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Recreating a functioning forest soil in reclaimed oil sands in northern Alberta Rowland, Sara Michelle 2008

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RECREATING A FUNCTIONING FOREST SOIL IN RECLAIMED OIL SANDS IN NORTHERN ALBERTA  by  SARA MICHELLE ROWLAND B. Sc., University College of North Wales, Bangor, 1992  A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF MASTER OF SCIENCE in THE FACULTY OF GRADUATE STUDIES (Forestry)  THE UNIVERSITY OF BRITISH COLUMBIA (VANCOUVER) FEBRUARY 2008  © Sara Michelle Rowland, 2008  Abstract During oil-sands mining all vegetation cover, soil, overburden and oil-sand is removed, leaving pits several kilometres wide and hundreds of metres deep. These pits are reclaimed by a variety of treatments using mineral soil or a mixed peat and mineral soil as the capping layer and planted with trees with natural colonisation from adjacent sites. A number of reclamation treatments covering different age classes were compared with a range of natural forest ecotypes to identify the age at which the treatments become similar to a natural site with respect to vegetation composition and key soil attributes relevant to nutrient cycling. Ecosystem function was estimated from plant community composition, litter decomposition, development of an organic layer and bio-available nutrients. Key response variables including moisture, pH, C:N ratios, bio-available nutrients and ground-cover were analysed by non-metric multidimensional scaling and cluster analysis to discover which reclamation treatments were moving towards or merging with natural forest ecotypes and at what age this occurs. On reclaimed sites, bio-available nutrients including nitrate generally were above the natural range of variability but ammonium, phosphorus, potassium, sodium and manganese were generally very low and limiting to ecosystem development. Plant diversity was similar to natural sites from 5 years to 30 years after reclamation, but declined as reclaimed sites approached canopy closure. Grass and forb leaf litters decomposed faster than aspen or pine in the first year, but decomposition on one reclamation treatment fell below the natural range of variability. Development of an organic layer appeared to be facilitated by the presence of shrubs, while forbs correlated negatively with first-year decomposition of aspen litter. The better restoration amendments for tailings sands involved repeated fertilisation of peat: mineral mixtures in the early years of plant establishment, these became similar to a target ecotype at about 25 years. Good results were also shown by subsoil laid over non-saline overburden and fertilised once, these became similar to a target ecotype at about 15 years. Other treatments receiving a single application of fertiliser remain entrenched in the early reclamation phase for up to 25 years.  ii  Table of Contents Abstract .................................................................................................................................... ii Table of Contents .................................................................................................................... iii List of Tables ............................................................................................................................ v List of Figures ......................................................................................................................... vi List of Abbreviations ............................................................................................................ viii Acknowledgements ................................................................................................................ ix  1.  LITERATURE REVIEW .................................................................................................. 1  1.1  Introduction to the problem .......................................................................................................... 1  1.2 The oil sands environment ............................................................................................................ 2 1.2.1 Climate and atmospheric factors .................................................................................. 2 1.2.2 Geology and geochemistry .......................................................................................... 3 1.2.3 Physical and chemical properties of oil sand tailings .................................................... 6 1.2.4 Making a ‘soil’ .............................................................................................................. 6 1.2.5 Planting practices ...................................................................................................... 10 1.3 Restoring an ecosystem in the oil sands ............................................................................... 10 1.3.1 Principles and practice ............................................................................................... 10 1.3.2 Natural range of variability ......................................................................................... 12 1.3.3 Ecosystem function .................................................................................................... 13 1.4  Introduction to the study .............................................................................................................. 14  2.  MATERIALS AND METHODS ..................................................................................... 16  2.1.  The study area and site descriptions ...................................................................................... 16  2.2  Sampling ............................................................................................................................................ 23  2.3 Examining key response variables........................................................................................... 23 2.3.1 Moisture content, pH and carbon:nitrogen (C:N) ratio ................................................ 23 2.3.2 Decomposition of litter measured by mass loss ......................................................... 23 2.3.3 Litter input and development of an organic layer ........................................................ 25 2.3.4 In situ bio-available nutrients...................................................................................... 25 2.3.5 Nitrate and ammonium production in incubations ....................................................... 27 iii  2.3.6 Plant community assessment .................................................................................... 28 2.4 Statistical interpretation ............................................................................................................... 29 2.4.1 Computer software..................................................................................................... 29 2.4.2 Non-metric multidimensional scaling .......................................................................... 29 2.4.3. Multi-Response Permutation Procedures ................................................................... 30 2.4.4 Cluster analysis method ............................................................................................. 31 3.  RESULTS ..................................................................................................................... 32  3.1  Moisture content, pH and C:N ratios ........................................................................................ 32  3.2  Litter decomposition...................................................................................................................... 35  3.3  Fermented and humified litter (FH) ........................................................................................... 39  3.4  In situ bio-available nutrients ..................................................................................................... 42  3.5  Nitrate and ammonium production in incubations ............................................................. 51  3.6  Ground-cover ................................................................................................................................... 54  3.7 Statistical interpretation ............................................................................................................... 61 3.7.1 Nonmetric multidimensional scaling ........................................................................... 61 3.7.2 Multi-Response Permutation Procedures ................................................................... 61 3.7.3 Cluster analysis ......................................................................................................... 64 4.  DISCUSSION ..................................................................................................................................... 66  5.  CONCLUSIONS AND RECOMMENDATIONS ............................................................. 74  Bibliography ........................................................................................................................... 77 Addendum .............................................................................................................................. 83  iv  List of Tables Table 1:  Selected natural soils in the oil sands region............................................................ 5  Table 2:  Materials used for oil sands reclamation .................................................................. 9  Table 3:  Natural forest ecotypes .......................................................................................... 20  Table 4:  Reclamation treatments ......................................................................................... 22  Table 5:  Total N, total C, C:N ratios and mass loss by decomposition of four boreal species leaf litters ............................................................................................................... 38  Table 6:  Regressions describing FH development with age ................................................. 41  Table 7:  Squared Pearson correlations ................................................................................ 63  Table 8:  Groups of plots as defined by cluster analysis ....................................................... 64  v  List of Figures Figure 1:  Location of plots near Fort McMurray in north-western Alberta, Canada .................17  Figure 2:  Inserting PRS™ probe into root-exclusion tube.......................................................27  Figure 3:  Mean moisture content of Litter, FH and Soil. .........................................................33  Figure 4:  Mean pH of litter, FH and soil ..................................................................................34  Figure 5:  C:N ratios of litter, FH and soil. ...............................................................................35  Figure 6:  Mean mass loss of aspen leaf litter during one year of decomposition ....................36  Figure 7:  Mass loss of aspen leaf litter related to soil C:N ratio ..............................................37  Figure 8:  Mass of litter and FH per square metre at the start of the growing season, by treatment. ...............................................................................................................40  Figure 9:  Mass of litter and FH per square metre at the start of the growing season, by age class .......................................................................................................................40  Figure 10: Treatment effect for FH development with age........................................................41 Figure 11: Soil moisture content for in situ root-free and rooted soil after 3 months in rootexclusion tubes. ......................................................................................................43 Figures 12 to 25: Nutrient capture by PRS™ probe method in root-free and rooted soils Figure 12: Figure 13: Figure 14: Figure 15 Figure 16: Figure 17: Figure 18: Figure 19: Figure 20: Figure 21: Figure 22: Figure 23: Figure 24: Figure 25:  Nitrate.................................................................................................................44 Ammonium .........................................................................................................45 Phosphorus ........................................................................................................45 Potassium ...........................................................................................................46 Calcium ..............................................................................................................46 Magnesium .........................................................................................................47 Iron .....................................................................................................................47 Manganese .........................................................................................................48 Copper ................................................................................................................48 Zinc .....................................................................................................................49 Boron ..................................................................................................................49 Sulphur ...............................................................................................................50 Sodium ...............................................................................................................50 Aluminium ...........................................................................................................51  Figure 26: Figure 27: Figure 28: Figure 29:  Mean NO3-- N production from FH during a 3-month incubation at 20oC ..............52 Mean NO3-- N production from soil during a 3-month incubation at 20oC .............52 Mean NH4+- N production from FH during a 3-month incubation at 20oC..............53 Mean NH4+-N production from soil during a 3-month incubation at 20oC ..............53  vi  Figures 30 to 39: Mean percent ground-cover by cover class Figure 30: Figure 31: Figure 32: Figure 33: Figure 34: Figure 35: Figure 36: Figure 37: Figure 38: Figure 39:  Bare ground...........................................................................................................55 Moss ....................................................................................................................55 Lichen ..................................................................................................................56 Grasses ...............................................................................................................56 Forbs ...................................................................................................................57 Shrubs .................................................................................................................57 Broadleaves .........................................................................................................58 Spruce .................................................................................................................58 Pine .....................................................................................................................59 Woody debris .......................................................................................................59  Figure 40: Figure 41:  Cumulative mean ground-cover classes by ecotype or treatment ........................60 Cumulative mean ground-cover classes by ecotype or age class ........................60  Figure 42: Figure 43:  NMS plot in 3-D, by treatment or grouped natural ecotypes ................................62 NMS plot in 3-D by age class or grouped natural ecotypes .................................63  Figure 44:  Dendrogram showing cluster analysis from bio-geochemical data .......................65  vii  List of Abbreviations  C  Carbon  N  Nitrogen  P  Phosphorus  K  Potassium  Ca  Calcium  Mg  Magnesium  Fe  Iron  Mn  Manganese  Cu  Copper  Zn  Zinc  NH4+  Ammonium  NO3-  Nitrate  S  Sulphur  Na  Sodium  Al  Aluminium  pH(w)  Acidity measured in water  m ha kg  Metre 10,000m  2  3  10 grams  g μg  Hectare Kilograms Grams  10-6 grams  Micrograms  o  Temperature in degrees Celcius  dS/m  DeciSiemens per metre (a measure of electrical conductivity)  NMS  Non-metric multidimensional scaling  r2  Correlation coefficient  p  Probability value  C  viii  Acknowledgements Simultaneous with the three year journey of this Masters’ degree I began a new life in Canada that has involved numerous ups and downs along the way. There are many people who, in one way or another, have supported me through this and made things a lot easier than otherwise they