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Some effects of forest floor displacement on soil properties and lodgepole pine productivity in the Boundary… Hickling, James S. B. 1997

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S O M E E F F E C T S OF F O R E S T FLOOR DISPLACEMENT ON SOIL P R O P E R T I E S AND L O D G E P O L E PINE PRODUCTIVITY INTHE BOUNDARY FOREST DISTRICT By James S. B. Hickling B. Sc. (Agr.), The University of British Columbia, 1993 A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE .IN THE FACULTY OF GRADUATE STUDIES DEPARTMENT OF SOIL SCIENCE We accept this thesis as conforming to the required standard THE UNIVERSITY OF BRITISH COLUMBIA August 1997 © James S. B. Hickling In presenting this thesis in partial fulfilment of the requirements for an advanced degree at the University of British Columbia, I agree that the Library shall make it freely available for reference and study. I further agree that permission for extensive copying of this thesis for scholarly purposes may be granted by the head of my department or by his or her representatives. It is understood that copying or publication of this thesis for financial gain shall not be allowed without my written permission. Department of The University of British Columbia Vancouver, Canada Date DE-6 (2/88) ABSTRACT Forest floor displacement occurs during ground skidding and other forest harvesting and silvicultural operations. It includes excavation, scalping, mineral soil exposure, and burial of the forest floor. Two aspects of forest floor displacement can result in soil degradation and may impede forest regeneration; the redistribution and loss of nutrients, and the exposure of unfavourable rooting medium. Current soil conservation regulations, introduced by the new British Columbia Forest Practices Code, limit the amount of forest floor displacement that is acceptable during harvesting operations. However, the limits imposed by the Code are contentious, especially in Boundary Forest District, where the prevailing soil conditions are regarded as being particularly sensitive to forest floor displacement by some foresters and particularly tolerant of it by others. Evidence regarding the long-term biological sustainability of forest floor displacement is needed to confirm or amend the current restrictions on forest floor displacement. A retrospective study of the effects of forest floor displacement on lodgepole pine productivity was performed. Soils were surveyed for forest floor displacement atfive sites and samples were analyzed to assess the impact of forest floor displacement on soil nutritional properties. Foliar tissue samples were collected at four sites and analyzed to assess the nutritional impact of forest floor displacement on lodgepole pine trees. Growth of trees ranging in age from three to twenty seven-years-old was assessed at four sites and stem productivity was related to the amount of forest floor displacement present around each stem. Soil organic matter content, total N and total C are reduced by forest floor displacement, while available P increases and the C:N ratio decreases. Soil chemical analysis suggests that although forest floor displacement results in a reduction in total soil nutrient content, the nutrients present on scalped soils may be more available for root uptake. Changes in the above soil properties are shown to be temporary as soils appear to recover from forest floor displacement over time. Statistically significant differences in foliar nutrient concentration are infrequent among seedlings growing on scalped and control soils. The mean foliar concentrations of macronutrients are often slightly higher in scalp trees compared to control trees. Statistically significant differences in seedling productivity are also infrequent, and no permanent, negative impacts on lodgepole pine productivity related to forest floor displacement are indicated. Possible ancillary effects of forest floor displacement on regeneration and stocking density are discussed. Recommendations regarding some aspects of the forest floor displacement regulations and future study are given. TABLE OF CONTENTS ABSTRACT TABLE OF CONTENTS LIST OF FIGURES LIST OF TABLES ACKNOWLEDGMENTS INTRODUCTION: BACKGROUND AND RATIONALE Problem Statement LITERATURE REVIEW Introduction Some Methods of Studying Soil Disturbance The Smith and Wass Era Mechanical Site Preparation in Central British Columbia Summary OBJECTIVES METHODS Field Methods Site Selection Soil Survey Soil Sampling Seedling Selection and Productivity Measurement Laboratory Methods Soil Chemical Analyses Foliar Nutrient Concentration Analysis Statistical Methods Box Plots Descriptive Statistics and Pearson Correlations Analysis of Variance and Related Statistical Tests Special Statistical Methods For Tree Productivity Analysis Using Soil Disturbance Levels to Classify Seedlings Soil Fertility Index Soil Fertility Code Percent Mineral Soil Exposure ,The Regression and ANOVA Models RESULTS AND DISCUSSION Site Histories: How They Affect the Analysis A Comparison of Survey Methods , Soil Chemical Analysis Correlations of Soil Properties and the Importance of Soil Organic Matter iv Comparison of Soil Properties Between Sites 47 Comparison of Soil Chemical Properties of Different Soil Disturbance Types Within Each Site 51 Soil Results Compared With Other Studies 55 Analysis of Variance for Organic Matter Content (LOI) 56 Recovery of Soil Properties After Disturbance 63 Foliar Nutrient Concentration Analysis 68 Comparing Foliar Nutrient Concentrations Between Sites 71 Comparing Foliar Nutrient Concentrations of Trees on Different Soil Conditions Within Each Site 72 Results of Analysis of Variance of Foliar Nutrient Concentrations 74 Diagnosis of Nutrient Deficiencies by Computer Program 76 Results of Seedling Classification 80 A Note on the Presentation of Results of Seedling Productivity Analysis 81 Introduction to the Seedling Productivity Data Set 82 Growth Curve Analysis 92 Analysis of Variance of Seedling Growth Data 103 Discussion of the Principal Factors of the ANOVA Model 106 Site Effects 106 Tree-Age Effects 108 Soil Fertility Effects 111 Regeneration Delay, Seedling Productivity and Soil Disturbance 114 Soil Disturbance and Regeneration Delay 118 SUMMARY AND CONCLUSIONS 120 APPENDICES 135 Appendix I 124 Site Maps, Site Descriptions and Photos Appendix II 155 Pearson Correlation Matrices for Soil Properties Appendix III 159 Box Plots of Soil Properties By Soil Disturbance Type Appendix IV 166 Box Plots of Soil Properties By Site Location Appendix V 172 Analysis of Variance and Least Squares Means for Soil Properties Appendix VI 192 Box Plots of Foliar Nutrient Concentrations by Site Location and Fertility Code Appendix VII 198 Tabulated Results of ANOVA on Foliar Nutrient Concentration Data Appendix VIII 202 Box Plots of Total Height and Root Collar Diameter at Each Site by Fertility Code. REFERENCE LIST 210 LIST OF FIGURES Figure 1. Box Plot of Organic Matter Content of Natural Stand Soils at Different Sites. 56 Figure 2. Box Plot of Total Nitrogen of Natural Stand Soils at Different Sites. 48 Figure 3. Box Plot of Total Carbon of Natural Stand Soils at Different Sites. 48 Figure 4. Box Plot of Available Phosphorus of Natural Stand Soils at Different Sites. 48 Figure 5. Box Plot of C/N Ratio of Natural Stand Soils at Different Sites. 49 Figure 6. Box Plot of Organic Matter Content of Different Soil Disturbance Types at Rathmullen. 52 Figure 7. Box Plot of Total Nitrogen Content of Different Soil Disturbance Types at Rathmullen. 52 Figure 8. Box Plot of Total Carbon Content of Different Soil Disturbance Types at Rathmullen. 53 Figure 9. Box Plot of Available Phosphorus Content of Different Soil Disturbance Types at Rathmullen. 53 Figure 10. Box Plot of C:N Ratio of Different Soil Disturbance Types at Rathmullen. 53 Figure 11. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Soil Disturbance Type. 59 , Figure 12. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Location. 61 Figure 13. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Soil Disturbance Type for Each Location 62 Figure 14. Chronosequence Showing Decline and Recovery of Soil Organic Matter Content Over Time by Different Soil Disturbance Types. 66 Figure 15. Chronosequence Showing Decline and Recovery of Total C Content Over Time by Different Soil Disturbance Types. 66 Figure 16. Chronosequence Showing Decline and Recovery of Total N Content Over Time by Different Soil Disturbance Types. 67 Figure 17. Chronosequence Showing the Change in Available P Content Over Time by Different Soil Disturbance Types. 67 Figure 18. Chronosequence Showing the Change in C:N Ratio Over Time by Different Soil Disturbance Types. 68 Figure 19. Box Plot of Foliar Nitrogen Contents by Location and Treatment. 69 Figure 20. Box Plot of Foliar Phosphorus Contents by Location and Treatment. 69 Figure 21. Box Plot of Foliar Sulphur Contents by Location and Treatment. 70 Figure 22. Histogram showing Fertility Index Distribution of Seedlings. 80 Figure 23. Probability Plot Comparing Fertility Index Distribution with Normal Distribution. 80 Figure 24. Box Plot of Total Heights at Different Seedling Ages Under Scalp and Control Soil Conditions at Beaverdell. 83 Figure 25. Box Plot of Root Collar Diameter at Different Seedling Ages Under Scalp and Control Soil Conditions at Beaverdell. 84 Figure 26. Box Plot of Total Heights at Seedling Ages Greater Than Ten Years Under Scalp and Control Soil Conditions at Lassie Lake. 85 Figure 27. Box Plot of Root Collar Diameters at Seedling Ages Greater Than Ten Years Under Scalp and Control Soil Conditions at Lassie Lake. 86 Figure 28. Mean Height Growth Curve with Standard Deviation Error Bars for Seedlings at Beaverdell. 93 Figure 29. Mean Height Growth Curves of Control Seedlings at Different Sites. 94 Figure 30. Mean Diameter Growth Curves of Control Seedlings at Different Sites. 94 Figure 31. Mean Height Growth Curves of Scalp Seedlings at Different Sites. 95 vi Figure 32. Mean Diameter Growth Curves of Scalp Seedlings at Different Sites. 95 Figure 33. Overall Mean Height Growth Curve of Seedlings Across all Sites. 96 Figure 34. Overall Mean Diameter Growth Curve of Seedlings Across all Sites. 97 Figure 35. Mean Height Growth Curve of Seedlings at Carmi. 98 Figure 36. Mean Diameter Growth Curve of Seedlings at Carmi. 98 Figure 37. Mean Height Growth Curve of Seedlings at Rathmullen. 99 Figure 38. Mean Diameter Growth Curve of Seedlings at Rathmullen. 99 Figure 39. Mean Height Growth Curve of Seedlings At Beaverdell. 100 Figure 40. Mean Diameter Growth Curves of Seedlings at Beaverdell. 101 Figure 41. Mean Height Growth Curves of Seedlings at Lassie Lake. 102 Figure 42. Mean Diameter Growth Curve of Seedlings at Lassie Lake. 102 Figure 43. A Graph of the Squared multiple R values of the Omnibus Model vs. Tree Age for the Three Seedling Growth Variables Evaluated. 103 Figure 44. Age 3 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. 115 Figure 45. Age 5 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. 115 Figure 46. Age 7 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. 115 Figure 47. Age 8 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. 115 Figure 48. Age 3 Least Squares Means of Root Collar Diameter (mm) by Harvest-related Tree Age at Beaverdell. 117 Figure 49. Age 5 Least Squares of Root Collar Diameter (mm) by Harvest-related Tree Age at Beaverdell. 117 Figure 50. Age 7 Least Squares Means of Root Collar Diameter (mm) by Harvest-related Tree Age at Beaverdell. 117 Figure 51. Age 8 Least Squares Means of Root Collar Diameter (mm) by Harvest-related Tree Age at Beaverdell. 117 Figure 52. Histogram of the Frequency of Recruitment as a Function of Percent Mineral Soil Exposure for the Six Years Following Harvest at the Beaverdell Site. 119 vii LIST OF TABLES Table 1. Fertility Index for Several Hypothetical Soil Disturbance Survey Results. 39 Table 2. Comparison of Results for %NAR Disturbed at the Henderson Creek Study Site Using Two Different Survey Methods. 45 Table 3. Estimated Forest Floor Displacement at Three Study Locations Where Seedlings Were Sampled Expressed as a Percent of the Net Area to be Reforested. 57 Table 4. Overall Pearson Correlation Matrix for Soil Properties Across Sites and Soil Disturbance Types. 47 Table 5. Mean and Standard Deviation of Soil Properties at Different Sites by Soil Disturbance Type. 50 Table 6. Mean and Standard Deviation of Chemical Properties of Different Soil Disturbance Types By Location. 54 Table 7. Results of Analysis of Variance for Organic Matter Content 57 Table 8. Percent Organic Matter Content (LOI) Least Squares Means by Soil Disturbance Type 59 Table 9. Percent Organic Matter Content (LOI) Least Squares Means by Location. 61 Table 10. Estimated Percentage of Original Soil Levels of Various Soil Chemical Properties by Soil Disturbance Type Eleven Years After Clearcutting. 65 Table 11. Foliar Concentrations of Some Nutrients of Control Trees at Different Sites 71 Table 12. Foliar Concentrations of Some Micronutrients of Control Trees at Different Sites. 71 Table 13. Foliar Concentrations of Some Nutrients at Different Sites. 73 Table 14. Foliar Concentrations of Some Micronutrients at Different Sites. 74 Table 15. Summary Table of Nutrient Deficiencies Diagnosed Using Ballard, Carter and Emanuel's Computer Program (Ballard and Carter 1983). 78 Table 16. Seedling Distribution Among Fertility Codes. 81 Table 17. Mean and Standard Deviation of Total Heights (cm) at the Three Study Sites. 88 Table 18. Mean and Standard Deviation of Root Collar Diameters (mm) at the Three Study Sites. 88 Table 19. Mean and Standard Deviation of Total Heights (m) at the Lassie Lake Study Site, 90 Table 20. Mean and Standard Deviation of Root Collar Diameters (mm) at the Lassie Lake Study Site. 91 Table 21. Results of Analysis of Variance for Seedling Total Height at Ages 2 through 9 years.. 105 Table 22. Results of Analysis of Variance for Seedling Annual Height Increment at Ages 2 through 9 years. 105 Table 23. Results of Analysis of Variance for Seedling Root Collar Diameter (RCD) at Ages 2 through 9 years. 106 Table 24. Results of Analysis of Variation for Seedling Annual Height Increment at Ages 3 through 9 years. 112 Table 25. Results of Analysis of Variation for Seedling Root Collar Diameter at Ages 3 through 9 years. 112 Table 26. Results of Analysis of Variation for Seedling Total Height at Ages 3 through 9 years. 113 ACKNOWLEDGMENTS The author wishes to express his gratitude for the funding provided by Forest Renewal British Columbia in conjunction with Science Council of British Columbia. The author would like to thank Dr. Mike Curran, Mike Klassen and Leseargent of the BC Ministry of Forests for their assistance and kind support. George Delisle and Randy Trerise of Pope and Talbot Ltd. were instrumental in the facilitation of field work. George Delisle in particular provided many useful ideas and insights and often went far out of his way to make this project a success. Outstanding contributions were made by Dr. Tim Ballard of the Department of Soil Science, University of British Columbia. Dr. Ballard provided guidance, support and much expert advice during the three years over which the project was running. The author also appreciates the participation and encouragement provided by Dr. Les Lavkulich and Dr. Chris Chanway, Department of Soil Science, UBC. In addition, this project could not have been completed without the angelic assistance of Ms. Alexis Clague also of the Department of Soil Science, UBC. Friend and mentor Rob Scagei was most accommodating, making himself available for many iate night phone calls and tutoring sessions. Mr. Scagel's contribution cannot be underestimated. Finally, the author would like to thank his field assistants Chris DeWreede, Jason Charles and Sam Chase for their help in completing the held work, data entry, etc. Despite the assistance of several great organizations and the best advice of many dedicated people, the author still made several faux pas. Any errors or omissions are entirely his responsibility. 1 INTRODUCTION: BACKGROUND AND RATIONALE Forest floor displacement during harvesting and site preparation is an important issue addressed by the new British Columbia Forest Practices Code. The Soil Conservation Guidebook for the implementation of the Forest Practices Code specifies that no more than 30 percent of the net area to be reforested (NAR) may be subject to forest floor displacement where such displacement is the leading disturbance hazard, regardless of soil sensitivity rating. Where soil compaction is the leading hazard, 25 or 30 percent of NAR is the limit, depending on sensitivity (Guidebook, 1995a). The dry interior forest site conditions that exist in much of Boundary District give rise to unique concerns regarding the long term effects of forest floor displacement. Among foresters in Boundary Forest District there is some disagreement with respect to the level of forest floor displacement that is biologically sustainable on ground skidded sites. Excessively stringent criteria impose operating constraints and raise costs for industry, while insufficiently stringent criteria may threaten long-term productivity. Evidence to justify or improve these criteria is needed. Given their mandate to foster or enforce sustainable forestry practices, the Ministry of Forests believes the currently acceptable levels of forest floor displacement to be generous. Industry contends that the forest floor displacement ceiling in the regulations is too low and that anecdotal and historical evidence on site disturbance and regeneration support their position. Literature relating to the effects of forest floor displacement is scarce and inconclusive, and related topics are dominated by short term studies. Studies of displacement should ideally include longer-term assessments of effects, particularly with regard to tree growth. This project was initiated by the Ministry of Forests and Pope and Talbot Ltd. in cooperation with the University of British Columbia, and funded by Forest Renewal British Columbia in order to begin the collection of data for the justification or amendment of the forest 2 floor displacement regulations. This report presents the results of soil disturbance and seedling productivity surveys as well as foliar nutrient concentration and soil chemical analysis on four sites comprising a range of ages on site conditions that commonly occur in Boundary Forest District. PROBLEM STATEMENT The forest floor is normally the principal source of nutrients for growing seedlings. Such nutrients as N , P and S are held principally in organic form by the forest floor matrix, protected from leaching but susceptible to release in available form by mineralization. Normally, most of the fine root mass of conifer seedlings is found in the forest floor. Forest floors protect mineral soil against temperature extremes, desiccation, surface erosion, and some structural degradation processes. Over calcareous materials, forest floors are especially important, providing a rooting medium of lower pH, and releasing organic acids which neutralize calcium carbonate. On fine-textured soils, thick forest floors can somewhat reduce mineral soil susceptibility to compaction through load spreading (Ballard, 1997) However, removal of forest floor can in some cases improve seedling growth and site productivity. For example, forest floors may be subject to desiccation and may contribute to high contact resistance for water flow into seedling roots. On cold sites, the low thermal diffusivity of forest floor can retard and limit subsurface soil warming in response to surface temperature increases during the growing season. Readily decomposable organic materials with low nutrient concentrations may be subject to extreme nutrient immobilization, limiting nutrient availability until later stages of decomposition. In addition, forest floor displacement, like agricultural cultivation, may provide short-term benefits by controlling vegetation competition. The scientific literature on the effects of forest floor removal on seedling productivity could be described as inconclusive. It is important to recognize that effects on seedlings may be 3 the net result of both positive and negative effects, e.g. improvement of temperature regime but deterioration of nutrient supply early in regeneration. The Forest Practices Code literature states that "Forest floor displacement...involves excavation, scalping, mineral soil exposure, and burial of the forest floor. The effects range from beneficial to detrimental,...two aspects of forest floor displacement can produce soil degradation: • redistribution and loss of nutrients • exposure of unfavourable rooting medium." The Code requires that harvested sites be surveyed for soil disturbance. Forest floor displacement is one of the classes of soil disturbance that is measured in the survey. The level of forest floor displacement that is considered acceptable is determined using the Hazard Assessment Keys for Evaluating Site Sensitivity to Soil Degrading Processes Handbook (MoF, 1995). The forest floor displacement hazard rating is determined for each site depending on site conditions such as depth and type of forest floor (L, F and/or H layers), soil texture, slope, aspect, etc. In Boundary District, almost all sites have a high to very high forest floor displacement hazard rating by virtue of their thin dry-interior forest floor type and coarse textured glacial fluvial soils. Most sites in Boundary are therefore subject to the most limiting regulations regarding forest floor displacement. Compliance with the code under these conditions requires that no more than 30% of the NAR be effected by forest floor displacement. However, for the past fifteen or twenty years many of the kinds of soil disturbance now considered forest floor displacement in Boundary District were understood to be beneficial for regenerating pine and were encouraged through the use of mechanical site preparation and broadcast burning. After all, pine may be described as being a "pioneer species", i.e. they are particularly well adapted to growing on disturbed soil. It could also be argued that since the natural stands are subject to plenty of recurring forest floor disturbance due historical stand 4 replacing fires, that forest floor displacement in these fire-adapted ecosystems is not detrimental. Industry foresters maintain that nutrients in the forest floor are not lost from the site, they are simply redistributed and remain available to tree roots. They do not concede that forest floor displacement negatively affects long term productivity. They argue that the burying and mixing of forest floor and mineral soil that accompanies displacement is beneficial to seedling growth. Current soil disturbance survey methods may tend to include mixed forest floor in displacement, depending on site conditions and surveyor experience. After viewing examples of forest floor displacement caused by ground-skidding operations at a recently logged site, it is clear that forest floor integrity can be classified into four broad categories; 1. Scalped - forest floor scraped away exposing mineral soil 2. Mixed - forest floor buried or mixed within mineral soil 3. Undisturbed - intact forest floor within clearcut 4. Natural Stand - intact forest floor outside of clearcut Based on the assumption that forest floor displacement causes soil degradation by redistribution of nutrients and exposure of unfavourable rooting medium, one could reasonably expect differences in soil chemical and nutritional parameters within the scalped, mixed, and undisturbed types of forest floor that are present after ground skidding operations. It also seems reasonable that any effect on seedling productivity caused by loss of nutrients should be related to the type forest floor displacement its roots encounter, or stated more specifically, by the integrity of the forest floor around each seedling. 5 L I T E R A T U R E R E V I E W INTRODUCTION Previous studies of soil disturbance by ground skidding most often been concerned with soil compaction on skidtrails, or with the exposure of deep subsoils during skidroad construction. The present study is concerned with quite the opposite; shallow disturbances on sites with low sensitivity to compaction. The kinds of soil disturbances now classified as forest floor displacement have only recently become a contentious issue and so are only now beginning to be intensively studied. Consequently, few previously published studies are appropriate for direct comparison. The present study is made unique by the qualities of soil disturbances it examines, and is further differentiated from other work by being limited to very specific site conditions. Site conditions are an important consideration when interpreting soil disturbance as soil degradation. Many of the other studies of soil disturbance by forest harvesting operations focus on problematic site conditions in sensitive ecosystems. According to a classification system devised by Smith and Wass (1980), the site conditions studied in the current project would be rated as least sensitive to soil disturbance, given the dry climate and slightly acid, non-calcareous, deep, well drained, coarse-textured soils. However, site conditions in Boundary Forest District are generally rated as having a high forest floor displacement hazard by the Forest Practices Code Hazard Assessment Keys for Evaluating Site Degrading Processes Guidebook (Guidebook, 1995b). The present study is unique, but many of the issues being investigated are not exclusive to it. Some studies on skidtrail disturbance, and others on mechanical site preparation, do address issues that are related to the present investigation of the effects of forest floor displacement. Later in this review and in the discussion, comparisons will be made using some of the more applicable skidtrail and mechanical site preparation studies. Before reviewing that 6 literature, there are a handful of other studies that are germane to the methodology of the present study. S O M E METHODS OF STUDYING SOIL DISTURBANCE Nyland et al. (1979) were probably the only group to document a study that focused solely on forest floor displacement and its effects on tree growth and soil nutrition. What sets their study apart is the method used to achieve forest floor displacement without gouging into the mineral soil. On the site of a former 35 year-old pine plantation, the authors raked and removed the forest floor from 10 x 20m plots. Thus they achieved the minimum disturbance depth necessary for classification as forest floor displacement in the Forest Practices Code regulations. By raking away forest floor on plots of known dimensions, Nyland et a/. (1979) avoided confounding the effects of pure forest floor displacement with those of increasing bulk density with depth and exposure of infertile subsoils, i.e. they avoid confusing forest floor displacement with gouging. In addition, Nyland et al. (1979) explicitly examined one of the underlying assumptions in the regulations regarding forest floor displacement. The authors' assessment of tree growth and foliar nutrients at different distances from undisturbed soil tests the assumption that larger areas of forest floor displacement are more detrimental to forest regeneration and that proximity to a disturbance or to undisturbed soil may influence seedling growth performance. This kind of direct assessment of the effect of scalp dimensions on soil productivity was one of the original objectives of the present study (one which we eventually found impossible to perform retrospectively). Nyland et al. (1979) reported that after nine growing seasons a trend existed in which mean total heights were reduced with increasing distance from undisturbed forest floor. The best growing seedlings, however, were those seeded into a narrow strip of exposed mineral soil (1.25 x 5m). The authors concluded that "removing Titter from areas no wider than a few meters 7 depleted nutrient resources and inadvertently caused deficiencies in Norway spruce". Yet, that statement is slightly misleading. It is true that seedlings grown in control areas away from the raked plots had the highest mean foliar nutrient concentrations and that seedlings on areas where forest floor had been removed had reduced mean foliar nutrient concentrations that differed significantly from control trees. However, only those seedlings furthest removed from the undisturbed forest floor (>3m) were rated as being nutrient-deficient for growth. Their results as reported actually showed that growth reductions and foliar deficiencies occurred when seedlings were 3 m or further away from the nearest undisturbed forest floor. A seedling encountering those conditions would have to be located at the center of a 6 x 6 m clearing of forest floor (more than the "few metres" wide stated in the conclusion). Also, the reader should note that unlike the present study, the species being assessed was Norway spruce (a climax species) and the soil conditions were described as being shallow, poorly drained, medium textured and underlain by shale. The shallow and poorly drained soil may rate the site as being more sensitive to disturbance than much of Boundary Forest District according to Smith and Wass (1980). Unfortunately no other geographical or climatological information is given for extrapolation to other sites. Nevertheless, the Nyland et al. (1979) experiment is a well designed effort at a controlled experiment to test the effects of forest floor removal on seedling growth and soil nutrition. The authors enjoyed the advantage of creating scalps of known dimensions, seeding of regeneration in a controlled manner, and returning later to collect data. Researchers performing retrospective studies on operationally harvested sites are not always so fortunate. A study by Clayton et al. (1987) is probably the only study that attempts to assess retrospectively the impact of soil disturbance created by ground skidding on moderate and low angle slopes. Working in central Idaho, the authors measured "lateral soil displacement" and penetration resistance in 15 and 19 year old stands of lodgepole pine. The Clayton et al. (1987) study is worth noting because it illustrates some of the difficulties in finding literature on forest 8 floor displacement for comparison, and some of the difficulties in assessing forest floor displacement retrospectively. For example, Daddow and Warrington (1983) reviewed results of several published studies and concluded that growth-limiting bulk densities are texture dependent. Compaction by machinery is itself texture dependent. Although some of the site conditions studied by Clayton et al. (1987) (such as slope, elevation, climate, etc.) sound comparable with the present study and soils are deep and well-drained, the soil texture at the surface is silt loam with a considerable content of volcanic ash in the surface 30 cm. Clayton et al. (1987) indicated that soil displacement was correlated with increased resistance to penetration, especially on finer textured soils, indicating that a compaction effect may well have occurred. Differences in soil texture may confound any comparisons or extrapolations of Clayton et al.'s results to conditions in the Boundary Forest District. It is instructive to compare Clayton et al.'s (1987) approach to assessing the impact of soil disturbance on stem productivity with the methods used in other studies. They defined "lateral soil displacement" as removal of part or all of the surface soil (top 30 cm) that would occur on the study plot assuming all plots had a soil profile similar to adjacent undisturbed sites. Thus they measured what they assumed to be truncated soil horizons and compared them to undisturbed soil horizons as an indicator of depth of disturbance. The authors felt that this was reasonable in view of the uniformity of the soils studied on stable 0-10 percent slopes but it seems to me there may be some loss of precision using such a method. The authors did note that after 20 years the evidence for lateral soil displacement was not always readily apparent. Other and more specific methodology regarding the categorization of disturbed areas are not reported. It is likely that the lateral soil displacement measured by Clayton et a/.(1987) routinely includes soil displaced to a greater depth than is the focus of the current study. The depth of soil disturbances studied is in fact one of the major obstacles in comparing results of different studies on soil disturbance. 