.5 6.6 4 M 2.0 13.1 0.3 3.5  B 0.2 0.6 0.7 0.8 1.0 0.8 1.4 0.4 0.4 0.6 0.6 0.6 1.0 0.7 1.1 1.8 0.9 1.3  S 2 20 51 23 17 17 16 24 18 21 29 27 17 64 24 21 46 42  Pb 0.2 8.4 4.0 5.5 1.4 3.5 3.8 4.9 2.1 0.9 6.1 1.8 6.8 3.4 4.8 2.3 2.3 4.3  0.5 0.7  24 35  2.0 2.9  1.6 1.3  31 27  10.6 5.0  2.0 1.4  13 17  5.9 4.9  0.7 0.9 0.9 0.6 1.4 1.8  18 40 28 20 30 36  5.4 5.9 3.0 1.0 4.6 4.6 180  Location Sample #Cation Method Detection Limits (mdl): MH 262 4 MH 263 3 MH 264 3 MH 265 4 MH 266 3 MH 267 1 MH 268 4 MH 269 4 MH 270 4 CWH 271 4 CWH 272 3 CWH 273 4 CWH 274 4 CWH 275 4 CWH 276 3 CWH 277 4 CWH 278 4 CWH 279 4 CWH 280 4 CWH 281 4 CWH 282 4 CWH 283 4 CWH 284 4 CWH 285 4 CWH 286 4 CWH 287 4 CWH 288 4 CWH 289 4 CWH 290 4 CWH 291 3 CWH 292 4 CWH 293 4 CWH 294 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 0.5 8.7 0.4 2.7 4 H <mdl 9.5 0.3 3.6 4 M <mdl 10.8 0.4 3.8 4 F <mdl 4.6 0.4 5.6 4 H 2.7 4.7 0.2 5.4 0 M N/A N/A N/A N/A 5 F 0.8 15.4 <mdl 3.3 4 H 0.8 27.0 0.2 5.8 4 M 1.3 10.6 <mdl 4.9 4 F 4.7 23.5 0.3 4.0 4 H 4.6 2.6 0.2 1.4 4 M 3.0 11.5 0.2 3.9 4 F 25.5 10.8 0.3 2.3 4 H 18.5 23.2 0.3 3.9 4 M 14.5 20.4 0.2 3.5 4 F 8.3 23.0 0.3 4.2 4 H 5.6 6.7 <mdl 2.5 4 M 5.2 16.1 <mdl 3.2 4 F 4.3 23.5 <mdl 4.0 4 H 9.9 19.1 0.2 3.6 4 M 6.1 10.0 <mdl 1.9 4 F 8.1 32.4 0.3 3.6 4 H 7.4 17.7 0.2 3.2 4 M 6.3 18.4 <mdl 3.6 4 F 0.5 29.3 <mdl 2.3 4 H 1.5 5.2 <mdl 1.8 4 M 2.8 1.8 <mdl 3.3 4 F 2.3 8.8 <mdl 1.2 4 H 1.2 22.4 0.3 4.1 4 M 2.4 2.7 <mdl 1.5 4 F 2.6 25.9 0.2 5.0 4 H 2.8 10.0 0.2 11.1 4 M 3.3 5.3 <mdl 2.3  B 0.2 1.6 0.9 1.2 0.8 0.6 N/A 0.3 0.2 0.7 0.4 0.7 0.6 0.7 0.8 0.4 0.7 0.8 0.8 1.2 2.1 2.3 1.1 1.0 1.6 0.7 0.8 0.8 0.8 0.4 0.4 0.8 1.1 0.9  S 2 19 34 32 24 50 N/A 13 26 24 28 28 26 45 35 35 31 29 25 27 37 30 32 23 23 23 35 21 20 30 16 34 34 27  Pb 0.2 11.5 7.9 19.4 4.3 3.4 N/A 1.3 2.5 2.1 0.6 0.3 0.7 3.3 3.8 3.6 1.1 0.9 0.5 1.3 2.3 0.6 2.3 1.3 1.7 0.3 0.7 1.6 0.3 2.4 0.5 3.1 3.0 0.9 181  Location Sample #Cation Method Detection Limits (mdl): CWH 295 4 CWH 296 4 CWH 297 4 CWH 298 4 CWH 299 4 CWH 300 4 CWH 301 4 CWH 302 4 CWH 303 4 CWH 304 4 CWH 305 4 CWH 306 4 CWH 307 4 CWH 308 4 CWH 309 4 CWH 310 4 CWH 311 4 CWH 312 4 CWH 313 4 CWH 314 4 CWH 315 4  PRS™-Probe Supply Rate (mg/10cm2/burial period) #Anion Layer Fe Mn Cu Zn 0.4 0.2 0.2 0.2 4 F 1.2 12.0 <mdl 1.6 4 H 1.9 18.4 <mdl 4.7 4 M 2.1 