may have been. First, I must thank my patient supervisors, who at times must have wondered whether I would stay the course – to Dr Cindy Prescott, heartfelt thanks for her tremendous support in difficult times; to Dr Sue Grayston for many off-the-topic and on-the-topic discussions at our weekly progress meetings; to Dr Sylvie Quideau for overseeing the field activities and responding quickly when things did not go to plan, and to Dr Gary Bradfield for showing me how I would be able to conclude something statistically. At the University of Alberta I thank Jennifer Lloyd for much stoic support from the lab and in the field, and to her husband David for giving up his weekends away from the family and loaning the ATV’s that made the adventure of finding the farthest flung sites a whole lot more fun for everyone, as well as introducing us to the ritual of the Saturday evening Martini. To Erin Humeny and Steve Clark thank you for dedicated attention to the task and for outstanding driving skills that got us out of several awkward situations. To Isabelle Turcotte, Mark Beasse, Jess Leatherdale for ad hoc field support and advice and to Sandy Hemstock for her friendship in and off the field. There is a cast of hundreds at Syncrude, Suncor and Albian Sands and Soil and Environmental Consulting Inc. who gave me the training, mine access and logistical support necessary to undertake the field research safely - if not without incident; to everyone involved, thank you. Special thanks to Clara Qualizza at Syncrude and Wayne Tedder at Suncor for facilitating our work day-to-day and for providing much useful information, not least the whereabouts of bears! At the University of British Columbia I must thank Kate Del Bel for prompt ordering of supplies and Carol Dyck for her careful analysis of hundreds of samples, the preparation of which was made a lot easier to bear with the assistance, good humour and chatting of Joyce Shen during long days in the lab. Without the humour and friendship of my first- and secondyear companions I might never have got this far – though they may have moved on to pastures new I have fond memories of silly moments in the office with Rachelle Lalonde, Shannon Burnett (nee Daradick), Sara Leckie, Veneta Yolova and Jocelyn Campbell. To my other UBC friends, thanks for all those excuses to go out for a meal or host drinking parties! And to  ix  everyone else who I’ve had the great pleasure to know these past three years in Vancouver, thank you for accepting me into your circle. Finally, though it was not easy for them to understand my decision to quit my career path and life in the UK I wish to thank my parents Chris and Lesley Rowland for their moral support and quiet encouragement to stick with the plan.  x  1.  LITERATURE REVIEW  1.1  Introduction to the problem  There are an estimated 1.6 trillion barrels of oil within the oil sands located in the Peace River, Cold Lake and Athabasca River regions of northern Alberta, of which 311 billion barrels in the Athabasca region are believed to be recoverable with current surface mining technology (Government of Alberta – Environment website, 2007). During mining, all vegetation cover is removed and about 15m – 50m of overburden is stripped away to reveal the oil sand, which is then itself stripped away and transported to the extraction plant. This creates very large, open pits several kilometres wide and hundreds of metres deep into which unwanted mine residues may be tipped. The oil companies operating in the region are obliged to work within a regulatory framework to conserve and reclaim their operational land as set out within the Land Surface Conservation and Reclamation Act 1973 and the Environmental Protection and Enhancement Act 1992 (Government of Alberta, 1999). The aim of reclamation is to use mine residues to create soil-like materials capable of supporting stable plant and microbial communities. The reclamation of the Athabasca oil sands mines is a massive soil-forming and landscaping exercise of a scale perhaps never seen before, and after about 30 years of research it remains a challenging undertaking for all involved. Three major oil companies operating in the Athabasca region around Fort McMurray are Syncrude Canada Ltd (owned by a consortium of 8 US and Canadian oil companies including Exxon and Petro Canada), Albian Sands (a subsidiary of Shell) and Suncor Energy Inc. These three companies have contributed to the funding of this research project. Syncrude’s long-term plan is to achieve 50% self-sustaining forest producing commercially useful timber, 20% grassland, 10% wetlands and 20% open water. By 2023, licensing agreements are expected to have allowed 150000 ha in the Athabasca region to be disturbed by mining (Syncrude website, 2005). Unless mining technology such as subterranean steam extraction becomes economically and technically viable, and the oil companies are counting on this to achieve even greater productivity (W. Tedder, pers. comm. 2005), the next 50 years or so will see cut-and-fill surfacemining progress over swathes of what is presently undisturbed boreal forest comprising mainly aspen (Populus tremuloides Michx., P. balsamifera L.), spruce (Picea glauca (Moench) Voss, P. mariana (Mill.) Britton,Sterns & Poggenb.) with jack pine (Pinus banksiana Lamb), fir (Abies balsamea (L.) P. Mill) or tamarack (Larix lariana (Du Roi) Koch) (Rowe, 1972). The reclaimed sites are under license agreements to be handed back to the Province once they are in a satisfactory state likely to achieve a defined ecosite phase. For economic reasons the companies seek ways to shorten this term as much as possible, and Syncrude 1  would like to achieve sequential site handover to the province 15 years after being ‘reclaimed’ (C. Qualizza, pers. comm. 2005). Provincial guidelines for reclamation recognize the region’s social needs and encourage the oil companies to establish commercial forest and, secondarily, wildlife habitat capabilities similar to pre-disturbance conditions as described for a Central Mixed Wood Sub-Region of the Boreal Forest (Government of Alberta, 1999). Whilst operators are not required to return to repair failed sites, a process of adaptive management is expected so that observed trends can be addressed with new reclamation methods introduced to establish the required eco-phase (Oil Sands Vegetation Reclamation Committee, 1998). Therefore the guidelines remain a fluid and flexible instrument. Each company practices its own range of reclamation prescriptions within this framework, leading to great variability between sites and tremendous challenges and opportunities for scientific study. The biology and productivity of reclaimed soils (rather, soil-forming materials) are still not understood and no-one has yet shown that the living components of the soil are functioning effectively (Oil Sands Vegetation Reclamation Committee, 1998). The University of Alberta and the University of British Columbia are working collaboratively on a large project to help understand whether reclaimed soils in the oil sands are on a trajectory to function like a natural forest soil with respect to hydrology, decomposition, nutrient cycling and plant succession; the author of this thesis is a member of that team.  1.2  The oil sands environment  1.2.1 Climate and atmospheric factors Climate is generally harsh, the ground surface does not thaw until late May or early June and the growing season extends for around 95 days until September. By month, the mean temperatures range from –220C to +17oC (Visser, 1985) and summer frosts are possible; only 2 months of the year are deemed frost-free. Summer precipitation is relatively uncommon, usually falling as brief showers. Total precipitation is around 470 mm per annum of which about 300 mm falls as rain. Precipitation in the Fort McMurray area is moderately to strongly acid, generally around pH 4.8 but pH 4.1 has been recorded (The NOx and SO2 Management Working Group Science Colloquium, 2000). Atmospheric deposition may be significant, from gas burnt to heat water during the oil sands processing but also from very heavy vehicles used for transporting oil sand to the processing facility. When the mines are at full capacity, probably sometime around 2010, it is predicted the nitrate deposited from the atmosphere will be up to 814 kg/ha/yr, decreasing with distance from the source but still in the hundreds of kilograms per hectare over the mine sites (The NOx and SO2 Management Working Group Science Colloquium, 2000). 2  1.2.2 Geology and geochemistry The oil sands are the delta deposits of an ancient tropical sea, subsequently compressed by extensive glacier formations and invaded by oil migrating from elsewhere. The local Clearwater shale formation north of Fort McMurray lies over the oil-rich sand deposits and is saline (Marshall, 1982); these consolidated deposits have strongly sodic or saline pore fluids where electrical conductivity exceeds 6 dS/m (Stolte et al. 2000, Purdy et al., 2005) and contain highly dispersive sodium-clays that are stable only whilst confined in situ. The shales are physically stable while at an angle of repose not exceeding 7o or 12%, resulting in a gently undulating landscape. Glacial till from the Pleistocene Epoch-era forms a clay-rich mineral soil layer over this except in some places where fluvial sand persists (Stolte et al. 2000). Natural soils within the oil sands are classified by the soil sub-groups of Orthic Brunisols, Orthic or Gleyed Luvisols, Gleysols or Organic (Table 1), this following a moisture gradient from xeric to subhydric and from aerobic to increasingly anaerobic and reducing conditions (The Oil Sands Vegetation Reclamation Committee, 1998). Broadly, this gradient in soil development is caused by impeded water flow due to increasing clay content in the mineral sub-soils, from glacio-fluvial sands to glacio-lacustrine muds to fine-clay glacial till. However, soil development from a Brunisol to a Luvisol may happen over time as clays are leached from surface layers and accumulated at depth (National Research Council of Canada, 1998). Natural soils are generally saline. Typically there is peat and peat-like soil at the surface (Stolte et al., 2000). In order to mine the oil sands the soils and shale overburden must be removed. The organic-mesic fibrosols are salvaged to supply the material used to manufacture the peat:mineral mixture used in reclamation. The litter and organic horizons from other soils are also salvaged and used to create soils on other reclamation treatments but this is on a very limited scale due to the small volume available (C. Qualizza, pers. comm. 2005). In removing the shale overburden it becomes fragmented and increases in volume, so when replaced as waste in the mined pit it is finally banked up at an angle greater than the 12% at which it can remain stable. The loosening allows rainwater and surface water flow to percolate through the now unstable material. This causes weathering; the sodium clays disperse rapidly leading to slumping and erosion (Stolte et al. 2000; Barbour et al., 2001). For these reasons all saline overburden (the Clearwater formation) must be protected from precipitation percolating below the root zone and this is generally achieved by capping with 80 cm of non-saline overburden or till. The hydration and swelling of clays within these various mixtures appears to impede water percolation by blocking micro-pore routes but macro-pore infiltration still happens. A significant salt concentration gradient down the profile develops to the point of salt precipitation at toes of slopes (Stolte et al., 2000). The physical and chemical properties of non-saline overburden varies, those selected for reclamation prescriptions from shallow depth (up to 1 m below soil 3  level) have significant but highly variable clay content and a pH range of between 4.9 and 6.6. At deeper levels, 1-2 m below soil level, the overburden may have a much higher pH 8 and higher base-cation content (Danielson et al., 1983).  4  Table 1: Name  Selected natural soils in the oil sands region1 Description  Poorly developed mineral-organic horizons  Parent material or principle mineral component  Coarse-textured glacial till or glacio-fluvial sands of low base status  Eutric dystric brunisol  Physio-chemical properties  Acidic pH < 5.5 Sub-xeric to sub-mesic moisture regime Well-drained to imperfectly drained Low to moderate fertility  Well-developed mineral-organic horizons  Fine-textured basesaturated glaciolacustrine or glacial till  Elluvial grey A-horizons and illuvial clay-enriched Bhorizons Neutral – alkaline  Orthic gray luvisol  pH > 5.5 Mesic moisture regime; imperfectly drained Moderately good fertility  Mesic fibrosol  Deep, relatively undecomposed fibric material in Sphagnum-bogs and poor fens, or sedgedominated rich fens. Bogs rely on precipitation for water; fens receive ground or surface flow with mineral enrichment  1  Organic material overlaying clay loam or clay-rich glacial till  >17% organic C >30% organic matter Rich fens pH > 5.5 Bog pH < 4.5 Typical peat contains 79% organic C Very wet, with poor or no drainage  Sources: Danielson et al. 1983; National Research Council of Canada 1998; University of Alberta Devonian Garden website 2008  5  1.2.3 Physical and chemical properties of oil sand tailings The oil in the oil sands is extracted by the Clark hot-water extraction method, a hot water and steam process with added sodium hydroxide (NaOH) that separates the bitumen from the sand. The sand is ejected as tailings while the bitumen is sent for processing into petroleum. During the process considerable abrasion occurs within the pipework at the plant as the tailings slurry is tumbled and discharged; at Syncrude the equivalent of 2 pick-up trucks of steel is abraded every day (Syncrude website, 2005). Although before processing the oil sands are moderately acid (pH 5.5 – 6), after the removal of oil and with the addition of NaOH during the separation process the tailings become alkaline (pH 8 ±0.5) (Lesko, 1974; Fedkenheuer et al., 1980; Visser 1985). The microbially-sterile tailings slurry is ejected into large settling basins. The tailings are 95% ( 1%) sand, 4% silt and 1% clay (Danielson et al., 1983; HBT AGRA 1994). The sand rapidly settles to form dykes for containment of fine tailings and tailings water but may be reworked by machine to create appropriate contours for reclamation. The tailings sands drain freely so that after about 5 years all traces of tailings water in the pores have disappeared (C. Qualizza, pers. comm. 2007). But fine tailings and tailing water that remain held within the basin includes the smallest solids that settle out very slowly, or possibly do not settle out at all but remain suspended in strongly alkaline aqueous mixture; this resembles yoghurt with a solids content of around 30% by volume. Once pumped to the settling basin these fine tailings sit beneath a thin layer of water that has no potential for recreation or wildlife. Due to residual bitumen floating at the surface, the lake must be protected constantly against migratory wetland or water fowl (C. Qualizza, pers. comm. 2005). Over a billion cubic metres of fine tailings will have been produced by 2025. This represents a significant on-going liability and efforts are being made to stabilise the fine tailings such that they can eventually be reclaimed. Presently this is done by adding gypsum (CaSO4, rate 800 -1200 g m-3) as the tailings slurry is discharged from the plant; the calcium replaces the sodium ions in the fine tailings clays, ejecting water and resulting in a sulphate-rich mix that settles out within a few days with a solids content of 80% by volume (Li & Fung, 1998; C. Qualizza, pers. comm. 2007).  1.2.4 Making a ‘soil’ The desirable characteristics of soil-forming materials used for reclamation are: (i)  ability to supply sufficient water and nutrients within the root zone for plant growth;  (ii)  physical stability for root development and resistance to erosion, and  (iii)  chemically and biologically active to serve both as a buffer against changing environments and to maintain nutrient status.  