9 In a manner similar to the soil disturbance assessments performed in the present study, Clayton et al. (1987) measured soil disturbance in a limited area round each stem and classified disturbance levels according to the percent of that area occupied by disturbance. They chose the crown drip line as their assessment area, and categorized the degree of lateral displacement as; None (<25 % displacement), Moderate (25 to 49 % displacement), and High (>50 % displacement). Unfortunately no soil chemical or foliar analysis is reported. Data collection was limited to stem d.b.h., height measurements and a subjective test of soil density by shovel penetration. Results indicated that soil displacement was associated with decreased d.b.h. on both fine textured and gravely sites, but that unlike the other two sites, the deep well drained gravely site (most similar to site conditions in Boundary District) indicated no effect on height growth by lateral soil displacement. The authors concluded that productivity losses resulting from soil disturbance are difficult to predict and that actual losses depend on the percentage of area impacted, associated growth decline for a given level of impact, and the rate of soil recovery. In spite of the differences in method, Clayton et al.'s (1987) study is important to acknowledge and review since it is one of a very few retrospective studies on ground skidding not associated with contour-built skidroads. There are a few studies worth noting in which soil disturbance was surveyed shortly after harvesting. Although little or no stem productivity measurements were made, the methods and results of the survey-type of studies are significant to the present study. Schwab and Watt (1981) surveyed soil disturbance after tractor logging and cable yarding on steep slopes in the Quesnel Highlands, near Williams Lake, B.C. Most of the original research in this report compares the frequency of different types and depths of disturbances that occur with different harvesting systems. The effect of soil disturbance on regeneration is discussed briefly but is not based on any original, systematically collected data. Nevertheless, as with Clayton et al. (1987), it is interesting to note the depth ranges used for classification of soil disturbance. 10 Schwab and Watt (1981) classified points on a transect as; no disturbance, forest floor disturbed but no mineral soil exposure, shallow disturbance <25 cm, deep disturbance >25 cm and a grouping of shallow and deep disturbances classed as mineral soil exposed. Note that Schwab and Watt's (1981) shallowest disturbance category could include up to 25 cm-deep gouges which likely differ substantially from simple forest floor displacement. Assessing soil disturbance after ground skidding is a difficult task due to the complexity and range of disturbance types. The soil disturbance assessment used in the present study on forest floor displacement had elements of both the Clayton et al. (1987) and the Schwab and Watt (1981) methodologies. The most important difference involves the depth-related classification of disturbances. It is very common in studies on soil disturbance by ground skidding to assess and classify soil disturbance on the basis of depth of disturbance. Clayton et al. (1987) and Schwab and Watt (1981) are but two of many examples of studies in which the authors classified soil displacement as shallow if it was <25-30 cm deep. This range of depths (0-30 cm) is substantially greater than that currently assessed as simple forest floor displacement. However, the 30 cm depth is not without precedent in assessing soil disturbance for lodgepole pine. Corns (1987) performed a greenhouse trial that indicated that increased bulk density resulted in growth reductions for lodgepole pine in a variety of soil textures. In the coarse-textured soil type, decreases in pine growth were found to occur at densities only encountered at 30 centimeters and below in a recent clearcut. No forest floor was used in the greenhouse experiment. Available comparisons between these studies and the present one are limited in that a point or area gouged to a depth of 30 cm is likely more strongly affected by increased bulk density and other depth-related restrictions to rooting than by reduced nutrients from forest floor displacement. It seems that only Nyland et al. (1979) assessed the effects of true, surficial forest floor displacement on soils and tree growth. 11 Of course there are exceptions; one is the study by Krag et al. (1986) in which the authors surveyed the extent and type of soil disturbance on steep slopes in Nelson Forest Region, comparing ground-skidding with cable yarding. This study included a class of disturbance (light, 0-5 cm deep) which is analogous to forest floor displacement as it is now known in B.C. Points along a transect were categorized by depth of disturbance (light, 0-5 cm; deep, 5-25 cm; very deep, >25 cm). The disturbance categories erected by Krag et al. (1986) were modifications of those used in earlier soil disturbance survey studies including Smith and Wass (1976,1979, 1980), Bockheim et al. (1975), Dyrness (1965) and Garrison and Rummell (1951) all of which had a 0-5 cm 'light' disturbance category. The frequency and origin of "light" disturbance was recorded but not much remarked on in these papers presumably because of the predominance of and greater concern regarding the more obvious gouge-type disturbances and roads, backspar trails and skid roads. The Krag et al. (1986) study and its precursors were disturbance-type survey and frequency analysis sorts of studies similar in approach to the work by Schwab and Watt (1981) discussed above. There was no attempt to assess the impact of soil disturbance on seedling growth or soil properties. Based on previous studies, especially the now classic series of reports by Smith and Wass (1976,1979,1980,1985), the authors may be forgiven a quickness to assert in their report that depth of disturbance is synonymous with severity of disturbance and that light soil disturbance is beneficial as a silvicultural prescription for seedbed preparation. Krag et al. (1986) should perhaps have acknowledged Smith and Wass more explicitly in their text than they did since their own study is so clearly modeled on Smith and Wass' body of work. Let me not make the same mistake. T H E SMITH AND W A S S ERA The reason that Smith and Wass' work is important to acknowledge in this report is not that it is directly related to the present topic of forest floor displacement; Smith and Wass concentrated on the effects of very deep disturbances associated with skidroads. It is important 12 because it is one of the most thorough and comprehensive group of studies on the effects of soil disturbance after forest harvesting anywhere - and it happens to have been performed in the Nelson Forest Region, nearby to the present study's locations. Concern over the amount of soil disturbance associated with clearcut logging is not new to the Nelson Forest Region. In 1973 general Forest Service regulations were formulated to govern ground skidding operations. This was followed by the formation of the multi-agency Steep Slope Committee, which had as one of its objectives a description of the extent and severity of soil disturbance over a range of sites and logging methods. These activities gave first impetus to the Smith and Wass series of reports (Smith and Wass, 1976). The two Canadian Forest Service researchers have since devoted twenty years to studying how forest harvesting practices affect soils and growing trees, and information from these studies have contributed to current Forest Practices Code regulations. Recommendations made by Smith and Wass over twenty years ago continue to reverberate in this present study. As early as 1976 Smith and Wass recommended more surveys and research to quantify the effects of the various types and degrees of soil disturbance on tree survival and growth over a range of ecosystem types for each biogeoclimatic zone. In many ways their work serves as a model applied to a different kind of soil disturbance in the present study. Many of the results of their studies are confirmed in the present study and will be further compared in the discussion section, nevertheless it is important to be aware of the differences in objectives and methodology used by Smith and Wass compared with the current study on forest floor displacement. Firstly, the Smith and Wass study sites were typically located at high elevations, on very steep slopes where densely spaced skid roads were used to remove logs. Over the years Smith and Wass (1976,1979,1980,1985,1994ab) reviewed a variety of logging systems and post-logging treatments over four main biogeoclimatic zones including the ESSF, IWH, IDF, ICH and transitional subzones. [Note that biogeoclimatic zone and subzone classification in this report 13 follows the system of Braumandl and Curran (1992)]. The disturbances they studied were most often a direct result of skidroad construction and were usually categorized as gouges or deposits (equivalent to cut slopes and sidecast). However, soil disturbance between roads including "litter disturbed" (possibly analogous to forest floor displacement) was also acknowledged, although it was reported to occur much less frequently than road related disturbances (Smith and Wass, 1976). In later works, Smith and Wass (1979,1980,1994ab) performed retrospective analyses of seedling growth on areas subject to skidroad disturbance which are akin to the retrospective strategy of this present study. They investigated seedling productivity and stocking, and soil and foliar nutrient concentrations as related to soil disturbance. In contrast to the present study they mainly examined regeneration of spruce and fir (and occasionally lodgepole pine) established on, above and below contour skidroads in steep, high elevation clearcuts. Based on their retrospective work on tree growth, Smith and Wass (1980) devised a system to rate sites on their sensitivity to disturbance in terms of tree growth on the disturbed surfaces. In general, tree growth was reduced most on calcareous soils, or in wet climates, on strongly acid, coarse textured or shallow soils. The least sensitive sites were those with slightly acid, non-calcareous, medium to moderately coarse textured soils in dry climates. The site conditions in Boundary Forest District match the description for least sensitive sites well. Also significant to the present study, they stated that attempts to determine the effects of skidroad construction on tree growth on intervening surfaces by comparing skidded and adjacent unskidded slopes were inconclusive. At two study locations with site conditions similar to those in the present study there were no significant differences in growth rates of lodgepole pine between skidded and unskidded areas based on tallest tree per plot. Because of the lack of significant growth differences, no growth reduction or enhancements could be attributed to the skidroad surfaces. Generally higher stocking between roads may have favoured trees growing on the skidroad 14 surfaces, trees on undisturbed surfaces were 1-2 years older than trees from the disturbed section (Smith and Wass, 1979; 1980). Very similar results are reported in the present study on forest floor displacement. However Smith and Wass' sites differed from those of the present study by being located on areas that had been burned in wildfires and then salvage logged with contour skidroads. Such areas may have had much of the forest floor removed or altered by fire thus concealing the impact of forest floor displacement by machinery. Smith and Wass also retrospectively studied the impacts of soil disturbance on soil chemical and physical properties (1985) approximately 20 years after harvesting. Most of their sites were in the ESSF subzone, at high altitudes and on steep slopes and were regenerating to spruce. Typical sites had coarse-textured soils and acidic conditions, some were alkaline and finer textured. Their results include some data for nitrogen and carbon content in various soil horizons which will be compared with results from the present study in a chapter to follow. Although they measured soil carbon and nitrogen levels in various disturbance categories, Smith and Wass emphasized increases in soil density and nutritional imbalances inherent in alkaline soils in their explanation of tree growth patterns. Their study of soil properties (Smith and Wass, 1985) indicated a slow rate of amelioration of disturbed soils, particularly in terms of density, penetrability, carbonate concentration and pH, but marked increases in organic carbon and total nitrogen as natural revegetation proceeds over a 20-year period. Tree growth on site conditions most comparable to those sampled in the present study showed either no marked pattern, or reduced growth on the inner skidroad only (the deepest, most compacted form of disturbance studied). Most recently Smith and Wass have published reports on the impacts of stump uprooting and skidroad construction on properties of a calcareous loamy soil and on planted seedling performance (Smith and Wass 1994a, 1994b). At the stumping study location, soils differed from the present study by being loam and silt loam textured, and derived from calcareous glacial till. Carbonates were detected where mineral soil was brought to the surface 15 in the form of deposits. Also, the block had been burned after harvesting, thus possibly confounding any extrapolation for the analysis and comparison of effects of forest floor displacement. They found that seventy-two percent of the area was disturbed by the stump uprooting operation, about equally divided between gouges and deposits. Growth rates for seedlings planted on deposits were similar to those for undisturbed ground (except where the deposit included calcareous material). Vegetative cover increased more slowly on tracks than on deposits and included a number of species not frequently found on deposits or undisturbed ground. After 8 years, lodgepole pine seedlings planted in tracks were on average significantly (12%) shorter than those planted on undisturbed ground, after 5 years lodgepole pine survival was greater on tracks than on deposits. Similar growth reductions associated with soil disturbance were found for Douglas-fir seedlings but lodgepole pine consistently exceeded Douglas-fir in survival rates, with ninety percent of the mortality taking place within two years after planting. Lodgepole pine productivity was negatively correlated with calcareous material content. There were highly significant correlations (Pearson's) between the depth of the track below the original soil surface and height diameter and volume of both Douglas-fir and lodgepole pine, indicating that the deeper the track or disturbance, the slower the growth rate. Similar correlations for depth of deposit did not exist or were very weak. A parallel study on the impact of skid roads on soil properties and seedling productivity was published separately (Smith and Wass, 1994b). Although a general trend existed for higher organic contents and C / N ratios in disturbed compared with undisturbed mineral soil, they reported that the most marked differences among disturbance categories in soil chemistry c involved the presence or absence of carbonates and the p H level in the upper mineral soil. Trends in growth and survival of seedlings in response to soil disturbance were similar to those 16 described above in the stumping project; lodgepole pine height growth was reduced but survival was improved where seedlings were growing in disturbances. In addition, the nutrient status of lodgepole pine was evaluated using a computer program developed at the University of British Columbia (Ballard and Carter, 1983; Ballard and Carter, 1986). Foliar nutrient deficiencies were noted irrespective of soil disturbance and were probably naturally occurring. Lodgepole pine foliage was on average considered to be severely deficient in N , moderately deficient in P, possibly slightly to moderately deficient in Mg and likely deficient in active Fe for all disturbance categories. Sulphur was considered deficient for all disturbance categories except for seedlings growing in the outer track for which diagnosis was "possibly deficient". Additionally, K was diagnosed as possibly slightly deficient for seedlings in all disturbance categories except the undisturbed soil. Smith and Wass (1994b) noted some differences in foliar nutrient concentration relative to soil disturbance. Nitrogen concentration was lowest in foliage from seedlings growing in the inner track and sidecast, but differences among disturbance categories were not significant. The concentration of K was also relatively low for foliage of seedlings growing in the inner track, berm and sidecast, significantly less than for foliage from seedlings growing in undisturbed soil. Manganese concentration was particularly low for lodgepole pine foliage from skid road surfaces, significantly less than from undisturbed soil. The concentration of P for lodgepole pine foliage from seedlings growing in undisturbed soil was significantly higher than for foliage from seedlings planted in the sidecast. Foliar calcium concentration may reflect the increasing abundance of carbonates with depth. The concentration of Ca in foliage from seedlings planted in undisturbed soil was significantly less than that from the inner track. Foliage from seedlings planted in the inner track had significantly higher concentrations of Ca than for any other skidroad disturbance category. Smith and Wass (1994b) concluded that the occurrence of calcareous horizons close to the soil surface in the study area resulted in a high susceptibility to degradation from soil 17 displacement. Coupled with this is a moderate susceptibility to degradation of the loamy soil from mechanical compaction. Construction of skidroads along the contour and their subsequent use severely test both these susceptibilities. Soil displacement greatly increased the presence of free carbonates in the soil surface; the deeper the displacement, the higher the indicated concentration of carbonates. In addition to having high carbonate levels, soils on the running surface of skidroads were considerably denser and less penetrable to a depth of at least 20 cm than for equivalent depths in undisturbed mineral soil. Despite the sometimes substantial differences in site conditions and types of soil disturbances studied, Smith and Wass' work has been useful as a model for application to a different class of soil disturbance on different kinds of site conditions (forest floor displacement on coarse textured soils). Some of their tree productivity results, especially those concerning lodgepole pine are important for comparisons with the results of the present study. In addition, many of their soil chemical and foliar nutrient concentration data sets are benchmarks against which comparisons with the present study can be made. Smith and Wass developed many of the principles of soil fertility in relation to soil disturbance in forestry. Several of the experimental results and conclusions of the Smith and Wass studies are echoed in the current project and in doing so lend a sense of confidence to the interpretations that follow. MECHANICAL SITE PREPARATION IN CENTRAL BRITISH COLUMBIA Another area of study that may be applicable to the current issue of forest floor displacement is that of mechanical site preparation, especially the studies on scarification. Scarification refers to the scraping away of slash, organic matter and organic soil horizons to reveal a mineral soil planting site. Scarification-type techniques were applied to over 300,000 hectares between 1976 and 1986 (BCMOF annual reports). For instance, mechanical scarification has been used widely in silvicultural operations in the Prince George Region for increasing soil temperature and vegetation control. 18 There has been a considerable amount of research into scarification and seedling establishment conducted in British Columbia especially in the Prince George Forest Region, and to a lesser extent in the Caribou and Kamloops Forest Regions. Most of the studies on scarification in BC are short-term projects on microclimatic effects such as soil temperature and moisture content and frost occurrence. Comparatively little research has been published concerning the longer-term implications of scarification on tree growth. However, as a group, the studies on scarification types of treatments tend to be well designed and have replicated scarification disturbances with very consistent dimensions. As well, the occasional paper was published which included data concerning foliar nutrient contents and/or long-term growth patterns associated with the removal of forest floor by scarification. The microclimatic effects of scarification include changes in soil and air temperature, and in soil moisture contents. Scarification is often successfully applied to sites where harsh microclimatic and soil conditions may inhibit seedling establishment. For example, forest regeneration at high latitudes such as those found in the central interior of BC present a difficult set of problems. Seedlings must become established over a relatively cool, short growing season. The organic mat that typically accumulates as a result of slow decomposition, can act as an insulating layer reducing the rate at which underlying mineral soil in the rooting zone warms in the spring. Many studies have investigated the effect of scarification of on soil temperature. Black and Mitchell (1991), working on a pinegrass dominated site in Kamloops Region monitored soil temperatures and found daily average growing season soil temperatures at the 5 cm depth were 3 to 6 degrees C higher in the scarified than in the control. Researchers have found increases in soil temperature by scarification to be dependent on site conditions such as soil moisture content and soil texture. Macadam (1990) tested several mechanical site preparation treatments including spot scarification in three moisture regimes of an interior spruce plantation. She observed that spot scalping on mesic sites resulted in slight temperature increases but had little effect on hygric and subhygric sites. Similarly, Bassman 19 (1989) noted no difference in soil temperature between treatments during moist periods and that scarification did raise temperatures only at the 5 cm depth. Dobbs and McMinn (1973) compared the effects of site preparation on soil temperature on three soil disturbance types in the Prince George Region. Plots were established on fine sand, silt loam and clay loam soils. Scalping treatments were applied and a marked increase in daily maximum temperature at 5 cm was noted on all scalped plots, especially the finer-textured ones. As well as raising soil temperatures during the growing season, scarification has been shown to influence frost occurrence and frost damage. Killing frosts can occur during the growing season in the interior of BC. The exposures of mineral soil by scarification can reduce the occurrence and intensities of growing season frosts (usually radiative frosts) by night release of heat stored during the day (Black and Mitchell, 1991; 1990b). On the other hand, Scagel and Evans (1992) found that frost penetration (or soil freezing) in blade-scarified plots near Prince George was more severe than on control plots. They concluded that the exposed mineral soil freezes sooner, deeper and more firmly than does a humus protected soil. Scagel and Hickling (1993) found that on fine textured soils near Prince George even a small, dinner plate-sized screef can significantly increase frost heave occurrence in plantations. Besides a general consensus that scarification raises growing season soil temperature below the surface, it has also been shown to modify the soil moisture regime by reducing water loss by evapotranspiration on grass dominated sites. In the Southern Interior of BC, around Kamloops, Black and Mitchell (1990a, 1990b, 1991) found that mechanical site preparation techniques like scarification and ripping were successful in improving the soil moisture regime. All treatments, including herbicide, helped to conserve water during the growing season probably because the dominant vegetation on these sites was pinegrass - a deep-rooting species which competes with seedlings for water. As a result of water conservation, seedlings in the treated plots showed virtually no growth limitation (Black and Mitchell, 1990a). 20 In contrast, near Williams Lake, B.C. Bassman (1989) found that during the early part of the growing season when precipitation is high, soil water potential was also high. There were no differences between treatments during this moist period. However, with decreases in precipitation later in the season, scarified patches had lower soil water potentials than controls 5 cm deep into the mineral soil. Plamondon et al. (1980) also found significantly lower water potentials in scarified plots between 0 and 15 cm depths, possibly due to the exposure and loosening of the mineral soil resulting in increased evaporation. Loosening surface mineral soil can reduce evaporative loss by reducing hydraulic conductivity, as is sometimes done in dry-land agriculture (Ballard, 1997). Although no formal investigation of soil water contents was made in the present study, anecdotal observations seem to support the examples illustrated by Bassman (1989) and Plamondon et al. (1980), that areas where forest floor displacement has occurred tend to have a reduced soil water content. Most of the studies on the effects of scarification on seedling productivity are short term and /or are made largely irrelevant to the present topic by differences in site conditions and methodologies. For example, Bassman (1989) found that seedlings in scarified patches were similar to controls in diameter and height, but had slightly greater total weights. The study site was near Williams Lake at a high elevation location in the ESSF subzone. The soil texture was silt loam and the profile included a 5 cm-deep forest floor layer and 5- to 8 cm-deep A horizon. Scarified patches occupied 0.5m2 from which all vegetation and the organic material were removed down to bare mineral soil (this size of patch would not qualify as a scalp under current regulations). Seedlings were planted on the north side of such patches with close access to the adjacent decomposing organic material. In spite of the difference in ecosystem and soil profile qualities the small patch size and the planting location make any evaluation of the impact of forest floor displacement on growth dubious in the context of the present study. The long term effects of mechanical scarification on tree productivity have not been very thoroughly documented and seem to be site and species specific. Dr. Bob McMinn of the 21 Canadian Forest Service was one of the few researchers to have established and maintained long term studies on mechanical site preparation in the Prince George Forest Region. Herring and McMinn (1980) reported that 21 years after harvest, performance of spruce natural regeneration on mineral soil exposed by blade scarification was poor in comparison to advanced regen. They attributed reduced productivity to the removal of organic and top mineral soil horizons beyond the immediate reach of seedlings. Thomson and McMinn (1989a, 1989b) reported that after 10 years, lodgepole pine had achieved greater mean total heights on scalps than undisturbed areas at two different sites, possibly as a result of reduced vegetation competition. The response of white spruce to scalping was less consistent. Hickling (1993) and Scagel et al. (1993), using one of McMinn's old trial sites, examined soil properties and white spruce growth on scarified trails compared with control areas 20 years after planting. Soils were fine textured and had a thin, 3 cm-deep forest floor(L, F and/or H layers). Hickling found that although some forest floor development had occurred after twenty years on scarified trails, the mineralizable nitrogen concentration in the forest floor remained thirty percent lower on scarified trails. Mean total height growth and annual height increment of white spruce were also reduced by thirty percent by scarification. After excavating intact root systems Hickling concluded that increased bulk density and reduced nutrient availability had led to the formation of root systems that were composed of fewer, smaller roots on scarified trails. Scagel et al. (1994) performed a retrospective investigation of the effect of V-plowing on lodgepole pine and white spruce near Fraser Lake in central BC. V-plowing was performed on sites in the SBSdk and mc2 subzones as a form of vegetation control and to improve planter access. The treatment produced extensive areas of potentially detrimental disturbance types. Seedlings were planted in the berms created beside the bladed trails. Eight to ten years after treatment the height growth of both lodgepole pine and white spruce was found to exceed the 22 normals developed for the SBSmc2. Lodgepole pine roots had successfully colonized and proliferated into the middle of trails. The authors concluded that the prognosis for V-plowing submesic to subhygric sites on fSaL and SaL soils appears to be excellent for both species. In a study similar in scope to that of Nyland et al. (1979), Ballard (1984) used foliar analysis to evaluate white spruce nutrition on untreated clearcut areas and on areas where surface horizons had been scalped during harvesting or mechanical site preparation. The frequency of serious N and Cu deficiencies was much higher and the average severity of N and Cu deficiencies was worse on scalped areas than where no treatment had been done. SUMMARY To date, few studies have specifically investigated the effects of very shallow soil disturbances such as forest floor displacement. Most studies of soil disturbance after logging confound the effect of forest floor displacement with the effects of increased bulk density at depth or by compaction. Although forest floor displacement has been shown to factor in reduced growth and nutrient deficiencies in climax species (Nyland et al, 1979; Ballard, 1984) the response of lodgepole pine has been more ambiguous. Lodgepole pine is a fast-growing pioneer species with an outstanding ability to occupy deep permeable soils (Eis, 1970). Some forms of soil disturbance can cause reductions in lodgepole pine growth but such growth reductions are usually accompanied by improved survival and stocking (Smith and Wass, 1994ab). More often a lack of marked differences in growth patterns is reported where lodgepole pine and soil disturbance are concerned (Smith and Wass; 1979,1980,1985). When growth reductions have occurred, researchers emphasized the role of increased bulk density and the presence of carbonate materials as causes rather than the displacement of nutrients or nutrient-rich soil layers (Smith and Wass, 1994ab). It is certain that removal of even a thin layer of organic matter can influence both soil nutrient content and microclimatic conditions. Evidence of the beneficial effects of humus on 23 seedling growth and nutrient status is not uncommon. Hallsby (1994) reported that Norway spruce seedling growth could be improved by gathering extra humus around the planting position. Graham, et al. (1987) reported that the removal of the organic matter layer decreased the productivity of western white pine and Douglas-fir. The amounts of macronutrients removed by scalping a 1 cm-thick humus layer vary. Ballard (1986) estimated amounts in kg/ha to be 150 (N), 20 (S), 15 (P, Ca, Mg, K), judging from the bulk density and concentration data of Gessel and Balci (1965) and Carter (1983). Several studies have shown very slow amelioration of soil properties like bulk density and carbonate content after soil disturbance (Clayton et al. 1987, Smith and Wass, 1994ab) but organic matter and nitrogen (the major factors affected by forest floor displacement) have been reported to accumulate comparatively quickly (Smith and Wass, 1994ab). The ultimate impact of forest floor displacement on tree growth depends on the net effect on growth-determining properties, and will therefore be site-specific. Site conditions studied in Boundary Forest District are relatively insensitive to many forms of soil disturbance such as compaction, and lodgepole pine is a proven colonizer of disturbed sites. Due to litterfall and other nutrient cycling processes, nutrient redistribution gradually occurs as the stand develops. Also, tree roots may extend into adjacent undisturbed soil. Thus the detrimental nutritional impact of forest floor displacement on lodgepole pine growth as it occurs under normal harvesting procedures may be expected to be small, temporary or even negligible. Under different site conditions with species other than lodgepole pine, the impact of scalped areas may be more serious and more permanent. Certainly it should be emphasized that more than one retrospective study on the effects of shallow soil disturbance will be necessary to fully explore the ramifications of forest floor displacement. 