3.2 <mdl 1.6 4 F 2.2 12.7 <mdl 1.4 4 H 4.3 8.4 <mdl 2.8 4 M 2.3 5.2 <mdl 1.5 4 F 32.3 8.8 0.9 5.6 4 H 21.4 5.9 0.9 3.9 4 M 4.6 1.2 <mdl 1.3 4 F 12.9 6.0 0.3 2.7 4 H 7.4 2.9 0.2 2.4 4 M 6.5 2.3 0.2 2.2 4 F 9.5 2.7 0.2 2.0 4 H 4.4 0.7 0.3 2.4 4 M 9.8 2.5 0.4 3.5 4 F 9.0 5.4 0.2 2.5 4 H 15.9 9.7 0.4 3.1 4 M 10.2 7.7 0.4 2.4 4 F 4.1 2.9 <mdl 2.2 4 H 8.4 3.8 0.3 2.8 4 M 10.8 4.8 0.3 2.8  B 0.2 0.8 1.3 1.2 0.8 1.3 0.8 1.0 1.4 1.9 2.0 2.0 2.3 3.0 1.7 2.3 1.5 2.8 3.2 2.4 3.3 2.7  S 2 11 17 14 15 24 16 31 41 31 31 23 21 22 31 25 24 27 27 26 19 28  Pb 0.2 0.2 1.3 0.8 <mdl 1.0 0.8 5.6 5.7 0.4 1.8 1.4 1.7 0.4 1.3 1.9 1.3 2.7 2.7 0.4 1.4 2.1  182  183  PRS™-Probe Supply Rate (mg/10cm2/burial period) Location Sample # #Cation #Anion Layer Method Detection Limits (mdl): IDF 1 4 4 F IDF 2 4 4 H IDF 3 4 4 M IDF 4 4 4 F IDF 5 4 4 H IDF 6 4 4 M IDF 7 4 4 F IDF 8 4 4 H IDF 9 4 4 M IDF 10 4 4 F IDF 11 4 4 H IDF 12 4 4 M IDF 13 4 4 F IDF 14 4 4 H IDF 15 4 4 M IDF 16 3 4 F IDF 17 4 4 H IDF 18 4 4 M IDF 19 4 4 F IDF 20 3 4 H IDF 21 4 4 M IDF 22 4 4 F IDF 23 4 4 H IDF 24 IDF 25 4 4 F IDF 26 4 4 H IDF 27 IDF 28 4 4 F IDF 29 4 4 H IDF 30 IDF 31 2 2 F IDF 32 4 3 H IDF 33 IDF 34 4 4 F IDF 35 IDF 36 IDF 37 4 4 F IDF 38 4 4 H IDF 39 IDF 40 3 2 F IDF 41 4 4 H IDF 42 IDF 43 4 4 F IDF 44 4 4 H IDF 45 ESSF 46 4 4 F ESSF 47  Al 0.4 9.2 10.4 10.2 8.1 7.3 7.7 9.2 9.9 8.8 7.7 8.6 9.1 9.8 12.9 9.7 20.9 23.6 29.8 21.5 17.5 18.5 16.1 19.8 16.2 19.9 23.0 21.5 15.0 35.5 25.2  22.4 21.1 26.0 24.3 32.1 25.7 40.7  184  PRS™-Probe Supply Rate (mg/10cm2/burial period) Location Sample # #Cation #Anion Layer Method Detection Limits (mdl): ESSF 48 4 4 M ESSF 49 4 4 F ESSF 50 ESSF 51 4 4 M ESSF 52 3 3 F ESSF 53 ESSF 54 4 4 M ESSF 55 4 4 F ESSF 56 ESSF 57 4 4 M ESSF 58 4 4 F ESSF 59 ESSF 60 4 4 M ESSF 61 4 4 F ESSF 62 4 4 H ESSF 63 4 4 M ESSF 64 4 4 F ESSF 65 4 4 H ESSF 66 4 4 M ESSF 67 4 4 F ESSF 68 4 4 H ESSF 69 4 4 M ESSF 70 4 4 F ESSF 71 4 4 H ESSF 72 4 4 M ESSF 73 4 4 F ESSF 74 4 4 H ESSF 75 4 4 M ESSF 76 4 4 F ESSF 77 4 4 H ESSF 78 4 4 M ESSF 79 4 4 F ESSF 80 4 4 H ESSF 81 4 4 M ESSF 82 4 4 F ESSF 83 4 4 H ESSF 84 4 5 M ESSF 85 4 4 F ESSF 86 4 4 H ESSF 87 4 4 M ESSF 88 4 4 F ESSF 89 H ESSF 90 4 4 M PP 91 4 4 F PP H PP 93 4 4 M PP 94 4 4 F  Al 0.4 55.9 42.2 43.7 37.8 49.1 38.6 67.5 48.5 47.1 45.2 44.5 41.4 52.2 36.9 