6  Reclamation materials used for pedogenesis comprise the various types of ‘waste’ or residuals from mining operations, including mixed surface peats or soil organic layers and mineral topsoils, mineral subsoils, tailings sand, overburden, saline-sodic Clearwater-formation shales and lean oil sand (Table 2). The proportion of sand, silt, clay and organic matter affects the soil structure, with a ‘good’ soil needing both micro- and macro-pore spaces for the proper balance of air and water to support plant and microbe communities. Stable aggregates (i.e. those aggregates that resist breakdown when gently agitated in water) of mineral and organic materials are essential to good soil structure (Li & Fung, 1998). The addition of peat to add organic C-content, improve water-holding capacity and permit aggregation of tailings sand is practised as a general principle. Peat is frequent in the boreal region, but peat that is salvaged from new mine operations is not sufficient in volume to provide the required cover to all reclaimed sites over the full term of mining activities and so this practice is unsustainable (C. Qualizza, pers. comm. 2005). Therefore there is interest in finding alternative reclamation methods that require less or no peat – bio-solids from the local sewage plant being one possible resource (W. Tedder, pers. comm. 2006). Until sometime in the1980’s both Suncrude and Syncor reclaimed their sites with the equivalent of 15 cm peat and 10 cm mineral soil (glacial till) mixed directly into the tailings sand. Early trials with various tree and shrub species showed that some were much less successful than others on this mix. The most variable survival rate (55% - 91%) in trees was exhibited by aspen while pine, spruce and larch conifers had better than 90% survival on all sites. Rose was also highly successful, with 100% survival (Fedkenheuer et al., 1980). The mixing of peat into tailings sand was soon discontinued due to unsatisfactory plant growth (Hardy BBT Ltd, 1990) and replaced by one with placing stratified layers of peat or mineral soil over tailings sand (Danielson et al., 1983). From the mid-1980’s onward all sites were capped with a surface layer of varying proportions of peat:mineral soil of variable depth (typically 20 cm at Suncor sites, 70 cm at Syncrude sites) that encouraged natural plant invasion, particularly on the shallow cap depths (Hardy BBT Ltd, 1990). Prescriptions at Syncrude have been modified so now the equivalent of 15 cm of peat is spread with 20 cm of mineral soil (C. Welham, pers. comm. 2005). The peat:mineral soil is mixed coarsely during the salvage operation where peat is overstripped, taking with it the required depth of underlying till (C. Qualizza, pers. comm. 2005). Where lean oil sands (unprocessed, raw-state oil sands with <6% oil by weight) and saline overburden, rather than tailings sand, is being reclaimed an intermediate capping layer of up to 80 cm clean (non-saline) subsoil material is used to bury the material and protect from water and plant root ingress (Oil Sands Vegetation Reclamation Committee, 1998; Stolte et al., 2000; C. Qualizza, pers. comm. 2005;). There is as yet no standard prescription or ‘recipe’ for reclamation - the oil companies adopt different methods of reclamation with different materials, 7  different cap depths and with or without peat (S. Quideau, pers. comm. 2004; C. Welham pers. comm. 2005).  8  Table 2: Name  Peat:mineral  Tailings sands  Materials used for oil sands reclamation1 Origin and description  Principle mineral component  Peat and clay mineral topsoils salvaged and mixed by stripping from fibrosol sites  Clay loam or clay overburden (shallow depth)  Cretaceous-era marine sands with occasional shale beds  95% quartz sand, 4% silt (feldspar and mica), 1% clay (kaolinite, illite and montmorillonite)  Physio-chemical properties  2% - 17% organic C Near-neutral pH P:M ratio by volume varies by company, between 3:2 to 3:4  Ejected as waste after bitumen has been extracted  Hydrophobic after air-drying due to residual 0.1% - 0.4% hydrocarbons Virtually nil plant nutrients Sand grains very fine to fine-grained (95% between 50- to 250-μm) Excessively drained, unstable, subject to wind and water erosion  Pleistocene Epoch glacial drift Subsoil  Overburden  Shallow depth (<3m from surface), comprising the Band C-horizons Pleistocene Epoch glacial drift over Cretaceous-era sedimentary deposits  Silt-clay shales and glacial clay-rich tills (kaolinite, illite and montmorillonite)  Non-saline pH 5.0 – 8.0 Low organic C (<2%) but may be locally enriched by peat during salvage operation  Silt-clay shales with clay-rich tills (kaolinite, illite and montmorillonite)  Fine- or coarse textured Non-saline Slightly alkaline (pH 8.0+) in situ, may be acidified by oxidation of sulphites (eg. pyrite, FeS2) in the reclamation landscape Low to nil organic C  Lean oil sand  Clearwater shales  Cretaceous marine sediments, with migrated bitumen  Cretaceous-era, impermeable marine shales deposited from ancient Boreal Sea  Relatively impermeable, noncemented quartz sand with shales, silts and clays  < 6% bitumen by weight  Silt shales with swelling clays (illite, montmorillonite)  Fine-textured  pH 5.5 – 6.0 Rejected as ‘ore’ because clay content too high  Saline-sodic (10-20dS/m) pH 8.0+ Sodium-saturated (SAR >20)  1  Sources: Mossop, 1980; Danielson et al. 1983; Hardy BBT 1990; C. Welham, pers. comm. 2005; C. Qualizza, pers. comm. 2007; M. MacKinnon, pers. comm. 2008  9  1.2.5 Planting practices Where erosion is likely to occur, and this is the case for almost all reclaimed oil sands sites, barley (Hordeum vulgare L.) is sown to provide quick vegetation cover and erosion control. Barley is used because it is a poor competitor and is readily invaded by local flora within the first few years (Hardy BBT Ltd, 1990). It also helps to bulk up soil organic matter content and supply nutrients to the desired secondary crop. Early trials in the 1970’s with hydro-seeding of mixed grasses and clover resulted in significant problems of reduced tree establishment and rapid invasion by agronomic species (alfalfa and red clover) at the expense of native species (Hardy BBT Ltd, 1990). It is noted that hydro-seeding occurred at a time when peat was incorporated into the tailings sand, rather than laid on top, and this may have been a factor in the problematic establishment of trees. Direct seeding is still permitted in the boreal forest subject to use of only native species within specified percentages (Government of Alberta – Sustainable Resource Development website, 2005). Suncor continued with direct seeding from 1983 using a legume/native grass mixture. Whilst this was less competitive with trees it still was prone to rapid invasion by agronomic species from neighbouring, older reclaimed sites (Hardy BBT Ltd, 1990). The seeding practice was abandoned sometime in the late 1980’s in favour of natural plant re-colonisation from adjacent reclaimed sites. This has been found to be successful for grasses and forbs but the re-colonisation varies with depth of peat and/or mineral capping and the time that the capping materials spent in stockpile. With the exception of peat transferred directly from one site to the next and containing viable root cuttings of aspen or willow, woody plant and tree establishment has not been significant and tree planting is deemed necessary (Hardy BBT Ltd, 1990; W. Tedder, pers. comm. 2005). On sites reclaimed for forestry, tree planting occurs usually at the next available planting season although delays do occur, for example where there are disputes over compliance with licenses (W. Tedder, pers. comm. 2006). The oil companies plant nursery-grown white spruce and aspen (W. Tedder, pers. comm., 2006) of which the latter appears from field observation to be more successful.  1.3  Restoring an ecosystem in the oil sands  1.3.1 Principles and practice Restoration ecology as a science began gaining momentum in the mid-1990’s, diverging from wildlife and conservation biology, where effort concentrated on manipulating environmental features to benefit particular species, to a broader scope of experimenting in the creation of entire ecosystems (Morrison, 1995; Dobson et al., 1997; Young, 2000). Ecological restoration is the concept of returning a damaged ecosystem to something that is within acceptable limits set by reference to a less-disturbed or non-disturbed one (Palmer et al., 2006; Society for 10  Ecological Restoration (SER) International website, 2008). It differs from ecological recovery (that is the process by which an ecosystem repairs or re-establishes itself over time) in that it includes an element of human interference or, rather, assistance to achieve the desired result sooner or for less cost (Temperton, 2007). Strictly, ecological restoration seeks to recover what was there before or what exists locally (Brown & Lugo, 1994; Miller & Hobbs, 2007); if one accepts a different but functional ecosystem to that which was damaged - for example, converting from forest to farmland - that would be ecological replacement (Dobson et al., 1997) or rehabilitation (Brown & Lugo, 1994). Reclamation is where one seeks to make a landscape of better productive usefulness than it was deemed to have at the start (Brown & Lugo, 1994) but ecological restoration implies a degree of ecosystem complexity above and beyond that of reclamation. Ecological restoration has developed through the necessity of mitigation for environmental damage inflicted by human activities in the exploitation of natural resources, providing research opportunities to test hypotheses on succession (Bell et al., 1997) and community assembly (Dobson et al., 1997; Lockwood, 1997). Eventually this information may lead to new legislation that may seek to remove the source of disturbance if, for example, damage cannot be rectified within an acceptable timeframe, or to improve practises and set goals for better or quicker restoration (Bell et al., 1997). Practical experience in ecological restoration has focussed attention on three areas deemed to be key to achieving success; these are (1) simplification of activities and consequent cost-reduction; (2) accelerating or kick-starting the restoration process and (3) determining the appropriate yardstick by which to measure results (Sublette et al., 2007). Historically, success in restoration ecology has been measured using at least one attribute drawn from (i) diversity (ii) vegetation structure and (iii) ecological processes (Ruiz-Jaen & Aide, 2005). The SER, although acknowledging the practical and cost limitations, recommends that measurement of success be by reference to (1) similar community structure in comparison with reference sites; (2) indigenous species; (3) functional groups for long-term stability; (4) physical parameters for sustaining populations; (5) normal function; (6) landscape integration; (7) elimination of potential threats; (8) resilience to natural disturbance and (9) self-sustainability (SER website, 2008). The question still remaining is whether any disturbed community returns to predisturbance state, or to a variety of alternative stable states that may be considered ecological replacement (Temperton, 2007). A big problem with the recovery phase is that biological organisms in a disturbed environment may behave in an undesirable way (Dobson et al. 1997) and lead to uncertainty as to the endpoint where the system could be said to be restored (RuizJaen & Aide, 2005). It may be of no use to take a snapshot of species composition and abundance and compare this to the target when the process is dynamic and the rate of change in composition and abundance may be more relevant (Hobbs & Norton, 1996). Long-term 11  monitoring of recovering sites is more likely to inform and guide us, but this is an ongoing managerial responsibility that requires archiving of data and communication between stakeholders, incurring significant expense and slow results (Allen et al., 1997; Michener, 1997; Temperton, 2007). The main issues behind success in the field are what factors limit restoration and how have those factors been perceived previously? Thus, until a few years ago abiotic constraints were considered over-riding limiting factors until researchers discovered biotic constraints were just as important – for example, the need to have facilitative organisms such as mycorrhizae (Ryszka & Turnau, 2007). The knowledge of which abiotic factors and which biotic factors are limiting whether separately or linked together, or when an abiotic limiting factor switches to biotic one, is now an essential part of ecological restoration (Temperton, 2007). At present we have relatively few examples of the longer-term changes of restored ecosystems and their viability (Temperton, 2007). As an example of how few studies of ecological restoration on highly disturbed sites have been undertaken, only 68 of the 468 articles published in the journal Restoration Ecology between its inception in 1993 and 2005 referred to planted or seeded sites (Ruiz-Jaen & Aide, 2005). There is a need to create a systematic scientific approach (that is, restoration ecology) as well as addressing socioeconomic, political and ecological constraints (that is, ecological restoration) (Miller & Hobbs, 2007, Temperton, 2007) which can come with knowledge gained from each additional study. But a particular site will require specific activities to restore it, so no generic set of recommendations will fit all cases (Miller & Hobbs, 2007). Although some form of ecological model is required to guide restoration, the use of reference sites as targets that represent a particular natural state has been argued to be unduly constraining on restoration efforts, setting unrealistic and unattainable goals (Hobbs & Norton, 1996). But the use of more than one reference site, or target, is recommended to help capture the temporal and spatial natural range of variability among different natural systems (Ruiz-Jaen & Aide, 2005) that will give a wider window of acceptable standard for ecosystem restoration.  1.3.2 Natural range of variability The natural range of variability is the range of ecosystem phases encountered over a long time period and before disturbance by non-aboriginal humans (Gayton 2001). Natural range of variability provides a yardstick by which to describe or measure disturbance processes, and the ecosystem variability that these disturbances create. Ecosystems are thought to be more sustainable if managed so their disturbance regime falls within the natural range of variability, spatially and/or temporally (Gayton, 2001). The natural range of variability may be estimated by 12  sampling a number of different eco-phases, or ecotypes, that occur in an environment that historically was subjected only to natural disturbances (such as by fire, flood or storm) to obtain data on spatio-temporal dynamics (Ministry of Forests, British Columbia, 2003; Nonaka & Spies, 2005).  1.3.3 Ecosystem function Ecosystem function depends on processes of primary production, decomposition and nutrient cycling. For an ecosystem to be self-sustaining, nutrients must be released during decomposition and mineralisation, and captured quickly by growing plants and not lost from the system so that net primary production increases over time. There is an implicit need for ‘synchronicity’, with nutrient release occurring at a pace and at times when they can be taken up by growing plants. Decomposing matter should also remain long enough to provide a long-term nutrient reservoir, support soil fauna and build up an organic layer which will permit the growth of plant roots and reduce the risk of drought stress. A measure of ecosystem function is whether an organic layer is developing from decomposing plant litter on the surface (Swift et al., 1979). Decomposing litter (which may include leaves, fruits, woody debris and microbial decomposers) that is no longer recognisable as the original litter type and is dark brown or black colour is referred to as fermented (F) and humified (H) organic matter – or as FH where no discrimination between the two types is deemed necessary. FH provides a low-pH, moisture-conserving environment for microbial transformation of inorganic substrates into nutrients available for plant uptake as well as providing a temporary store for dissolved organic carbon leached from decomposing litter that may then be released and assimilated by microbes (Park et al., 2002; Prescott, 2005). A feedback loop may occur as the decomposing litter feeds the new crop of plants that then drop their litter to create the FH, and so on; in this manner an ecosystem may achieve functional integrity, a self-sustaining state with respect to nutrient cycling, although this is never a completely closed loop since nutrients are also input from the atmosphere and weathering of rocks and lost by leaching, denitrification or fire (Swift et al., 1979; Attiwill & Adams, 1993). Decomposition can be very slow - in the boreal forest, pine needles remained as litter for 6 months, decomposed in the F-layer for 9 years (Millar, 1974) and as humified remains in the H-layer for 353 years (Cole & Rapp, 1981), after which time the organic material is completely mineralised as nutrients for synthesis into new cellular components in other organisms, or released as waste CO2 (Prescott, 2005). Litter quality may be indicated by C:N ratio, which varies among species and functional types and is considered a good predictor for decomposition rate among species, being correlated negatively with early mass loss (Prescott, 2005). The chemical composition (N,P,K, etc.) of the leaf litter of perennial plants is relatively 13  low; in addition to having more structural and protective compounds such as cellulose and lignin, large amounts of photosynthates and key nutrients N and P are withdrawn into the plant’s roots and woody tissues and stored for the next growing season. In contrast, annual plants have less structural and protective materials and die without fundamental alteration of their biochemistry before death. Therefore a greater proportion of annual plant litter is likely to increase rates of C and N mineralization and microbial biomass and cycle nutrients at a selfsustaining level (Vinton & Burke, 1995). The volume and chemical quality of plant litter from annual plants, encompassing soft-leaved and palatable species such as grass and N-fixing legumes, may be crucial to nutrient status over the term while an ecosystem becomes established. The more recalcitrant litter from perennial plants (e.g. conifer needle litter) through their slower decay and humification may be more important in the development of a surface organic layer (Cole & Rapp, 1981).  