24 OBJECTIVES The global objective for the present study is to determine the effects of forest floor displacement on tree growth and on major growth-determining soil properties for Highly and Very Highly Sensitive sandy soils in the Boundary Forest District. (Sensitivity classes are defined in the Hazard Assessment Keys for Evaluating Site Sensitivity to Soil Degrading Processes Guidebook (Guidebook, 1995b). Specific objectives for this study are: 1. Relate such effects to the percentage of N A R (net area to be reforested) subject to displacement. 2. Relate such effects to scalp size and proximity to scalp edge. (As noted below, this objective proved inappropriate due to the nature of the disturbances, which often were not characterized by discreet scalps. Consequently an alternative was used.) 3. Relate such effects to forest floor properties (including thickness) and characteristics of the underlying mineral soil. 4. For assessment of longer-term effects, evaluate appropriate treatment units established about 25 years ago in the Lassie Lake area, and compare with shorter-term effects of treatments on nearby similar sites. 5. Evaluate result in relation to requirements associated with the Forest Practices Code, and suggest modifications of the requirements where appropriate. 25 M E T H O D S FIELD METHODS Site Selection Industry and government foresters have co-operated by identifying sites that had been ground-skidded between 1974 and the present. In Boundary Forest District, the great majority of older sites have been treated with silvicultural site preparation methods, either by broadcast burning or by mechanical site preparation (e.g. windrowing, blading, mounding, and twist-and-turn). Only recently, with the implementation of limits and regulations on the use of such techniques, has ground skidding alone become common practice. Therefore, there are not a lot of older, untreated sites available for such a retrospective study. Locating useful, comparable sites has been further complicated by the fact that many of the remaining older untreated sites have been subject to subsequent management practices such as thinning or fertilization, or they represent uncommon site conditions. Eventually it became evident that intensive sampling on a few very appropriate sites was likely to be more revealing than extensive sampling encompassing more problematic sites. Therefore the study was designed as an intensive rather than extensive survey of a few comparable sites representing a range of ages within one commonly occurring biogeoclimatic subzone. The advantages of reviewing sites over a range of ages include that one might better observe an effect that is evident during one period in the regeneration process, but obscured during another, and that a chronosequence detailing soil recovery from disturbance may be deduced. Potential study sites were viewed and locations were selected for study based on their suitability for the objectives; i.e. within the old ICHc2 subzone, ground-skidded but otherwise 26 untreated, sufficiently re-stocked, with identifiable soil disturbance types, and not subject to extreme animal, pest or disease influences. The vast majority of candidate sites were rejected because of site factors that were inappropriate for study and comparison. Sites were rejected if ecological site conditions, low stocking density, inconsistent species composition, seedling age less than three years old, or extensive cattle use made the site incomparable with other potential sites. On some sites extensive grass invasion, often associated with ranching operations, obscured soil disturbance types and otherwise influenced site conditions such that the site was rejected. Once selected, each study site was stratified into homogenous units of moisture and nutrient regime (Braumandl and Curran, 1992) and degree of site disturbance. Transects were laid through the stratified unit most appropriate for comparison with other sites. The sites selected for study are named Beaverdell, Carmi, Henderson, Lassie Lake and Rathmullen in the text of this report. They are located between the towns of Kelowna and Grand Forks. Al l of the study sites were subject to soil sampling. The Henderson site was very recently logged and seedlings had yet to become established on this site. Therefore no sampling of foliar tissues or seedling productivity measurements were made at the Henderson site. However, soils at Henderson were sampled in order to compare survey techniques and for use in the soil chronosequence analysis (both of which are described in the Results section). How the ground skidding operations were applied depended entirely on the operator's objectives and abilities, and were not directed by this study. Each study location was described and classified by soil type (CSSC, 1987), humus form (Klinka, et al. 1981), site description (Luttmerding, et al. 1990), terrain unit (Howes and Kenk, 1988), biogeoclimatic zone and site association (Braumandl and Curran, 1992). Site descriptions and photos are presented in Appendix I. 27 Soil Survey The initial intent to examine tree growth and soil properties in relation to scalp size and the distance from scalp edge was eventually recognized as inappropriate in many cases because of short-range variation in disturbance and because a well-defined scalp edge could often not be identified unambiguously (due to the naturally occurring thin forest floor and, in some cases, post-harvest litterfall from trees and other vegetation). Within each study site, four forest floor disturbance conditions (a.k.a. soil disturbance types) were evaluated: adjacent natural stand (outside of clearcut), undisturbed (within clearcut), scalped (mineral soil exposed) and mixed (organic horizons buried or mixed). Unbiased selection of survey points was made along a transect. From each transect survey point, the closest qualifying seedling was selected for measurement and represented the centre of a soil sampling area. Forest floor displacement in the vicinity of sampled seedlings was evaluated by examining four systematically spaced points on a concentric circle at each of three radii (0.5,1.0 and 1.5 m) from the seedling stem, giving a total of 12 sample points within a 1.5 m radius of the measured seedling. Forest floor disturbance conditions were identified at sample points by digging shallow pits to expose and examine the profile of the soil surface horizons. Given that some new litter fall had often occurred since harvesting but that sufficient time has not elapsed for the development of a well humified organic soil layer, the presence or absence of an H layer was often used as the criterion to distinguish between undisturbed and scalped soil disturbance types. The mixed soil disturbance type was fairly obvious due to its buried organic matter soil horizons; in addition, it often occurred where soil had been obviously pushed into a pile or berm, for example at the margins of a wheel rut. A circular area of 1.5 m radius around each stem was selected to represent the seedling's immediate environment. This area was selected because it is small enough that a 28 detailed soil survey would be manageable, yet large enough that it was always greater than the drip line or crown diameter (except perhaps for some of the oldest trees at the Lassie Lake site). It is assumed that, given the relatively small spread of lodgepole pine root systems (Eis, 1970) the soil conditions within this area would be those most likely to control seedling productivity. The frequency of different soil disturbance types occurring around each seedling was determined using the 12 point sample survey described earlier. Thus by calculation the soil survey yields an estimate of the percentage of the area of the 1.5 m radius plot occupied by each of the three soil disturbance types encountered. The percentages of scalped, mixed and undisturbed soil around each seedling sum to 100%. Results of this survey are used to classify seedlings for analysis of growth with respect to soil disturbance. The Forest Practices Code soil disturbance survey counts forest floor displacement in two ways; i.) at any survey point where forest floor has been'removed and mineral soil exposed, and ii.) where forest floor displacement has occurred over an area of specific dimensions, for example a wide scalp (80% of forest floor removed from a 1.8 m by 1.8 m area) or a very wide scalp (80% of forest floor removed from a 3 m by 3 m area). One of the disadvantages of the 12-point survey is that it does not directly address scalp dimensions. However when ten of the twelve survey points are recorded as being scalped, such conditions do approximate the criteria for identifying a very wide scalp. This approximation is used in the analysis of foliar nutrient concentration, where trees grown on conditions approximating very wide scalps are compared with trees on undisturbed or control conditions, and in the growth curve analysis both of which are described in more detail later. The soil survey also yields an estimate of the % NAR affected by the different types of forest floor displacement. The % NAR subject to scalping can be calculated by dividing the number of scalp points found by the total number of points examined. This estimate can be used to compare growth at sites with different levels of forest floor displacement, which is one of the objectives of this study. However, to properly and confidently assess the role of the % 29 NAR subject to disturbance in stand growth, one would need more replication than is offered in this study. Soil Sampling After the completion of soil surveying using the 12-point method described above, ten sample points were randomly selected for each of the four soil disturbance types identified. Sample points for the natural stand soil type were located at 10 m spacing along transects placed through the stand adjacent to the study area. Natural stand transects were located >1.5 tree lengths away from clearcut edges to avoid collecting samples subject to an edge effect. Samples of constant depth and volume were obtained by driving a cylindrical soil core device into the soil surface and extracting the soil cores. The dimensions of the cylindrical core were: height 10 cm, diameter 9 cm. Sampling in this way meant that both the forest floor and the underlying mineral soil were collected together in the same sample. Once the samples reached the lab they were dried and sieved so that the fine organic matter (< 2 mm) was diluted within the mineral soil. It had been suggested that ideally the forest floor and the mineral soil would be sampled and analyzed separately. However, the method of combining the organic fines with the mineral fines was necessary in order to compare the qualities of the mixed soil disturbance type samples with the others. Typically the mixed soil disturbance types were heterogeneous enough that sampling of the organic matter separate from the mineral soil would have been problematic and introduced additional error into the analysis. Also, separate sampling of horizons does not necessarily facilitate root zone nutrient comparisons for microsites with dissimilar forest floor depths. It was decided therefore that constant volume and depth samples would be taken and the organic matter and mineral soil combined in order to best compare between soil disturbance types. 30 Seedling Selection and Productivity Measurement Transects were laid out within study areas such that the sample points formed a 5 x 10 m grid. At each sample point, the closest appropriate tree was selected for measurement. Trees were determined to be appropriate if they were at least half as old as the cut block, not within 50 cm of a competing stem and not subject to severe form defects or disease. The trees at the oldest site (Lassie Lake, 20 to 30 years-old) were selected without reference to the age criteria. A n initial sample size of 50 trees per site proved to small to yield clear results so the number of stems sampled per site was increased to 150 at Beaverdell and Rathmullen, 100 at Carmi and 30 scalp and 30 control trees each at Lassie Lake, for a total of 460 trees. After surveying the soil for forest floor displacement around each stem, the seedlings were measured for total height. Total height at previous ages was determined by measuring the height at successively lower annual whorls. Annual height increment, or internodal lengths, were calculated by subtracting the total height of a particular year from the total height of the subsequent year. Care was taken not to include the "false" or polycyclic whorls which lodgepole pine can produce. Each stem was then felled and a slice of stem was sawn off above the root collar (approximately 10-15 cm above ground level). Ages were determined by counting rings, and ring diameters were measured with electronic calipers to determine diameters at previous ages. Some trees were dropped from the data set prior to analysis if the number of rings could not be reconciled with the number of internodal lengths. This occurred when polycyclic whorls failed to be properly identified and as a result tree heights related to specific ages were ambiguous. In addition, a foliar sample was collected from each seedling's stem leader in October and retained for analysis. Several studies have examined seasonal and spatial variability of nutrient elements in foliage; they have generally concluded that between 10 and 30 trees per stand should be sampled depending on the nutrient(s) of interest. Samples from the current year's foliage should be collected from open grown foliage located in the upper half or third of 31 the live crown (Carter, 1992). In the present study, foliar samples were collected according to the above recommendations by Carter (1992). Twenty foliar samples of seedlings growing on a completely scalped area (roughly equivalent to a very wide scalp in the Forest Practices Code) were collected at each site, along with twenty samples from seedlings growing on completely undisturbed soil conditions. Thus comparisons between foliar nutrient concentration of scalp and control trees were made and are presented in the results section. In addition, a computer program developed by Ballard and Carter (1986) at UBC was used to diagnose foliar nutrient deficiencies. The distance to each of the three nearest competing stems was measured for each seedling and used to estimate local stem density. A competing stem was defined as any seedling at least half the age of the sample tree. 32 LABORATORY METHODS Soil Chemical Analyses Soil samples were dried and sieved (2 mm sieve) and submitted to Soilcon Laboratories Ltd. for chemical analysis. Four soil variables were tested for each sample. Total organic matter content was determined by Loss on Ignition, total organic C content and total N were determined using the Leco method and apparatus, and available P was measured by the Bray PI method. Although total organic C content can be implied from loss on ignition data, such results regarding C content are dependent on laboratory procedures, and soil and organic matter characteristics. The Leco apparatus was used to determine C content with more precision. Foliar Nutrient Concentration Analysis Foliar samples were dried and submitted to Pacific Soil Analysis Inc. for analysis. Total Nitrogen, Phosphorus, Calcium, Magnesium and Potassium were determined on a peroxide-sulfuric acid digest. Total Nitrogen and Phosphorus were measured colorimetically, and the remaining cations were measured on an Atomic Absorption Spectrophotometer. Total S was measured using a Leco Sulfur analyzer. Total Copper, Zinc, Iron and Manganese were determined on a dry ash. Total Boron was determined on a dry ash using Azomethine-H. STATISTICAL METHODS Box Plots Some properties of a data set can be summarized graphically using box plots. In this study box plots are used to introduce the soil chemical and foliar nutrient concentration data sets. Box plots reveal the location and spread of data well, albeit at the risk of losing the detail 33 that plotting the raw data provides. This graphical technique is one of a number developed by John Tukey for work in exploratory data analysis (Tukey, 1977; cited in Wilkinson et al, 1996). The summary features of box plots are based on ranks rather than sums. This makes these displays relatively less susceptible to the influence of a few extreme values. In addition, box plots reveal skewness (nonsymmetry) in the data. In the plot, the center horizontal line marks the median of the sample. The edges of the box separate the first and fourth quartiles. The median splits the ordered batch of numbers in half, and the box ends split the remaining halves in half again - that is, the central 50% of the values fall within the range of the box. The whiskers show the range of values that fall within 1.5 times the difference between the box ends from each of the upper and lower box ends. Outside values are plotted with asterisks and far outside values with open circles (SYSTAT, 1996). Descriptive Statistics and Pearson Correlations Means and standard deviations are frequently used to compare the variables of groups or subsets of the main data sets (soil chemical, foliar nutrient concentration, and seedling growth performance). Separated t-tests, and both Tukey's and Bonferroni's post hoc tests for the comparison of means are used where appropriate to indicate statistically significant differences between means. Pearson correlation coefficients are calculated to illustrate how the different soil chemical variables are related to each other. 34 Analysis of Variance and Related Statistical Tests Analysis of variance was applied to soil, foliar and tree productivity data. The following categorical values were used: • Soil Disturbance Type (4 levels): Natural Stand, Mixed, Undisturbed, and Scalped • Site Location (4 levels): Beaverdell, Carmi, Henderson, and Rathmullen • Treatment (2 levels): Scalp and Control Typically A N O V A was applied to a particular data set as a whole using an omnibus form of the model appropriate for that data set. Then A N O V A was applied to subsets of the main data sets where appropriate using a more specific and limited model in order to try to identify any relationships that might be obscured by the noise of extraneous data. For example, the omnibus model used for the analysis of soil organic matter content was; Soil Organic Matter Content = f (Soil Disturbance Type, Site Location, Soil Type x Site Location) where x signifies interaction. To test for relationships or differences that may exist between sites an A N O V A model was applied to control tree data only as; Soil Organic Matter Content = /(Site Location) To test for relationships or differences that may exist between soil disturbance types within each site the A N O V A model applied was; Soil Organic Matter Content = /(Soil Disturbance Type) Results of the various A N O V A s can be used to identify and confirm nuances in the data that otherwise could have appeared blurred and ambiguous. Models will be introduced in the text before discussing results. In addition to analysis of variance, two related techniques (least squares means analysis and partial eta squares) that are uncommon in the forestry literature were applied and are explained below. 35 Analysis of Least Squares Means was performed on each term of the omnibus A N O V A models and results are presented where appropriate. The least squares means technique allows one to examine individual treatment means with the effects of the other treatments (or A N O V A model terms) removed. Simply put, least-squares means, or "population marginal means", are the expected value of class or subclass means that you would expect for a balanced design involving the class variable with all covariates held at their mean value (SAS Inst., 1985). Thus, it is possible using the entire soil data set to compute and examine the estimated means and standard errors of each soil disturbance type independent of the location and the soil disturbance type by site location effects. This technique is often applied in studies of livestock productivity, for example, and is useful in identifying and confirming general trends with respect to specific A N O V A model factors within a complex data set. However, since we are dealing with means estimated using the A N O V A or regression model, a least squares mean approach to analysis can really only be considered as accurate as the squared multiple r suggests. The more conventional analysis, using the measured data (rather than data predicted by least squares) provides an alternative evaluation which may be preferable where the squared multiple r is not very high. Partial Eta-squares or "interclass correlation coefficients" are somewhat analogous to the concept of the squared multiple r. Partial Eta-squares are a measure of the relative strength of relationship between the measured variable and a term in the regression or A N O V A model. They are expressed as a percent and calculated for each term by dividing the sum of squares for that term by the total some of squares not including the error. Thus it is a measure of that term's contribution to the total explained variation. SPECIAL STATISTICAL METHODS FOR T R E E PRODUCTIVITY ANALYSIS The analysis of tree growth performance with respect to soil disturbance is necessarily more complex than the analysis of the soil and foliar data sets. Although the principal 36 techniques already discussed are applied to the tree productivity data set, some additional information is necessary for the interpretation of results.. First, seedlings were classified according to the results of the survey of soil disturbance types around each stem described earlier, and by soil chemical analysis. Then growth variables of seedlings of different soil disturbance classes were compared using i.) analysis of variance, multiple general linear regression and related techniques and ii.) growth curve analysis. Analysis of variance and related techniques allow for the measurement of the strength of the relationship between soil disturbance, other site factors, and seedling growth variables. Growth curve analysis provides a convenient visual comparison of seedling growth on different levels of forest floor displacement. Using Soil Disturbance Levels to Classify Seedlings The soil environment experienced by a young seedling on a recently harvested site can be extremely complex. In this study, scalp characteristics including size and proximity were not good, measurable indicators of the scale of forest floor displacement influencing a seedling or young tree for three reasons: • because of new litter accumulation, brush and grass invasion, stand density, etc. the original size of the scalp can be very difficult to determine consistently for comparison between sites retrospectively. • scalps are seldom individual discreet units, i.e. often multiple scalps of various sizes and qualities may impact on a seedling simultaneously. It is not unreasonable to expect the productivity of a seedling to be a function of the type of soil(s) it encounters. Since seedling root systems may experience different levels of each soil disturbance type (undisturbed, mixed, scalped) simultaneously, a system of classification needed to be developed to reflect the contribution of each soil disturbance type to each seedling's environment. 37 It would be wrong to ignore the assumptions and uncertainties in developing and assigning trees to a system of classification based on local soil disturbance patterns. In this study it is assumed that the soil factors that exert the greatest influence on seedling productivity are located within the 1.5 m radius plot. Lodgepole pine root systems, although small relative to other species (Eis, 1970) are known to extend beyond 1.5 m from the stem (Scagel and Evans, 1992; Scagel et al, 1994). Furthermore, for the range of stems sampled, the frequencies of the three soil disturbance types in question combine to form a continuum which runs from totally scalped at one extreme to totally undisturbed at the other, with varying amounts of the mixed soil disturbance type included. At some level, arbitrary divisions have to be made to separate the continuum into distinct classes. These divisions may or may not reflect real classes occurring in the field and as described by the Forest Practices Code, although the very wide scalp class of disturbances can be approximated using the results of the soil survey. In order to limit the liability of relying on an imperfect classification, several different systems were developed to describe and classify the soil disturbance around each stem. These classification systems used different composite indices that were created to reflect the measured soil environment around each stem. Only the system most successful in classifying seedlings and explaining the observed variation in seedling growth data is reported here, that of the soil fertility index. Soil Fertility Index The system described herein classifies seedlings using numerical values that index the total soil fertility occurring within a 1.5 m radius. The surrogate evaluation of total soil fertility (called the soil Fertility Index) is derived by summing a weighted frequency of each soil disturbance type occurring within a stem's immediate environment. The frequencies of soil disturbance types occurring within 1.5m of the stem are weighted using the results of the chemical soil analysis reported earlier. A Fertility Index value is created for each seedling by 38 multiplying each soil disturbance type frequency by it's "Fertility Factor" and summing the resultant weighted frequencies. It was assumed that the scalped soil disturbance type represents a baseline of soil fertility because every point in the study areas is essentially a scalp (i.e. mineral soil) enriched at the surface with varying amounts of organic matter. The soil chemical analysis indicated that for the soil factors of organic matter and soil nitrogen, the undisturbed soil was 33.5% greater than the scalp and the mixed soil disturbance type was 62.0% greater than the scalped type. The value of the Fertility Factor refers to the amount of enrichment by organic layers rather than an absolute measure of fertility. Therefore the scalp, being an unenriched baseline was assigned a Fertility Factor of 0.000, the undisturbed soil type was assigned a Fertility Factor of 0.335 and the Fertility Factor of the mixed soil disturbance type was 0.620. Thus the equation used to calculate the Fertility Index is: Fertility Index = (% area scalped * 0.00) + (% area undisturbed * 0.335) + (% area mixed * 0.620). The results of the Fertility Index equation are presented in Table 1 for several different soil survey results so that the reader may see how the fertility index distinguishes between seedlings on different soil disturbance conditions. The results of this classification by Fertility Index and the distribution of values of sampled seedlings are presented in the Results and Discussion section. Table 1. Fertility Index for Several Hypothetical Soil Disturbance Survey Results. Soil Environment Percent of Area Percent of Area Percent of Area Fertility Index Scalped (%) Undisturbed (%) Mixed (%) Value see above example 35 35 30 30.3 all scalped 100 0 0 0 all undisturbed 0 100 0 33.5 even distribution 33.3 33.3 33.3 31.8 half un/half scalp 50 50 0 16.8 half un with some mix 25 50 25 32.35 half mixed 25 25 50 39.4 39 The advantage of this system is that the contribution of each soil disturbance type to total soil fertility is expressed as a single unit based on the results of chemical analysis and an intensive, unambiguous and repeatable soil survey. Thus the system combines two levels of real data (soil survey and soil chemical analysis) in estimating total fertility. This system can successfully differentiate between seedlings based on their exposure to the various soil disturbance types and it allows for realistic comparisons of soil total fertility in spite of differing levels of the various soil disturbance types. Furthermore, the numerical fertility index values are usable in both A N O V A and Multiple General Linear Regression analysis and thereby can be used to analyze and compare tree growth in a very complex soil environment, i.e. not only are trees on scalps compared with trees on undisturbed ground, but trees experiencing partial scalping may also be used in the analysis. Soil Fertility Code Six groups with roughly the same population size were created by combining seedlings with similar Fertility Index values. These six groups are referred to as Fertility Codes (see Table x in the results section below). Grouping seedlings and assigning a categorical class to them allows for some additional comparisons through A N O V A and growth curve analysis. Since the range of the Fertility Index values of study seedlings is a continuum in which population distribution is roughly equal (except for all scalp and all undisturbed groups which are relatively large), group population was used to determine the boundaries between classes. Those seedlings surrounded by undisturbed soil conditions (fertility index=33.5) are central to the study because they demonstrate the ideal soil growing conditions as implied by the Forest Practices Code. This bracket also happened to be the most populous single index value. For reasons that are intuitive, seedlings with a fertility index value of 33.5 were assigned to their own group for analysis. Since A N O V A is best performed on groups with equal sample sizes the other group boundaries were determined such that seedlings were assigned to groups with 40 approximately the same population. In total six groups were formed, one being the totally undisturbed, four with lesser ranges of Fertility Index values and one with a range of Fertility Index values above completely undisturbed ( a result of the improved nutritional qualities of the mixed soil disturbance type). Fertility Codes were used in A N O V A and growth curve analysis, as well as foliar nutrient concentration analysis. Percent Mineral Soil Exposure For soil survey methods please refer to the chapter on soil analysis. Percent mineral soil exposure was determined by summing the frequencies of the scalped and mixed soil disturbance types which by definition exhibit exposed mineral soil at the surface. This index is expressed as a percent. The Regression and ANOVA Models The omnibus form of the A N O V A model used was: Seedling Growth Variable = /(Site Location, Tree Age, Soil Fertility Index, % Exposed Mineral Soil, Local Density) A number of variations of the omnibus A N O V A model were used during different phases of the analysis and will be described where results are presented. The following categorical values were used: • Site Location (3 levels): Beaverdell, Carmi, and Rathmullen • Tree Age (9 levels) if chronological age: 3 through 11 yrs old or (6 levels) if measured as germinated x years after harvest: x=l through 6 yrs • Fertility Code (6 levels): scalp, very low, low, med., control, super (an index of total soil fertility) 41 Recall that the Henderson site lacked established seedlings and so is not included in the analysis of seedling productivity. The Lassie Lake site was analyzed separately because of its different site history, i.e. it was mechanically scarified as part of an older, 1960s site preparation trial, and is included in this report in order to provide some evidence for longer-term effects of forest floor displacement (see next section). The different measures of tree age are explained later in the results section. In addition to the omnibus model being applied to the entire tree productivity data set, A N O V A was performed on individual sites to test for effects of soil disturbance independent of site. Additional A N O V A models were used in some instances and will be introduced before the presentation of results. 