43.1 52.5 43.8 37.9 48.0 58.0 83.0 68.9 51.2 61.6 54.3 44.7 82.9 55.7 73.1 77.6 53.8 38.8 44.7 35.1 76.7 114.1 41.0 64.3 37.4 19.6 14.9 185  PRS™-Probe Supply Rate (mg/10cm2/burial period) Location Sample # #Cation #Anion Layer Method Detection Limits (mdl): PP 95 H PP 96 4 4 M PP 97 4 4 F PP 98 H PP 99 4 4 M PP 100 4 4 F PP 101 H PP 102 M PP 103 4 4 F PP 104 H PP 105 M PP 106 4 4 F PP 107 H PP 108 M PP 109 4 4 F PP 110 H PP 111 M PP 112 4 4 F PP 113 H PP 114 M PP 115 3 4 F PP 116 H PP 117 M PP 118 4 4 F PP 119 H PP 120 M PP 121 4 4 F PP 122 H PP 123 M PP 124 4 4 F PP 125 H PP 126 M PP 127 4 4 F PP 128 H PP 129 M PP 130 4 4 F PP 131 H PP 132 M PP 133 4 4 F PP 134 H PP 135 M BWBS 136 4 3 F BWBS 137 4 4 H BWBS 138 4 4 M BWBS 139 4 4 F BWBS 140 4 4 H BWBS 141 4 4 M  Al 0.4 19.8 18.8 34.8 36.5  36.4  40.7  55.2  43.3  30.6  51.9  29.1  22.7  27.9  24.3  29.8  45.9 40.3 47.5 30.8 31.6 43.5 186  PRS™-Probe Supply Rate (mg/10cm2/burial period) Location Sample # #Cation #Anion Layer Method Detection Limits (mdl): BWBS 142 4 4 F BWBS 143 4 4 H BWBS 144 4 4 M BWBS 145 5 4 F BWBS 146 4 4 H BWBS 147 4 4 M BWBS 148 4 4 F BWBS 149 4 4 H BWBS 150 4 4 M BWBS 151 3 4 F BWBS 152 4 4 H BWBS 153 4 4 M BWBS 154 4 3 F BWBS 155 4 4 H BWBS 156 4 3 M BWBS 157 3 3 F BWBS 158 4 4 H BWBS 159 4 4 M BWBS 160 4 4 F BWBS 161 4 4 H BWBS 162 4 4 M BWBS 163 4 4 F BWBS 164 4 4 H BWBS 165 4 4 M BWBS 166 4 4 F BWBS 167 H BWBS 168 4 4 M BWBS 169 4 4 F BWBS 170 H BWBS 171 4 4 M BWBS 172 4 4 F BWBS 173 H BWBS 174 4 4 M BWBS 175 4 4 F BWBS 176 H BWBS 177 4 4 M BWBS 178 4 4 F BWBS 179 H BWBS 180 4 4 M ICH 181 4 4 F ICH 182 H ICH 183 4 4 M ICH 184 4 4 F ICH 185 H ICH 186 4 4 M ICH 187 4 3 F ICH 188 H  Al 0.4 32.1 33.7 42.0 41.1 37.7 58.0 28.4 28.9 40.7 23.8 39.9 38.3 49.1 48.8 46.2 45.7 53.3 49.8 24.5 39.5 56.5 32.6 37.0 41.2 30.5 46.5 28.2 59.0 28.3 74.7 36.5 67.4 40.5 45.5 42.4 66.7 33.9 40.2 46.4  187  PRS™-Probe Supply Rate (mg/10cm2/burial period) Location Sample # #Cation #Anion Layer Method Detection Limits (mdl): ICH 189 4 2 M ICH 190 4 4 F ICH 191 4 4 H ICH 192 4 4 M ICH 193 4 4 F ICH 194 4 4 H ICH 195 4 4 M ICH 196 4 4 F ICH 197 4 4 H ICH 198 4 4 M ICH 199 4 4 F ICH 200 4 4 H ICH 201 4 4 M ICH 202 3 4 F ICH 203 H ICH 204 4 2 M ICH 205 4 4 F ICH 206 4 3 H ICH 207 4 5 M ICH 208 4 4 F ICH 209 4 4 H ICH 210 