1.4  Introduction to the study  This project is part of a wider study that aims to discover whether reclaimed sites are on a predictable path to become functioning ecosystems similar to those naturally present in the boreal region and, if so, within what timescale. An estimate of the range of natural variability for a variety of response variables in reference (‘target’) natural boreal forest ecotypes were compared with those of a number of oil sands reclamation amendments across a range of ages. The purpose was to determine how restored and natural systems differ and how these change over time. The aim was to discover which type(s) of amendment is, or is likely to, best fit the natural range of variability present in the natural forest ecosystem and by what age this will occur. In most cases mean values were calculated for each ecotype. The range of means was selected to represent the natural range of variability and compared with the treatment means; this overcame the problem of having a very large range of variability between maxima and minima for each ecotype or treatment measured at the plot level. The variables measured represented two of the three major attributes identified by RuizJaen & Aide (2005), thus (i) vegetation structure and (ii) ecological processes and five of nine attributes identified by SER, thus (1) similar community structure in comparison with reference sites; (2) presence of functional groups (for example, grasses) for long-term stability; (3) adequate physical parameters for sustaining populations, for example, soil moisture and developing FH; (4) normal function, such as decomposition, and (5) self-sustainability, explored by measuring changes on plots that differed in age as a proxy for a chronosequence.  14  Vegetation structure and functional groups were estimated by assessing ground-cover types (this included bare ground and woody debris). Ecological processes or functions were measured by assessing bio-available nutrients in situ in root-free and rooted soil, litter decomposition rates, development of FH and N-mineralisation during laboratory incubations. The data collected was used to identify trends or develop models to help explain why differences occur and what factors may be the most important in the oil sands to achieve better and/or quicker restoration of a target ecosystem. Non-parametric methods of analysis were employed to identify which treatments were on a satisfactory trajectory towards a natural ecotype. A number of questions were formed to help direct the research in both field and laboratory experiments. These were:-  1. Does a peat:mineral soil mix, applied according to a variety of reclamation prescriptions, mimic the FH layer or mineral soil of natural forest ecotypes with respect to key response variables such as moisture, pH and nutrient status?  2. Does ecosystem development on reclaimed sites require an early kick-start from added fertilisers?  3. How does FH accumulate on the various reclamation treatments with time and how does this compare to the range of variability in natural site FH?  4. (a) Does litter decompose at different rates across all reclamation treatments and how does this differ from that of natural ecotypes? (b) How do the rates of decomposition of various litter types compare?  5. By what age will plant communities on reclaimed sites (planted or not) be similar to those communities found on natural forest ecotypes?  6. Are reclaimed sites on a predictable path to become functioning ecosystems similar to those naturally present in the boreal region and, if so, within what timescale?  15  2.  MATERIALS AND METHODS  2.1. The study area and site descriptions The study area is within the Wood Buffalo region of northern Alberta, Canada (Figure 1). The study area covers a geographic range of about 135 km north-south and 50 km east-west, centred approximately 35 km north of Fort McMurray (57o00’ N, 111o28’ W). It consists of 47 plots, all but one of these plots were chosen from an inventory of about 100 long-term monitoring plots that were established from 2000 to 2006 by Paragon Soil and Environmental Consulting Inc; the other plot (WA5, a re-colonised peat waste area in the Suncor facility) was established for this study and has since been adopted by others for research purposes. Each long-term monitoring plot measures 10 m x 40 m and is identified on the ground by corner posts of 1-m-tall white plastic tubing or, in the case of WA5, pink flagging tape tied to the nearest tree and, in the case of Albian, the extent of current instrumentation set up on site by other researchers. Navigation to plots was by a combination of prior knowledge, satellite coordination (eTrex™, Garmin Ltd, Kansas, USA), map reading, compass orientation – and perseverance. Baseline data on these plots, excepting the most recent ones (Albian, WA5, FF), was available from AMEC Earth & Environmental/Paragon Soil and Environmental Consulting Inc. (2005). Site age was calculated as the interval between capping year (not planting year) to the year of first sampling, either 2005 or 2006.  16  Figure 1: Location of plots near Fort McMurray in north-western Alberta, Canada. Plot symbols are not to scale.  (i)   (ii)  (i) : Northern section of study area Natural ecotypes Reclaimed  5km  17  (ii): Southern section of study area Natural ecotypes Reclaimed  5km  Fort McMurray  18  Target ecotypes Ecosystem types (‘ecotypes’) have been classified by Beckingham & Archibald, 1996 (Table 3). These varied according to landscape, drainage and soils, with different plant communities developing with different proportions of trees including jack pine, aspen and white spruce. The acceptable target ecotypes with the potential end-use of commercial forestry were classed by predominant indicator species and described thus: - blueberry (b), Labrador teamesic (c), low bush cranberry (d), dogwood (e) and horsetail (f) (Oil Sands Vegetation Reclamation Committee, 1998). Preliminary comparisons of recently reclaimed site treatments for Syncrude and Suncor (Quideau, pers.comm. 2004) with soil capability class (Beckingham & Archibald, 1996) suggests that target ecosite d applies to imperfectly drained reclaimed sites and b to well-drained reclaimed sites. The reference, or target, plots in this study comprised of 3 replicates of each of 6 natural ecotypes drawn from classes a1, b1, b3, d1, d2 and d3, thus 18 natural plots in all; one plot representing a b3 ecotype was destroyed mid-study by the expansion of a mine. Ecotype b2 was excluded as reference plots were too distant to be considered local or relevant and ecotypes e and f were excluded because they represent a very wet regime unlikely to be achieved using current reclamation methods; ecotype c was not included as it is not found locally or represented within the long-term monitoring plot inventory.  19  Table 3: Ecotype  Natural forest ecotypes (after Beckingham & Archibald, 1996) Moisture  Soil subgroup  Description  regime  Replicates by plot number  Eluviated a1  Xeric  Dystric  Lichen - jack pine  10, 26, 27  29, 62, 63  Brunisol Eluviated b1  b3  Sub-xeric to  Dystric  Blueberry,  xeric  Brunisol  jack pine – aspen  Eluviated  Blueberry,  2, 49  Sub-mesic to  Dystric  aspen – white  64 destroyed  mesic  Brunisol  spruce  winter 2005  Low-bush cranberry,  4, 8, 61  Orthic Gray d1  Mesic  Luvisol  aspen  d2  Mesic  Orthic Gray  Low-bush cranberry,  Luvisol  aspen – white  19, 50, 57  spruce Orthic Gray d3  Mesic  Luvisol  Low bush cranberry,  20, 21, 23  white spruce  20  Reclamation treatments There were 29 reclaimed plots covering different reclamation prescriptions (Table 4). Older treatments were described as A, B, E, F, H, I, M after AMEC Earth & Environmental/Paragon Soil and Environmental Consulting Inc. (2005). Additional, or newer, treatments are Albian Sands’ instrumented slope site (Albian), a waste peat dump at Suncor (WA5) and a recently reclaimed site at Syncrude that is similar to treatment M (mesic peat) but utilises fibric peat – called Fireweed Fibric (FF) since fireweed (Epilobium angustifolium L.) is frequent on this site. Note that horizon depths in all treatment profiles are not strictly defined and range widely depending on the change in practices and over time and by the challenges of grading mixed materials in large-scale landscaping. The majority of the plots, including several natural forest plots, are within the mine lease areas of Syncrude, Suncor and Albian Sands; the remainder are natural plots located on Crown land outside of the leased mining areas. Syncrude and Albian currently practice restricted access policies at their site(s). Special protection is lacking on Suncor sites so some sites here have been disturbed by mining operations. All measurements and sampling occurred within 1 m - 2 m of the outside of the plot perimeter to avoid disturbing ground-cover and preserve the integrity of the environment within the plot. There is no inherent experimental design (eg. Latin square or Completely Randomised Block) to adequately represent the variety of reclamation treatments introduced since mining began in the area in the late 1960s. There are numerous confounding factors that include uneven numbers of replicates (frequently, only one per treatment), age since reclamation, age at time of planting, planting species, proximity to other reclaimed sites or disturbed areas (related to natural colonisation potential), fertilisation treatment, peat type and prior stock-piling (if recorded), altitude and aspect. This created problems with using classic statistical methods. Unless described otherwise, the capping layer designed for supporting plant growth on reclamation treatments is called ‘soil’.  21  Table 4:  Reclamation treatments 1  .  TREATMENT  Depth  A  B  E  F  H  I  M  /cm 10  Peat:Mineral  Peat: Subsoil  20  Mineral  Peat:Mineral  Peat:Mineral  Subsoil  30 40  Subsoil  Waste  Albian  Fibric  Area 5  Sands  Mesic  Fibric  peat:  peat:  Mineral  Mineral  Peat  Subsoil  Peat: Mineral  Overburden  50 Tailings sand  60  (Shales) Subsoil  70  Subsoil  Tailings  O’burden  80 90  Fireweed  Tailings sand  Tailings sand  (Shales)  sand O’burden (Shales)  100 ....  CWS  Comp.  Syncrude  Suncor  Plots  36(11) 37(24)  3(14) 38(19)  7(12)  (Age)  40(14) 87( 5)  39(9) 42(11)  88( 4) 89( 5)  46(10)  12(14)  CWS  Syncrude  1(14) 16(9)  25(21) 28(22)  14(10)  17(13) 24(34)  32(21) 34(24)  43(22)  30(34)  75(3)  Fertiliser  1 x 250 – 350kg/ha*  1 x 300 kg/ha*  (N: P: K )  (35:46:44)  (71:33:20) 4 x 250kg/ha/yr*  86(6)  250 –  FF(4)  336kg/ha  LOS  Suncor  Albian  WA5 (22)  Albian(3)  None  None  350kg/ha* (10: 30: 15) (35:46:44)  (315:70:41) Key:  CWS – Clearwater shales LOS – Lean oil sand  1  Sources: The Oil Sands Vegetation Reclamation Committee 1998; * Lanoue, 2003; AMEC Earth & Environmental/Paragon Soil and Environmental Consulting Inc., 2005; C. Qualizza, pers. comm. 2005; W. Tedder. pers. comm. 2007  22  2.2  Sampling  Soils were sampled in June 2005, with the exception of Albian and FF that were sampled in June 2006. June is the month when maximum soil moisture typically is encountered after the spring thaw and before vegetation begins to grow strongly. Sampling was undertaken in a semisystematic method, making a composite sample from ten soil cores (7-cm diameter) taken within 1 m – 2 m of the perimeter of the plot - from four locations down each long side, and one at each short side. Due to the presence of large thorny vegetation, fallen trees or deeply eroded material it was not practical to select cores by either a completely random or systematic method, so cores were taken from accessible points at about 15 - 20 pace intervals. As each core was extracted it was laid out on a plastic bag on the ground. The litter from the top of the core was hand-picked into a plastic bag. The depth of FH, if any, was measured (mm) before it was harvested into a separate plastic bag; on some reclaimed sites close examination was necessary to determine where the thin FH layer ended and the original peat: mineral (P:M) mixture began, FH generally being darker brown/black. Any live vegetation was removed at this stage. Finally a generous handful of soil within the upper 10 cm was taken. Samples were composited x 10, well-mixed by hand and stored in re-sealable plastic bags on ice until the end of the day. Litter and FH layers were then weighed wet before a small subsample of the mixed contents was taken for research on microbial communities by another team member. All bulk samples were frozen (-18oC) at the Syncrude facility until transported to the laboratory for subsequent drying and analysis.  2.3  Examining key response variables  2.3.1 Moisture content, pH and carbon:nitrogen (C:N) ratio On receipt at the lab, the frozen samples of soil, FH and litter were defrosted overnight and a sub-sample (in some cases, the entire sample of FH or litter depending on its size) was placed in a drying oven (105oC) for 72 hours to obtain gravimetric moisture content and the total dry weight. Where the sample size was large enough, a second sub-sample was dried at c.50oC for several days before being sent for total C and N analysis by dry combustion with the remainder analysed for pH in water (pH(w)). All samples of FH and soil were sieved (<4mm) prior to analysis to remove stones and roots.  2.3.2 Decomposition of litter measured by mass loss In September 2005 fresh-fallen or newly senescent litter samples of a naturally-growing broadleaf (aspen Populus tremuloides Michx Lamb), conifer (jack pine Pinus banksiana Lamb), 23  forb (fireweed Epilobium angustifolium L.) and an unidentified tall grass species were taken from within 50 m of natural forest plots 10 (ecotype a1) and 61 (ecotype d1). Petioles were kept intact on aspen leaves. The litter was stored in paper bags and allowed to dry indoors for at least 48 hours prior to use. A sub-sample was stored dry for later analysis of total C and N concentrations. Litter bags were used to determine decomposition of the four different litter types across the range of treatments and ecotypes. Litter bags were made from 10 cm x 10 cm squares of nylon mesh fabric (approximate mesh size 0.8 mm x 0.125 mm) sewn with polyester thread. Litter samples were weighed (c.0.50 g) and placed inside a bag with a numbered metal tree-tag for identification. The open end was sealed using a hot-glue gun with general purpose glue sticks. Approximately 1.5 m of 4-lb-breaking-strain nylon fishing line was sewn to one corner of the litter bag and a loop made at the free end. In September 2005 litter bags containing only aspen litter were placed on a single replicate of all the treatments and ecotypes included within the study - with the exception of plots Albian and FF that were not added to the study until 2006. No other replicated plots were included for any treatment or ecotype due to resource constraints. In the vicinity of plot 17 (treatment H) were placed litter bags for all four litter types. By comparing how four different litters decompose on this plot and how aspen litter decomposition differs between that plot and others, some information could be extrapolated to predict decomposition of different litters across all treatments and ecotypes. Two sets of seven bags of each litter type were laid out in the field in spoke-wheel arrangements. Random placement of bags would have been preferred but it was feared that this would increase the risk that bags would be lost. Each bag position was marked with a UVresistant pin flag and the set of seven anchored by another pin flag inserted through the loop in the fishing lines; the length of fishing lines were worked under surrounding vegetation or buried where possible to avoid accidental injury to or disturbance by wildlife. One set of seven bags was retrieved after 365 (±4) days, the second set left for retrieval at an unspecified future date. The bags were air-dried and the contents removed and weighed. At this time the choice of plot 17 (treatment H) as the receiver of all litters was found to be a poor one; litter bags had become buried by tailings sands eroded from upper slopes or blown into the site and the finest sands were concreted to the decomposing aspen litter (other litters were not badly affected) and were extremely difficult to remove. The results from three bags only were used for mass loss determination of aspen litter for this plot.  24  2.3.3 Litter input and development of an organic layer An estimate of start-of-season litter and FH biomass by ecotype/treatment and ecotype/age class was obtained from the original wet litter and wet FH sample weights adjusted by accounting for gravimetric moisture to obtain dry weight per m2 (the original sample being a composite of ten 7-cm cores, total area harvested 0.038 m2). The dry weight FH (as the original value, i.e. g/0.038m2) and the mean (n=10) FH depth were transformed to natural log ln(x+1), this achieving normality in distribution. Dry weight FH, FH depth, pH, total N, total C and moisture content were regressed against site age to find a model for FH development. The age by which FH development would fall within the natural range of variability was estimated from the model.  