42 R E S U L T S A N D D I S C U S S I O N SITE HISTORIES: HOW T H E Y A F F E C T THE ANALYSIS The effects of forest floor displacement on seedling productivity were measured at three juvenile-aged sites (Beaverdell, Carmi and Rathmullen) and one intermediate-aged site (Lassie Lake) in the old ICHc2 biogeoclimatic subzone (ICHc2 subzone has since been split into the ICHmkl and MSdml). The site histories affect the analysis in a couple of ways. The age of the clearcut controls the age of the trees found there and the microclimatic conditions to which they have been exposed. In addition, each site has been affected by soil disturbance to a different degree. The %NAR disturbed on each site may influence the results of analysis. The Carmi site is the youngest of the three juvenile-aged sites, logged in 1991, with seedlings ranging in age from 3 to 6 years old. The Rathmullen site was logged in 1987, and the Beaverdell site was logged in 1985. Seedlings used for analysis range in age from 6 to 9 yrs old at Rathmullen and 6 through 11 years old at Beaverdell. Analysis of seedling productivity at juvenile-aged sites was performed across all sites where possible, and at each site separately. In addition, for longer-term data, trees at a 30 year old site near Lassie Lake were measured and compared. At Lassie Lake the trees ranged in age from 18 to 29 years old. The Lassie Lake site is an old mechanical site preparation and broadcast burning trial installed in the mid 60's and early 70's. Much of the integrity of the original trial has been lost due to the lack of site monumentation and the occasional reorganization of block boundaries into current management units. Most of the currently existing blocks have been juvenile spaced, in some cases repeatedly, through the late seventies to the present time without regard to mamtaining valid comparisons between treatments. By careful examination of the existing data on the Lassie Lake site, and a substantial field reconnaissance effort, we were able to identify the two blocks best suited to the purpose of studying the longer-term effects of forest floor displacement 43 on pine productivity. Wherever useful, results from the Lassie Lake site will be reported in the text along with the other juvenile-aged sites. A COMPARISON OF SURVEY METHODS The twelve-point survey for soil disturbance was developed for two reasons; it yields consistent and reproducible results when applied to differently aged sites, and it yields a measure of forest floor displacement for each sampled tree as well as an estimate of the %NAR affected by dispersed soil disturbance or forest floor displacement. It was necessary to test and i compare the results for %NAR disturbed using both the twelve-point and the Ministry of Forests audit level soil surveys. Fortunately, Pope and Talbot Ltd. had recently commissioned an in-house report which among other things compared pre-Code soil disturbance levels with post-Code disturbance levels on a few blocks. I was able to extract and use some results reported by a certified soil disturbance surveyor and compare his results with my own using the twelve-point method. One block was selected, the Henderson Creek site, which was particularly apt for comparisons of survey methods because it had a range of disturbance levels, stratified and mapped, each unit covering fairly extensive areas. Each of these stratified units was surveyed again using the 12 point method and results regarding % N A R subject to forest floor displacement were compared. Results from this study's 12 point soil survey are compared with results obtained by a certified soil disturbance surveyor at a number of locations comprising a range of levels of forest floor displacement (Table 2). Given that both the 12 point survey and the standard soil disturbance survey rely on converting frequencies of point estimates from transects into percent of area measurements, it is not suprising that the twelve-point survey results closely shadowed those of the certified surveyor. This is the case when only scalped points are counted during the 12-point survey method; if both scalp and mixed points are counted the estimates of % NAR disturbed diverge. 44 Table 2. Comparison of Results for %NAR Disturbed at the Henderson Creek Study Site Using Two Different Survey Methods. Audit Level 12-Point 12-Point (only scalps) (scalps+mixes) 0-5 % 6.0% 13.33% 5-10 7.64 12.5 10-13 14.58 26.39 17-25 19.48 42.21 >25 39.53 52.03 The main difference between the survey methods is that the Ministry's audit level survey extends sample points along a linear axis while the twelve point survey method clusters sample points around a point of centre. In fact, for a given area, the twelve point survey method evaluates six times as many survey points, probably making it more accurate than the audit level survey. This process of comparing survey methods helped lend confidence to the ability of the twelve point survey to accurately survey the %NAR affected by forest floor displacement. Different levels of forest floor displacement were encountered at each site depending on the intensity of the ground skidding disturbance (Table 3). In fact, since the plot centers for the twelve point survey are tree stems systematically selected without bias, the accuracy of the 12-point survey depends on equal and unbiased seedling distribution across the site. A preference by seedlings for disturbed or undisturbed soil could result in an over or an underestimate of soil disturbance respectively. Inferences regarding the effect of the general level of forest floor displacement that exists on a site will be discussed later. 45 Table 3. Estimated Forest Floor Displacement at Three Study Locations Where Seedlings Were Sampled Expressed as a Percent of the Net Area to be Reforested. SOIL CHEMICAL ANALYSIS Correlations of Soil Properties and the Importance of Soil Organic Matter Table 4 presents the overall Pearson correlation matrix for soil properties across all sites and soil disturbance types. Correlation matrices for soil properties between sites and between soil disturbance types are presented in Appendix II. It is important to recognize how different soil chemical properties relate to each other in interpreting the effect of soil disturbance. Correlation analysis may also help to identify a subset or a particular individual property that can be used to describe soil fertility generally without having to analyze for each nutrient. For example, carbon concentration in organic matter is relatively invariable/and although nitrogen concentration fluctuates more widely, nearly all of the soil's fixed nitrogen is stored in organic form. Consequently, the patterns of C and N distribution should approximately resemble that of organic matter. With enough baseline data on a particular soil type, one may be able to infer N and C levels as well as other nutrients without additional and more expensive analysis. Site %NAR Displaced Beaverdell Carmi Rathmullen 33.8 47.2 21.5 46 Table 4. Overall Pearson Correlation Matrix for Soil Properties Across Sites and Soil Disturbance Types. LOI T O T A L C T O T A L N A V A I L P C:N LOI 1.000 T O T A L C 0.846 1.000 T O T A L N 0.863 0.846 1.000 A V A I L P -0.069 -0.059 -0.109 1.000 C:N 0.258 0.525 0.050 0.032 1.000 Number of observations: 195 For pooled soil data from the whole study, the Pearson correlation coefficients of C and N concentration with loss on ignition were 0.846 and 0.863, respectively. On an individual site basis, the correlation coefficient for N with loss on ignition was 0.770 or higher. The Carmi site had an unusually low correlation of C with loss on ignition: 0.695, and the materials there tested negative for presence of calcium carbonate. Despite the generally strong correlations of both N and C with loss on ignition, the latter was poorly correlated with the C / N ratio: the correlation coefficient for pooled samples was only 0.258. As the C / N ratio is often roughly indicative of mineralizable N and short-term N availability, there seems little connection between the latter and the amount of undisplaced forest floor. The Pearson correlation coefficient for "available" P with loss on ignition was -0.069 for pooled data, and on individual sites ranged from +0.160 to -0.076. Although in some coastal podzols "available" P assessed by the Bray P-l method is closely linked with soil organic matter content, this is not at all the case in these soils from Boundary Forest District (Ballard, 1997). Comparison of Soil Properties Between Sites Figures 1 through 5 are box plots comparing soil chemical properties in the natural stand adjacent to each study site. The adjacent natural stand was sampled for an estimate of the soil conditions in the study site prior to harvesting. Comparison of adjacent natural stand soil 47 properties should indicate how the study sites differ from one another. Box plots comparing other soil disturbance types are presented in Appendix III. Figure 1. Box Plot of Organic Matter Content of Figure 2. Box Plot of Total Nitrogen of Natural Stand Natural Stand Soils at Different Sites. Soils at Different Sites. <F Location Location Figure 3. Box Plot of Total Carbon of Natural Stand Soils at Different Sites. Figure 4. Box Plot of Available Phosphorus of Natural Stand Soils at Different Sites. Location 48 Figure 5. Box Plot of C/N Ratio of Natural Stand Soils at Different Sites. 60 50 o 40 cu • a. 0 30 I 20 T 10 In general, there is not much separation of the ranges of soil properties between sites. This is further illustrated in the Tables that follow Which indicate only a relatively few number of statistically significant differences. A couple of those significant differences are visible in where available P content at the Beaverdell site exceeds all others. Results of the box plots infer the following ranking of general soil fertility by site; Rathmullen > Henderson > Beaverdell > Carmi. Only the box plots for the adjacent natural stand soils are presented above, however the same relative ranking occurs for each soil property throughout the various soil disturbance types (see Appendix III). The effects of soil disturbance as studied in this project do not appear to affect any one site disproportionately compared to the others. The mean values for some soil properties are grouped by soil disturbance type and presented with their standard errors in Table 5. This Table allows for the comparison of soil disturbance types at different locations. Tukey's post hoc test for comparison of multiple means was used to determine significant differences at the 0.05 level, and results are indicated in the Table by letter codes. Figure 2, where the total N content at the Rathmullen site exceeds all others, and in Figure 4, 49 Table 5. Mean and Standard Deviation of Soil Properties at Different Sites by Soil Disturbance Type. Values with the same letter code do not differ significantly at the 0.05 level. Location Soil Disturbance N % Organic % Total C % Total N Available P CN Ratio Type Matter Carmi Natural Stand 10 6.3+ 2.7a 3.9 ± 1.7a 0.11 ±0.05a 17.0+10.2a 35.7 ±9.8a Beaverdell Natural Stand 10 5.9±1.6a 3.1 +1.0a 0.10 ±0.01a 50.2 ±31.6b 31.6±7.7a Henderson Natural Stand 10 8.0 ±3.4a 3.7 +1.7a 0.13±0.05a 28.1 +3.4a 29.7 ±11.3a Natural Stand 10 8.5 ± 2.8a 4.7 ± 2.4a 0.17 ± 0.04b 33.9 + 9.3ab 26.4 ± 8.6a Carmi Mix 10 3.9 ± 1.3a 2.3 ± 1.4a 0.08 ±0.03a 55.8 ±43.6a 29.6 ±6.2a Beaverdell Mix 15 5.7±1.4ab 3.2 ± 1.0a 0.09 ± 0.02a 44.8 ±20.7a 36.8±8.2a Henderson Mix 9 7.0 ± 1.7b 4.0 +1.2a 0.11 + 0.03a 36.0+6.1a 35.5 ± 10.9a Rathmullen Mix 10 10.8±3.5c 5.6+1.9b 0.18±0.04b 31.8±7.4a 30.0 ±5.0a Carmi Undisturbed 10 3.9 ± 1.5a 1.8+0.9a 0.07 ± 0.02a 36.7+9.1a 25.5 ±7.4a Beaverdell Undisturbed 15 5.4 +1.5a 3.0 ± 1.5a 0.09 ±0.03ab 65.4 ± 26.8b 32.7 ±7.6a Henderson Undisturbed 10 5.8 ±2.4ab 2.9 +1.5a 0.09 ±0.03ab 38.9 ±7.6a 34.2 ± 15.5a Undisturbed 11 7.5 +1.5b 3.7 ± 1.0b 0.14 + 0.03b 34.9 ± 9.6a 25.9 ± 3.0a Carmi Scalp 10 3.4 ± 1.4a 1.4 + 1.0a 0.05 ± 0.02a 36.6 ±21.9a 24.6±9.3a Beaverdell Scalp 15 4.2+1.7a 2.0 ±0.9a 0.07 ± 0.03a 40.2 ± 12.0a 29.6±9.6a Henderson Scalp 10 4.2 ±2.0a 1.8+0.9a 0.06 +0.02a 29.5±5.6a 30.0 ± 10.5a Rathmullen Scalp 10 5.5 ± 2.2a 2.6 ± 1.7a 0.10 +0.04b 32.6 ± 12.1a 23.8 ± 5.7a Organic matter content is naturally quite low at all of the study sites. When comparing soils between sites or soil disturbance types organic matter content is worth focusing on because it is one of the main drivers of soil nutrient content, availability and general soil fertility. In the natural stand soil type at different sites organic matter content ranges from 5.9 to 8.5 percent on average. Soil organic matter contents are reduced at all study sites in the undisturbed soil type within the clearcut, ranging from 3.9 to 7.5 percent on average. Thus exposure of the forest floor to clearcut site conditions resulted in a decrease in soil organic matter content. In scalped soils the mean organic matter content is further reduced to between 3.4 and 5.5 percent. The mixed soil disturbance type has the widest range of organic matter contents from 3.9 to 10.8 percent. This increase in range probably reflects variation in the mixing process from simple burying with mineral soil to the bunching and concentrating of organic matter layers. Differences in soil properties between sites are small in magnitude, probably reflecting the generally similar site conditions, i.e. dry climate, sandy soil texture and thin forest floor 50 layer. Statistically significant differences in soil properties occur occasionally. The soils at the Rathmullen site, for instance, are relatively rich in organic matter, nitrogen and carbon compared to other sites (note that the Rathmullen site has a finer textured mineral soil than found at other sites). The soils of the Beaverdell site are usually the richest in available phosphorus. Exposing the forest floor by clearcutting resulted in a reduction in soil organic matter content by between 9 and 39 percent depending on the site (soil in the natural stand compared with the undisturbed soil within a clearcut). Increases in the rate of organic matter decomposition and reductions in soil organic matter content after clearcutting are commonly reported in the literature (Hickling and Scagel, 1995; Hughes and Reynolds, 1991; Entry, 1986, 1987; Hendrickson et al., 1985; Lundgren, 1982; Sundmanetal, 1978). In fact, consistently across all sites in the present study, a greater proportion of the original soil's organic matter is lost by exposure to clearcut conditions than by scalping. Scalping intensifies a generally occurring, clearcut-induced loss of soil organic matter in localized areas. Comparison of Soil Chemical Properties of Different Soil Disturbance Types Within Each Site Figures 6 through 10 use box plots to compare soil variables in the different soil disturbance types at the Rathmullen study site. Box plots comparing soil disturbance types at other sites are presented in Appendix IV. There is a fairly consistent pattern in the ranking of median soil variable values by soil disturbance type. The scalp soil disturbance type is consistently the lowest ranking soil type regardless of soil property being evaluated (except perhaps for C:N ratio where scalps are low but highly variable (see Appendix IV)). At Rathmullen, the mixed soil disturbance type often ranks highest, followed closely by the natural stand and undisturbed soils respectively. At other sites, magnitudes of properties of the natural stand soil usually exceed those for the mixed soil 51 disturbance type slightly. Otherwise, very similar trends between soil disturbance types are evident at other study sites (see Appendix IV). Thus there appears to be a consistent trend in soil properties by soil disturbance type within each study location where; Natural Stand >= Mix > Undisturbed > Scalp. Figure 6. Box Plot of Organic Matter Content of Figure 7. Box Plot of Total Nitrogen Content of Different Soil Disturbance Types at Rathmullen. Different Soil Disturbance Types at Rathmullen. Mix NalSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types 52 Figure 8. Box Plot of Total Carbon Content of Different Soil Disturbance Types at Rathmullen. Figure 9. Box Plot of Available Phosphorus Content of Different Soil Disturbance Types at Rathmullen. Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types Figure 10. Box Plot of C:N Ratio of Different Soil Disturbance Types at Rathmullen. Mix NatSt Scalp Un Soil Disturbance Types Table 6 presents the same statistics as in Table 5 but grouped by location in order to compare soil disturbance types. Tukey's post hoc test for comparison of means was used to determine significant differences at the 0.05 level, and results are indicated in the Table by letter codes. 53 Table 6. Mean and Standard Deviation of Chemical Properties of Different Soil Disturbance Types By Location. Values with the same letter code do not differ significantly at the 0.05 level. Location Soil Disturbance N Organic Total C Total N Available P CN Ratio Type Matter (%) (%) (%) (PPm) Beaverdell Natural Stand 10 5.9 ± 1.6a 3.1 ± 1.0a 0.10±0.01ab 50.2 + 31.6ab 31.6 ± 7.7a Beaverdell Undisturbed 15 5.4±1.5ab 3.0 ± 1.5a 0.09 ± 0.03a 65.4 ± 26.8a 32.7 + 7.6a Beaverdell Mix 15 5.7 + 1.4a 3.2 ± 1.0a 0.09 ± 0.02abc 44.8 ± 20.7ab 36.8 ± 8.2a Beaverdell Scalp 15 4.2 ± 1.7b 2.0 ± 0.9b 0.07 ± 0.03c 40.2 ± 12.0b 29.6 ± 9.6a Carmi Natural Stand 10 6.3 ± 2.7a 3.9 ± 1.7a 0.11 + 0.05a 17.0 ± 10.2a 35.7 ± 9.8a Carmi Undisturbed 10 3.9 ± 1.5b 1.8 +0.9b 0.07 ± 0.02b 36.7±9.1ab 25.5 ± 7.4b Carmi Mix 10 3.9 ± 1.3b 2.3 ± 1.4b 0.08 ± 0.03ab 55.8 ± 43.6b 29.6 ± 6.2ab Carmi Scalp 10 3.4 ± 1.4b 1.4 ± 1.0b 0.05 + 0.02b 36.6 + 21.9ab 24.6 ± 9.3b Henderson Natural Stand 10 8.0 ± 3.4a 3.7 ± 1.7a 0.13 +0.05a 28.1 +3.4a 29.7 +11.3a Henderson Undisturbed 10 5.8 ± 2.4ab 2.9 ± 1.5bc 0.09 + 0.03abc 38.9 ± 7.6b 34.2 +15.5a Henderson Mix 9 7.0 ± 1.7a 4.0 ± 1.2ac 0.11 ±0.03ab 36.0 ±6.1 be 35.5 +10.9a Henderson Scalp 10 4.2 ± 2.0b 1.8 ± 0.9b 0.06 + 0.02c 29.5 ± 5.6ac 30.0 +10.5a Rathmullen Natural Stand 10 8.5 ± 2.8ab 4.7 ± 2.4a 0.17 ± 0.04a 33.9 ± 9.3a 26.4 ± 8.6a Rathmullen Undisturbed 11 7.5 ± 1.5a 3.7±1.0ab 0.14±0.03abc 34.9 ± 9.6a 25.9 ± 3.0a Rathmullen Mix 10 10.8 ± 3.5b 5.6 ± 1.9a 0.18±0.04ab 31.8 ± 7.4a 30.0 ± 5.0a Rathmullen Scalp 10 5.5 ± 2.2c 2.6 ± 1.7b 0.10 ± 0.04c 32.6 ± 12.1a 23.8 ± 5.7a Lassie Undisturbed 10 7.1 ± 2.1a 3.6 ± 1.4a 0.12 ± 0.04a 40.2 ± 7.8a 29.4 ± 7.9a Lassie Scalp 10 7.8 ± 1.4a 4.0 ± 1.2a 0.13 +0.03a 38.2 ± 5.7a 32.4 + 6.8a As noted earlier, the natural stand is consistently the richest soil type in terms of organic matter and total nitrogen contents, except at the Rathmullen site. At Rathmullen, the mixed soil disturbance type is consistently richest (although it does not differ significantly from the natural stand soil type). The elevation of measured soil properties in the mixed soil at Rathmullen may be the result of the concentrating of organic-rich soil layers in mixed soils (i.e. organic layers folded on top of each other). Mixing and burying may also protect soil organic matter from exposure to the microclimatic and other site conditions that promote soil organic matter loss in clearcuts. Chemical properties of the undisturbed soil type are reduced compared to the natural stand soil type. However, statistically significant differences between mean soil properties of 54 natural stand and undisturbed soils occur infrequently, except at the Carmi site where the natural stand soil is often significantly richer than other soil disturbance types. Scalping reduced soil properties such as organic matter total nitrogen contents, and available phosphorus and total C. Though significant differences do not always exist, scalps are consistently the lowest of all soil disturbance types within different site locations in organic matter, total C and total N contents, and among the lowest in available P and C:N ratio. With respect to the C:N ratio, during stand opening and soil disturbance carbon seems to be preferentially removed compared to total nitrogen, thus both clearcutting and scalping result in reductions in C:N ratio and therefore nitrogen availability may be enhanced at the same time as total nitrogen content is reduced by displacement. Results from the Lassie Lake site indicate that the effects of scalping on soil properties are temporary in this kind of ecosystem. Thirty years after scarification treatment there are no significant differences between measured soil properties of scarified and undisturbed soils. However, the reader should note that these two treatment units are not located on the same block and therefore are or "may be" confounded by site differences. The issue of soil recovery after forest floor displacement will be discussed further in the Lassie Lake chapter and later in this section. Soil Results Compared With Other Studies Smith and Wass (1985) retrospectively analyzed soil properties on sites disturbed by ground skidding in Nelson Forest Region and reported values for total nitrogen and carbon that are not unlike those found in the present study. They found total nitrogen to range from 0.04 to 0.15 percent and total carbon from 0.75 to 4.41 percent across all disturbance types at five locations (compared with 0.05 to 0.18 percent N and 1.5 to 5.6 percent C in the present study). C:N ratio ranged from 20.2 to 32.2 on Smith and Wass' sites, and 23.8 to 36.8 in the present study. However, the disturbance types evaluated and the soil sampling technique employed by 55 Smith and Wass (1985) were substantially different, precluding any direct comparisons. First of all, unlike the present study, Smith and Wass sampled the surface F H and mineral horizons separately, a fact which probably accounts for the generally lower range of total N and C contents and C:N ratios. The disturbance types they evaluated were portions of constructed contour skid roads, i.e. inner track, midtrack, outer track, and berm, and their "undisturbed" soils were not defined the same as in the present study. Instead of comparing the surface soil of a disturbance with the surface soil of an undisturbed area Smith and Wass (1985) sampled the surfaces of disturbances and compared them to samples taken at an equal depth in an undisturbed profile. Thus their results often indicate higher levels of N and C at disturbed surfaces compared with the "undisturbed" soil which actually lies below the original surface in undisturbed soils - clearly the differences in nutrient content are a result of litter accumulation and other nutrient cycling processes. Analysis of Variance for Organic Matter Content (LOI) More formal statistical techniques can be applied to confirm the results of the above introductory analysis, and to provide further insight into the soils analysis data. To this end the results of A N O V A and Least Squares Means analysis for soil organic matter content are presented below. For the sake of brevity, results of A N O V A and Least Squares Means analysis for each soil chemical property measured are presented in Appendix V. The omnibus A N O V A model used in this analysis was; Organic Matter Content =/(study site location, soil disturbance type, and an interaction effect) The omnibus A N O V A model accounted for approximately forty-seven percent of the total variation in the organic matter content data (squared multiple R=0.467). Both the soil disturbance type and site location effects were highly significant and accounted for approximately sixteen and twenty-three percent of the total variation respectively. The soil 56 disturbance type by site location interaction effect was significant, but relatively less so, accounting for only seven percent of the total variation (See Table 7). Table 7. Results of Analysis of Variance for Organic Matter Content. Source Sum-of- DF Mean-Square F-Ratio P Squares Soil 215.668 3 71.889 16.193 0.000 Disturbance Type Site Location 313.041 3 104.347 23.504 0.000 Type x Location 96.456 9 10.717 2.414 0.014 ERROR 705.894 159 4.440 Total , 1331.059 174 In summary the omnibus model was most successful at explaining the variance in total soil N content (59 % explained), followed by organic matter content (47 %), total carbon content (39 %), and available P content (30 %). The model was less successful at explaining the variance in C:N ratio (18 % explained). Both the soil disturbance type and site location were consistently highly significant as indicated by the probability statistic. The soil type by site location interaction effect was also consistently significant but relatively less so, except in the analysis of C:N ratio where the interaction effect was assigned a probability statistic of 0.388 and accounted for less than five percent of the variance. Location was always the more significant than soil disturbance type except in the analysis of total carbon content, and the interaction effect was always least significant except in the analysis of available phosphorus. That the interaction effect and the location effect were both highly significant in the analysis of available P, while the significance of the soil disturbance type was relatively low indicates that treatment effect on available P seems site-specific and the data therefore provide no basis for generalizations applicable to the study as a whole. Analysis of the Least Squares Means of soil organic matter content indicates some interesting associations between soil disturbance types. Based on Tukey's HSD Post hoc Test for 57 Comparison of Means, the least squares mean organic matter contents of the natural stand and the mixed soils were not significantly different. However, both the natural stand and mixed soils had significantly greater mean organic matter content than the undisturbed soil, which in turn had a significantly greater mean organic matter content than the scalped soil disturbance type (see Figure 11). The least squares means of organic matter content for different soil disturbance types ranged from 7.2 % in the natural stand soil to 4.3 % in the scalped soil. Accordingly, results indicate that on average the natural stand and the mixed soil disturbance types have approximately 62 % more organic matter than the scalped soil disturbance type. The undisturbed soil type has about 33.5 % more organic matter than the scalped soil disturbance type (Table 8). 58 Figure 11. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Soil Disturbance Type; M = mixed, N = natural stand, S = scalped, and U = undisturbed. Least Squares Means Table 8. Percent Organic Matter Content (LOI) Least Squares Means by Soil Disturbance Type (See graph above). Soil Type LS Mean (%) SE N Natural Stand 7.19a 0.333 40 Mixed 6.85a 0.324 44 Undisturbed 5.63b 0.315 46 Scalped 4.33c 0.319 45 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. 59 Analysis of the Least Squares Means indicated that the mean organic matter contents at particular site locations differed significantly from each other. The Carmi site tended to have the lowest mean organic matter content at 4.3 %, while the Rathmullen site was the richest in organic matter with a least squares mean of 8.1 % (See Figure 12 and Table 9). The significance of the soil disturbance type by site location interaction effect seems to be generated in particular by the pattern of organic matter content by soil disturbance type at the Rathmullen site. In contrast to the other three sites, at Rathmullen the Least Squares Mean of organic matter content for the mixed soil disturbance type is greater than that of the natural stand soil type. The relatively small standard errors for the Beaverdell site in Figure 13 may reflect higher number of samples per soil disturbance type that were taken at the Beaverdell site (n=15) compared to the other three sites (n=10). In this analysis, increased sample size will result in more compact standard errors (see Figure 13). Figure 12. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Location. Least Squares Means Table 9. Percent Organic Matter Content (LOI) Least Squares Means by Location(See graph above). Location LS Mean (%) SE N Rathmullen 8.08c 0.329 41 Henderson 6.26b 0.338 39 Beaverdell 5.29ab 0.289 55 Carmi 4.36a 0.333 40 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. 61 Figure 13. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Soil Disturbance Type for Each Location; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means BEAVERDELL CARMI M N S U M N S U TYPES TYPE$ 62 In general, results of analysis of least squares means of total C and total N closely parallel that of organic matter content. For available P and C:N the interpretation is not so straightforward (please refer to Appendix V).. Analysis of variance indicates that site location is slightly more important than soil disturbance type in predicting soil chemical variables. Comparisons of soil properties by soil disturbance type between sites are confounded by site differences, so that for example a scalp at Rathmullen may in fact be more nutrient rich than an undisturbed soil type at Beaverdell. Comparison of soil disturbance types is probably only relevant within the context of each individual site. This effect is reflected in the tree productivity analysis presented in a later chapter, in which site is by far the most statistically important growth determining factor, far more important overall than soil disturbance type. Nevertheless, the fairly consistent trend in soil chemical properties by soil disturbance type within sites indicated by the preliminary analysis, was confirmed by analysis of variance and least squares means. The only inconsistency between the results of the different analysis is in how the mixed soil disturbance type compares to the natural stand soil type overall and at individual sites. The difference in this relationship is probably due to variability in the processes that produced the mixing of soil layers. Recovery of Soil Properties After Disturbance Clearcutting and soil disturbance have been shown above to contribute to the decline of several soil chemical properties. Since the soil data were collected from cutblocks of various ages it is possible to construct a chronosequence in which the decline and recovery of soil properties over time can be estimated. Each site represents a cutblock at a particular age after harvesting; Henderson was sampled one year after harvesting, Carmi sampled three years after harvesting, and Rathmullen and Beaverdell sampled ten and eleven years after harvesting respectively. In addition, the 63 Lassie Lake sites were sampled some thirty years after harvest. Unlike at other sites, the differences between scalped and undisturbed soil properties at Lassie Lake were consistently not statistically significant, possibly indicating near-complete recovery. Lassie Lake soil recovery results, however, are not presented in the graphical analysis which follows, for two reasons; unlike at other sites, results from Lassie Lake may be confounded by site differences, and including the Lassie Lake data would drastically alter and negatively affect the graphical presentation of results. A chronosequence of changes in soil properties of different soil disturbance types over time was developed as follows: the mean value of each soil property for all soil disturbance types was calculated as a percentage of the mean value of the adjacent natural stand. So for example, if the mean organic matter content of the natural stand soil at Beaverdell was 5.9 percent and the mean organic matter content of the scalped soil at Beaverdell was 4.2 percent, then the scalp soil type was estimated to have 4.2/5.9 or seventy one percent of its original organic matter content after eleven years. After performing the same calculation on data from Henderson (1 yr. old), Carmi (3 yrs. old), and Rathmullen (10 yrs old) we can plot four points for each soil type and track the changes in soil properties that have occurred over time. The results of such calculations are presented in Figures 14 through 18. Soil chemical properties such as organic matter content, total C, total N and the O N ratio tend to decline for a period of about five years after harvest. After this initial decline, new litter-fall and other nutrient cycling processes are probably responsible for the subsequent increases in these soil properties. In contrast, available P initially increases above the level present in the natural stand after harvesting and soil disturbance, and subsequently declines towards the original levels. The initial increase in available phosphorus after disturbance is likely a result of its liberation from organic matter by the increased mineralization that often occurs after harvesting. It is interesting to note that the chronosequence line representing the mixed soil type experiences an initial decline in organic matter content, total C and N , but remains above other 64 soil disturbance types found in the clearcut. Mixed soil disturbance types remain high, probably as a result of the concentration of organic matter during mixing and burying. Recovery of the mixed soil type proceeds most rapidly and may exceed the levels found in the natural stand soil after about ten years. This effect is probably due to the additive effect of preservation and concentration of buried organic horizons and new surface accumulations of litter. Both undisturbed and scalped soil disturbance types also experience a period of decline in soil properties followed by a period of recovery. Soil properties of the scalped soil type are consistently the lowest and slowest to recover. However, some recovery is evident. Table 10 indicates the level of recovery experienced by different soil disturbance types. Eleven years after harvest undisturbed soils are within 10 percent of the original levels of soil properties, scalps are within 30 to 40 percent, and mixed soils have fully recovered and may even exceed the original levels. Table 10. Estimated Percentage of Original Soil Levels of Various Soil Chemical Properties by Soil Disturbance Type Eleven Years After Clearcutting. Soil Disturbance Type Organic Matter Content (%) Total N (%) Total C (%) Available P (%) C:N Ratio (%) Mixed 95 to 125 92 to 104 100 to 110 100 116 Undisturbed 90 92 98 100 104 Scalped 70 70 60 100 94 65 Figure 14. Chronosequence Showing Decline and Recovery of Soil Organic Matter Content Over Time by Different Soil Disturbance Types. Years After Harvest Figure 15. Chronosequence Showing Decline and Recovery of Total C Content Over Time by Different Soil Disturbance Types. - 150 m o 0 2 4 6 8 10 12 Years After Harvest 66 Figure 16. Chronosequence Showing Decline and Recovery of Total N Content Over Time by Different Soil Disturbance Types. 