4 4 M ICH 211 4 4 F ICH 212 H ICH 213 4 4 M ICH 214 4 4 F ICH 205 H ICH 216 4 4 M ICH 217 4 4 F ICH 218 H ICH 219 4 4 M ICH 220 4 4 F ICH 221 H ICH 222 4 3 M ICH 223 4 4 F ICH 224 H ICH 225 4 4 M MH 226 4 4 F MH 227 4 4 H MH 228 4 3 M MH 229 4 4 F MH 230 4 4 H MH 231 4 4 M MH 232 4 4 F MH 233 4 4 H MH 234 4 4 M MH 235 4 4 F  Al 0.4 69.8 36.7 36.5 41.5 44.3 45.3 51.1 27.3 36.7 46.5 56.0 49.5 49.2 36.1 97.1 41.4 56.3 35.8 46.3 35.4 34.7 35.2 38.8 47.2 45.0 41.1 47.5 39.1 59.0 32.2 42.1 23.7 26.2 50.5 40.0 21.9 69.8 47.1 24.0 45.5 43.2 188  PRS™-Probe Supply Rate (mg/10cm2/burial period) Location Sample # #Cation #Anion Layer Method Detection Limits (mdl): MH 236 4 4 H MH 237 4 4 M MH 238 4 4 F MH 239 4 4 H MH 240 4 4 M MH 241 4 4 F MH 242 4 4 H MH 243 4 4 M MH 244 4 4 F MH 245 4 4 H MH 246 M MH 247 4 4 F MH 248 4 4 H MH 249 M MH 250 4 4 F MH 251 4 4 H MH 252 M MH 253 4 4 F MH 254 4 4 H MH 255 M MH 256 4 4 F MH 257 4 4 H MH 258 4 4 M MH 259 3 4 F MH 260 5 4 H MH 261 4 4 M MH 262 4 4 F MH 263 3 4 H MH 264 3 4 M MH 265 4 4 F MH 266 3 4 H MH 267 1 0 M MH 268 4 5 F MH 269 4 4 H MH 270 4 4 M CWH 271 4 4 F CWH 272 3 4 H CWH 273 4 4 M CWH 274 4 4 F CWH 275 4 4 H CWH 276 3 4 M CWH 277 4 4 F CWH 278 4 4 H CWH 279 4 4 M CWH 280 4 4 F CWH 281 4 4 H CWH 282 4 4 M  Al 0.4 49.4 38.0 48.0 43.8 47.1 48.3 64.4 41.5 60.8 54.9 56.6 62.3 77.3 57.9 44.9 44.2 50.0 41.0 26.5 10.4 32.3 34.6 35.2 26.7 29.2 18.3 23.3 N/A 20.9 30.2 36.3 29.6 32.0 42.1 72.2 75.9 57.1 54.6 36.7 60.8 37.5 39.8 37.4 189  PRS™-Probe Supply Rate (mg/10cm2/burial period) Location Sample # #Cation #Anion Layer Method Detection Limits (mdl): CWH 283 4 4 F CWH 284 4 4 H CWH 285 4 4 M CWH 286 4 4 F CWH 287 4 4 H CWH 288 4 4 M CWH 289 4 4 F CWH 290 4 4 H CWH 291 3 4 M CWH 292 4 4 F CWH 293 4 4 H CWH 294 4 4 M CWH 295 4 4 F CWH 296 4 4 H CWH 297 4 4 M CWH 298 4 4 F CWH 299 4 4 H CWH 300 4 4 M CWH 301 4 4 F CWH 302 4 4 H CWH 303 4 4 M CWH 304 4 4 F CWH 305 4 4 H CWH 306 4 4 M CWH 307 4 4 F CWH 308 4 4 H CWH 309 4 4 M CWH 310 4 4 F CWH 311 4 4 H CWH 312 4 4 M CWH 313 4 4 F CWH 314 4 4 H CWH 315 4 4 M  Al 0.4 23.4 26.0 50.6 12.9 15.0 27.3 11.4 25.8 17.7 51.2 64.1 36.1 32.4 31.9 37.9 26.9 47.7 59.2 50.5 92.1 46.5 42.3 62.6 55.4 42.7 46.9 69.0 57.0 83.6 88.6 48.4 64.9 64.1  190  

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