2.3.4 In situ bio-available nutrients Plant Root Simulator (PRS™) probes (Western Ag Innovations Inc., Saskatchewan, AB, Canada) were used to determine bio-available nutrients within the soil. The PRS™ actively competes with other plant roots and the rhizosphere for available nutrients and so is an indicator of nutrient bio-availability in that environment. The PRS™ consists of a plastic peg with a window cut in its centre into which is inserted a re-useable plastic membrane that is coated with an ion-exchange resin; once inserted in a soil this absorbs available ions from it. The captured ions are subsequently flushed from the resin using HCl and analysed; the re-charged PRS™ is then available for re-use. To examine what effect, if any, the presence of plant roots and mycorrhizae had on nutrient bio-availability, in situ root-free soil was achieved by inserting hollow root-exclusion tubes into the ground in June 2006 on all plots except plot 64 that had by then been destroyed. The tubes were designed to sever existing roots and prevent new roots or mycorrhizal hyphae from entering the soil contained within. The tubes were made from 28-cm lengths of 10-cm diameter plastic rainwater piping – this length was chosen on the basis of literature research on rooting depth in the boreal forest that suggested a 20 cm rooting depth. Four 2.5-cm-diameter holes on opposing sides of each tube were drilled to permit lateral water movement and covered with 0.5-micron nylon mesh (Plastok, Birkenhead, United Kingdom) stuck to the side of the tube using non-toxic, waterproof Plumbers Goop™ adhesive (Eclectic Products Inc., Eugene, Oregon); this prevented roots and hyphae from entering into the tube. Two smaller holes were drilled on opposite sides close to one end (= the top) to allow for a metal bar to be threaded through the top of the tube to facilitate its removal from the ground at the end of the experiment. The lower edge of the pipe was sharpened using a sanding belt and toothed by hacksaw to help with the insertion and root severance.  25  The tube was tapped into the mineral soil or P:M layer using a heavy mallet. A wooden block was placed over the top of the tube to protect it, with the wooden block being struck by the mallet. The teeth at the base of the tube effectively severed fine roots (up to 5 mm) when the tube was gently rotated as it was driven into the soil; larger roots, where encountered, were severed using a pruning saw slid down the sides of the tube. On a few occasions on very stony sites a hole was dug for the tube and the tube back-filled with the excavated soil and gently tamped down. The column of soil now inside the tube was hand-weeded to remove all live vegetation. Each tube was marked with flagging tape and its location marked using pin flags. Four tubes per site were installed within 1 m of the long sides of the long-term monitoring plot, two per side. An exception was plot 24 where all four tubes were installed on the same long side because mining operations had excavated within 1 m of the other side. Into each tube a pair (one cation, one anion) of PRS™ probes was inserted (Figure 2). On sites with shallow or no FH, the probes were inserted so that the resin window was just buried with most or all of the resin in the soil below; for consistency, on sites with a deep FH or peat layer the probes were buried deep so that the midpoint of the resin window was at the FH/soil interface. A second pair of probes was installed in the open ground close by, each probe being marked by flagging tape. Thus each plot yielded four cation probes and four anion probes for both the root-free and rooted soil, a total of 16 probes per plot. Approximately 6 weeks later (44 ± 6 days) in July-August 2006, all plots were re-visited and the probes removed and replaced with fresh probes that were left for a second period (51 ± 6 days). Any seedling weeds within the tubes were removed by hand during this visit. The second set of probes and the tubes were removed in September 2006. All removed probes were thoroughly washed with de-ionised water and sent to the manufacturer for analysis of nutrients.  26  Figure 2:  Inserting PRS™ probe into root-exclusion tube  At the end of the experiment, upon retrieval of the root exclusion tubes, two composited soil samples were collected for each plot using a melon-balling utensil (as this was found to be strong and easy to work in situ down through the confined soil in the tube) for comparison and possible correlation of gravimetric moisture inside and outside the tubes, in case this affected the levels of ion exchange in solution. One composited sample (x4) was made for each plot from soil taken inside each tube at the approximate mid-depth of the resin window of the PRS™ probe and the second composited sample was taken from the same depth from the wall of soil exposed in the vacated hole. Bio-available nutrient data from both PRS™ probe burial periods for ‘outside’ (normally rooted) or ‘inside’ (root- and rhizosphere-free) soils were combined to create a cumulative amount for the 3-month growing season and converted to micromoles per 10 cm2 of resin per combined burial period (95 ± 9 days).  2.3.5 Nitrate and ammonium production in incubations Since there had been a period of drought during the first 6-week PRS™ probe burial period with almost nil quantities of in situ N (as nitrate NO3- and ammonium NH4+) observed on most of the plots, all soil samples and any FH still remaining were incubated in the laboratory for 3 months to estimate their potential for NO3- or NH4+ production under near-ideal conditions of moisture and temperature. This would also give an estimate of ecosystem function.  27  The incubation vessels were made from clear acrylic pipe (5-cm internal diameter) cut into sections to form a cylinder, to the base of which was glued with Plumbers Goop™(Eclectic Products Inc., Eugene, Oregon) a circle of galvanised wire mesh. A length of self-adhesive rubber draught-proofing tape was wound around and stuck to the outside of each cylinder about 6 cm from its base, this forming a seal when the cylinder was placed inside a filter cup. A 5-cm diameter fibreglass filter paper was placed into the base of each cylinder, followed by a plug of fibreglass wool that was gently tamped down to serve as a primary filter and particle trap. Onto this pad of fibreglass either 50 g of FH (wet weight) or, if less, whatever mass was remaining, or 100 g of soil (wet weight) was placed. The cylinders with their contents were then each placed inside a filter cup over a filter flask with a vacuum side-arm connected by flexible tubing to a vacuum. 100 ml of distilled water was slowly added to the cylinder contents to help settle the material and leach out any preliminary NO3- or NH4+ produced during the freezing, thawing and disturbance of the sample. The excess water was collected under suction as leachate before being discarded. A small square of low-density polyethylene film (‘cling-wrap’) was secured with rubber bands over the top of the cylinder to reduce moisture loss while still permitting gaseous diffusion and the cylinder placed into an unlit cupboard at 20oC to incubate. After one week the cylinders were removed and leached under suction with 100 ml of distilled water. Thereafter the cylinders were leached at two-weekly intervals for a total of 10 weeks. The collected leachate was then measured for total volume and analysed for N as NO3and NH4+ using an auto-analyser. The values were then adjusted for equivalent oven-dry weight FH or soil. At the end of the experiment a number of leached soils and FH were analysed for end-point pH(w).  2.3.6 Plant community assessment During July and early August 2006 ground-cover was surveyed on all plots except plot 64 which had by then been destroyed. Ten 1 m x 1 m quadrats were laid systematically around each plot starting with one corner and at 10-m intervals along each long side and at the 5-m mid-point of the ends. Within each quadrat a visual estimate of all layers of ground-cover < 2m above ground level was scored as % cover (one 10 cm x 10 cm square = 1%), classing vegetation and other materials by type, thus pine, spruce, broadleaf, woody shrubs, forbs, grasses, mosses, lichens, woody debris (>5 cm long or >1 cm diameter) and bare ground. Any ground-cover class that occurred at <1% cover was scored as 1%. Aerial cover (above 2 m) was also estimated visually and the % cover (this assuming a single layer of vegetation) added to that plant type score. To explore how ground-cover type affects ecosystem restoration, models were sought to see whether this could be correlated with moisture (in particular, whether grass has a thatching 28  or mulching effect), decomposition, nutrient bio-availability or mass of FH. The effects of age class or treatment were explored using graphs with cumulative ground-cover to see whether time is all that is required.  2.4 Statistical interpretation 2.4.1 Computer software Where possible, data were analysed using SAS 9.1 (SAS Institute Inc., Cary, NC, USA) to identify models and treatment effects using classical statistical methods, as reported. Much data had non-normal distribution and despite numerous transformations could not be made to fit with normality. Thus it was necessary to find alternative methods for statistical analysis. PC-Ord 5.0 (MJM Software Design, Gleneden Beach, Oregon) had specific relevance to biological community data and was used to apply non-parametric analyses where appropriate.  2.4.2 Non-metric multidimensional scaling An ordination technique known as non-metric multidimensional scaling (NMS), was chosen as it is able to manipulate a large range of variables where normality and linear relationships are not a pre-requisite for analysis (McCune & Grace, 2002) and it was a method used for evaluating community-environment relationships in ecosystems (Laughlin & Abella, 2007). It produces an image of the plots in an ordination space that provides a visual aid to interpreting similarity among entities, those more similar being clustered closer together. Ground-cover data from quadrat surveys were transformed from raw % cover to 2/pi arcsine square-root(x) as this has the effect of narrowing the class limits for very high and very low proportion data while maintaining a broader class spread for intermediate values, thus giving more equal weight to rare or ubiquitous ground-cover (McCune & Grace, 2002). NMS ordinations were undertaken using PC Ord v.5.0 supported by reference to McCune & Grace (2002, and references therein). Two data matrices were used, one containing ground-cover data and the second containing chemical data for the soils (pH(w), C:N ratio, moisture content, bio-available nutrients). An NMS ordination was performed on the groundcover data, and the chemical variables in the second matrix correlated and overlaid on the NMS axes. The second matrix also contained class variables to describe treatment or ecotype (and for ease of interpretation all natural ecotypes were grouped together as one) and age class. The distance measure used was Sorensen (Bray-Curtis). The ‘auto-pilot’ and ‘medium thoroughness’ options were selected. This undertook a series of repeated computations (iterations) to adjust the position of entities (plots) in ordination space. It began with a 4-D solution and reduced to 1-D for each of 50 runs with real data starting with random co-ordinates, then 50 runs with shuffled data (that is, shuffled within columns – each column being one 29  variable). This shuffling forms the basis for a Monte Carlo test of significance of each dimensionality (McCune & Grace, 2002). The best solution was the number (n) of dimensions beyond which additional dimensions do little to reduce stress. Stress is the departure from monotonicity in the plot of distance in the original n-dimensional space versus distance in the ordination space, thus the closer data points lie to a monotonic line, the lower is the stress (McCune & Grace, 2002). A final stress < 20 was deemed satisfactory for ecological community data (McCune & Grace, 2002). A plot of stress versus iteration (and the maximum number of iterations was 200) showed if and when the stress declined to a constant, and at that point was deemed to be stable, or if it fluctuated (in which case, more iterations required), with a final instability value automatically calculated from the standard deviation in stress over the preceding x iterations (McCune & Grace, 2002); an instability criterion of 0.00001 was set as a ‘target’ by the auto-pilot mode. The auto-pilot then ran a final, best solution (low stress, low instability value) using starting co-ordinates taken from an automatically-saved file for a previous run that had produced the better solution in n dimensions. After the first NMS ordination, stress levels were too high (>35, but stable). An adjustment in the data was made with pine and spruce variables (raw % cover) combined to create a new variable of ‘conifer’ that was transformed as before. The NMS ordination was repeated with this new variable in the ground-cover matrix. The resulting 3-D image was rotated on screen until a subjective decision was made to select a ‘best view’ showing a relatively clear separation between groups. The categories within the viewing options were then changed from ‘treatment’ to ‘age class’ (by 5-year increments, excepting natural ecotypes which were arbitrarily assigned to a top age class), and the new image was viewed. Correlation coefficients (r) expressing linear relationships of each variable with each axis were revealed by selecting the Pearson Correlation option; the square (r2) expresses the proportion of variation on that axis that is ‘explained’ by the variable in question (McCune & Grace, 2002).  2.4.3. Multi-Response Permutation Procedures Multi-Response Permutation Procedures (MRPP) were undertaken in PC Ord. MRPP is a nonparametric analysis of within group and between group distances, to test for significant differences among 2 or more groups. The results provide a measure of heterogeneity or homogeneity while giving a p-value to distinguish between pairs of groups where n > 1 (McCune & Grace, 2002). MRPPs were undertaken first by treatment versus natural ecotypes (grouped together) and, next, by treatment versus each ecotype from a1 to d3. The distance measure chosen was Sorensen (Bray Curtis). A test statistic, the chance-corrected within-group agreement was examined - this indicates whether entities within groups are identical to one 30  another (value = 1.0) or as different as would be expected by chance (value = 0) or more different than would be expected by chance (value < 0), with typical community ecology values being < 0.30 (McCune & Grace, 2002).  2.4.4 Cluster analysis method A cluster analysis using PC Ord v.5.0 was performed on the ground-cover (biological) data, and the environmental (chemical) data were used in a subsequent step to help interpret the cluster groups of plots. Groups of plots that were similar to one another appeared in a simple-tounderstand diagram (dendrogram).  31  3.  RESULTS  3.1  Moisture content, pH and C:N ratios  3.1.1 Moisture Ranking reclamation prescriptions in order of increasing soil moisture gave the sequence F < H < Albian < B < I < WA5 < M < A < E< FF (Figure 3). This rank order shall be followed in the presentation of graphs. Generally, natural ecotypes followed the expected trend for increasing moisture as the classification moves from a1 to d3. In six of the reclaimed treatments (I, WA5, M, A, E and FF), soil moisture fell outside the range of natural variability, these treatments having a more hygric moisture regime. In the other treatments (F, H, Albian and B), soil moisture fell within the natural range of variability for the b3 to d3 ecotype mineral soils. Litter moisture was generally similar to FH moisture in all treatments, and FH moisture in the reclamation treatments was within the range of natural variability. Where litter moisture is higher than the natural range of means, such as for WA5 and M, it may have been due to a short interval since rainfall at the time of sampling.  