0 2 4 6 8 10 12 Years After Harvest Figure 17. Chronosequence Showing the Change in Available P Content Over Time by Different Soil Disturbance Types. 67 Figure 18. Chronosequence Showing the Change in C:N Ratio Over Time by Different Soil Disturbance Types. FOLIAR NUTRIENT CONCENTRATION ANALYSIS Trends and differences in foliar nutrient concentration associated with forest floor displacement were tested for using box plots, ANOVA and a computer program developed at the University of British Columbia (Ballard and Carter, 1983; Ballard and Carter, 1986). Foliar analysis provides an index of the amount of nutrients actually taken up by the tree. At present, foliar analysis is most useful for identifying severely deficient nutrients, though it can be used to identify certain incipient deficiencies and nutrient interrelationships (Carter, 1992). 68 Figures 19 through 21 illustrate the spread of N, P and S foliar concentrations that exist between trees grown in scalps and trees grown on undisturbed forest floor. Box plots of other foliar nutrient concentrations are presented in Appendix VI. Figure 19. Box Plot of Foliar Nitrogen Contents by Location and Treatment. 2.5, £ . 2.0' c B c c g 1 1.0 Treatment 0.5 & sT tff* Location • Scalp Control Figure 20. Box Plot of Foliar Phosphorus Contents by Location and Treatment. 0 30 c 0.25 S. c o " 0.20 O L. o % 0 1 5 o CL 0.10 Treatment o Scalp Control Location 69 Figure 21. Box Plot of Foliar Sulphur Contents by Location and Treatment. 0 . 2 0 Treatment 0 . 0 5 • Scalp Control 1* Location No striking or consistent pattern of differences in foliar nutrient concentration between treatments is evident at any of the four study sites examined. Foliar nutrient concentrations tend to be more variable for control trees at Carmi and Rathmullen, but foliar concentrations of scalp trees are more variable at Beaverdell and Lassie Lake. Box plots indicate that substantial differences in foliar nutrient concentrations between scalp and control trees occur infrequently. Only P, Zn, Cu and Fe contents show any difference in range between scalp and control trees, and even then differences seem to occur on only one of four sites (see Appendix VI). Differences in P, Zn, and Fe are most pronounced at the Lassie Lake study site, the only site where results on a treatment basis may in fact be confounded by site differences. In general, there is more often a greater separation of seedling foliar nutrient concentrations at different locations than between seedlings grown on different soil conditions at the same site. Beaverdell and Lassie Lake trees generally have lower foliar macronutrient concentrations than trees at either Carmi or Rathmullen. This pattern is consistent for all foliar macronutreint concentrations measured. A parallel pattern does not occur in soil nutritional properties, therefore factors other than simple soil abundance and availability are controlling macronutrient uptake. 70 Comparing Foliar Nutrient Concentrations Between Sites Table 11 and Table 12 present the mean and standard deviation values for foliar concentrations of various nutrients in control trees at different sites. Tukey's post hoc test for comparison of means was applied and significant differences at the 0.05 level are indicated by letter code. Differences in foliar nutrient concentration between sites are quite small in magnitude. As far as foliar concentration of macronutrients (N, P, K, S) are concerned sites are consistently ranked as follows; Carmi > Rathmullen > Beaverdell > Lassie Lake. Statistically significant differences do occur and usually divide the four sites into two groups: Carmi and Rathmullen vs. Beaverdell and Lassie Lake. Statistically significant differences in micronutrient concentrations also occur, but no particular pattern is evident. Table 11. Foliar Concentrations of Some Nutrients of Control Trees at Different Sites. Values with the same letter code are not significantly different at the 0.05 level. Location N P K (%) S Ca Mg Beaverdell 1.18 ±0.13a 0.14 ±0.02a 0.58 ±0.08ac 0.11 ±0.02a 0.21 ±0.05a 0.09 ±0.02a Carmi 1.53 ±0.22b 0.20 ±0.03b 0.72 ±0.06b 0.14 ±0.02b 0.21 ±0.05a 0.09 ±0.01 a Rathmullen 1.36 ±0.12c 0.18 ±0.03b 0.64 ±0.10c 0.13 ±0.02b 0.23 ±0.06ab 0.10 ±0.02a Lassie 1.11 ±0.14a 0.15 ±0.02a 0.49 ±0.08d 0.10 ±0.01a 0.26 ±0.07b 0.08 ±0.02a Table 12. Foliar Concentrations of Some Micronutrients of Control Trees at Different Sites. Values with the same letter code are not significantly different at the 0.05 level. Location Cu Zn Fe Mn B Al (ppm) Beaverdell 5.73 ±0.59a 58.15 ±9.08a 53.62 ±9.56a 302.1 ±90.8a 27.93 ±7.05a 603.2 ±121.4ab Carmi 4.62 ±1.08b 55.06 ±10.20a 47.28 ±6.45b 321.3 ±106.1a 18.15 ±4.24b 660.5 ±180.0b Rathmullen 5.82 ±1.46a 61.04 ±11.02a 51.78 ±8.00ab 283.1 ±74.0ab 30.14 ±4.80a 521.5 ±115.8a Lassie 6.19 ±0.93a 46.62 ±9.23b 40.64 ±4.95c 219.9 ±53.8b 16.15 ±4.61 b 549.1 ±135.2a 71 Comparing Foliar nutrient concentrations of Trees on Different Soil Conditions Within Each Site Table 13 and Table 14 present the mean and standard deviation values of foliar concentrations of various nutrients for trees grown on scalps and on control areas at each site. Tukey's post hoc test for comparison of means was applied and a few adjustments made where necessary using Bonferroni's post hoc test for comparison of means, since the small number of means to be compared may violate the assumptions in Tukey's test. Differences in foliar nutrient concentrations between control and scalp trees are usually small in magnitude. Statistically significant differences between scalp and control trees within sites are more likely to indicate higher foliar nutrient concentrations in scalp trees than in control trees. In fact, foliar nutrient concentrations of P, B and Fe are significantly higher for scalp trees at 50 % or more of the study sites. For control trees, only foliar A l is significantly higher at 50 % of the four study sites. CD > in o CD "5 c CO o "E ' t o o n CD ca CD X> o u CD e _» CD E CO cn CD CD CO > cn CD CO co CO cu E o CO "o CO c CD u 1= o o co u CO c CD E co 9> c o •is u o co £• 2 CO -£ CD CD § §» CO CO CD CU m m a- S CO •£ £ E co co o u CO c o ! ; CO c o ! o ; co a> c o ; CO _ Q C M ±114. ^— i C M ; *r— +1 c o M " +1 C D i c o ; +1: C D C O +i L O ; . — ' +i: L O c o +1 L O C O +1 C O C M ; L O ; c o L O ; Ovi C O C O • o • c o ; C O C M C D • c o • c o ; C D L O C O C M • L O ; c o C O a> L O CO C M O c o : C M i O ' C O C D c o ! o ! CO C M C D c o ; C M i o • C M C D CO C M C D O +l o : +1; C D +1 C D ! +1; C D +1 o 1 +1; C D +1 C D +1 c n ; O i O D o c n : O i ^— C D 1 O c o o o o 1 C D C D ! C D C D ! C D C D CO h -O CO '• L O ; C D ; CO C O O CO ' L O : C D ; CO C O o CO ' c o ; o ; CO C O O CO r--o O +l C D ; +i: C D +1 C D ; +i: C D +1 C D ; +i: C D +1 C D +1 C O C M C M C O C M C M C M C M C O • C M ' C O C M C O C M O C D C D C D C D C D C D C D CO C M O CO C M O CO O _ o C M C D J a O CO C M C D - O O ca C D O +l O +1 C D +1 C D +1 C D +1 C D +1 C D +1 C D +1 ••- c o •>s- c o C M o o o C D C D C D C D C D o CO C O C D CO oo o CO o j3 C O C D CO C O C D CO O . O CO oo o o +l C D +1 C D +1 C D +1 C D +1 C D +1 C D +1 C D +1 C D L O c o L O c o C O C M C O M " C O c o L O C D - 3 -o C D C D o C D C D C D C D CO C M C D - 0 C M O CO C M O CO c o C D CO C M O CO C O O . o c o C D CO C M C D O +l C D +1 C D +1 C D +1 C D +1 C D +1 CD* +1 C D +1 C O oo o C M C O O O , o i C M L O o C D C D C D C D C D ; C D C D CO C M CO C O 1 CO ! c o C O C M C M ! co ; >*• CO C M ' CO i C O CO i— O +l C D +1 ' C D : +i C D +1 • C D : +i C D +1 • C D : +i C D +1 o C M 0 0 ! L O • C O L O I C O C O C O ! o ; C M £• 2 co -p CD CD E E co ca tx tr ZL 73 Table 14. Foliar Concentrations of Some Micronutrients at Different Sites. Values with the same letter code are not significantly different at the 0.05 level. Location Treatment Cu Zn Fe (ppm) Mn B Beaverdell Scalp Control 5.34 ±1.09a 5.73 +0.59a 59.07 ±15.40a 58.15 +9.08a 62.73 ±7.90a 53.62 ±9.56b 254.3 ±53.2a 302.1 ±90.8a 34.22 ±8.50a 27.93 ±7.05b Carmi Scalp Control 6.34 ±1.21a 4.62 +1.08b 55.05 ±11.94a 55.06 +10.20a 57.24 ±17.15a 47.28 ±6.45b 235.2 ±83.7a 321.3 ±106.1b 15.81 ±3.24a 18.15 ±4.24a Rathmullen Scalp Control 4.80 +0.59a 5.82 +1.46b 60.18 ±10.38a 61.04 +11.02a 45.67 ±7.77a 51.78 ±8.00b 249.3 +72.7a 283.1 ±74.0a 28.09 ±5.42a 30.14 ±4.80a Lassie Lassie Scalp Control 6.84 ±1.25a 6.19 ±0.93a 70.16 ±9.32b 46.62 +9.23a 50.88 ±8.26b 40.64 ±4.95a 236.1 ±85.7a 219.9 ±53.8a 21.35 ±6.98b 16.15 ±4.61 a The mean foliar nutrient concentrations presented in the above Tables are not unlike those reported for lodgepole pine by Smith and Wass (1994a). For example, they found foliar N concentrations ranged from between 1.23 to 1.37 percent compared to the 1.11 to 1.53 percent reported in this study. Foliar samples analyzed in this study tend to be slightly higher in P, Zn, Mg, and Mn. Smith and Wass reported no significant differences in foliar nutrient concentration between seedlings grown on different soil disturbance types. Results of Analysis of Variance of Foliar Nutrient Concentrations Analysis of variance was applied to the foliar nutrient concentration data in a few different ways. First the omnibus model; foliar nutrient concentration = /(site location, Fertility Code, site x Fertility Code) was applied to the data set as a whole. The Fertility Code is a categorical factor based on the Fertility Index and in this case compares seedlings grown on undisturbed soil (i.e. Fertility Code of "control") to seedlings grown on scalps (Fertility Code of "scalp"). Fertility Codes are 74 described in the Methods section and in the section entitled Results of Seedling Classification. Complete results of this analysis are presented in Appendix VII. Later, a variant of the omnibus model was applied to subsets of the data to more explicitly test the between and within site variance. Results of these analyses are described below. The omnibus model explained between 6 and 57 % of the variance in foliar nutrient concentration data depending on the nutrient being analyzed. The model explained N , P, S, B and A l concentrations best. Squared multiple R values ranged from 0.420 (for Al) to 0.570 (for B). The site location factor was more significant than Fertility Code factor in predicting foliar concentrations of those nutrients, as indicated by the probability statistic. The Fertility Code factor was not a significant in determining the N , S, B, or A l concentrations at the 0.05 level. Fertility Code was a significant factor in the analysis of available P data. The site location x Fertility Code interaction effect was always significant, except when predicting Ca content, when none of the independent factors had a probability statistic value indicating significance associated with them. The omnibus model explained over 25 % of the variance in K, Cu, Fe, and Zn concentrations. Again, location was a significant factor, Fertility Code was not at the 0.05 level. The model was less successful in predicting the foliar concentrations of Ca, Mg, and Mn concentrations (less than 18 % of the variance explained), and the probability statistic for location and Fertility Code are likely not important under such conditions. In summary, the omnibus model is reasonably successful at predicting the nutrient concentrations of some nutrients. The omnibus model suggests that the site location is a more important factor than the soil conditions (as indexed by Fertility Code) in determining foliar nutrient concentration. This inference was further tested by applying A N O V A to data from control trees only, in order to test the significance of site location independent of the additional complication of soil conditions. Similarly, A N O V A was applied to data from each site 75 individually in order to test the significance of soil conditions (i.e. Fertility Code) within each site. Thus the two additional models used were; foliar nutrient concentration =/(site location) and foliar nutrient concentration = /(Fertility Code) When A N O V A is performed on foliar nutrient concentration of control trees using the former model above, the model explains between 8 and 59 percent of the variance depending on the nutrient being analyzed for. The model explains more than 50 percent of the variance of N, P, K, S and B contents. The model explains less than 30 % of the variance in Ca, Mg, Cu, Zn, Fe, Mn, and A l concentrations. When A N O V A is performed on foliar nutrient concentrations of control and scalp trees at individual sites using the latter model described, only between 1 and 34 % of the variance is explained depending on the nutrient being analyzed for. No consistent pattern in predicting the foliar nutrient concentrations with respect to soil conditions (i.e. scalp vs. control) emerges. Overall, results of A N O V A suggest that forest floor displacement has little value as a predictor of foliar nutrient concentrations. Results from Lassie Lake may appear to be an exception to this assertion, but remember that the Lassie Lake scalp trees are on one site and the control trees an entirely different location, similar in many respects, but still, site and soil conditions may have been confounded there. Diagnosis of Nutrient Deficiencies by Computer Program The box plots and A N O V A results are presented to illustrate trends and differences in foliar nutrient concentrations. These analyses do not, however, give any indication of how foliar nutrient concentrations may be influencing growth. Possible nutrient deficiencies were analyzed 76 for using a computer program developed at UBC (Ballard and Carter, 1983; Ballard and Carter, 1986). Of the several approaches widely employed in evaluating foliar chemical data, the most common is the use of critical levels. This method compares the concentration of each element in the foliage to interpretive criteria normally developed from fertilizer and/or pot trials. "Critical level" is generally defined in the agricultural literature as the nutrient concentration that is just deficient for maximum growth (Carter, 1992). Another method of evaluating foliar nutrient data makes use of nutrient ratios in which the ratios of several nutrients in the foliar material are compared with those known to occur in healthy, growing tissues. The program developed by Ballard and Carter (1983,1986) uses both the concept of critical levels and nutrient ratios in diagnosing nutrient deficiencies in conifer species. Results of the critical value comparison are expressed as a percent and may be expressed in text also depending on the nutrient being analyzed. Results of the nutrient ratio analysis are expressed in text (see Table 15). 77 Table 15. Summary Table of Nutrient Deficiencies Diagnosed Using Ballard, Carter and Emanuel's Computer Program (Ballard and Carter 1983). All nutrients not listed were rated as being Adequate for Maximum Growth. Location Nutrient Control Scalp Deficiency Rating Deficiency Rating (% + / - critical value) (% + / - critical value) Beaverdell N Severe (-19%) Severe (-18%) S None (-34%) None (-34%) Mg Little or None (-6%) Adequate (+6%) P Slight (-9%) Adequate (+9%) Carmi N Adequate (+5%) Slight to Moderate (-1 %) S None (-12%) Possible (-18%) Mg Little or None (-6%) Slight to Moderate (-14%) P Adequate (+34%) Adequate (+22%) Rathmullen N Slight to Moderate (-7 %) Slight to Moderate (-2 %) S Possible (-19%) Possible (-16%) Mg Little or None (-5%) Adequate (+8%) P Adequate (+21 %] Adequate (+29 %) Lassie Lake N Severe (-24%) Severe (-18%) S None (-37%) None (-27%) Mg Slight to Moderate (-17%) Adequate (+1 %) P Adequate (+2%) Adequate (+34%) K Slight to Moderate (-2 %) Adequate (+16%) Fe Possible (-10%) None (+13%) Analysis of mean nutrient contents of scalp and control trees by location using the Ballard and Carter (1983) computer program indicated that trees on study sites are generally deficient in N, S, and Mg. Furthermore, the P / A l ratio is suggestive of a P deficiency across all sites and treatments. Note that the S content is often relatively far below the critical value for lodgepole pine (expressed as a negative percentage) but since N is also low, and S deficiencies are rated by the N:S ratio, the analysis of S deficiency stated in the text is usually "none" or "slight". Nitrogen was the only nutrient ever rated as being severely deficient. Where nitrogen is severely deficient it is so in both scalp and control trees, suggesting no effect by soil disturbance. Few deficiencies were found that could be related specifically to the effect of forest floor 78 displacement. In fact on average, trees growing in undisturbed soil are more likely to be more deficient in N , P, and Mg than trees grown on scalps (excepting perhaps the Carmi site). Results suggest that either the scalped mineral soil has enough nutrient storage to support tree growth as well as undisturbed soil or that adjacent mixed and undisturbed soils, relatively high in soil nutrients, are accessible to root systems growing in scalps. Foliar analysis is very popular but somewhat simplistic for the following reasons: (1) foliage samples collected at the end of the growing season may not accurately reflect nutrient status during the growing season, particularly for nutrient elements such as boron that are i subject to periodic, acute deficiencies; (2) interpretations cannot stand alone, but must be related to stand growth; and (3) interpretative data are associated with considerable imprecision; performance and site ecological characteristics (Carter, 1992). As to the first point, one has no choice but to rely on the technology available and acknowledge imprecision. Carter's second concern was addressed with expert advice, and both growth performance and site characteristics have been assessed for each site in this study. Foliar nutrient concentration data may therefore be considered a very useful addition to this investigation of the effects of forest floor displacement. 79 RESULTS OF SEEDLING CLASSIFICATION Fertility Index values ranged from 0.00 (all scalped) to 51.67. The maximum possible value would have been 62.0 (all mixed). The most populous single value was 33.5 (n= 66) which reflects undisturbed soil conditions. Otherwise the distribution of Fertility Index values among seedlings was close to normal. The distribution of seedlings by Fertility Index is illustrated in Figure 22. Figure 23 is a probability plot which compares the Fertility Index distribution with the values expected in a normal distribution. A straight forty-five degree line would indicate a perfectly normal distribution. Based on previous experience with probability plots and biological variables, these results indicate that the distribution of Fertility Index among seedlings is close to normal. Figure 22. Histogram showing Fertility Index distribution of seedlings. Figure 23. Probability plot comparing Fertility Index distribution with normal distribution. 0 10 20 30 40 50 60 Fertility Index 0 10 20 30 40 50 60 Fertility Index The distribution of seedlings among the different Fertility Codes is presented in Table 16. Seedlings on completely undisturbed soils (Fertility Index=33.5) were the most populous group and were assigned the Fertility Code of "Control". Seedlings with soil conditions dominated by scalps (Fertility Index of 0.00 -13.13) were dubbed "Scalp". The remaining seedlings were fairly evenly distributed among the other Fertility Codes. Although analysis has been performed which tested seedling productivity related to all of the Fertility Codes, only results reflecting the scalp, control and medium soil disturbance conditions are presented 80 herein. Seedlings of these codes were selected for analysis and presentation in this document to compare the extremes of forest floor displacement (scalp vs. control), as well as conditions which reflect partial forest floor displacement (medium). Table 16. Seedling Distribution Among Fertility Codes. Scalp Very Low Fertility Code Low Medium Control Super Fertility Index range n % of pop. 0.0-13.13 50 15.15 13.54-23.9 54 16.36 24.3-27.5 47 14.24 27.9 - 33.1 57 17.27 33.5 66 20 33.8-51.7 56 16.97 A NOTE ON THE PRESENTATION OF RESULTS OF SEEDLING PRODUCTIVITY ANALYSIS The analysis of seedling productivity with respect to soil disturbance was necessarily more complex than the analyses of the soil and foliar data sets. Relationships between seedling productivity and forest floor displacement were tested using a variety of analytical techniques. Presenting the results of all the analyses attempted would require a considerable quantity of paper, and the reader would require many hours of effort to assimilate all of the information. However, all of the analyses performed indicate that there is no noteworthy, large or consistent relationship between lodgepole pine productivity and forest floor displacement. Therefore, only the most compelling and straightforward analyses are presented herein. Furthermore, in order to maintain a consistency in style of presentation throughout the report, the seedling productivity data set is introduced through selected box plots and descriptive statistics. Growth curves are then used to condense the above information and illustrate the trends occurring over time. Results of the most accurate A N O V A and multiple general linear regression models are used to identify the factors most important in controlling seedling productivity. Finally, a possible coincident effect of forest floor displacement on seedling recruitment and regeneration delay is explored. 81 INTRODUCTION TO THE SEEDLING PRODUCTIVITY DATA S E T A complete set of box plots of seedling growth variables is presented in Appendix VIII. In general there is little or no separation between the growth data of scalp and control trees at any of the study sites. Box plots of total height and root collar diameter for control and scalp seedlings at Beaverdell and Lassie Lake are presented in Figures 24 through 27 . The Beaverdell Figures include results up to about age ten, the Lassie Lake Figures pick up from there with ages ten to about age twenty-seven. Soil disturbance-based differences in variability are not consistent across all sites, i.e. scalp trees are more variable at one site, control trees at another. However there does appear to be an increase in variability of total height and root collar diameter with age. This is not uncommon in juvenile stands. At Lassie Lake, by age ten the variability in total height has become more or less constant, although increasing variability continues with age for root collar diameter, especially for the seedlings grown on scalps. i Figure 24. Box Plot of Total Heights at Different Seedling Ages Under Scalp and Control Soil Conditions at Beaverdell. ro Z 100h 8 10 12 Fertility-Code • SCALP CONTROL Seedling Age Figure 25. Box Plot of Root Collar Diameter at Different Seedling Ages Under Scalp and Control Soil Conditions at Beaverdell. 4 6 8 Seedling Age 10 12 Fertility-Code • SCALP CONTROL 84 Figure 26. Box Plot of Total Heights at Seedling Ages Greater Than Ten Years Under Scalp and Control Soil Conditions at Lassie Lake. 15 20 25 Seedling Age 30 Treatment • scalp control 85 Figure 27. Box Plot of Root Collar Diameters at Seedling Ages Greater Than Ten Years Under Scalp and Control Soil Conditions at Lassie Lake. 200 15 20 25 Seedling Age 30 Treatment • scalp control 86 The mean and standard deviation of total heights for scalp and control trees at Beaverdell, Carmi and Rathmullen are presented in Table 17. Significant differences between means were tested for using the separated t-test which does not require equal variances among groups being tested. Significant differences are infrequent and where they do occur the scalp trees consistently outperform the control trees. By age four, scalp trees consistently have greater mean total heights compared with control trees at all sites. The mean and standard deviation of root collar diameters for scalp and control trees at Beaverdell, Carmi and Rathmullen are presented in Table 18. Significant differences between means were tested for using the separated t-test. Significant differences are infrequent and where they do occur the scalp is usually outperforming the control trees. Scalp trees consistently outperform control trees in terms of mean root collar diameter at Beaverdell and Carmi. At Rathmullen, control trees initially have greater mean root collar diameters. However, the rate of growth of scalp trees is greater and by age seven scalp trees have caught up and later surpass control trees in mean root collar diameter. T 3 co u cn '55 cu CO CO CO •o o u cu cu 3 CO (Z co • ca ca cu —^» CO > . 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Significant differences between means were tested for using the separated t-test. Significant differences are infrequent and where they do occur the control trees consistently outperform the scalp trees. In fact, scalp trees appear to be almost exactly one year behind control trees in terms of total height. However, by about age ten or twelve, the scalp trees have more or less closed the gap in total height. The mean and standard deviation of root collar diameters for scalp and control trees at Lassie Lake are presented in Table 20. Significant differences between means were tested for using the separated t-test. Significant differences are infrequent and where they do occur the scalp is usually outperforming the control trees. A pattern similar to the one indicated in the analysis of total height is evident. Scalp trees appear to be almost exactly one year behind control trees in terms of root collar diameter. However, by about age twelve the scalp trees have more or less closed the gap in root collar diameter, and actually go on to exceed control trees slightly up to the end of measurement at about age 21. < in o o CD CD O ) < c o c o o o •H +1 i— L O C D C D to CM CD < +1 O C M C £ CD 3 = "•5 CD < I— CO o o +1 +1 C D O L O C D to CM CD cn < CO u CO CD w CO t o CD •o o 0 1 CD CD < CD cn < C D r-~ o o -H -H C O L O o o CO c o CM CD < co C M CD C n < c o o CD . 0 ) 5 CO • co L O C O o o CD cn < CD c o CO L O c o o o -H +1 i— CO CO CO CM CM CD cn < CM CD cn < +" -H o o o c o i-1 o -H +1 LO O CD CO a> 00 3 55 CD c a _ l CD 'CO " — 1 CD CD ~ ~ C O CD cn < CD < L O C D d O +1 +1 c q cvj c o -d- L O o o -H -H i - CO Cvi CM CM CD cn < CD cn < o c o o +1 +1 o CO CO c n c o o o •H -H L O CO c o c o E c •S- CO <n « 1> & E l £ •-g> 0 ca s E .2 CD * - »— co co > CO CD ^ Q CO •s '§ -2 « = ? ca £ 1 " CO ra = 5? co S CD CD cn < CD cn < CU cn < CD cn < o u L O o o +1 -H C D C O T - ^ C M C O L O o o -H -H CM CO CO O O -H -H C O -=f O ^ T- C O o o +H +1 C D CO O O Q . O CO ~ u CA c o u T3 CD C o o co CD cn < CD cn < CP cn < CD C D < CD T 3 O U c o c o o o +1 -H O C O c o c o CO o o o -H -H c o c o c o c n o o +1 -H c o c n CD o +i +i c o o c o t-^ CO u to 68 cn < C O C D co co cn < cn co CM •>a- co ^ — — 1 — cu CD +1 +) cu cn < •"3" L O 3= C D O T3 r— co j>. 0 0 :an 1 — co co :an i— * . . — CD +1 - H "E cn < r— co cn c d co "co co r— CD CO X I co co •>-CO 0 T-• C M CD > DO r ge - H - H U < C O C O i— cu C O C O C M ~ £ Z cn C O "* ere CD cn +• * < « • ! L O hdi C M c< L O § * S L O • CO 00 •b Mean Age 44.5 ±; 53.4 ± 1 CO ca xi C M C O CD ••—• C D C O CO CD cn +1 +1 > . < co T3 C D to co -=J-Ui CD ca xi O L O sieLa co CD cn L O C O +H +1 sieLa < C O L O CO C O C D co C M C O _ l > CD CO x> • C 0 C M •>*• LO • " T r-" m)a o _ CD cn - H - H m)a c: < O C M E, co C M ad 0 C M C M CO CD ign CO X I C O 1 -ame CO C O C D ame > . CD cn +1 - H nal < C O L O L O O C O 'cn 1 — C M Coll mat ca .a r-~ C D £ CO C M Roi <sa CD cn < • H - H co co o CO O C O c CD o '-£ as CM co co •> x: I"" C O CD Q CD cn +1 - H •a CO < 1 - ; C O c c co r-" CO CO "O CD tan * 1— 0 1 - " T3 vel. Age - H +1 an vel. Age h- C M C a> C M C O CO LO CD 0 0 o CM CD X I at the Code Scalp Control co 1-in CM CD cn < CM CD cn < CO CM CD cn < CM CM CD cn < CM CD cn < CM CD cn < CD cn < CD cn < CD cn < •a CD 3 O u CM X i CO CD cn < CD cn < o U C M • H C O C O I O C O C O C M C O L O C D C O C M • H c o C D C M C O +1 +1 C D C O o co -3-C D •>*• C M T-- H - H C O T-C M C O C O C M ^ LO C M 1 — - H - H r~- C O co co C O L O C M 1 — - H +1 r-~ - -co c o co C M C M 1 — - H +1 -3- C M C M 1 — - H +1 t-~ C M C O C O o o C O 10 C O C D CO ^ ~ ^ +1+1 cn cn C D *X C M cd o o C O C O C O L O - H +1 co 10 co* c d cn cn co ->3-1^  10 +1 +1 co co O C M C O C O °- s co "I 06 Patterns of growth between scalp and control trees at the Lassie Lake site differ from those at the Beaverdell and Carmi sites where scalp trees consistently outperform control trees. The patterns of relative growth in total height and diameter between scalp and control trees at Lassie Lake are more consistent with the patterns exhibited at Rathmullen. The seedlings at Lassie Lake and Rathmullen may be responding to the effect of stand density. Unlike Lassie Lake and Rathmullen, the Beaverdell and Carmi study sites were not excessively stocked and canopy closure had not yet occurred. Initially, at both Rathmullen and Lassie Lake, control trees perform slightly better, on average, than scalp trees. However, as the stand ages and canopy closure begins to occur, competition in these densely stocked stands becomes more intense. If the scalped soils are less well stocked, as anecdotal observation suggests, scalp trees may be favoured for a period of time. This may explain why scalp trees grow more quickly and eventually catch and exceed control trees at Rathmullen and Lassie Lake. Seedling growth is determined by a number of factors and their performance with respect to soil disturbance is the result of both negative and positive influences. Seedlings at different sites may differ in their response to soil disturbance depending on how growth limiting factors specific to that site are impacted. The main conclusion that can be drawn from the box plots and Tables of mean total height and diameter is that differences between scalp trees and control trees are small and infrequently statistically significant. GROWTH CURVE ANALYSIS Growth curve analysis allows for a visual comparison of trends in the growth of seedlings experiencing different levels of forest floor displacement. The growth curves presented below were created by plotting the mean values of total height or root collar diameter that occur over time. Error bars for the mean values have been removed to prevent the Figures from becoming cluttered. For example, Figure 28 is a height growth curve for control and scalp seedlings at Beaverdell. The separation between mean values is about as large as is ever seen in this study; yet with error bars, the difference is shown to be minimal. By removing error bars, 92 the curves can be seen more clearly. Comments on the significance of differences between curves are presented in the text where appropriate. Figure 28. Mean Height Growth Curve with Standard Deviation Error Bars for Seedlings at Beaverdell. Three levels of increasing incidence of forest floor displacement are contrasted in the growth curves. Levels correspond to the previously described Fertility Codes; control (no displacement), medium (some displacement), and scalp (near-complete displacement). Figures 29 and 30 compare height and diameter growth between sites for control trees. Figures 31 and 32 compare height and diameter growth between sites for scalp trees. Clearly Rathmullen is the most productive site. In addition, Rathmullen scalp trees appear to outperform their control neighbors in terms of mean total height; however, error bars generously overlap. 93 Figure 29. Mean Height Growth Curves of Control Seedlings at Different Sites. 400 2 4 6 8 10 12 Seedling Age (Years) Fertility-Code rathmullen x carmi • beaverdell Figure 30. Mean Diameter Growth Curves of Control Seedlings at Different Sites. "5 E CO b o O o o c t 60 50 40 30 20 10 0 0 2 4 6 8 10 12 Fertility-Code rathmullen < carmi • beaverdell Seedl ing Age 94 Figure 31. Mean Height Growth Curves of Scalp Seedlings at Different Sites. 400 2 4 6 8 10 12 Seedling Age (Years) Fertility-Code rathmullen carmi • beaverdell Figure 32. Mean Diameter Growth Curves of Scalp Seedlings at Different Sites. 60 E E 50 1 4 0 5 30 k_ (0 =5 20 0 1 10 or o o I . I i I— i _L_ 2 4 6 8 Seedling Age 10 12 Fertility-Code rathmullen carmi • beaverdell In general, there were no substantial differences between seedling growth on the different soil conditions evaluated at any of the study sites. Where differences in mean growth variables were noted, trees growing on scalps were often more productive than their control 95 neighbours. Results of growth curve analysis are presented for the overall data set (Figures 33 and 34) and for each study site individually. In Figure 33 , mean overall height growth of trees grown on scalps appears to be reduced compared to that of trees grown on control soil conditions; however if standard deviation error bars were included in the figure, it would be clear that differences in height growth related to the level of soil disturbance are negligible. Note that results for trees aged greater than eight years old in this overall analysis are dubious because of shrmking sample size and increasing site influences. The apparent decrease in total height at age nine is the result of a small sample size dominated by trees at the older, less productive Beaverdell site. Figure 33. Overall Mean Height Growth Curve of Seedlings Across all Sites. Figure 34 displays the overall mean growth curves for root collar diameter of trees grown on different soil conditions. Root collar diameter is even less responsive overall to forest floor displacement than is total height. Root collar diameter growth curves of trees on different soil conditions track each other until age nine when the same considerations regarding the dominance of the Beaverdell site must be applied to interpretation of results. The influence of the Beaverdell site on the overall diameter growth curve is increased drastically at age nine. 96 Figure 34. Overall Mean Diameter Growth Curve of Seedlings Across all Sites. |40 E SCALP MED t CONTROL Fertility-Code or 01 0 2 4 6 8 Seedling Age , J L L 10 12 The Carmi site is the youngest study site and was selected for study specifically in order to test the effects of forest floor displacement on early growth, effects which may have been obscured in the assessment of older trees. Forest floor displacement has no effect on height growth during the first five years of growth at the Carmi site (Figure 35). There appears to be some separation of root collar diameter growth curves at about age five in Figure 36 . Mean root collar diameter of scalp trees exceeds that of control trees by approximately 5 mm at age five. Factors affecting root collar diameter may include competition intensity. For example if control soils were more heavily stocked than scalps, this may favour scalp root collar diameters. More likely, however, the difference in mean diameter at age five is a result of the extremely small sample size (there is only one five-year-old control tree at Carmi). 97 Figure 35. Mean Height Growth Curve of Seedlings at Carmi. Figure 36. Mean Diameter Growth Curve of Seedlings at Carmi. Results of growth curve analysis at the Rathmullen site indicate no substantial differences in productivity between trees growing on different soil conditions (Figures 37 and 38). If any effect was to be noted it may be that scalp tree productivity starts to exceed that of control trees at about age eight, but had error bars been included in the Figures it would be impossible to draw such a conclusion with any confidence. However, Smith and Wass (1994b) noted that lower stocking densities on scalps may favour scalp trees as canopy closure approaches. Figure 37. Mean Height Growth Curve of Seedlings at Rathmullen. Figure 38. Mean Diameter Growth Curve of Seedlings at Rathmullen. 99 Results of growth curve analysis for the Beaverdell site represent another instance in which scalp trees tend to perform better than their control neighbours on the basis of mean total height and mean root collar diameter (Figures 39 and 40). However, once again it would be misleading not to note that with the inclusion of error bars, the differences between soil disturbance types seem negligible. Figure 39. Mean Height Growth Curve of Seedlings At Beaverdell. 100 Figure 40. Mean Diameter Growth Curves of Seedlings at Beaverdell. Fertility-Code S C A L P MED • CONTROL Seedling Age The growth curves of seedlings at the Lassie Lake study sites indicate no substantial differences in seedling productivity with respect to forest floor displacement after twenty years (Figures 41 and 42). Results of the Lassie Lake analysis confirm that forest floor displacement has no measured negative impact on lodgepole pine seedling productivity in the short and in the medium-long term. A difference in total heights appears to develop at about age ten in which scalp trees are reduced slightly (<0.5m) on average. However, this difference is negligible when standard deviations are taken into consideration. Figure 41. Mean Height Growth Curves of Seedlings at Lassie Lake. Seedling Age (Years) Figure 42. Mean Diameter Growth Curve of Seedlings at Lassie Lake. 102 ANALYSIS OF VARIANCE OF SEEDLING GROWTH DATA The most successful and accurate omnibus model used in the analysis of seedling productivity growth was; tree productivity = /(site, tree age, fertility index, exposed mineral soil, local density) The model was applied to tree growth variables at different seedling ages. Figure 43 illustrates the accuracy of the omnibus model over the lives of the seedlings studied. In addition, one can compare the strength of influence of the different factors used in the model for predicting seedling growth variables (see Tables 21, 22, and 23). Figure 43. A Graph of the Squared multiple R values of the Omnibus Model vs. Tree Age for the Three Seedling Growth Variables Evaluated. 1.0 0.81 or % 0.61 S 0-4 3 CT CO 0.2 0.0' JL _ i _ 2 3 4 5 6 7 8 9 Age of Measurement (Years) 10 Growth Parameters Ann. Increment Diameter Height The omnibus model is reasonably accurate when predicting seedling growth parameters. Squared multiple R values of greater than 0.20 are routinely reported in the literature and accepted as important in ecological studies. The model is consistently most 103 accurate when predicting seedling root collar diameter, indicating that the terms used in the model more strongly influence seedling diameter than total height or annual height increment. During the first several years of growth, the relatively low R squared values indicate that seedling growth variables are largely independent of the environmental factors included in the model. This observation is not entirely unexpected since lodgepole pine seedlings have been known to take a "running start", only after which environmental stresses will significantly impact on growth. The generally positive slope of the lines in the above graph illustrates that the regression model is better able to predict growth parameters as seedlings age. Some time after the third year, the seedlings' growth parameters begin to respond to and be determined by environmental conditions. Results of the overall A N O V A analysis for seedling growth properties are presented in Table 21 (total height), Table 22 (annual increment), and Table 23 (root collar diameter). So far in this report there has been little evidence to suggest that forest floor displacement has a significant effect on seedling productivity. By applying A N O V A to the seedling productivity data set the major environmental factors controlling productivity may be identified. Partial eta-squares are used to compare the relative contribution of each factor in the model in explaining the variance in the data. For a complete definition of partial eta-squares, refer to the methods section. 104 Table 21. Results of Analysis of Variance for Seedling Total Height at Ages 2 through 9 years. Partial Eta-Squares are presented to compare the influence of the experimental terms. Partial Eta-Square Values Height at N Squared Site Tree Age Fertility Exposed Local Age Multiple R (%) (%) Index Mineral Soil Density (Years) (%) (%) (%) 2 279 0.425 89.53" 4.97" 0.66 0.19 4.66" 3 315 0.370 89.03" 10.13" 0.04 0.30 0.50 4 305 0.469 90.25" 8.90" 0.00 0.75 0.09 5 261 0.611 88.30" 10.64" 0.54 0.29 0.23 6 242 0.636 74.38" 21.46" 1.11 0.61 2.45" 7 235 0.665 81.56" 14.49" 1.90" 0.35 1.70* 8 175 0.677 90.02" 4.84" 3.07" 0.47 1.60* 9 96 0.785 84.24" 10.84" 4.43" 0.32 0.16 ** indicates statistical significance at the p<0.100 level, * indicates marginal statistical significance at the p<0.150 level. Table 22. Results of Analysis of Variance for Seedling Annual Height Increment at Ages 2 through 9 years. Partial Eta-Squares are presented to compare the influence of the experimental terms. Partial Eta-Square Values Height N Squared Site Tree Age Fertility Exposed Local Increment at Multiple R (%) (%) - Index Mineral Soil Density Age (Years) (%) (%) (%) 2 94 0.239 74.84** 1.53 6.03 2.08 15.51 3 191 0.158 83.28" 14.83" 0.02 1.09 0.78 4 227 0.215 85.34" 6.62* 0.07 7.96* 0.00 5 241 0.400 60.87** 35.53** 2.46 0.00 1.13 6 242 0.383 65.79** 21.10" 0.29 0.03 12.78" 7 235 0.515 96.69** 0.00 . 2.40* 0.39 0.53 8 175 0.549 88.52** 0.97 8.40* 0.80 1.31 9 96 0.597 95.31" 1.92" 1.83 0.65 0.29 " indicates statistical significance at the p<0.100 level, * indicates marginal statistical significance at the p<0.150 level. 105 Table 23. Results of Analysis of Variance for Seedling Root Collar Diameter (RCD) at Ages 2 through 9 years. Partial Eta-Squares are presented to compare the influence of the experimental terms. Partial Eta-Square Values RCD at Age N Squared Site Tree Age Fertility Exposed Local (Years) Multiple R (%) (%) Index (%) Mineral Soil (%) Density (%) 2 240 0.278 30.92" 65.92" 0.79 0.06 2.31* 3 242 0.242 7.02" 90.93" 0.79 0.23 1.03 4 242 0.305 2.74 95.57" 0.04 0.63 1.03 5 242 0.392 33.03" 65.18" 0.14 0.67 0.98 6 242 0.421 70.84" 26.43" 0.09 1.16 1.47 7 235 0.423 80.55" 14.51" 0.42 0.67 3.85" 8 175 0.460 81.48" 13.43" 0.38 1.45 3.27* 9 95 0.695 57.07" 30.31" 6.09" 0.01 6.52" " indicates statistical significance at the p<0.100 level, * indicates marginal statistical significance at the p<0.150 level. DISCUSSION OF THE PRINCIPAL FACTORS OF THE ANOVA MODEL Site Effects With very few exceptions, the site effect is consistently the most important term in the A N O V A model. For all of the seedling growth variables (height, diameter, and height increment) site remains the most significant effect throughout the first nine years of seedling growth (see Tables 21, 22, 23). During this period there are few other clearly consistent trends in the effect of site on seedling growth parameters. The site effect can account for between 75 and 90% of the total explained variation in total seedling height. For annual increment it accounts for between 60 and 95% of the total explained variation. The effect of site on seedling diameter is something of an exception. It accounts for between 2.75 and 81% of the total explained variation and seems to become more important as the seedlings age. That the site effect is the dominant effect in the A N O V A model is consistent with previous analysis which also showed a reasonably important site effect on soil and foliar nutrient concentration and mean growth curves. The dominance of the site term in the A N O V A model indicates a large amount of between site, or within subzone, variation. Efforts were 106 made to select sites that were as comparable as possible, but given the limited number of useful sites available, site similarities were roughly limited to general soil conditions (i.e. texture and depth of forest floor) and biogeoclimatic subzone. The site effect on growth is likely the result of the differences that exist between sites in moisture / nutrient regime, aspect, elevation, stand density, etc. In addition, at least a portion of the site effect may be related to the differences in the general level of soil disturbance (% NAR disturbed) occurring at each site. Presumably, all other tilings being equal, a seedling growing on a less disturbed site (fewer, less extensive scalps) will have greater access to the soil nutrient reserves in organic matter than a seedling on a more highly disturbed site. However, increased soil warming and drainage in spring and fall may extend the growing season on scalps. Rathmullen was the least disturbed site (22 % NAR) followed by Beaverdell (34 %) and Carmi (47%). Analysis of seedling growth data across all study sites is useful for identifying general trends which may be occurring throughout the old ICHc2 subzone (currently the ICHmkl and the MSdml subzones). However, the dominance of the site effect on the overall analysis may obscure the less obvious, but no less relevant relationships with other site parameters such as soil disturbance indices. It is interesting to note that the effect of the age of the tree being measured also accounts for a disproportionately large amount of the variation in seedling growth parameters (discussed below). Therefore, a strategy of analyzing the data independently of site and tree age may be necessary in order to identify, quantify and interpret the effect of soil disturbance on seedling productivity. However, regression analysis performed on this basis failed to indicate any important relationships between forest floor displacement and seedling productivity at any of the study sites. Tree-Age Effects With very few exceptions, the tree-age effect is consistently the second most important term in the A N O V A model. For total height and diameter, tree-age remains a significant effect 107 throughout the first nine years of seedling growth (see Tables 21, 22, and 23). The significance of tree-age on total height growth remains fairly stable, and for the most part it accounts for between about 5 and 15% of the total explained variation. For diameter and annual height increment, the significance of tree age tends to be reduced with time. This trend of decreasing significance of tree age is most pronounced in the analysis of annual height increment, where at its peak, tree-age accounts for over 35% of total explained variation at age 5, falling to below 2% for ages 7,8 and 9, thus losing its statistical significance as an effect in the A N O V A model. Similarly, at its peak, tree-age accounts for over 95% of the total explained variation of seedling diameter at age 4, but it then falls to around 14% at ages 7 and 8. This trend probably indicates that once a seedling has been established, its annual growth proceeds at a rate that is independent of tree age. There are two equally plausible explanations for the importance of the tree age term. A tree age effect could be the result of variability in climatic conditions (e.g. precipitation, growing season temperatures, etc.) or it could be the result of other site processes occurring over time. A period of stress-free climatic conditions could help a seedling to perform well in future years, whereas a stressful period of climatic conditions may contribute to future poor performances. Other site processes could include any site conditions that change over time. In a young plantation these may include site amelioration via green-up, organic matter solarization, changes in soil microbial ecology, seedling density and other competition relationships, etc. In the analysis of variance, if the observed tree age effect was the result of a purely climatic effect, it should coincide with calendar year and therefore analysis of the tree age effect should be carried out on a chronological tree age basis (as in Tables 21,22 and 23). For example, a six-year-old tree at Beaverdell has experienced the same general climatic conditions as a six-year-old tree at Rathmullen, and so they may be compared on that basis. In contrast, if the observed tree age effect was the result of a purely site processes effect it should create a chronosequence in the data at each site and the data should be analyzed according to how long 108 after harvest the seedling was germinated. For example a seedling having germinated three years after harvest at Beaverdell should be comparable to a seedling that germinated three years after harvest at Rathmullen if the site processes that have occurred are not unique to any individual site. It is likely that some interaction occurs between the two sources of variation ascribed to tree age, e.g. the effect of a dry season on growth will depend on when in the site processes chronosequence it occurs. However, it is useful for the analysis to recognize that the effect of the tree age factor can be related to at least two different sources. Based on the results of soils analysis that indicate a chronosequence in soil properties after harvesting, it may be informative to test seedling productivity in relation to a similar chronosequence effect. Both methods of assessing tree age were tested by A N O V A and the chronological age (climatic effect) consistently produced slightly higher squared multiple R values and was consistently more significant on the basis of the probability statistic. Nevertheless, the idea of a post harvest chronosequence in seedling productivity is intriguing and will be further explored later in this section, but only as it relates to forest floor displacement. Interestingly, the use of one or the other tree age factors impacts on the probability statistic for the soil fertility index factor and the local density factor. Using the chronological tree age factor (assumed to be climate-related) in the model tends to increase the significance of the soil fertility term slightly compared with the results obtained when harvest-related age factor is used. This may reflect a relationship between annual weather patterns, seedling productivity and soil organic matter content, i.e. weather may effect soil water availability, soil temperature, or some other organic matter related property which in turn effects seedling performance. When the harvest-related age factor is used, the local density significance is increased compared with the results obtained with the chronological age factor. This likely reflects an increasingly important relationship between seedling performance and stand density as the 109 block ages. It should be emphasized that in the context of the results of A N O V A using the omnibus model, these nuances are trifling. Also of note is that the sum of squares and partial Eta-squared values for the location factor is much greater when the harvest-related age factor is used, indicating a stronger site-dependent component to the harvest-related age factor. These results are consistent with the idea that the chronological tree-age effect is mostly a climate effect and the harvest-related age factor is mostly a site processes effect. Which method of accounting for tree age is better to use and why? Both types of age factors are consistently highly significant for all growth parameters. Although using the chronological tree age factor results in a larger percentage of the variation explained (higher squared multiple R values), the harvest-related age factor is probably more useful for comparing results across sites. Al l harvest-related ages in the data set are represented at each site. Al l of the chronological ages in the data set are not represented at each site. Therefore to perform analysis across all sites using the chronological tree age will introduce a site bias into that factor. When analysis is performed on data from individual sites the chronological and harvest-related age factors result in identical groupings of seedlings and therefore have exactly the same effect. Including both factors in the overall regression model results in an increase in squared multiple r value of around 5 to 10 % depending on the growth parameter being evaluated. However, I found it preferable to forgo small increases in model accuracy because of the analytical advantages of the harvest-related age factor over the chronological age factor. In addition, when the harvest-related age factor is used in the analysis the loss in magnitude of explained variation is simply loaded into the error factor. Unless we can get climate data, climatic effects belong in the error factor because they are not experimental treatment effects and cannot be controlled or predicted. 110 Soil Fertility Effects The impact of Soil Fertility Index on seedling growth at individual sites has been previously assessed using mean and standard deviations as well as growth curves and Bonferroni's test for comparison of means. A N O V A was applied in order to assess the relative importance of the Fertility Index on seedling growth performance overall, and results are described below. A N O V A results have suggested that for when analyzing factors across sites, the harvest-related tree age factor is most appropriate. Therefore, for the analysis of overall soil fertility index effects, A N O V A was reapplied using the harvest-related tree age in the omnibus model. In the overall analysis of variance the Fertility Index is far less important than the site effect, and generally less important than the tree age effect (see Tables 24, 25, 26). The fertility index effect on seedling growth variables appears to become more significant as the seedlings age. It achieves statistical significance for total height after age seven but never accounts for more than four percent of the total explained variance. For annual height increment and root collar diameter, fertility index is rarely significant and results do not indicate any clear pattern or trend. 111 Table 24. Results of Analysis of Variation for Seedling Annual Height Increment at Ages 3 through 9 years. Partial Eta-Squares are presented to compare the influence of the experimental terms. Height Increment at Age (Years) N Squared Multiple R Site (%) Partial Eta-Square Values Tree Age Fertility Exposed (%) Index Mineral Soil (%) (%) Local Density (%) 3 279 0.123 69.32" 18.84 6.47* 4.37 1.00 4 291 0.227 80.16" 14.63" 0.41 4.54" 0.26 5 260 0.433 71.80" 26.56" 1.48* 0.08 0.07 6 242 0.395 79.61" 14.73" 0.32 0.00 5.34" 7 235 0.536 91.96" 5.67" 1.94" 0.12 0.31 8 175 0.567 81.92" 6.61" 8.94" 1.75 0.78 9 96 0.597 193.38" 2.73 2.54 0.94 0.41 ** indicates statistical significance at the p<0.100 level, * indicates marginal statistical significance at the p<0.150 level. Table 25. Results of Analysis of Variation for Seedling Root Collar Diameter at Ages 3 through 9 years. Partial Eta-Squares are presented to compare the influence of the experimental terms. Diameter at Age (Years) N Squared Multiple R Site (%) Partial Eta-Square Values Tree Age Fertility Exposed (%) Index Mineral Soil (%) (%) Local Density (%) 3 330 0.541 63.75" 35.32** 0.06 0.30 0.57 4 304 0.481 67.29" 30.90" 0.03 0.43 1.36" 5 261 0.459 66.87** 32.10" 0.22 0.09 0.72 6 242 0.440 81.92" 16.54** 0.16 0.43 0.94 7 235 0.44 85.53" 11.59" 0.44 0.26 2.18" 8 175 0.504 83.44" • 14.02" 0.73 0.01 1.80" 9 95 0.704 j 76.28** 16.51" 3.88" 0.06 3.27** ** indicates statistical significance at the p<0.100 level, * indicates marginal statistical significance at the p<0.150 level. 112 Table 26. Results of Analysis of Variation for Seedling Total Height at Ages 3 through 9 years. Partial Eta-Squares are presented to compare the influence of the experimental terms. Height at Age (Years) N Squared Multiple R Site (%) Partial Eta-Square Values Tree Age Fertility Exposed (%) Index Mineral Soil (%) (%) Local Density (%) 3 315 0.379 84.74" 14.52" 0.01 0.25 0.48 4 305 0.477 87.19" 11.94" 0.00 0.79 0.08 5 261 0.631 85.24" 13.74" 0.74* 0.16 0.12 6 242 0.656 84.03" 14.13" 0.79* 0.18 0.87" 7 235 0.68 87.98" 10.01" 1.27" 0.12 0.62* 8 175 0.708 87.95" 8.80" 2.70" 0.01 0.55 9 96 0.789 88.51" 7.67" 3.38** 0.33 0.11 indicates statistical significance at the p<0.100 level, * indicates marginal statistical significance at the p<0.150 level. No clear patterns or trends emerge when fertility index is regressed against seedling growth parameters independent of site and tree age. Likewise, percent exposed mineral soil and local density account for only very small portions of the explained variance and regression analysis indicated no clear pattern of effects on seedling growth variables. Local density, however, was a statistically significant factor in the model when applied to root collar diameter at age seven and above. This result probably indicates the effect of increasing competition, but it is very slight, never accounting for more than four percent of the explained variance (Table 25 )• 113 REGENERATION DELAY, SEEDLING PRODUCTIVITY AND SOIL DISTURBANCE It was noted earlier that an intriguing post harvest chronosequence in seedling productivity may be occurring. In such a scenario, the seedlings that germinate soon after harvest have a reduced growth performance compared to seedlings that germinate several years after harvest. This section explores how such an effect may be related to forest floor displacement. A comparison of the least squares means of total height at various ages indicates a consistent trend in which seedlings that germinated in the early years after harvest perform less well than seedlings that germinate later. This is especially true at the Beaverdell site (see Figures 44 through 47). Seedlings at the Carmi site respond similarly, but the response at Rathmullen is more ambiguous. For seedling total heights up to age eight, a three-year delay after harvest seems to produce the best performing seedlings. A regeneration delay of more than three years results in a slight decline in expected mean seedling total height growth. Yet seedlings germinating four to five years after harvest still perform better in general than early germinators (Figures 44 through 47). It should be noted that Tukey's HSD Test for the Comparison of Means rarely indicates statistically significant differences between the expected means of total height of the various germinators. This is not suprising given the variation in the data and the large standard errors associated with the expected means. 114 Figure 44. Age 3 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. Least Squares Means 60.0 25.6 1 2 3 4 5 6 YRSFRMHRVSTS Figure 45. Age 5 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. Least Squares Means m 101 91 81 71 61 51 41 I I I I • _ \ -/ 1 \ -_ i I -1 2 3 4 5 6 YRSFRMHRVSTS Figure 46. Age 7 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. Least Squares Means 1 2 3 4 5 YRSFRMHRVSTS Figure 47. Age 8 Least Squares Means of Total Height (cm) by Harvest-related Tree Age at Beaverdell. Least Squares Means 104 1 2 3 YRSFRMHRVSTS 115 Trends consistent with those observed for total height are evident from the least squares means analysis of root collar diameter. Seedlings that germinated in the early years after harvest typically have smaller root collar diameters than seedlings that germinate later. This is especially true at the Beaverdell site (Figures 48 through 51) and the Carmi site, but once again the response of seedlings at Rathmullen is more ambiguous. For seedling growth up to age seven, a three year delay after harvest seems to produce the best performing seedlings. A regeneration delay of more than three years results in a slight decline in expected mean seedling root collar diameter. Yet seedlings germinating four to five years after harvest generally perform about equal to early germinators. It should be noted that Tukey's HSD Test for the Comparison of Means rarely indicates statistically significant differences between the means of root collar diameter. This is not suprising given the variation in the data and the large standard errors associated with the expected means. 116 Figure 48. Age 3 Least Squares Means of Root Collar Figure 49. Age 5 Least Squares of Root Collar Diameter (mm) by Harvest-related Tree Age at Diameter (mm) by Harvest-related Tree Age at Beaverdell. Beaverdell. Least Squares Means Least Squares Means 1 2 3 4 5 6 YRSFRMHRVSTS 1 2 3 4 5 6 YRSFRMHRVSTS Figure 50. Age 7 Least Squares Means of Root Collar Figure 51. Age 8 Least Squares Means of Root Collar Diameter (mm) by Harvest-related Tree Age at Diameter (mm) by Harvest-related Tree Age at Beaverdell. Beaverdell. Least Squares Means Least Squares Means 11.0 40 35 30 25 20 1 1 -/ / • A --i 1 ! 1 . 2 3 4 5 YRSFRMHRVSTS 1 2 3 YRSFRMHRVSTS 117 Soil Disturbance and Regeneration Delay The previous analysis suggests a chronosequence type of effect on seedling performance. Increasing competition from grass, brush and other seedlings are obvious sources of a chronosequence effect, as is the amelioration of harsh site conditions that often occurs in the years after harvest. What soil processes may be occurring on a site that influence or control this apparent chronosequence? A drawback of retrospective studies is that many important processes and changes in site conditions have already occurred and cannot be observed. However, sometimes it is possible to reconstruct events of the past using data collected after the fact. Exploratory data analysis comparing tree age with soil disturbance indicated the occurrence of temporal lag in seedling recruitment related to soil disturbance which may help explain the observed performance chronosequence and harvest-related age effect. In general, undisturbed microsites (0% mineral soil exposure) are consistently the most preferred germination sites until three or four years after harvest. During the fifth year after harvest, only the microsites with 50% or more exposed mineral soil are still being colonized. Over time there appears to be a shift in recruitment from less disturbed to more highly disturbed micro-sites (disturbance measured as percent mineral soil exposed). A histogram of the incidence of recruitment by percent mineral soil exposed over the six years after harvest at Beaverdell is presented in Figure 52. This Figure was selected because the effect in question is most pronounced at Beaverdell. The Carmi site exhibits an effect that is consistent with that observed at Beaverdell. However, the trend at the Rathmullen site is less clear and generally contrary to the effects observed overall and at the other two sites evaluated. At Rathmullen, although there is an increase in the frequency of recruitment on more disturbed sites as the block ages, the undisturbed soil environment remains the preferred germination location at least for the first four years after harvest. By comparison, such a trend is not as evident at any of the study sites when we consider recruitment as a function of the soil fertility index. 