32  Figure 3:  Mean moisture content of litter, FH and soil at natural (left) and reclaimed (right) sites. Error bars represent one standard deviation.  % H2O, by dry weight  200  Litter FH Soil  150  100  50  Ecotype 3.1.2  E FF  A  M  W I A5  B  AL H BI AN  F  d2 d3  b3 d1  a1 b1  0  Treatment  pH(w)  Reclamation treatments were 1 to 2 pH units higher than natural ecotypes, except treatment FF that retained near-natural pH values (Figure 4). On reclaimed sites (excepting FF) the pH of litter ranged from 6.3 to 6.9, FH 6.3 to 7.2 and soil 6.1 to 7.5; in the natural forests the pH of litter ranged from 5 to 6.3, FH 4.9 to 5.8 and soil 4.4 to 5.5. The pH of litter in natural ecotypes showed an increasing trend from pine (a1) to aspen and forbs with spruce (b1, b3, d1) and fell again as spruce and Sphagnum dominated (d3).  33  Figure 4:  Mean pH of litter, FH and soil in natural (left) and reclaimed (right) sites. Error bars represent one standard deviation.  Litter FH Soil 8  pH  6  4  2  Ecotype 3.1.3  E FF  A  M  W I A5  B  AL H BI AN  F  d3  d2  d1  b3  b1  a1  0  Treatment  C:N ratios  C:N ratios of FH on reclaimed sites were below the natural range of variability (Figure 5). WA5 (the abandoned peat dump) had the lowest C:N ratio in litter, soil, and the second lowest for FH. The C:N ratios in reclaimed treatments generally followed the order of litter > soil > FH which differs from the pattern in natural ecotypes where the C:N ratios decreased with depth (litter > FH > soil). The C:N ratio of natural soils decreased as site moisture status increased and the C:N ratios for all three d-ecotypes was low (<20).  34  Figure 5:  C:N ratios of litter, FH and soil at natural ecotypes (left) and reclamation treatments (right). Error bars represent one standard deviation.  70  Litter FH  Soil  60  C:N  50  40  30  20  10  Ecotype 3.2  E FF  A  M  W I A5  B  AL H BI AN  F  d2 d3  b3 d1  a1 b1  0  Treatment  Litter decomposition  Mass loss of aspen litter on the reclamation treatments (except treatments A, I and WA5) was below the range of natural variability (Figure 6). Treatment B (plot 3) had the lowest mass loss (11.5%).  35  Figure 6:  Mean mass loss of aspen leaf litter during one year of decomposition, at natural ecotypes (left) and reclamation treatments (right), ranked in order of increasing soil moisture. Error bars indicate one standard deviation. 50  % Mass loss  40  30  20  10  0  a1 b1 b3 d1 d2 d3  F  H  B  I WA5 M  A  E  Ecotype or treatment Note: The mean value for treatment H is the mean of 3 litter bags; all other treatment means are for 7 litter bags  Using SAS 9.1 a model was sought to explain decomposition rate of aspen litter across all treatments in year 1 using variables of moisture, pH and C:N ratios of both FH and soil. The only variable of significance (p = 0.0021) was soil C:N ratio (Figure 7). The model (r2 = 0.5608) is: %mass loss = 48 – 0.80(C:NSOIL)  36  Figure 7:  Mass loss of aspen leaf litter related to soil C:N ratio  50  % mass loss  40  30  Group i 20  r2 = 0.5608  Group ii  10  0 0  10  20  30  40  Soil C:N ratio Data point Regression: % mass loss = 48 - 0.80 (C:NSOIL)  There appeared to be two clusters (Figure 7). Group (i) describes soils of lower (≤ 20) C:N ratio that are found on ecotypes d1, d2 and d3, and treatment WA5. Group (ii) comprises other treatments and ecotypes a1, b1 and b3, with higher soil C:N ratios. Using SAS 9.1 a model was sought to explain litter mass loss (% of original dry mass) in relation to soil nutrient chemistry. Only Zn and K were significant (p < 0.05). If soil C:N ratio was included (as above) both this and Zn were significant (p < 0.05) but K dropped out, the revised model achieving a reasonably good fit (r2 = 0.6998).  The revised model was: %mass loss = 40.03 – 0.77(C:NSOIL) + 15.13 ln(Zn+1) With respect to ground-cover type (raw % cover) only forbs were significant (p = 0.01, 2  r = 0.4275). That model was:%mass loss = 37.56 – 0.28 (forbs) 37  Litter quality The C and N content of four leaf litters (grass, fireweed, aspen and jack pine) were examined at (a) original harvested state and (b) after one year of decomposition in litter bags and compared with the litter decomposition rate (Table 5). Table 5:  Total N, total C, C:N ratios and mass loss by decomposition of four boreal species leaf litters at (a) original harvested state and (b) after decomposing for one year on treatment H  Total N  Total C  %  %  C:N ratio  Mass loss % initial dry weight  LITTER a  b  a  b  a  b  Grass  0.7  1.2  42.4  35.1  57  30  37.5  Fireweed  0.6  (lost)  42.1  (lost)  68  (lost)  37.8  Aspen  0.8  1.2  44.7  42.0  55  36  24.7  Pine  1.1  1.1  47.6  47.5  44.1  42.9  9.6  Note: C:N ratios were calculated from the original data before rounding all values to 1 decimal place  Fireweed had the higher C:N ratio of 68 at the start and decomposed at the fastest rate (37.8%), but this rate was not significantly different from that of grass (37.5%) which had a lower C:N ratio of 57. The C:N ratio of aspen at the start was 55, which is not that much lower than that for grass, yet the decomposition rate was lower (24.7%). Pine had the lowest C:N ratio of 44 at the start and decomposed by 9.6% in the first year; however its N and C proportional content at the end of the first year of decomposition was virtually equal to that at the start. The sequence for decomposition rate of these litters on a reclaimed site was:-  fireweed = grass >> aspen >>> pine  38  3.3  Fermented and humified litter (FH)  The dry weight of FH on all reclaimed sites fell below the range of natural variability (Figures 8 & 9). On reclaimed sites above age 20, undecomposed litter found at the start of the growing season was within the range of natural variability. Across the natural ecotypes the FH dry weight sample range was 168 g to 1479 g (equivalent to 4.4 kg/m2 to 38.4 kg/m2) for a1 to d3 sites. FH dry weight on natural sites varied significantly and negatively with pH (p < 0.05, r2 = 0.3881). FH development on reclaimed sites was best predicted with a model using site age and FH dry weight (p < 0.0001, r2 = 0.6057). The model was:ln(dryFH+1) = 1.25 + 0.15(age)  Very little additional information could be gleaned by forcing the addition of FH depth and moisture (r2=0.6315) while pH, N and C concentrations were not significant. Applying the model as a best guess (since 40% of the variability remains unexplained), the minimum time by which FH will be within the lower range of natural variability (i.e. 168 g) is 26 years. There appeared to be a treatment effect with different slopes for different treatments (Figure 10 and Table 6). The r2 for treatment I is extremely high (0.9493) and suggests a very strong model but the spread of data for this treatment is poor with a single low-value entry and a cluster of highvalue ones, so the true spread of information in the mid-range is unknown. Treatments B and E have high p-values (p > 0.15) for Age, so Age is not reliable in models for these treatments.  39  Figure 8:  Mass of litter and FH per square metre at the start of the growing season at natural (left) and reclaimed (right) sites by treatment. Error bars represent one standard deviation.  Dry weight, kg/m2  40  Litter FH  30  20  10  E FF  A  M  W I A5  B  H BI AN  F  AL  d3  d2  d1  b3  b1  a1  0  Ecotype  Mass of litter and FH per square metre at the start of the growing season at natural (left) and reclaimed (right) sites, by age class. Error bars represent one standard deviation.  40  Litter FH  30  20  10  Ecotype  15 610 11 -1 5 16 -2 0 21 -2 5 31 -3 5  d3  d2  d1  b3  b1  0  a1  Dry weight, kg/m2  Figure 9:  Treatment  Age class  40  Figure 10:  Treatment effect for FH mass with age  ln(dry FH + 1)  B  7  A A  6  H E  5 4 3 2  Treatment I r2 0.9493  1 0  0  5  10  15  20  25  30  35  40  Age/years A ALBIAN B E F FF H I M WA5 Regression  Note: Only the regression for treatment I is significant (p < 0.05); other regression lines are shown here simply to help distinguish among the different treatments  Table 6: Regressions describing FH development with age by treatments A, B, E, H and I Treatment  Slope  Model r2  p-value, Age  A  0.17(age)  0.6433  0.0549  B  0.16(age)  0.4363  0.2250  E  0.07(age)  0.4627  0.5238  H  0.07(age)  0.6096  0.1191  I  0.20(age)  0.9493  0.0049  41  3.4  In situ bio-available nutrients  3.4.1  Effect of root exclusion tube on soil moisture.  Six treatments (F, H, Albian, B, WA5 and M) showed little difference between moisture contents in rooted and root-free soil (Figure 11). The difference in moisture content in a rooted and rootfree soil was more pronounced on the other four treatments (I, A, E and FF) where, excepting FF, the rooted soil was drier. On natural ecotypes the rooted soil was drier than the root-free soil.  3.4.2  Bio-available nutrients.  Reclaimed sites generally had high NO3- but low NH4+, P and K. Sodium and Mn were much reduced, but Ca, Mg, Fe, S, Cu, Zn, boron, S and Al were all generally elevated on reclamation treatments (Figures 12 to 25). The levels of nutrients on most reclamation treatments was outside (above or below) the natural range of variability. Albian had extremely low P and K but very high Fe and S. Albian and WA5, both unfertilised, had higher NO3- than the fertilised reclamation treatments and the natural ecotypes. Repeatedly fertilised sites H and I, compared with the once-only fertilised treatments (A, B, E, F, M, FF), generally had slightly higher mean values or an extended range of values (as indicated by the standard deviation) in most nutrients. There was no difference between Al, Ca and boron nutrient bio-availability (p-values = 0.5860, 0.8140 and 0.9176 respectively) in rooted and root-free soil when using the PRS™ probe method. Other than Al, boron and Ca, normality of distribution in data for nutrients could not be achieved despite numerous transformations; classical statistical interpretation of treatment effect was not possible for non-normal data. There appeared to be no difference between rooted and root-free soil nutrients, except for Mg on the two b3 sites where the range of all measured values for rooted soil were higher than the range of all measured values for root-free soil.  42  Figure 11:  Soil moisture content for in situ root-free and rooted soil after 3 months in root-exclusion tubes. Error bars represent one standard deviation.  Root-free Rooted  180  140 120 100 80 60 40 20  Ecotype  E FF  A  M  W I A5  B  AL H BI AN  F  d2 d3  b3 d1  0  a1 b1  % H2O, by dry weight  160  Treatment  43  Figures 12 - 25:  Nutrient capture by PRS™ probe method in root-free and rooted soils as micromoles per 10 cm2 per 95 ± 9 days between June and September 2006. Error bars represent one standard deviation.  Figure 12:  Nitrate  NO3--N Root-free Rooted  12 10 8 6 4 2  Ecotype  E FF  A  M  W I A5  B  F  AL H BI AN  d2 d3  b3 d1  0 a1 b1  Micromoles per 10cm2 per 95 days  14  Treatment  44  Ammonium  Figure 13:  Micromoles per 10cm2per 95 days  NH4+-N 3.5  Root-free Rooted  3.0 2.5 2.0 1.5 1.0 0.5  Ecotype  E FF  A  M  I W A5  B  AL H BI AN  F  d2 d3  b3 d1  a1 b1  0.0  Treatment  Phosphorus  Figure 14:  3.0  Root-free Rooted  2.5  2.0  1.5  1.0  0.5  E FF  A  M  W I A5  B  H BI AN  F  AL  d2 d3  b3 d1  0.0  a1 b1  Micromoles per 10cm2 per 95 days  PO4 -P  Ecotype  Treatment  45  Potassium  Figure 15  35  Root-free Rooted  30 25 20 15 10 5  E FF  A  M  W I A5  B  H BI AN AL  F  d2 d3  b3 d1  0 a1 b1  2 Micromoles per 10cm per 95 days  K+  Ecotype  Treatment  Calcium  Figure 16:  Root-free Rooted  180 160 140 120 100 80 60 40 20  E FF  A  M  W I A5  B  H BI AN AL  F  d2 d3  b3 d1  0  a1 b1  Micromoles per 10cm2 per 95 days  Ca2+  Ecotype  Treatment  46  Magnesium  Figure 17:  60  50  40  2  Micromoles per 10cm per 95 days  Mg2+  30  20  10  E FF  A  M  W I A5  B  N  H AL  BI A  F  d2 d3  b3 d1  a1 b1  0  Root-free Rooted  Ecotype or treatment  Iron  Figure 18:  5  Root-free Rooted  4  2  Micromoles per 10cm per 95 days  Fe2+/3+  3  2  1  Ecotype  E FF  A  M  W I A5  B  F  AL H BI AN  d3  d2  d1  b3  b1  a1  0  Treatment  47  Manganese  Figure 19:  2.5  Root-free Rooted  2.0  1.5  1.0  0.5  E FF  A  M  W I A5  B  F  H BI AN AL  d2 d3  b3 d1  0.0  a1 b1  Micromoles per 10cm2 per 95 days  Mn2+  Ecotype  Treatment  Copper  Figure 20:  0.030  Root-free Rooted  0.025  0.020  2  Micromoles per 10cm per 95 days  Cu2+  0.015  0.010  0.005  E FF  A  M  W I A5  B  H AN AL  BI  F  d3  d2  d1  b3  b1  a1  0.000  Ecotype  Treatment  48  Zinc  Figure 21:  Root-free Rooted  0.16 0.14 0.12 0.10 0.08 0.06 0.04 0.02  E FF  A  M  W I A5  B  H BI AN  F  AL  d2 d3  b3 d1  0.00 a1 b1  Micromoles per 10cm2 per 95 days  Zn2+  Ecotype  Treatment  Boron  Figure 22:  0.7  Root-free Rooted  0.6 0.5 0.4 0.3 0.2 0.1  E FF  A  M  W I A5  B  F  H BI AN AL  d2 d3  b3 d1  0.0  a1 b1  Micromoles per 10cm2 per 95 days  BO33--B  Ecotype  Treatment  49  Sulphur  Figure 23:  100  Root-free Rooted  80  60  40  20  E FF  A  M  W I A5  B  H AN AL  BI  F  d2 d3  b3 d1  0  a1 b1  Micromoles per 10cm2 per 95 days  SO42--S  Ecotype  Treatment  Sodium  Figure 24:  350  Root-free Rooted  300 250 200 150 100 50  E FF  A  M  W I A5  B  H AN AL  BI  F  d2 d3  b3 d1  0  a1 b1  Micromoles per 10cm2 per 95 days  Na+  Ecotype  Treatment  50  Aluminium  Figure 25:  3.0  Root-free Rooted  2.5  2.0  1.5  1.0  0.5  E FF  A  M  W I A5  B  H AN AL  BI  F  d2 d3  b3 d1  0.0  a1 b1  Micromoles per 10cm2 per 95 days  Al3+  Ecotype  3.5  Treatment  Nitrate and ammonium production in incubations  Reclamation treatments were about four times richer in NO3- but only about half as rich in NH4+ compared with natural ecotypes (Figures 26 to 29). All reclamation treatments were outside the range of natural variability for NO3- produced from FH. All except B, F and FF were outside the range of natural variability for NO3- produced from soil. Reclamation treatments B, F, H and M fell outside the range of natural variability for NH4+  produced from FH. Treatments Albian, E, FF and M were outside the range of natural  variability for NH4+ produced from soil. Only treatment M fell out the range of natural variability in all four incubated-N measurements. On a dry-weight basis, the amount of NH4+ and NO3- produced from FH was about ten times that of soil. NH4+ production from soils was almost negligible (<2.5ug/g dry soil/3 months). NO3--N was about ten times more abundant than NH4+-N in all cases. pH was altered by the leaching process. In reclamation treatments, mean pH was shifted +0.2pH units for FH and +0.4pH units for soils; in natural ecotypes mean pH was shifted +0.5pH units in FH and +1.0pH units in soils.  51  Mean NO3-- N production from FH during a 3-month incubation at 20oC  Figure 26:  FH NO3-  ug N per g dry FH  800  600  400  200  0 a1 b1 b3 d1 d2 d3  F  H  Ecotype  B  I WA5 M  A  E  Treatment  Mean NO3-- N production from soil during a 3-month incubation at 20oC  Figure 27:  Soil NO3-  100  60  40  20  Ecotype  E FF  A  M  W I A5  B  Al H bi an  F  d2 d3  b3 d1  0  a1 b1  ug N per g dry soil  80  Treatment 52  Mean NH4+- N production from FH during a 3-month incubation at 20oC  Figure 28:  25  FH NH4+  ug N per g dry FH  20  15  10  5  0 a1 b1 b3 d1 d2 d3  F  H  B  Ecotype  I WA5 M  A  E  Treatment  Mean NH4+-N production from soil during a 3-month incubation at 20oC  Figure 29:  Soil NH4+  5  3  2  1  Ecotype  E FF  A  M  W I A5  B  Al H bi an  F  d3  d2  d1  b3  b1  0  a1  ug N per g dry soil  4  Treatment 53  3.6  Ground-cover  There was considerable variability within and among ecotypes and treatments for all ten ground-cover classes measured (Figures 30 to 40). There was also variability with age irrespective of treatment, with younger and older reclaimed sites having fewer ground-cover classes than middle-age ones, and with an apparent ‘tailing off’ of cumulative ground-cover (an indicator of vegetation structure, with multiple layering of vegetation of differing heights) after a peak at the 21-25 year age class (Figure 41). Albian and WA5 were the least similar to the other treatments with Albian having almost nil plant cover while WA5 was densely populated by shrubs and broadleaves. The general trend was for reclaimed sites to have more bare ground, grasses and forbs but less cover of moss, lichen, shrubs, broadleaves, spruce, pine or woody debris than natural sites. The only treatments that had any lichen cover were H and I. Among the natural ecotypes, d1 appeared to be the most different, having the lowest mean values for moss and lichen but the highest mean values for forbs, shrubs and broadleaves. Using the r-square method, 44% of soil moisture was explainable using every groundcover variable except grass (p < 0.50, r2 = 0.4376), i.e. using bare ground, moss, lichen, forbs, shrub, broadleaves, spruce, pine, woody debris. Thus grass had no effect on soil moisture. A second stepwise method selected pine, woody debris, shrub and broadleaf as useful (p < 0.15) in a model to explain soil moisture. Exploring FH development (as natural log of FH dry weight), shrub cover was highly significant (p < 0.0001) contributing 10% to the model; age and pH accounted for about 50% and 20% of the model, respectively. The model (r2 0.7952) was:ln(dryFH + 1) = 0.24 + 0.03(age) + 0.43(ph(w)) + 0.04(shrubs)  Woody debris and pine were also significant (p < 0.01) but contributed little more to the model (r2 = 0.8469). The full model was: ln(dryFH + 1) = 0.15 + 0.03(age) + 0.40(pH(w)) + 0.04(shrubs) + 0.03(pine) + 0.09(woody debris)  54  Figure 30 to 39`:  Mean percent ground-cover by cover class. Error bars represent one standard deviation.  