118 Figure 52. Histogram of the Frequency of Recruitment as a Function of Percent Mineral Soil Exposure for the Six Years Following Harvest at the Beaverdell Site. Recruitment by Percent Exposed Mineral Soil at Beaverdell 1 1 0 o CO aj i_ <U C T3 CD CO O Q . X tu c cu o l_ <D D_ 70 30 - 1 0 I 1 1 ooooooo 1 OOOO 1 o c o o o -_ o c o o o OOO 0 o oo CO OOO o oo CO OOO o c o CO 0 0 - o OO o - o CO OO 0 o c oo COO -o oo O oo o c ooo o o c o o o c 0 o c o ooo oo -o c o 1 0 1 COO 1 00 1 1 0 2 4 Years After Harvest 119 SUMMARY AND CONCLUSIONS Results of this study were consistent with the literature reviewed which suggested that, in the absence of soil compaction and alkaline materials, one could expect little or no negative impact of soil disturbance on lodgepole pine productivity. In fact, in several cases results of this study suggest that lodgepole pine seedlings growing on scalped soils may perform slightly better than their neighbours on undisturbed ground. However, differences were rarely statistically significant at the 0.05 level. Analysis of soil nutrient content and foliar nutrient concentrations generally supported the results of analysis of seedling productivity with respect to forest floor displacement. Why should nutrients be more available to trees growing on scalps? Scalped soils, although generally reduced in organic matter content and total N , had higher available phosphorus and lower C:N ratios suggesting that these nutrients may actually be more available on scalps than under the residual forest floor. Much of the total nutrient content in the soils studied may be bound in organic matter and not readily available. It is possible that soil microorganisms within the undisturbed forest floor compete with roots for available nutrients, quickly immobilizing those that do become available. It is also conceivable that scalp soils may benefit from lateral nutrient translocation as mineralization and leaching take place in adjacent undisturbed and mixed soil disturbance types. Furthermore, several researchers have reported that removal of the forest floor results in increased soil temperatures. Scalped soils may warm and drain sooner in the spring and remain warmer longer in the fall, thus extending the growing season and providing more time for nutrient acquisition. At any rate, nutrients in undisturbed and mixed soils very likely become available to the root systems of trees growing on scalps as they grow and extend into undisturbed areas. In addition, results of soil chemical analysis suggest that both organic matter and total N levels are restored relatively quickly after soil disturbance. Twenty-seven years after mechanical blade scarification, the depth of the forest floor on scalped soils was comparable to that of control soils at Lassie Lake. However, some qualitative differences in the 120 organic layers were noted, for example the relative depth of and degree of humification in the H layer. Given the above observations, the impact of forest floor displacement on soil and tree nutrition is likely to be temporary and does not appear to have a negative effect on lodgepole pine productivity. Analysis of foliar nutrient concentrations supports the hypothesis that nutrients are relatively more available to seedlings growing on scalps compared to seedlings growing on undisturbed soil. Differences between means were not often statistically significant, but mean foliar nutrient concentrations of macronutrients (N, P, S, K) were commonly greater on scalp trees compared with trees grown on undisturbed soil. Growth-retarding deficiencies in foliar nutrient concentrations occurred regardless of soil conditions but tended to be more severe in control trees. In general, seedlings on sites such as these should respond to nitrogen and sulphur fertilization regardless of forest floor displacement. There is no evidence to suggest any substantial, long term implications of forest floor displacement on lodgepole pine seedling productivity. After more than twenty years, analysis of growth data from the Lassie Lake study site indicated no lasting differences or trends in seedling growth. Similarly, the growth response of seedlings to forest floor displacement in juvenile-aged stands cannot be characterized as negative. The slight differences that occasionally do occur seem just as likely to result from differences in stocking density as any from soil nutritional differences that may be present. Anecdotal observations suggests that scalped areas are less well stocked and this effect may favour the growth of trees on scalps as canopy closure occurs. Such results are consistent with retrospective analysis performed by Smith and Wass (1985,1994b). In addition, scalped areas generally experience a regeneration delay of about three years during which time the harsh imcroclimatic conditions present immediately after clearcutting may be moderated. Foresters must be cautious when interpreting soil disturbance as soil degradation and must give consideration to both the site characteristics and species tolerances. The results of this 121 study are limited to the specific site conditions, scalp dimensions and conifer species investigated herein. Similar disturbances on other sites may result in more severe impacts on seedling productivity. Deeper, more compacted disturbances on site conditions similar to those investigated in this study may also result in more extreme growth response by lodgepole pine. The ability to properly extrapolate the results of this study to other sites in other biogeoclimatic zones is limited. One retrospective study is a beginning, but to fully explore the implications of forest floor displacement in this and other regions, further study is necessary. The value and irrefutability of Smith and Wass' work is the result of twenty years of research and at least seven published reports. Given the results of this study, and ancillary information derived elsewhere, the following recommendations seem appropriate; • Changes to the limits of forest floor displacement in the Forest Practices Code regulations are probably unnecessary. Ground skidder operators seem to be able to comply with Code regulations while harvesting. To my knowledge, during the past few years and since the inception of the regulations no blocks have been found to be out of compliance for forest floor displacement in Boundary District. Further, Megahan (1980) summarized results from 16 logging studies in the US and Canada and found that the average percentage of area disturbed by ground skidding (excluding roads) is 21 percent. So even fifteen years before forest floor displacement became a concern, ground skidding operations were routinely within Code limits. • In this study, scalped areas that approximated the largest (and therefore most potentially negative) scalp recognized in the MoF soil disturbance survey were shown to have no negative impact on lodgepole pine seedling growth or nutrition. This result implies that, where lodgepole pine is the target species for regeneration, the accounting of the smaller scalp sizes ( e.g. 1.5 x 1.5 m ) in the soil disturbance survey may be unnecessary. In order to settle the issue of scalp size, the effect of a range of scalp sizes, including larger areas, ought to tested on a variety of species in a manner similar to that reported by Nyland et al. (1979). Perhaps more important operationally than scalp size is the issue of scalp density within a limited area. • There were too few treatment replicates to assess the relevance of forest floor depth layers in forest floor displacement. Of the three juvenile stands assessed, Beaverdell had the thinnest forest floor and Rathmullen the thickest. Paradoxically it seems, seedlings at Beaverdell seemed to respond positively to forest floor displacement on average, while seedlings at Rathmullen responded slightly negatively, at least at first. At any rate, to best address issues related to site conditions, a catalogue of sites should be created and maintained which includes both site characteristics and survey results. Such a catalogue would be most useful in identifying locations appropriate for future retrospective analyses. Alternatively, forest floor 122 displacement should be intentionally applied at different levels on a variety of site conditions and treatments monumented for long term study. 123 APPENDIX I Site Maps, Site Descriptions and Photos 124 RATHMULLEN SITE LOCATION Opening Number: 82E01800-34 Harvested July 1987 125 RATHMULLEN SITE SUMMARY Table R.1. Site Properties. Terrain Unit Meso-slope Position Shape Slope Aspect Altitude Glaciofluvial Outwash Blanket Midslope straight to slightly concave 12 to 14% NW 270° 950m Table R.2. Ecosystem Properties. BGCSZ M/N regime S. A~ ICHc2 (old) 3C 04 ICHmkl (new) Table R.3. Soil Summary. Soil Classification Humus Form Humus Drainage Impediment Depth Eluviated Dystric Mormoder 6 cm well none Brunisol (patchy Lignomoder) drained Table R.4. Soil Chemical Analysis. Soil Type LOI Total C Total N Avail able P %OM (ppm) (%) (ppm) Natural 8.53 4.71 0.173 33.9 Mixed 10.8 5.56 0.182 31.8 Undisturbed 7.46 3.69 0.142 34.9 Scalped 5.52 2.61 0.103 32.6 Table R.5. Humus Description. Horizon Depth Description L 6-5 cm loose, fibrous, stick and needle debris. Fa 5-3 cm soft, slightly matted, commuted needles etc., dark brown to black, moist, few to plentiful yellow and white micellia. H 3-0 cm soft and felty, slightly greasy, matted, friable, well rooted (va fine, a medium, p-f coarse), dark brown to black, plentiful patchy dead wood, few white micellia. Table R.6. Soil Description. Horizon Depth Texture Colour gravel cobble stone CF Rooting Structure (%) (%) (%) (%) Ae 0-2 cm fSa 5YR8/1 0 0 0 ff,fm single grained Bm 2-50 cm fSaL 10YR7/4 10 10 10 30 af, a-pm, pc single grained Photo R.1. A general view of the Rathmullen study site. Photo R.2. The Rathmullen study site was the most nutritionally rich and most densely populated of the juvenile stands sampled. Despite some larch seed trees, Pli was by far the most abundant species.. 128 129 Photo R.5. Profile of a mixed soil type at the Rathmullen study site. Note the buried LFH layers and the dead wood which was plentiful but discontinous on this site. 130 Photo R.6. Profile of a scalped soil type at the Rathmullen study site. The strong brown colours at the surface are partially the result of a wetting front and do not indicate intact forest floor. Moss crusts on found on exposed mineral soil and the absence of an Ae horizon were clues indicating forest floor displacement at Rathmullen. 131 BEAVERDELL SITE LOCATION Opening Number 82E05600-09 Harvested May 1985 632ES PIFL 420M3 361000 358000 132 BEAVERDELL SITE SUMMARY Table B.1. Site Properties. Terrain Unit Meso-slope Position Shape Slope Aspe ct Altitude Glaciofluvial Outwash Blanket midslope straight to slightly convex 6-8% SE 130° 1085m Table B.2. Ecosystem Properties. BGCSZ M/N regime S. A. ICHc2 (old) 3 / B 03 ICHmkl (new) Table B.3. Soil Summary. Soil Classification Humus Form Humus Depth Drainage Impediment Orthic Dystric Mormoder 6 cm well none Brunisol drainded Table B.4. Soil Chemical Analysis. Soil Type LOI Total C Total N Avail able P %0M (ppm) (%) (ppm) Natural 5.90 3.11 0.097 50.2 Mixed 5.70 3.22 0.089 44.8 Undisturbed 5.37 3.02 0.090 65.4 Scalped 4.21 2.01 0.069 40.2 Table B.5. Humus Description. Horizon Depth Description L F H 6-4 cm 4-2 cm 2-0 cm loosed to slightly matted, fibrous, needles, stick and shrub debris, some moss, slightly matted, fibrous but sottish, sort of a blackened mat or weave of mostly commuted needles with stick and root debris, grades into H layer, very few white micellia, not abundantly rooted; few to pi. fines, v. few medium. matted, fibrous but sottish, very few mineral grains, very well rooted, dark brown to black, not greasy, 2cm thickness is generous, mix of fine particles and commuted needles and debris, va white micellia, no charcoal evident, rooting; va fines, a medium, f coarse. Table B.6. Soil Description. Horizon Depth Texture Colour gravel cobble stone CF (%) (%) (%) (%) Rooting Structure *Ae Bm C 0- 1 cm 1- 50 cm 50+ cm LSa 10YR8/1 Sa 10YR 6/6 20 10 5 35 Sa 10YR 6/2 20 10 5 35 af, am, fc vf coarse single grained single grained 133 Comments: *Ae horizon is thin and discontinuous. This site is sandy textured with a thin forest floor soil layer situated on a lower slope of glacial fluvial origin adjacent to Beaver River floodplain. Study area is moderately sloped, although it can approach 20% in one comer. Rooting occurs at surface mostly, decreasing root frequency with depth. Coarse fragments are subangular to subround and of mixed geology. Microtopography suggests slope was subject to mass flow events in the past. Photo B.1. A general view of the Beaverdell study site. Logged in 1985, this site has the oldest of the juvenile stands sampled. Sampled stems ranged in age from six to 11 years old. Photo B.3. A profile of an undisturbed clearcut soil at the Beaverdell study site. Located near a stump, this profile exhibits a thickness of LFH layers relatively generous for this site at about 3 to 4 cm deep. The library card shown for scale is 9cm in length. Photo B.4. The same profile as in photo 7, this picture shows the depth and characteristics of mineral soil horizons in an undisturbed clearcut soil at Beaverdell. Photo B.5. An excavation of a berm. Mixed soil disturbance types are typically found in structures such as this. 137 138 CARMI SITE LOCATION Opening Number: TFL8 9E-9 Harvested 1991 139 CARMI SITE SUMMARY Table C.1. Site Properties. Terrain Unit Meso-slope Position Shape Slope Aspect Altitude Glaciofluvial Outwash Blanket lower slope Broken (mostly flat) 0-12% 72° 1285m Table C.2. Ecosystem Properties. BGCSZ M/N regime S. A. ICHc2 (old) 4 / B 04 MSdml (new) Table C.3. Soil Summary. Soil Classification Humus Form Humus Depth Drainage Impediment Orthic Dystric Hemimor 10 cm well to none Brunisol moderate Soil Chemical Analysis. Soil Type LOI Total C Total N Avail able P %0M (ppm) (%) (ppm) Natural 6.31 3.94 0.112 17.0 Mixed 3.88 2.34 0.076 55.8 Undisturbed 3.87 1.79 0.068 36.7 Scalped 3.39 1.38 0.053 36.64 Table C.5. Humus Description. Horizon Depth Description L Fm H 10-9 cm 9-4 cm 4-0 cm loose, slightly matted, fibrous, mostly discoloured needles (Pine, Larch and Spruce) slightly compressed, matted or woven needle debris, fibrous grading to felty, moist, dark brown, very abundant milky white micellia, f-pl yellow micellia. matted but friable, soft, felty, moist, v. slightly greasy, dark brown to black, ab. root debris, vf mineral grains, rooting; va fine, pl-a med., few coarse. Table C.6. Soil Description. Horizon Depth Texture Colour gravel cobble stone CF Rooting Structure (%) (%) (%) (%) Ae 0-1 cm vfSa 10YR8/1 af, fm, fc single Bm 1-30 cm LSa 10YR6/7 10 5 0 15 p-ff, pm, pc grained BC 30-40 cm Sa 10YR6/7 15 5 0 20 ff, fm, fc single + 6/3 grained C 40+ cm Sa 10YR6/3 20 5 0 25 vfm, vfc single grained 140 Comments: The Carmi site is the smallest opening studied. Located on the upper elevation fringe of the ICHc2, the natural stand is the most diverse of all the study sites. The soil is very sandy in texture, glacial fluvial in origin and coarse fragments are subround. The forest floor soil layer is the thickest of any other study site and has a few other features which distinguish it from the other sites such as the compressed Fm layer indicative of snowpack and the relative abundance of fungal micellia. Photo C.2. A typical 4 year old Pli seedling at Carmi. 142 Photo C.3. An undisturbed clearcut soil profile at Carmi. The Carmi site had the thickest LFH soil layer of any study site at approximately 10cm deep. The library card shown for scale is 9cm tall. Photo C.4. Soil profile of a scalped area at the Carmi study site. This is a good example of the nature of forest floor displacement. 143 Photo C.5. A profile of a mixed soil type at Carmi. Note the buried LFH horizon with protruding roots and the berm or mineral soil capping on top. These types of disturbed microsites and their profiles are not unlike the mounds created during mechanical site preparation on wet ground. 144 HENDERSON SITE LOCATION Opening Number: CP2D-5 Harvested 1995 145 HENDERSON SITE SUMMARY Table H.1. Site Properties. Terrain Unit Position Shape Slope Aspect Altitude Glaciofluvial Outwash lower slope concave / 0-6 % SW 950 m Blanket broken 225 Table H.2. Ecosystem Properties. BGCSZ M/N regime S. A. ICHc2 (old) 4 / C 04 ICHmkl (new) Table H.3. Soil Summary. Soil Classification Humus Form Humus Depth Drainage Impediment Eluviated Dystric Mormoder 3 cm well none Brunisol Table H.4. Soil Chemical Analysis. Soil Type Mean LOI Mean Total C Mean Total N Mean Available P %OM (ppm) (%) (ppm) Natural 8.02 3.65 " 0.127 28.1 Mixed 7.03 3.98 0.114 36.0 Undisturbed 5.81 2.90 0.085 38.9 Scalped 4.19 1.77 0.057 29.5 Table H.5. Humus Description. Horizon Depth Description L F H 3-2 cm 2-0.5 cm 0.5-0 cm loose, fibrous, discoloured needles, shrub leaf and stick debris slightly matted non-compacted, discoloured needles, fibrous, dry, few white micellia, few fine roots light brown, slightly matted, soft, relatively well rooted but loose and friable, very thin and dry, plentiful frass, abundanct white micellis, patchy thin black humus, not greasy, pi mineral grains, pi. fine roots. Table H.6. Soil Description. Horizon Depth Texture Colour grave 1 (%) cobble (%) stone (%) CF (%) Rooting Structure Ae 0-1 cm Sa 10YR6/2 5 5 0 10 pf.fm.fc weak, Bm1 1-7 cm SaL 10YR5/4 5 5 0 10 pf, fm, fc Bm2 7- 27 Sa 10 YR 5.5/6 5 5 5 15 ff, fm, fcf subangular C 27 + Sa 10YR 6/2 5 5 5 15 vff, vfm, vfc blocky Comments: Coarse fragments are sub round. Photo H2. Three closely spaced skid trails at the Henderson Creek study site. Disturbances Such ar these do not count as "forest floor displacement" if some forest floor material remains as on the far right skid trail pictured below. 148 Lassie Lake Control Site Location Original 1960s Study Block Reference Number: 11 Harvested 1968 149 LASSIE L A K E CONTROL SITE SUMMARY Table LC.1. Site Properties. Terrain Unit Meso-slope Position Shape Slope Aspect Altitude Glaciofluvial Outwash Blanket Level Flat 0% Flat 1350 m Table LC.2. Ecosystem Properties. BGCSZ M/N regime S. A. ICHc2 (old) 3 / B 04 MSdml (new) (transitional) Table LC.3. Soil Summary. Soil Classification Humus Form Humus Depth Drainage Impediment Eluviated Dystric Mormoder 4cm well none Brunisol Table LC.4. Soil Chemical Analysis. Soil Type LOJ Total C Total N Avail able P %QM (ppm)' (%) (ppm) Undisturbed 7.06 3J>4 0123 40.2 Table LC.5. Humus Description. Horizon Depth Description L 4.5-3 cm slightly matted, discoloured pine needles, very few micelia, few fine roots Fa 3-2.5 cm moss crust, some dead wood, cone and bark debris, thin transition zone from L to H layers H 2.5-0 cm blocky, strong but friable, dark brown, well rooted, organic fines with pi mineral grains, not greasy, few to pi. white micellia, rooting; vaf, am, fc. Table LC.6. Soil Description. Horizon Depth Texture Colour gravel cobble stone CF Rooting Structure (%) (%) (%) (%) Ae 0-2 cm vfSa 5YR 7/1 af, fm, fc massive Bm 2-20 cm LSi 10YR6/4 2 2 2 6 a-plf, pirn, fc massive BC 20-30 cm LSi 0 0 0 0 ff, fm, vfc massive C 30+cm LSi 5YR 7/1 0 0 0 0 vff, vfc massive Comments: 150 Photo LC.1. Profile of an undisturbed or control soil profile at Lassie Lake. The total length of the marker (shown for scale) is 13cm, the marker cap alone is 3.5cm in length. The surface of the control soil profile is characterised by a 3 to 4 cm deep forest floor layer overlying a thin Ae horizon. 151 LASSIE L A K E SCALPED SITE LOCATION Original 1960s Study Block Reference Number: 6 Harvested 1965, Broadcast Burned 1965 and 1967, Blade Scarified 1967. 152 LASSIE L A K E SCALPED SITE SUMMARY Table LS.1. Site Properties. Terrain Unit Meso-slope Position Shape Slope Aspect Altitude Glaciofluvial Outwash Blanket Level Flat 0% Flat 1350 m Table LS.2. Ecosystem Properties. BGCSZ M/N regime S. A. ICHc2 (old) 3 / C 04 MSdml (new) (transitional) Table LS.3. Soil Summary. Soil Classification Humus Form Humus Depth Drainage Impediment Eluviated Dystric Mormoder 3 cm well/ none Brunisol moderate Table LS.4. Soil Chemical Analysis. Soil Type LOI Total C Total N Avail able P %0M (ppm)' (%) (ppm) Scalped 7.83 4.04 0.125 38.2 Table LS.5. Humus Description. Horizon Depth Description I L 3.5-2.5 cm slightly matted, discoloured pine needles, very few micelia, few fine roots Fa 2.5-1.5 cm discoloured needles, charcoal, some dead wood, cone and bark debris, thin transition zone from L to H layers H 1.5-0 cm blocky, weak, friable, black, well rooted, relatively coarse organic fines, abundant mineral grains, not greasy, few to pi. white micellia, rooting; vaf, am, fc. Table LS.6. Soil Description. Horizon Depth Texture Colour gravel cobble stone CF Rooting Structure (%) (%) (%) (%) Bm 0-11 cm LSi 10YR6/4 2 2 2 6 a-plf, pirn, fc massive BC 11-23 cm LSi * 0 0 0 0 ff, fm, vfc massive C 23+ cm LSi 5YR 7/1 0 0 0 0 vff, vfc massive Comments: * The BC horizon is coloured as follows; 5YR 7/1 matrix with abundant, diffuse 10YR 6/4 streaks. 153 Photo LS.1. Profile of the soil on a blade scarified trail at Lassie Lake. Note that the thickness of the forest floor has been substantially recovered over twenty five years or so. However, compared to the control forest floor, that of the scarified trail is less well humified and is therefore made up of comparitively coarser textured remains of needles and other plant debris. In addition, the depth of the Bm horizon appears to have been truncated although the relative abruptness of the boundary between B and C horizons may indicate different drainage and therefore soil forming conditions compared to the control soil shown in photo 18. 154 APPENDIX II Pearson Correlation Matrices for Soil Properties Overall Table II.1. Pearson Correlation Matrix Overall Soils LOI T O T A L C T O T A L N AVAILP LOI 1.000 T O T A L C 0.846 1.000 T O T A L N 0.863 0.846 1.000 AVAILP -0.069 -0.059 -0.109 1.000 C N 0.258 0.525 0.050 0.032 Number of observations: 195 Pearson Correlation Matrices for Soil Properties at Individual Sites Table II.2. Pearson Correlation Matrix for Soils at the Beaverdell Site LOI T O T A L C T O T A L N AVAILP LOI 1.000 T O T A L C 0.796 1.000 T O T A L N 0.769 0.804 1.000 AVAILP 0.038 0.082 0.088 1.000 C N 0.462 0.659 0.133 0.011 Number of observations: 55 Table H.3. Pearson Correlation Matrix for Soils at the Rathmullen Site Pearson correlation matrix LOI T O T A L C T O T A L N AVAILP LOI 1.000 T O T A L C 0.930 1.000 T O T A L N 0.888 0.891 1.000 AVAILP 0.160 0.065 0.010 1.000 C N 0.741 0.864 0.570 0.056 Number of observations: 41 Table II.4. Pearson Correlation Matrix for Soils at the Henderson Site LOI T O T A L C T O T A L N AVAILP LOI 1.000 T O T A L C 0.811 1.000 T O T A L N 0.839 0.754 1.000 AVAILP -0.062 0.043 0.046 •1.000 C N 0.190 0.571 -0.050 -0.079 Number of observations: 39 Table II.5. Pearson Correlation Matrix for Soils at the Carmi Site LOI TOTAL C TOTAL N AVAILP LOI 1.000 T O T A L C 0.695 1.000 T O T A L N 0.804 0.881 1.000 AVAILP -0.076 -0.167 -0.159 1.000 C N 0.422 0.746 0.400 -0.106 Number of observations: 40 Table II.6. Pearson Correlation Matrix for Soils at the Lassie Lake Site LOI T O T A L C T O T A L N AVAILP LOI 1.000 T O T A L C 0.717 1.000 T O T A L N 0.699 0.833 1.000 AVAILP 0.261 0.183 0.141 1.000 C N 0.280 0.566 0.044 0.140 Number of observations: 20 Pearson Correlation Matrices for Each Soil Type Table II.7. Pearson Correlation Matrix for Natural Stand Soils LOI T O T A L C T O T A L N AVAILP LOI 1.000 T O T A L C 0.755 1.000 T O T A L N 0.767 0.704 1.000 AVAILP -0.002 -0.034 -0.049 1.000 C N 0.171 0.550 -0.159 -0.040 Number of observations: 40 Table II.8. Pearson Correlation Matrix for Scalped Soils LOI T O T A L C T O T A L N AVAILP LOI 1.000 T O T A L C 0.929 1.000 T O T A L N 0.915 0.894 1.000 AVAILP -0.023 -0.032 -0.037 1.000 C N 0.435 0.581 0.206 -0.049 Number of observations: 55 Table II.9. Pearson Correlation Matrix for Mixed Soils LOI T O T A L C T O T A L N AVAILP C N LOI 1.000 T O T A L C 0.834 1.000 T O T A L N 0.875 0.895 1.000 AVAILP -0.181 -0.184 -0.206 1.000 C N 0.046 0.330 -0.098 -0.019 1.000 Number of observations: 44 Table 11.10. Pearson Correlation LOI T O T A L C LOI 1.000 T O T A L C 0.809 1.000 T O T A L N 0.836 0.808 AVAILP 0.001 0.029 C N 0.264 0.568 Matrix for Undisturbed Soils T O T A L N AVAILP C N 1.000 -0.092 1.000 0.028 0:142 1.000 Number of observations: 56 158 APPENDIX III Box Plots of Natural Stand Soil Properties Fig. 111.1. Box Plot of Organic Matter Content of Natural Stand Soils at Different Sites. Fig. III.2. Box Plot of Total Nitrogen of Natural Stand Soils at Different Sites. Location Fig. III.3. Box Plot of Total Carbon of Natural Stand Soils at Different Sites. Fig. III.4. Box Plot of Available Phosphorus of Natural Stand Soils at Different Sites. Location *0> rf>^ V / Location 159 Fig. III.5. Box Plot of C/N Ratio of Natural Stand Soils at Different Sites. 4? Box Plots of Undistrubed Soil Properties Fig. III.6. Box Plot of Organic Matter Content of Undisturbed Soils at Different Sites. Fig. III.7. Box Plot of Total Nitrogen of Undisturbed Soils at Different Sites. 0.20 0.10 Soil Disturbance Types Soil Disturbance Types Fig. 111.8. Box Plot of Total Carbon of Undisturbed Fig. 111.9. Box Plot of Available Phosphorus of Soils at Different Sites. Undisturbed Soils at Different Sites. Soil Disturbance Types Soil Disturbance Types Fig. 111.10. Box Plot of C/N Ratio of Undisturbed Soils at Different Sites. Soil Disturbance Types 161 Box Plots of Mixed Soil Properties Fig. 111.11. Box Plot of Organic Matter Content of Mixed Soils at Different Sites. Fig. 111.12. Box Plot of Total Nitrogen of Mixed Soils at Different Sites. Soil Disturbance Types Soil Disturbance Types Fig. 111.13. Box Plot of Total Carbon of Mixed Soils at Fig. 111.14. Box Plot of Available Phosphorus of Different Sites. Mixed Soils at Different Sites. 4F Soil Disturbance Types Soil Disturbance Types Fig. 111.15. Box Plot of C/N Ratio of Mixed Soils at Different Sites. Soil Disturbance Types 163 Box Plots of Scalped Soil Properties Fig. 111.16. Box Plot of Organic Matter Content of Scalped Soils at Different Sites. Fig. 111.17. Box Plot of Total Nitrogen of Scalped Soils at Different Sites. Soil Disturbance Types Soil Disturbance Types Fig. 111.18. Box Plot of Total Carbon of Scalped Soils Fig. 111.19. Box Plot of Available Phosphorus of at Different Sites. Scalped Soils at Different Sites. Soil Disturbance Types Soil Disturbance Types Fig. 111.20. Box Plot of C/N Ratio of Scalped Soils at Different Sites. Disturbance Types 165 APPENDIX IV Box Plots of Beaverdell Soil Properties Fig. IV.1. Box Plot of Organic Matter Content of Fig. IV.2. Box Plot of Total Nitrogen Content of Different Soil Disturbance Types at Beaverdell. Different Soil Disturbance Types at Beaverdell. Mix NatSt Scalp Un Mix NatSt Scalp U Soil Disturbance Types Soil Disturbance Types Fig. IV.3. Box Plot of Total Carbon Content of Fig. IV.4. Box Plot of Available Phosphorus Content Different Soil Disturbance Types at Beaverdell. of Different Soil Disturbance Types at Beaverdell. Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types 166 Fig. IV.5. Box Plot of C:N Ratio of Different Soil Disturbance Types at Beaverdell. Mix NatSt Scalp Un Soil Disturbance Types Box Plots of Rathmullen Soil Properties Fig. IV.6. Box Plot of Organic Matter Content of Different Soil Disturbance Types at Rathmullen. Fig. IV.7. Box Plot of Total Nitrogen Content of Different Soil Disturbance Types at Rathmullen. Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types 167 Fig. IV.8. Box Plot of Total Carbon Content of Fig. IV.9. Box Plot of Available Phosphorus Content Different Soil Disturbance Types at Rathmullen. of Different Soil Disturbance Types at Rathmullen. Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types Fig. IV.10. Box Plot of C:N Ratio of Different Soil Disturbance Types at Rathmullen. 50 40> Mix NatSt Scalp Un Soil Disturbance Types 168 Box Plots of Carmi Soil Properties Fig. IV.11. Box Plot of Organic Matter Content of Different Soil Disturbance Types at Carmi. Fig. IV.12. Box Plot of Total Nitrogen Content of Different Soil Disturbance Types at Carmi. Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types Fig. IV.13. Box Plot of Total Carbon Content of Different Soil Disturbance Types at Carmi. Fig. IV.14. Box Plot of Available Phosphorus Content of Different Soil Disturbance Types at Carmi. & 150 8- 100 JS 50 \-Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types 169 Fig. IV.15. Box Plot of C:N Ratio of Different Soil Disturbance Types at Carmi. 1 0 i 1 1 1 i_ Mix NatSt Scalp Un Soil Disturbance Types Box Plots of Henderson Soil Properties Fig. IV.16. Box Plot of Organic Matter Content of Different Soil Disturbance Types at Henderson. Fig. IV.17. Box Plot of Total Nitrogen Content of Different Soil Disturbance Types at Henderson. Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types 170 Fig. IV.18. Box Plot of Total Carbon Content of Different Soil Disturbance Types at Henderson. Fig. IV.19. Box Plot of Available Phosphorus Content of Different Soil Disturbance Types at Henderson. Mix NatSt Scalp Un Soil Disturbance Types Mix NatSt Scalp Un Soil Disturbance Types Fig. IV.20. Box Plot of C:N Ratio of Different Soil Disturbance Types at Henderson. Soil Disturbance Types 171 APPENDIX V Analysis of Variance and Least Squares Means for Organic Matter Content Table V.1. Results of Analysis of Variance for Organic Matter Content Source Sum-of-S quares DF Mean-Square F-Ratio P TYPE$ 215.668 3 71.889 16.193 0.000 LOC$ 313.041 3 104.347 23.504 0.000 TYPE$*LOC$ 96.456 9 10.717 2.414 0.014 ERROR 705.894 159 4.440 Total 1331.059 174 The A N O V A model accounted for approximately forty-seven percent of the total variation in the organic matter content data (squared multiple R=0.467). Both the soil type and site location effects were highly significant and accounted for approximately sixteen and twenty-three percent of the total variation respectively. The soil type by site location interaction effect was significant, but relatively less so, accounting for only seven percent of the total variation (See Table V.l). Analysis of the Least Squares Means indicated some interesting associations between soil disturbance types. Based on Tukey's HSD Post hoc Test for Comparison of Means, the mean organic matter contents of the natural stand and the mixed soils were not significantly different. However, both the natural stand and mixed soils had significantly greater mean organic matter content than the undisturbed soil, which in turn had a significantly greater mean organic matter content than the scalped soil type. The least squares means of organic matter content for different soil disturbance types ranged from 7.2 % in the natural stand soil to 4.3 % in the scalped soil. Accordingly, the natural stand and the mixed soil disturbance types have approximately 65 % more organic matter than the scalped soil type. The undisturbed soil type has about 30 % more organic matter than the scalped soil type. (See Fig. V . l and Table V.2). Analysis of the Least Squares Means indicated that the mean organic matter contents at particular site locations differed significantly from each other. The Carmi site tended to have the lowest mean organic matter content at 4.3 %, while the Rathmullen site was the richest in organic matter with a least squares mean of 8.1 % (See Fig. V.2 and Table V.3). The significance of the soil type by site location interaction effect seems to be generated in particular by the pattern of organic matter content by soil type at the Rathmullen site. In contrast to the other three sites, at Rathmullen the Least Squares Mean of organic matter content for the mixed soil type is greater than that of the natural stand soil type. It is interesting to note that the relatively small standard errors for the Beaverdell site probably reflect that site's homogeneity of slope compared to the other sites which were more variable. In addition, more samples per soil type were taken at the Beaverdell site (n=15) compared to the other three sites (n=10). In this analysis, increased sample size will result in more compact standard errors (See Fig. V.3).. 172 Fig. V.1. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Soil Type; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means M N S U T Y P E S Table V.2. Percent Organic Matter Content (LOI) Least Squares Means by Soil Type (See graph above). Soil Type LS Mean (%) SE N Natural Stand 7.19a 0.333 40 Mix 6.85a 0.324 44 Undisturbed 5.63b 0.315 46 Scalp 4.33c 0.319 45 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. 173 Fig. V.2. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Location. Least Squares Means L O C $ Table V.3. Percent Organic Matter Content (LOI) Least Squares Means by Location (See graph above). Location LS Mean (%) SE N Rathmullen 8.08c 0.329 41 Henderson 6.26b 0.338 39 Beaverdell 5.29ab 0.289 55 Carmi 4.36a 0.333 40 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. Fig. V.3. Graph of Percent Organic Matter Content (LOI) Least Squares Means by Soil Type for Each Location; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means B E A V E R D E L L CARMI HENDERSON RATHMULLEN M N S TYPE$ 175 Analysis of Variance and Least Squares Means for Total C Content Table V.4. Results of Analysis of Variance for Total Carbon Content Source Sum-of-Squares DF Mean-Square F-Ratio P TYPES 102.728 3 34.243 17.779 0.000 LOC$ 69.723 3 23.241 12.067 0.000 TYPE$*LOC$ 29.416 9 3.268 1.697 0.094 ERROR 306.234 159 1.926 Total 508.101 174 The A N O V A model accounted for approximately thirty-nine percent of the total variation in the organic matter content data (squared multiple R=0.392). Both the soil type and site location effects were highly significant and accounted for approximately twenty and fourteen percent of the total variation respectively. The soil type by site location interaction effect could be described as marginally significant at the a = 0.05 level, it accounted for only about six percent of the total variation (See Table V.4). Analysis of the Least Squares Means indicated some interesting associations between soil disturbance types. As expected, the order and significance of the soil type least squares means follows the same pattern as that of organic matter content. Based on Tukey's HSD Post hoc Test for Comparison of Means, the mean total carbon of the natural stand and the mixed soils were not significantly different. However, both the natural stand and mixed soils had significantly greater mean total carbon than the undisturbed soil, which in turn had a significantly greater mean total carbon than the scalped soil type. The least squares means of total carbon for different soil disturbance types ranged from 3.9 % in the natural stand soil to 1.9 % in the scalped soil. Accordingly, the natural stand and the mixed soil disturbance types have approximately twice as much total carbon than the scalped soil type. The undisturbed soil type has about 50 % more total carbon than the scalped soil type. (See Fig. V.4 and Table V.5). Analysis of the Least Squares Means indicated that the mean total carbon of samples taken at the Beaverdell, Carmi and Henderson sites were not signifcantly different. The total carbon of soil at the Rathmullen site was significantly greater than the other locations. The Carmi site tended to have the lowest mean total carbon at 2.4 %, while the Rathmullen site was the richest in total carbon with a least squares mean of 4.1 % (See Fig. V.5 and Table V.6). The marginal significance of the soil type by site location interaction effect seems to be generated in particular by the pattern of total carbon by soil type at the Carmi site. In contrast to the other three sites, at Carmi the Least Squares Mean of the mixed soil type is less than that of the natural stand soil type. It is interesting to note that the relatively small standard errors for the Beaverdell site probably reflect that site's homogeneity of slope compared to the other sites which were more variable. In addition, more samples per soil type were taken at the Beaverdell site (n=15) compared to the other three sites (n=10). In this analysis, increased sample size will result in more compact standard errors (See Fig. V.6). 176 Fig. V.4. Graph of Percent Total Carbon Least Squares Means by Soil Type; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means 1 -M N S U T Y P E S Table V.5. Percent Total Carbon Least Squares Means by Soil Type (See graph above). Soil Type LS Mean (%) SE N Natural Stand 3.85a 0.219 40 Mix 3.77a 0.213 44 Undisturbed 2.85b 0.207 46 Scalp 1.94c 0.210 45 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. 177 Fig. V.5. Graph of Percent Total Carbon Least Squares Means by Location. Least Squares Means L O C $ Table V.6. Percent Total Carbon Least Squares Means by Location (See graph above). Location LS Mean (%) SE N Rathmullen 4.14b 0.217 41 Henderson 3.07a 0.222 39 Beaverdell 2.84a 0.190 55 Carmi 2.36a 0.219 40 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. 178 Fig. V.6. Graph of Percent Total Carbon Least Squares Means by Soil Type for Each Location; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means BEAVERDELL CARMI M N S U M N S U TYPES TYPES HENDERSON RATHMULLEN 179 Analysis of Variance and Least Squares Means for Total N Content Table V.7. Results of Analysis of Variance for Total nitrogen Source Sum-of-Squares DF Mean-Square F-Ratio P TYPE$ 0.078 3 0.026 26.634 0.000 LOC$ 0.134 3 0.045 45.610 0.000 TYPE$*LOC$ 0.019 9 0.002 2.159 0.028 ERROR 0.155 159 0.001 Total 0.386 174 The A N O V A model accounted for approximately fifty-nine percent of the total variation in the total nitrogen data (squared multiple R=0.594). Both the soil type and site location effects were highly significant and accounted for approximately twenty and thirty-five percent of the total variation respectively. The soil type by site location interaction effect was significant, but relatively less so, accounting for only five percent of the total variation (See Table V.7). Analysis of the Least Squares Means indicated some interesting associations between soil disturbance types. As expected, the order and significance of the soil type least squares means follows the same pattern as that of organic matter content. Based on Tukey's HSD Posf hoc Test for Comparison of Means, the mean total nitrogen of the natural stand and the mixed soils were not significantly different. However, both the natural stand and mixed soils had significantly greater mean total nitrogen than the undisturbed soil, which in turn had a significantly greater mean total nitrogen than the scalped soil type. The least squares means of total nitrogen for different soil disturbance types ranged from 0.127 % in the natural stand soil to 0.070 % in the scalped soil. Accordingly, the natural stand and the mixed soil disturbance types have approximately 80 % more nitrogen than the scalped soil type. The undisturbed soil type has about 37 % more nitrogen than the scalped soil type. (See Fig. V.7 and Table V.8). Analysis of the Least Squares Means indicated that the mean total nitrogen of samples taken at the Beaverdell and Carmi sites were not significantly different. The total nitrogen of soil samples taken at the Henderson site were not significantly different than the samples from Beaverdell. The total nitrogen of soils at the Henderson and Rathmullen sites were significantly different from each other and from the Carmi location. The Carmi site tended to have the lowest mean total nitrogen at 0.077 %, while the Rathmullen site was the richest in total nitrogen with a least squares mean of 0.150 % (See Fig. V.8 and Table V.9). The significance of the soil type by site location interaction effect seems to be generated in particular by the pattern of total nitrogen by soil type at the Rathmullen site. In contrast to the other three sites, at Rathmullen the Least Squares Mean of total nitrogen for the mixed soil type is greater than that of the natural stand soil type. Total nitrogen content was extremely variable for each soil type sampled across all sites (See Fig. V.9). 180 Fig. V.7. Graph of Percent Total Nitrogen Least Squares Means by Soil Type; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means 1 . 0 0.8 o r-0.6 0.4 M N S U T Y P E S Table V.8. Percent Total Nitrogen Least Squares Means by Soil Type (See graph above). Soil Type LS Mean (%) SE N Natural Stand 0.127a 0.005 40 Mix 0.115a 0.005 44 Undisturbed 0.096b 0.005 46 Scalp 0.070c 0.005 45 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. 181 Fig. V.8. Graph of Percent Total Nitrogen Least Squares Means by Location. Least Squares Means 1 . 0 0.8 h O 0.6 0.4 0.2 ab 0.0 L O C $ Table V.9. Percent Total Nitrogen Least Squares Means by Location (See graph above). Location LS Mean (%) SE N Rathmullen 0.150c 0.005 41 Henderson 0.096b 0.005 39 Beaverdell 0.086ab 0.004 55 Carmi 0.077a 0.005 40 a-cLeast Squares Means with the same letter are not significantly different at the 0.05 probability level according to Tukey's Multiple Comparison Test. 182 Fig. V.9. Graph of Percent Total Nitrogen Least Squares Means by Soil Type for Each Location; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means BEAVERDELL CARMI 1.0 Z 0.6 _ j < o 0.4 0.0 M N S TYPES 1.0 0.8 0.4 0.0 M N S TYPES HENDERSON RATHMULLEN 0.6 e 0.4 0.6 0.4 0.2 0.0 M N S TYPES 183 Analysis of Variance and Least Squares Means for Available P Content Table V.10. Results of Analysis of Variance for Available Phosphorus Source Sum-of-Squares DF Mean-Square F-Ratio P TYPE$ 4035.162 3 1345.054 3.902 0.010 LOC$ 9556.623 3 3185.541 9.242 0.000 TYPE$*LOC$ 9161.587 9 1017.954 2.953 0.003 ERROR 54803.031 159 344.673 Total 77556.403 174 The A N O V A model accounted for thirty percent of the total variation in the avialable P data (squared multiple R=0.301). The soil type and site location effects were highly significant and accounted for approximately five and twelve percent of the total variation respectively. The soil type by site location interaction effect was also highly significant, and accounted for about twelve percent of the total variation (See Table V.10). Analysis of the Least Squares Means indicated an interesting trend in phosphorus by soil disturbance types. Available P is low in natural stand and scalp soil disturbance types, and is high in mixed and undisturbed soils. The lowest mean available phosphorus content is found in the natural stand soil (32.32 ppm). The scalped soil, type has slightly greater mean available phosphorus (34.72 ppm), but does not differ significantly from the natural stand or the mixed soil type (42.11 ppm). The undisturbed clearcut soil type has the greatest mean available phosphorus (43.96 ppm), significantly different from all but the mixed soil type. (See Fig. V.10 and Table V.l l ) . Phosphorus is mostly bound in organic matter in soils and released through mineralization. It follows that the mixed and undisturbed soil disturbance types would be relatively high in available P because they have the highest organic matter contents, which are relatively rapidly decomposing in the clearcut environment. Analysis of the Least Squares Means indicated that the mean available phosphorus of samples taken at the Beaverdell site were significantly different from all other locations. None of the other sites differed significantly from each other in available phosphorus content (See Fig. V . l l and Table V.12). The high significance of the interaction effect coupled with the analysis of least squares means on that basis indicates that the treatment effect on available P is site specific. Consequently the data provide no basis for generalizations applied to the study as a whole (see Fig. V.12) Fig. V.10. Graph of Available Phosphorus Least Squares Means by Soil Type; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means b ab T N S U T Y P E S Table V.11. Available Phosphorus Least Squares Means by Soil Type (See graph above). TYPES LS Mean SE N Undisturbed 43.961 2.775 45 Mix 42.110 2.853 44 Scalp 34.719 2.810 46 Natural Stand 32.323 2.935 40 Fig. V.11. Graph of Available Phosphorus Least Squares Means by Location. Least Squares Means Table V.12. Available Phosphorus Least Squares Means by Location (See graph above). LOC$ LS Mean SE N Beaverdell 50.158 2.542 55 Carmi 36.550 2.935 40 Rathmullen 33.286 2.902 41 Henderson 33.118 2.976 39 186 Fig. V.12. Graph of Available Phosphorus Least Squares Means by Soil Type for Each Location; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means BEAVERDELL CARMI 52 h < M N S TYPES HENDERSON M N S TYPES < 29 M N S U TYPES RATHMULLEN M N S TYPES 187 Analysis of Variance and Least Squares Means for C:N Ratio Table V.13. Results of Analysis of Variance for C:N Ratio Source Sum-of-S quares DF Mean-Square F-Ratio P TYPE$ 796.853 3 265.618 3.362 0.020 LOC$ 1130.175 3 376.725 4.768 0.003 TYPE$*LOC$ 760.436 9 84.493 1.069 0.388 ERROR 12563.665 159 79.017 Total 15251.129 174 The A N O V A model accounted for only eighteen percent of the total variation in the C:N ratio data (squared multiple R=0.181). The soil type and site location effects were highly significant and accounted for approximately five and seven percent of the total variation respectively. The soil type by site location interaction effect was not significant, accounting for less than five percent of the total variation (See Table V.13). Analysis of the Least Squares Means indicated an interesting trend in C:N ratio by soil type. The lowest mean C:N ratio is found in the scalped soil type (27.02). The undisturbed soil type has a slightly greater mean C:N ratio (29.57), but does not differ significantly from the scalp or the natural stand soil disturbance types (30.832 ppm). The mixed soil type has the greatest mean C:N ratio (32.975), significantly different from only the scalped soil type. (See Fig. V.13 and Table V.14). Soil disturbance types that are relatively low in C content have the lowest O N ratios suggesting that there are N reserved stored in the mineral soil. Exposing soils to clearcut conditions and scalping seems to preferentially remove C compared to N. Similar to the results of anlaysis of avialable P by soil type, although most of the N on site may be found in organic matter, its availability is increased with soil disturbance (scalping) and the assumed increased rate of decomposiiton associated with clearcutting. Analysis of the Least Squares Means indicated that Beaverdell and Henderson had the highest C:N ratios, followed by Carmi and Rathmullen (see Fig. V.14 and Table V.15 ) C:N ratio analysis is inconsistent with that of available P and the hypothesis that both P and C:N ratio are controlled by organic matter content and decomposition rate. For example, Beaverdell soils have some of the lowest organic matter contents studied, yet they are highest in available P (suggesting relatively high decomposition rate) and also highest in C:N ratio (suggesting a relatively low decomposition rate). One answer may be that there is relatively little N storage in Beaverdell's coarse textured mineral soil, thus C:N ratio remains high even in conditions of rapid N and P mineralization. 188 Fig. V.13. Graph of C:N Ratio Least Squares Means by Soil Type; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means 38.0 34.6 31.2 z o 27.8 24.4 M N S U T Y P E S a Table V.14. C:N Ratio Least Squares Means by Soil Type (See graph above). TYPES LSMean SE N Mix 32.975 1.366 44 Natural Stand 30.832 1.405 40 Undisturbed 29.569 1.329 46 Scalp 27.023 1.346 45 189 Fig. V.14. Graph of C:N Ratio Least Squares Means by Location. Least Squares Means a a L O C $ Table V.15. C:N Ratio Least Squares Means by Location (See graph above). LOC$ LS Mean SE N Beaverdell 32.685 1.217 55 Henderson 32.331 1.425 39 Carmi 28.858 1.405 40 Rathmullen 26.525 1.389 41 Fig. V.15. Graph of C:N Ratio Least Squares Means by Soil Type for Each Location; M = mix, N = natural stand, S = scalp, and U = undisturbed. Least Squares Means BEAVERDELL CARMI 30.0 23.5 h HENDERSON RATHMULLEN 36.5 H 30.0 h 23.5 17.0 191 AP P E N D I X VI Box Plots of Foliar Nutrient Concentrations by Site and Fertility Code Figure VI.1 . Box Plot of Foliar Nitrogen Contents by Location and Fertility Code Treatment • Scalp Control Location Figure VI.2. Box Plot of Foliar Phosphorus Contents by Location and Fertility Code 0.30 0.25 e 5 o c n 3 o 0.20 0.15i 0.10 I ' T u S <f y Location Treatment • Scalp Control Figure VI.3. Box Plot of Foliar Potassium Contents by Location and Fertility Code 0.9 g0.8 1 0.7 c o 0 0.6 E 55 0.5 m ro 1 0.4 0.3 T J Location Treatment • Scalp Control Figure VI.4. Box Plot of Foliar Calcium Contents by Location and Fertility Code Figure VI.5. Box Plot of Foliar Magnesium Contents by Location and Fertility Code 0 . 1 6 Treatment Scalp Control Figure VI.6. Box Plot of Foliar Sulphur Contents by Location and Fertility Code 0 . 2 0 i i t 0.15 c o o Q. cn 0.10 0 . 0 5 Location Treatment • Scalp Control Figure VI.7. Box Plot of Foliar Iron Contents by Location and Fertility Code 150 i g 100 I c o ° 50 p Location Treatment • Scalp Control Figure VI.8. Box Plot of Foliar Manganese Contents by Location and Fertility Code 600 Q . 5 0 0 Q . l 400 O 300 | 200 co S 100 J Location Treatment i Scalp Control Figure VI.9. Box Plot of Foliar Zinc Contents by Location and Fertility Code 110 100 f 90 & 80 c 70 0) 60 , 50 « 40 30 20' c o O J Treatment n Scalp Control Location Figure VI. 10. Box Plot of Foliar Copper Contents by Location and Fertility Code 10 a. o U <3* U l rT Location Treatment • Scalp Control Figure Vl.11. Box Plot of Foliar Boron Contents by Location and Fertility Code 60 'A Location 197 APPENDIX VII Tabulated Results of ANOVA on Foliar Nutrient Concentration Data Table VII.1. Results of Analysis of Variance for Foliar N Concentration. Square Multiple R = 0.470 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 3.111 3 1.037 40.910 0.000 TREATMENT 0.026 1 0.026 1.015 0.315 LOCxTREAT 0.188 3 0.063 2.470 0.064 ERROR 3.726 147 0.025 Total 7.051 154 Table VII.2. Results of Analysis of Variance for Foliar P Concentration. Square Multiple R = 0.479 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 0.039 3 0.013 24.108 0.000 TREATMENT 0.011 1 • 0.011 20.859 0.000 LOCxTREAT 0.007 3 0.007 13.395 0.000 ERROR 0.001 147 0.001 Total 0.058 154 Table VII.3. Results of Analysis of Variance for Foliar Ca Concentration. Square Multiple R = 0.058 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 0.023 3 0.008 1.871 0.137 TREATMENT 0.000 1 0.000 0.002 0.963 LOCxTREAT 0.014 3 0.005 1.140 0.335 ERROR 0.591 147 0.004 Total 0.628 154 Table VII.4. Results of Analysis of Variance for Foliar Mg Concentration. Square Multiple R = 0.174 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 0.004 3 0.001 3.836 0.011 TREATMENT 0.003 1 0.003 8.978 0.003 LOCxTREAT 0.004 3 0.001 4.017 0.009 ERROR 0.047 147 0.000 Total 0.058 154 Table VII.5. Results of Analysis of Variance for Foliar K Concentration. Square Multiple R = 0.321 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 0.491 3 0.164 18.629 0.000 TREATMENT 0.001 1 0.001 0.089 0.766 LOCxTREAT 0.115 3 0.038 4.356 0.006 ERROR 1.290 147 0.009 Total 1.897 154 Table VII.6. Results of Analysis of Variance for Foliar S Concentration. Square Multiple R = 0.467 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 0.029 3 0.010 38.556 0.000 TREATMENT 0.000 1 0.000 1.258 0.264 LOCxTREAT 0.003 3 0.001 4.210 0.007 ERROR 0.036 147 0.000 Total 0.068 154 Table VII.7. Results of Analysis of Variance for Foliar Cu Concentration. Square Multiple R = 0.313 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 35.374 3 11.791 10.057 0.000 TREATMENT 2.247 1 2.247 1.916 0.168 LOCxTREAT 41.445 3 13.815 11.783 0.000 ERROR 172.355 147 1.172 Total 251.42 154 I/II.8. Results of Analysis of Variance for Foliar Zn Concentration. Square Multiple R = 0.265 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 607.945 3 202.648 1.695 0.171 TREATMENT . 1334.509 1 1334.509 11.160 0.001 LOCxTREAT 4175.719 3 1391.906 11.640 0.000 ERROR 17578.812 147 • 119.584 Total 23696.993 154 VII.9. Results of Analysis of Variance for Foliar Fe Concentration. Square Multiple R = 0.326 Source Sum-of-Squares DF Mean-Square F-Ratio P LOCATION 3280.133 3 1093.378 12.485 0.000 TREATMENT 1293.432 1 1293.432 14.769 0.000 LOCxTREAT 1782.561 3 594.187 6.785 0.000 ERROR 12873.731 147 87.576 Total 19229.857 154 Table VII.10. Results of Analysis of Variance for Foliar Mn Concentration. Square Multiple R = 0.160 200 Source Sum-of-Squares DF Mean-Square F-Ratio LOCATION TREATMENT LOCxTREAT ERROR 67902.437 55115.999 53664.366 932804.717 3 1 3 147 22634.146 55115.999 17888.122 6345.610 3.567 8.686 2.819 0.016 0.004 0.041 Total 1109487.519 Table Vll.11. Results of Analysis of Variance for Foliar B Concentration. Square Multiple R = 0.571 Source Sum-of-Squares DF Mean-Square F-Ratio LOCATION TREATMENT LOCxTREAT ERROR 5861.037 121.362 608.971 4955.512 3 1 3 147 1953.679 121.362 202.990 33.711 57.954 3.600 6.021 0.000 0.060 0.001 Total 127512.52 154 Table VII.12. Results of Analysis of Variance for Foliar Al Concentration. Square Multiple R = 0.415 Source Sum-of-Squares DF Mean-Square F-Ratio LOCATION TREATMENT LOCxTREAT ERROR 1509884.8 57011.153 341367.651 2523801.797 3 1 3 147 503294.933 57011.153 113789.217 17168.720 29.315 3.321 6.628 0.000 0.070 0.000 Total 4432065.378 154 201 A P P E N D I X VIII Box Plots of Total Height and Root Collar Diameter at Each Site by Fertility Code. Fig. VIII.1. Box Plot of Total Heights at Different Seedling Ages Under Scalp and Control Soil Conditions at Beaverdell. 300 o 200 CD X ro Z 100h 4 6 8 Seedling Age Fertility-Code • SCALP CONTROL Fig. VIII.2. Box Plot of Root Collar Diameter at Different Seedling Ages Under Scalp and Control Soil Conditions at Beaverdell. 4 6 8 Seedling Age 10 12 Fertility-Code • SCALP CONTROL 203 Fig. VIII.3. Box Plot of Total Heights at Different Seedling Ages Under Scalp and Control Soil Conditions at Rathmullen. 500 0 2 4 6 8 10 12 Seedling Age Fertility-Code • SCALP CONTROL 204 Fig. VIII.4. Box Plot of Root Collar Diameter at Different Seedling Ages Under Scalp and Control Soil Conditions at Rathmullen. 4 6 8 Seedling Age Fertility-Code • SCALP CONTROL 205 Fig. VIII.5. Box Plot of Total Heights at Different Seedling Ages Under Scalp and Control Soil Conditions at Carmi. 150 0 2 4 6 8 10 12 Seedling Age Fertility-Code • SCALP • CONTROL Fig. VIII.6. Box Plot of Root Collar Diameter at Different Seedling Ages Under Scalp and Control Soil Conditions at Carmi. 4 6 8 Seedling Age Fertility-Code • SCALP CONTROL 207 Fig. VIII.7. Box Plot of Total Heights at Seedling Ages Greater Than Ten Years Under Scalp and Control Soil Conditions at Lassie Lake. Treatment • scalp • control Seedling Age 208 Fig. VIII.8. Box Plot of Root Collar Diameters at Seedling Ages Greater Than Ten Years Under Scalp and Control Soil Conditions at Lassie Lake. 200 15 20 25 Seedling Age 30 Treatment • scalp control 209 COLOPHON This document was produced on an IBM-PC platform operating under Microsoft Windows™. It's native format is Microsoft Word for Windows™, Microsoft Excel for Windows ™, and SYSTAT 6.0 for Windows™. The font family used throughout the text is the Adobe PostScript Palatino ™ set at 10 point. The Tables and headers use Adobe PostScript Helevetica Narrow™ set at 10 point. Headings use Adobe PostScript Helevetica™ set at various sizes depending upon the order. REFERENCE LIST Ballard, T.M. and R.E. Carter. 1983. (ammended by J. E. Emanuel and R.E. Carter, 1984). Foliar Nutrient Analysis. A computer program developed at the University of British Columbia, Vancouver B.C. Ballard, T.M. 1984. Nutrition of Planted White Spruce. Contract Res. Report, BC MoF, Research Branch, Victoria BC. Ballard, T.M. and R.E. Carter. 1986. Evaluating Forest Stand Nutrient Status. BC MoF, Victoria. Land Management Rep. 20. Ballard, T.M. 1997. Pers. Comm., unpublished. Bassman, J. 1989. Influence of Two Site Preparation Treatments in Ecophysiology of Planted Picea engelmanni x glauca Seedlings. Can. J. For. Res. 19:1359-1370. BCMOF. 1976-1991. Annual Report(s) of the British Columbia Ministry of Forests, Victoria BC. Black, T.A. and B. Mitchell. 1990a. Effects of site Preparation Treatments on the Soil Moisture Regime in IDFdk, Msxk and ESSFxc Clearcuts. FRDA Research Memo, No. 162. Black, T.A. and B. Mitchell. 1990b. IDF Zone Seedling Survival and Growth After Site Preparation Treatements. FRDA Research Memo No. 166. Black, T.A. and B. Mitchell. 1991. Effects of Site Preparation Treatments on Seedling Survival and Grwoth in the MS Zone. FRDA Research Memo No. 167. Bockheim, J.G., T.M. Ballard, and R.P. Willington. 1975. Soil Disturbance Associated with Timber Harvesting in Southwestern British Columbia. Can. J. For. Res. 5:285-290. Braumandl, T.F. and M.P. Curran. 1992. A Field Guide for Site Identification and Interpretation for the Nelson Forest Region. MoF Land Management Handbook No. 20. BC MoF, Nelson BC. Canadian Soil Survey Committee (CSSC). 1987. The Canadian System of Soil Classification. 2nd ed. Agric. Can. Publ. 1646. Carter, R.E. 1983. Forest Floors Under Second Growth Duglas Fir Stands: Their Chemical Variability and Some Relationships to Produtivity. M.Sc. Thesis (Soil Science). UBC. Carter, R.E. 1992. Diagnosis and Interpretation of Forest Stand Nutrient Status. In Forest Fertilization: Sustaining andlmproving Nutrition and Growth of Western Forests, H.N. Chappell, G.F. Weetman, and R.E. Miller (eds.) University of Washington, Institute of Forest Resources Contribution No. 73. Chrosciewicz, Z. 1990. Site Conditions for Jack Pine Seedling. For. Chron. 66:579-584. 210 Clayton, J.L. and G. Kellogg and N. Forrester. 1987. Soil Disturbance-Tree Growth Relations in Central Idaho Clearcuts. USDA For. Serv., Research Note INT-372. Corns, I.G. 1987. Compaction by Forestry Equipment and Effects on Coniferous Seedling Growth on Four Soils in the Alberta Foothills. Can. J. For. Res. 18:75-84. Daddow, R.L. and G.E. Warrington. 1983. Growth Limiting Bulk Densities as Influenced by Soil Texture. Group Report WSDG-TN-00005. Fort Collins, CO: USDA, For. Serv., Watershed Systems Development Group. Dobbs, R.C. and R.G. McMinn. 1973. the Effects of Site Preparation on Summer Soil Temperatures in Spruce-Fir Cutovers in the BC Interior. Can. For. Ser. Bimon. Res. Notes. 29:6-7. Dobbs, R.C. and R.G. McMinn. 1977. Effects of Scalping on Soil Temperature and Growth of White Spruce Seedlings. In Energy, Water and the Physical Environment of the Soil, Report of te Sixth annual BC Soil Science Workshop. BC Min. of Agric, Victoria BC. p. 66-73. Dyrness, C T . 1965. Soil Surface Condition Following Tractor and High-Lead Logging in the Oregon Cascades. J. For. 63:272-275. Eis, S. 1970. Root-Growth Relationships of Juvenile White Spruce, Alpine fir, and Lodgepole Pine on Three Soils in the Interior of British Columbia. Can. For. Ser. Publication No. 1276. Entry, J.A., N.M. Stark, and H . Lowenstein. 1986. Effect of Timber Harvesting on Microbial Biomass Fluxes in a Northern Rocky Mountain Forest Soil. Can. J. For. Res., 16:1076-1081. Entry, J.A., N.M. Stark, and H. Lowenstein. 1987. Timber Harvesting: Effects on Degradation of Cellulose and Lignin. For. Ecol. Manage., 22:79-88. Garrison, G.A. and Rummell, R.S. 1951. First-year Effects of Logging on Ponderosa Pine Forest Range Lands of Oregon and Washington. J. For. 49: 708-713. Gessel, S.P. and A.N. Balci. 1965. Amount and Composition of Forest Floor Under Washington Coniferous Forests. In Forest-Soil Relationships in North America. C.T. Youngberg (ed.) Oregon State Univ. Press, Corvallis, OR, pp.11-23. Graham, RT, A.E. Harvey, J.R. Tann, and M.F. Jurgensen. 1987. Soil Organic Reserves and Their Importance to Conifer Performance in the Northern Rocky Mountains. USDA Forest Service, Intermount. For. Range Exp. Station, Moscow ID. Unpubl. Rep. Green RN, Trowbridge RL, Klinka K. 1993. Towards a taxonomic classification of humus form. For Sci Mono29. pp.47. Guidebook. 1995a. British Columbia Forest Practices Code Soil Conservation Guidebook. BC Ministry of Forests and BC Ministry of Environment, Victoria BC. Guidebook. 1995b. Hazard Assessment Keys for Evaluating Site Sensitivity to Soil Degrading Processes Guidebook. BC MoF and BC MoE, Victoria BC. Hallsby, G. 1994. The Influence of Different Forest Organic Matter on the Growth of One-year old Planted Norway Spruce Seedlings in a Greenhouse Experiment. New Forests 8:43-60. Hendrickson, O.Q., L. Chatarpaul, and J.B. Robinson. 1985. Effects of Two Methods of Timber Harvesting on Microbial Processes in Forest Soil. Soil Sci. Soc. Am. J., 49:739-746. Herring, L.J. and R.G. McMinn. 1980. Natural and Advance Regeneration of Englemann Spruce and Subalpine Fir Compared 21 Years After Treatment. For. Chron. 56:55-57. Hickling, J.S. 1993. Some Long Term Effects of Scarification on Soil Properties and Site Productivity. Undergraduate Thesis, UBC. Unpublished. 211 Hickling, J. and R. Scagel. 1995. Humus Survey: establishment report and initial survey, treatment at the stump vs. treatment at the landing. Report prepared for BC MoF, Silviculture Section, Victoria B.C. Howes, D.E. and E. Kenk (eds.). 1988. Terrain Classification System for British Columbia (Revised). MoE Manual 10. BC MoE, Victoria, BC. Hughes, S. and B. Reynolds. 1991. Effects of Clearfelling on Microbial Biomass Phosphorus in the Oh Horizon of an Afforested Podzol in Mid-Wales. Soil Use and Manage., 7(4): 183-188. Krag, R., K. Higginbotham, and R. Rothwell. 1986. Logging and Soil Disturbance in Southeast British Columbia. Can. J. For. Res. 16:1345-1354. Lundgren, B. 1982. Bacteria in a Pine Forest Soil as Affected by Clear-Cutting. Soil Biol. Biochem., 14:537-542. Luttmerding, H.A., D. Demarchi, E. Lea, D. Meidinger and T. Void. 1990. Describing Ecosystems in the Field, 2nd ed. M O E Manual 11, BC Ministry of Environment and BC Ministry of Forests. Victoria, BC. Macadam, Anne. 1990. Effects of Microsite Alteration on soil Climate and Interior Spruce Establishment in the Sub-Boreal Spruce Zone. FRDA Research Memo No. 157. Megahan, H.F. 1980. Nonpoint source Pollution From Forestry Activities in the United States. In Proceedings of Forestry and Water Quality: What Course in the '80s? Washington, D.C.: Water Pollution Control Federation: 92-151. Nyland, R., A. Leaf, and D. Bickelhaupt. 1979. Litter Removal Impairs Growth of Direct-Seeded Norway Spruce. For. Sci. 25:244-246. Plamondon, A., D. Ouellet, and G . Derv. 1980. Effets de la Scarification du Site sur le Microenvironment. Can. J. For. Res. 10:476-482. SAS Institute. 1985. SAS User's Guide: Statistics. SAS Institute, Carey, N.C. Scagel RK, and R.C. Evans. 1992. Exploratory survey of Pli and Si root growth, form, and symmetry on mechanically site prepared spots. Report prepared for BC MOF, Silviculture Branch. Scagel, R., and J. Hickling. 1993. Examination of frost heaving in sx-91-204-Q: relation of frost heaving to screefing. Report prepared for BC MoF, Silviculture Branch, Victoria B.C.. pp. 21. Scagel, R., R. Evans, and J. Hickling. 1993. Silvicultural perspectives of v-plowing in the SBSmc2. Report prepared for BC MoF, Silviculture Branch, Victoria B.C.. pp. 65. Scagel, R., R. Evans, and J. Hickling. 1994a. Exploratory excavations of spruce seedlings from mound and v-bladed trails in the ESSFwkl. Report prepared for BC MoF, Silviculture Section, Victoria B.C. pp. 21. Scagel, R., R. Evans, and J. Hickling. 1994b. Pli root egress under burnt and unburned disc trenching. Report prepared for BC MoF, Silviculture Section, Victoria B.C. pp. 23. Scagel, R., J. Hickling, and R. Evans. 1994c. Root excavation techniques: whole root and profile methods. Report prepared for BC MoF, Silviculture Section, Victoria B.C. pp. 6. Schwab, J.W. and W.J. Watt. 1981. Logging and Soil Disturbance on Steep Slopes in the Quesnel Highlands, Caribou Forest Region. BC MOF Res. Note No.88. Smith, R.B. and E.F. Wass. 1976. Soil Disturbance, Vegetative Cover and Regeneration on Clearcuts in the Nelson Forest district, British Columbia. Fisheries and Environment Canada, Can. For. Serv., Info. Rep. BC-X-151. 212 Smith, R.B. and E.F. Wass. 1979. Tree Growth on and Adjacent to Contour Skidroads in the Subalpine Zone, Southeastern British Columbia. Environment Canada, Can. For. Serv., Info. Rep. BC-R-2. Smith,R.B. and E.F. Wass. 1980. Tree Growth on Skidroads on Steep Slopes Logged After Wildfires in Central and Southeastern British Columbia. Environment Canada, Can. For. Serv., Info. Rep. BC-R-6. Smith,R.B. and E.F. Wass. 1985. Some Chemical and Physical Characteristics of Skidroads and Adjacent Undisturbed Soils. Can. For. Serv., Info. Rep. BC-X-261. Smith, R.B. and E.F. Wass. 1994a. Impacts of a Stump Uprooting Operation on Properties of a Calcareous Loamy Soil and on Planted Seedling Performance. Can. For. Ser. Info. Report BC-X-344. Smith, R.B. and E.F. Wass. 1994b. Impacts of Skidroads on Properties of a Calcareous Loamy Soil and on Planted Seedling Performance. Can. For. Ser. Info. Report BC-X-346. Standards. 1994. British Columbia Forest Practices Code: Standards With Revised Rules and Field Guide References. BC Ministry of Forests and BC Ministry of Environment, Victoria BC. Sundman, V., V. Huhta, and S. Niemela. 1978. Biological Changes in Northern Spruce Forest Soil After Clear-Cutting. Soil Biol. Biochem., 10:393-397. SYSTAT1996. SYSTAT 6.0 for Windows: Statistics. SPSS Inc., Chicago, II. Thomson, A.J. and R.G. McMinn. 1989a. Height Growth Rates of Young White Spruce and Lodgepole Pine. Can. J. For. Res. 19:257-261. Thomson, A.J. and R.G. McMinn. 1989b. Effects of Stock Type and Site Preparation on Growth to Crown Closure of White Spruce and Lodgepole Pine. Can. J. For. Res. 19:262-269. Wilkinson, L., G. Blank, and C. Gruber. 1996. Desktop Data Analysis with SYSTAT, Prentice Hall, Inc., Chicago II. 

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