Bare ground  Figure 30: 100  Mean % cover  80  60  40  20  E FF  A  M  W I A5  N  B  BI A  H  AL  F  d2 d3  b3 d1  a1 b1  0  Ecotype  Treatment  Moss  Figure 31: 100  60  40  20  E FF  A  M  W I A5  AN  B AL  BI  H  F  d3  d2  d1  b3  b1  0  a1  Mean % cover  80  Ecotype  Treatment 55  Lichen  Figure 32: 100  Mean % cover  80  60  40  20  A  E FF  A  E FF  M  W I A5  N  B  BI A  H  AL  F  d2 d3  b3 d1  a1 b1  0  Ecotype  Treatment  Grasses  Figure 33: 100  60  40  20  M  W I A5  N  B  BI A AL  H  F  d2 d3  b3 d1  0  a1 b1  Mean % cover  80  Ecotype  Treatment  56  Forbs  Figure 34: 100  Mean % cover  80  60  40  20  A  E FF  A  E FF  M  W I A5  N  B  BI A AL  H  F  d2 d3  b3 d1  a1 b1  0  Ecotype  Treatment  Shrubs  Figure 35: 100  60  40  20  M  W I A5  N  B  BI A AL  H  F  d2 d3  b3 d1  0  a1 b1  Mean % cover  80  Ecotype  Treatment  57  Broadleaves  Figure 36: 100  Mean % cover  80  60  40  20  A  E FF  A  E FF  M  W I A5  N  B  BI A AL  H  F  d2 d3  b3 d1  a1 b1  0  Ecotype  Treatment  Spruce  Figure 37: 100  60  40  20  M  W I A5  N  B  BI A AL  H  F  d2 d3  b3 d1  0  a1 b1  Mean % cover  80  Ecotype  Treatment  58  Pine  Figure 38: 100  Mean % cover  80  60  40  20  A  E FF  A  E FF  M  W I A5  N  B  BI A AL  H  F  d2 d3  b3 d1  a1 b1  0  Ecotype  Treatment  Woody debris  Figure 39: 100  60  40  20  M  W I A5  N  B  BI A AL  H  F  d2 d3  b3 d1  0  a1 b1  Mean % cover  80  Ecotype  Treatment  59  Figure 40:  Cumulative mean ground-cover classes by ecotype or treatment  Cumulative means, % cover  250  Woody debris 200  Pine Spruce  150  Broadleaf Shrubs Forbs  100  Grass Lichen  50  Moss Bare ground  Ecotype Figure 41:  E FF  A  M  W I A5  AL B BI AN  H  F  d2 d3  b3 d1  a1 b1  0  Treatment  Cumulative mean ground-cover classes by ecotype or age class  180 Woody debris 160 Pine 140  Spruce  120  Broadleaves  100  Shrubs  80  Forbs  60  Grasses Lichen  40  Moss 20 Bare ground  Ecotype  5  31  -3  5 -2  0 -2  16  21  5 -1  0 -1  11  6  -5 1  d3  d2  d1  b3  b1  0  a1  Cumulative means, % cover  200  Age class 60  3.7  Statistical interpretation  3.7.1 Nonmetric multidimensional scaling NMS produced a solution in 3-D with a final stress of 11 and a final instability of 0.00001 after 83 iterations (Figures 42 & 43). Most of the variability was expressed in axis 1 (r2 = 0.455), which was correlated strongly with bare ground and forbs (r2 > 0.500) (Table 7). Monte Carlo test: The proportion of 50 randomised runs with stress less than or equal to the observed stress (the probability that a similar final stress would be achieved by chance) was 0.0196, or 2%. The ecotypes clustered together along with some reclaimed sites from treatments E, H and I. Other reclamation treatments were more distant, so less similar to the ecotypes. The main variables vectoring site position in ordination space (r2 > 0.25) were pH(w), Na, boron and Ca. In general, reclaimed sites by 21 – 25 years, irrespective of treatment, had a status more similar to a natural site and less similar to a younger reclaimed one. There are exceptions, however, and notably the oldest plots for treatments A and B remained distant from the natural ecotypes after 24 and 19 years, respectively. The eldest sites (age class 31-35 years) also appeared less similar to natural ecotypes and different from the other reclaimed plots, projecting away into the page (Figure 43).  3.7.2 Multi-Response Permutation Procedures The test of chance-corrected within-group agreement, describing within-group homogeneity compared to random expectation (McCune & Grace, 2002) was 0.17. Treatments E and I as groups were indistinguishable from one another (p = 0.71) and not significantly different from the natural ecotypes group. Treatment I, as a group, was not significantly different from ecotypes b1and b3 and treatment E, as a group, was not significantly different from ecotype d1 (p > 0.05) Treatment H (a tailings sand treatment) as a group was different from the ecotypes group (p < 0.001), despite several plots exhibiting similar results to treatment I and having good overall candidacy for restoration. This treatment was, however, also different from all other treatments (p < 0.05) except E and I. Treatments A and B (both tailings sand treatments) as groups were indistinguishable from each other (p = 0.58) and different from the natural ecotype group (p < 0.00001). Pairs of treatment groups that were significantly different from each other were A versus H, B versus H, A versus I and B versus I (p < 0.01).  61  Figure 42:  NMS plot in 3-D, by treatment or grouped natural ecotypes Axis 3 r2 = 0.210  KEY Treatment A B E F  Axis 1 r2 = 0.455  H I M Albian FF WA5  Axis 2 r2 = 0.253  Natural ecotypes (a1 to d3)  62  Figure 43:  NMS plot in 3-D by age class or grouped natural ecotypes  KEY Age class 1–5 6 – 10 11 – 15 16 – 20 21 – 25 31 – 35 Natural ecotypes (a1 to d3)  Table 7:  Squared Pearson correlations with ordination axes (reporting r2 > 0.200); Pearson linear correlations (r) in parentheses Axis Variable 1 2 3 Bare ground 0.533 (- 0.730) Moss 0.551 ( 0.742) Lichen 0.306 (- 0.553) Grass 0.277 ( - 0.526) Forbs 0.773 ( 0.879) Shrubs 0.256 ( 0.506) Broadleaves 0.545 ( 0.738) Conifer 0.309 ( 0.556) 0.356 ( - 0.597) Debris 0.475 ( 0.689) pH(w) soil 0.298 ( - 0.546) Ca 0.339 ( - 0.582) K 0.215 ( 0.464) Fe 0.234 ( - 0.484) Boron 0.330 ( - 0.574) Na 0.262 ( 0.512)  63  3.7.3 Cluster analysis The dendrogram (Figure 44) was pruned at the 50% level, thus acquiring four groups (Table 8) Age class data were used to identify temporal changes in relationships. Treatments E, H and I clustered more closely with natural ecotypes than other treatments, and three plots (16, 28 and 34) very closely matched a d3 ecotype plot. Table 8:  Groups of plots as defined by cluster analysis, by ecotype and treatment by age class  GROUP  1  2  3  4  1 2 7 10 16 19 20  4 8 25 43 61 WA5  Albian  3 12 14 17 36 37  Plots  21 23 24 26 27  38 39 40 42 46  28 29 30 32 34  75 86 87 88 89  49 50 57 62 63  FF  Ecotypes a1 b1 b3 d2 d3  d1  Age class and treatment 1-5  Albian  A, A, A, I, FF  6 – 10  H  B, B, B, E, M  11 - 15  E, H  A, A, B, F, H  16 – 20  B  21 – 25  I, I, I  31 - 35  H, H  E, I, WA5  A  64  Figure 44:  Dendrogram showing cluster analysis from biogeochemical data  KEY A B E F H I M Albian FF WA5 Natural ecotypes  pruning cut  Distance measure: Sorensen (Bray-Curtis) Linkage method: Ward’s Percent chaining 3.28  65  4.  DISCUSSION  This discussion refers to the original questions that directed the research, as follows. 1. Does a P:M (peat:mineral) mix, applied according to a variety of reclamation prescriptions, mimic the FH layer or mineral soil of natural forest ecotypes with respect to key response variables such as moisture, pH and nutrient status?  Moisture content varied among the reclamation treatments. Broadly, the presence of tailings sands at depth led to reduced moisture content, while a P:M mix at the surface helped conserve moisture unless the P:M mix is directly over tailings sands as in treatment H. Subsoil caps (treatments B, F) were generally dry. Moisture content does not appear to be affected predictably by the presence/absence of plant root activity in root-tube experiments; Albian is almost devoid of plant growth so this may account for negligible difference between the ‘inside’ and ‘outside’ soils, but why the other treatments should show such little variability is not clear but could be due to confounding spatial variability in the type of peat substrate and % organic matter. It was noted on plot 30 that when the root tubes were retrieved from the field they were filled with white mycelia that was not apparent in the soil outside the tubes; this suggests that microbial proliferation was permitted by the removal of competition from plant roots and the rhizosphere, and this growth may account for why nutrient and moisture differences were not significantly different between rooted and root-free soils. On treatment FF where the root-free soil was much drier it is thought solar radiation may be heating up the black plastic tops of the tubes and transmitting that heat down, creating warmer conditions inside the tube where the peat acts like insulation and dries out. Since the PRS™ rely upon capturing ions in solution it was suspected that nutrient capture in drier soil would likely be lower than that in a moister soil, but the differences in nutrients between rooted and root-free soils were, where testable, insignificant so it seems that soils inside and outside the tubes were sufficiently moist for exchange purposes. Treatment FF was the only treatment with a pH similar to the natural ecotypes. This treatment is comprised of fibric peat that is very spongy and tends to ‘float’ or migrate to the surface and away from the mineral component that settles lower down the profile; thus when sampled to 10 cm depth one collected almost pure fibric peat of naturally low, un-amended pH. The shift in pH in other treatments is most likely to be attributed to the clay-rich, calciumadsorping mineral soil that is in the P:M mixture from the outset, but windblown deposits from exposed tailings may be a contributory factor.  66  Reclaimed sites differed from natural ecotypes in their nutrient status. With respect to macronutrients (N, P and K), NO3--N may not be limiting since it is present on all treatments, fertilised or unfertilised, at levels at or far above the natural range of means. Those treatments with levels of both macronutrients K and P at or reasonably close to the natural ranges were: (1) those with a shallow P:M cap that have received fertiliser over several years (treatments H, I); (2) with a shallow P:M cap over non-saline, clay-rich subsoil and overburden, fertilised once (treatment E), and (3) with a deep mineral soil cap laid over tailings sands (treatment B). This includes both young and more mature plots, so reinforcing the suggestion of a treatment rather than purely an age effect. Although oil sands soils were formed from marine sources and are saline with high Na, Na was generally very low on reclaimed sites – perhaps due to dispersal of Na from clays by the addition of gypsum during processing, but also from topographic effects where Na is leached from slopes. Plot 86 (treatment M) may have been expected to reveal high Na due to being laid over saline-sodic Clearwater shales, and this is a level plot at the base of a steep slope where it was presumed to be receiving run-off. But this plot also had low Na. This appears to contradict predictions that Na would become more prevalent in reclaimed oil sands landscapes (Purdy et al. 2005), although it does concur with the finding that a potentially saline reclaimed site (treatment M) was more similar to a non-saline forest system (Purdy et al, 2005). It was interesting that all the d-ecotypes had a relatively low soil-C:N ratio < 20, lower than all the other ecotypes and reclamation treatments except WA5. As Luvisols, the d-ecotype soils are clay-rich and it was noted when sampling these sites that some had a very high water table in early summer creating a ‘soup’ of clay at depth. One may assume that the eluvial Ae mineral horizon was sampled and this would have had low organic carbon content due to the leaching and washing of organic compounds released from the upper litter layer. That litter may be more rapidly humified (as suggested by the higher rate of aspen litter decomposition on decotypes) with greater evolution of waste CO2. These factors could account for the apparent low C:N ratio in the soil. The higher C:N ratio for P:M mixtures is explained by the presence of a greater proportion of organic material in the mixture while on subsoil-capped sites and other ecotypes it likely is due to the products of litter decomposition that are not leached in the drier environment.  67  2. Does ecosystem development on reclaimed sites require an early kick-start from added fertilisers?  Fertiliser is necessary to kick-start ecosystem development and should be applied incrementally when a P:M mix is used to cap tailings sands. This agrees with experience on other profoundlydisturbed and nutrient-limited sites (Norman et al., 2006; Sublette et al., 2007). Treatments H and I seemed to be performing better than other treatments and are both P:M cap treatments fertilised repeatedly in the early years of reclamation. These treatments achieved near-normal micronutrient bio-availability across an age range, notably with respect to Mn. This suggests that, once applied incrementally to a critical threshold, micronutrients are available at a rate sufficient to trigger good plant and microbial growth and are conserved within the nutrient cycling system. In contrast, treatment A, also a P:M mixture laid over tailings sands but with an intermediate subsoil layer, did not achieve target status 24 years after receiving a single application of fertiliser. On treatments where subsoil is used over shales and clays, whether capped by P:M or not (i.e. treatments E, F) a single application of fertiliser may be sufficient for long-term nutrient self-sustainability. However, the poor incidence of functional groups like shrubs on treatment F that received no peat amendment may be suppressing its restoration potential. Subsoil appears to be economically useful in reclamation treatments by off-setting fertiliser costs but this value may be wasted if it is laid directly over tailings sands as in treatment B where sites remained stuck in the reclamation phase for at least 19 years. Subsoil should, therefore, be reserved for reclamation of overburden - but not tailings sands. Tailings sands should be reclaimed using treatment H, with a P:M cap (to boost moisture holding capacity and organic C content) and applying fertiliser over five years. The two treatments with deep peat or P:M were Albian and WA5. Albian was a young (age 3) plot, virtually bare, not fertilised and had low P and K but abundant NO3--N. WA5 was a more mature plot, not fertilised and also had abundant NO3--N, low P, but plant growth on this site was not limited. This suggests that the factor limiting plant growth may be K, but other elements present at excessive or limiting levels may be contributing to a phytotoxic or infertile condition. The high NO3- values for in situ soil (from PRS™ probes) for Albian were not corroborated by high values from laboratory incubation. NO3- present in the sample at the start of incubation may have been lost on the first cleansing (by leaching) in the laboratory, or anaerobic denitrifiers may have reduced it to gaseous form during thawing. There may have been atmospheric deposition of N at Albian, sourced from the haul trucks and processing plant that were continuously operating a few hundred metres away, and this may account for the high values in situ although this has not been corroborated by work using resin bags by another member of the research team (S. Quideau, pers. comm. 2008). A more likely explanation is that 68  gradual drying of the deep peat after it was laid in the reclaimed landscape permits aerobic, heterotrophic decomposition of the extensive peat resource at a relatively rapid pace. 3. How does FH accumulate on the various reclamation treatments with time and how does this compare to the range of variability in natural site FH?  FH development varied among the treatments but the differences between treatments were not extreme. The model for predicting FH development by age suggested that it would take at least 26 years to achieve an FH layer similar to that found in an a1 ecotype, this being at the lowest end of the natural range of variability. In the field, only plot 30 (treatment H) achieved a dry weight FH that was very close to the lower end of the natural range and this plot is 34 years old; thus the model may underestimate FH development by several years. On reclaimed sites the input of litter necessary to sustain FH development appears after age 20 to be similar to that of natural sites. But litter mass was estimated from what remains un-decomposed at the start of the season and this may have very little relationship to what falls and decomposes during the growing season and into the winter; a better estimate would be by installing litter-fall trays to capture litter over the whole season. The model indicated that shrubs were important in the development of FH so it is suggested that shrubs should be encouraged to accelerate forest floor development. Planting additional shrubs may accelerate the desired restoration trajectory to a d1 ecotype since the d1 ecotype (which clustered separately from the other ecotypes) had a higher incidence of shrub cover (mean = 47%), likely favoured by relatively well-lit conditions and moderate pH. A shrub cover effect was noted by Castro et al. (2004), where it aided reforestation in a harsh environment by mitigating water stress through reduced solar radiation to the floor, and was also helpful in preventing seedling mortality from browsing. It has also been shown that the leeward sides of shrubs benefit soil NH4+ pools and soil organic matter, presumed due to interference with wind resulting in localised changes in micro-climate and plant litter deposition (Mummey et al., 2002). It may be that shrub cover in reclaimed oil sands sites plays a similar role with a side benefit of facilitating FH development. 4. (a) Does litter decompose at different rates across all reclamation treatments and how does this differ from that of natural ecotypes?  On the reclamation treatments, despite moisture levels generally above the natural range of variability, litter decomposition appeared to be depressed compared with that on natural ecotypes. Decomposition rate was especially low on treatment B, a subsoil capping mixture laid 69  over tailings sands. In treatment F, however, where subsoil is laid as a cap over overburden, although it was drier than treatment B (soil moisture content 17% versus 23%, respectively) decomposition was faster but still below than that of most P:M treatments. This suggests that if tailings sand is reclaimed with a subsoil cap, the tailings sand reduces that subsoil’s inherent microbial potential and may lead to nutrient deficiencies. Mass loss of aspen litter by decomposition was correlated negatively with soil C:N ratio, but the C:N ratio of the FH did not influence mass loss rate. It may be that the underlying soil and not the FH hosts an environment more suited to inhabitation by microbes, perhaps because it is less affected by fluctuating chemistry, and those microbes then inoculate the FH and litter as and when conditions permit. P:M amendments generally created better conditions than subsoil treatments for litter decomposition, which suggests that the peat component need not be of high C-content to maintain nutrient self-sufficiency. Vetch and yellow clover (both N-fixing legumes) were observed in high abundance only on plots 3, 39 and 42 (all treatment B), and plot 3 had the lowest aspen litter decomposition rate (plots 39 and 42 did not receive any litter bags). Lower decomposition and N-mineralisation rates have been observed under legumes with the suggestion that decomposers utilised N released from legume roots and did not extend their resource use into litter (Saj et al., 2007). Mass loss of aspen litter by decomposition was also correlated positively with Zn and K availability, but negatively with forb cover. It may be that forbs compete strongly for nutrients such as K and Zn so reducing substrate availability for cell function and synthesis of microbial enzymes. 4(b) How do the rates of decomposition of various litter types compare?  In one year, forb and grass litters decompose at the same rate, about 50% faster than aspen and 400% faster than pine litters. Initial C:N ratios were not reliable predictors of mass loss. The site chosen to receive all 4 litters (plot 17, treatment H) was subject to sand deposition and this may have affected the decomposition processes. The inference is that grasses and forbs are valuable for short-term decomposition and nutrient cycling (as well as performing other functions such as soil conservation) in the early part of ecosystem restoration while aspen performs a mid-term role. Pine litter may serve a role in creating a long-term reservoir of nutrients and developing an organic layer, or FH. This is corroborated by the model that predicts FH development by pine, albeit at a lesser significance than by shrubs. Spruce, although a needle-bearing conifer, did not contribute to a model for FH development and its value to decomposition and nutrient cycling is not clear; this may be related to the long lifespan of spruce needles which would postpone the contribution of spruce to the 70  development of an organic layer (Cole & Rapp, 1981). Others have noted that trees that are planted at the outset may not perform an obvious role in ecosystem development while understorey species create the conditions for restoration (Castro et al., 2004; Rayfield et al., 2005). The raw data for forb cover indicated that forbs were generally more dominant on all reclamation treatments – the two exceptions being Albian, where there were almost nil plants present, and WA5 that was dominated by shrubs and aspen. This dominance by ephemeral species has been reported on other mine reclamation sites where it persisted for 14 years (Norman et al., 2006) and the presence of forbs may be advantageous on reclamation sites by providing early nutrient cycling to support microbial communities and permit higher plants to develop later. It may be necessary to depend far more on primary colonizers such as grasses and forbs at the expense of secondary colonizers such as trees to build up an enriched environment in which the higher plants may later flourish (Mulder et al., 2004). Therefore although forbs and grasses are critical in establishing a nutrient cycle on reclaimed sites, trees and shrubs should be included to promote the development of an organic layer. 5. By what age will plant communities on reclaimed sites (planted or not) be similar to those communities found on natural forest ecotypes?  Plant communities on reclamation treatments E, H and I may be similar to those of a target ecotype by age 20-25. Diversity and proportion of different plant types on reclaimed sites, irrespective of treatment, by age 25 was similar to that of a natural forest ecotype, but treatments E, H and I had better diversity in more natural proportion than other treatments across the range of their age classes. It appears necessary to plant shrubs on treatment F, which received no peat amendment, to improve its overall performance and boost its restoration trajectory. But if peat resources should continue to be available, adding a peat amendment as per treatment E should achieve the desired results without the need for planting shrubs – presumably because the peat amendment provides the shrub propagules and/or the conditions for natural colonisation. Treatment F could yet prove to be of restoration merit when peat reserves have been exhausted, subject to shrubs being included in the planting mixture. The succession of plants from primary colonizers to high forest involves varying phases of nutrient cycling and changing ratios of species such that some uncommon types become more common, and vice versa (Mulder et al., 2004). Diversity on treatments appears to remain fairly stable before declining with advancing canopy closure at about age 31 - 35 when understorey plants disappeared. After canopy closure a shift back towards greater diversity may occur as trees compete against each other creating a more open high canopy similar to that in 71  the natural forest, producing woody debris and permitting other under-storey vegetation to flourish. There is some evidence that the initial floristic composition is maintained in the longterm with grasses forming a higher proportion of plant cover on reclaimed sites than on natural sites; other research indicates this is common to highly disturbed reclaimed sites with the same initial floristic composition as that which was originally sown still evident after 14 years (Norman et al., 2006), 25 years (Rayfield et al., 2005) and 45 years (Hodačová & Prach, 2003). Grasses may out-compete some species and prevent their establishment for decades, but I consider that the early benefit of grass litter decomposition outweighs the concern over whether all species will eventually be represented and in their natural proportion.  6. Are reclaimed sites on a predictable path to become functioning ecosystems similar to those naturally present in the boreal region and, if so, within what timescale? Yes - for treatments E (15 – 20 years), H and I (20 – 25 years) No - for treatments A and B  Different restoration or replacement trajectories occur with different treatments. The best-performing P:M treatments are E, H and I. There was a line of separation at the 16-20 year age range where a reclaimed E, H or I site shifted away from its reclaimed site status and towards the natural range. These treatments also showed high diversity of ground-cover, which also means functional groups, with 8 to 10 different cover classes represented. Treatment E, with one application of fertiliser, develops towards a target ecosystem by about 15 – 20 years. With treatments H and I and about 5 years of preparatory fertilising and planting, the system develops towards the target ecotypes in 20-25 years. The cluster analysis also revealed an association with natural ecotypes by this age, the exceptions being a young (age 3) replicate for treatment I and a 13-year-old replicate for treatment H that was subjected to significant sand deposition during the course of this study, which may be continually resetting the clock and maintaining its position in the early reclamation phase. The MRPP analysis revealed that treatment E, as a group, was no different from ecotype d1, while treatment I, as a group, was no different from b1 and b3. The two oldest sites in the study (age class 31 – 35 years) represent treatment H. Both these sites appear to be ‘moving’ away from the clustering formed by the natural ecotypes and away from their younger counterparts due to increasing canopy closure and more bare ground. The dissimilarity revealed by the MRPP between treatment H, as a group, and the natural ecotypes appears to be because of the heterogeneity of treatment H with its youngest and oldest members scattered more distantly in ordination space. For treatment H, 72  a tailings sand treatment, given the rather slow development of trees, the erosive nature of the substrate and the fragility of developing FH, public access should not be permitted until the plant community is at least 25 years old or about 30 years after reclamation, whichever is later. Since it would be difficult to justify the Province accepting responsibility for a 5-year-old land resource the tax-paying public cannot use for another 20 years or more, effort should be made to trial fertilisers and their application rates to hasten the development process and achieve a robust, natural condition so that tailings sand site handover can be achieved earlier. It may also be beneficial to ensure planting mixtures include shrubs for protection of moisture, developing FH and seedlings. Site handover back to the Province could be achieved very soon after the completion of planting and fertilising, subject to an assessment of adequate stocking density and composition. Site handover and public access may be appropriate as early as around 15 years for overburden (i.e. not tailings, lean oil sand or Clearwater shales) reclaimed using P:M and/or mineral subsoil (treatments E, I), fertilised and planted as previously described. Operational or risk-assessment considerations may be such that site handover does not occur that quickly. The worst-performing treatments A and B (both covering tailings sand) are indistinguishable and do not ‘move’ towards a natural system but remain stuck, as if in the early reclamation phase, for 25 years or so. Treatments A and B appear to be on a trajectory to a novel ecosystem that may or may not achieve the original aims of commercial forestry and wildlife habitat within an appropriate timescale; consideration may be given to amending these treatments to divert them back to a desired trajectory more similar to a natural ecotype by adding more fertiliser in the early years as per treatments H and I. Since there was only one replicate in each of treatments F, M, FF, WA5 and Albian, I am less confident in drawing conclusions as to their candidacy but it looks very likely that Albian, an outlier separate from all other treatments or natural ecotypes, will take decades to shift out of its reclamation state. WA5 was clustering with the natural ecotypes, but this treatment is not feasible on a large scale since it is a deep peat treatment and peat is a limited resource. As for treatments M (age 6) and FF (age 4), which differ in the type of peat used (mesic versus fibric), there is little evidence yet that either of these young sites is moving towards a natural condition. They should continue to be monitored to determine relative advantages of the different peat types, and fertilised to overcome low K and, with treatment M, low Mn. Treatment F (age 14) may be of particular interest when peat resources dwindle since it has no peat amendment and, with the exceptions of K and Mn, it is broadly similar to the range of variability in plant nutrient chemistry of natural ecotypes despite exhibiting low litter decomposition rates, but it requires shrubs to be planted to extend its functional group diversity and boost its restoration trajectory.  73  5.  CONCLUSIONS AND RECOMMENDATIONS  The best reclamation prescriptions appear to be treatments I, H and E, for which nutrient chemistry was generally within the natural range of variability and ground-cover was very similar to that of a target forest ecotype. The worst treatments appear to be A and B that remain static, failing to ‘move’ towards a target ecotype for at least 20-25 years. Further research is recommended to confirm the candidacy of other treatments. It appears that Albian, although still a young site and thus relatively undeveloped, will be unsuitable in the long-term without significant amendment. Treatments H and I are both P:M mixture treatments practiced by Suncor; some of the success could be attributed to management of the P:M mixture during the period between harvesting and reclamation. Therefore it may be beneficial to adopt the usual Suncor management practices for P:M placement. Those sites with a P:M cap require fertiliser with P, K, and trace elements including Mn to provide an early boost to ecosystem development. Fertilisation should aim to improve their bio-availability under elevated pH. Where P:M caps are applied over subsoil and overburden but not tailings sand these may be self-sufficient after a single application of fertiliser in year 1; it appears this will lead to a condition close to a target ecotype by 15-20 years. A single application of fertiliser is insufficient on P:M caps over tailings sands and P:M caps over subsoil over tailings sands, and may not achieve a condition similar to a natural ecotype until some (as yet undetermined) time well after 25 years. Annual fertiliser treatments for the first five years after planting as practiced at Suncor may be sufficient to boost a P:M-reclaimed site to a status similar to a natural forest site by age 20-25, whether over tailings sands or not. Repeated addition of fertiliser will assist plant growth and litter production, and hence development of a surface FH layer. Further work is recommended to understand how significant atmospheric inputs will be in contributing to the fertilisation and acidification of both natural and reclaimed sites in the oil sands region. Addition of N in fertilisers should be revisited in light of the observations here that suggest NO3--N is abundant on unfertilised sites. The present use of relatively clean and unaltered peat and glacial till resources is not sustainable and are unlikely to provide sufficient capping material for future reclamation on the scale currently proposed, so alternative prescriptions requiring lesser and/or alternative inputs are necessary.  74  The approach used, that of measuring two ecological attributes (plant community and ecological processes) on young reclaimed sites against those on a range of older, natural target ecotypes, appears to have been successful in allowing me to predict which treatments are on a predictable path towards becoming similar to the target ecotypes. This suggests that ecosystem development is indeed predictable. It was interesting to find that older reclaimed sites that look like a forest and could be assumed by a casual observer to have been restored successfully were not on target but rather are developing into a new non-target ecotype. Does this mean that restoration of these sites has been unsuccessful? It may be that expectation of restoring an ecosystem like that which was destroyed is unrealistic. Perhaps when dealing with a profoundlydisturbed landscape on this scale, an ecological landscape (i.e. a mosaic of different communities comprising forest, forest edge, grassland) is more important. The comparison between reclamation treatments and target ecotypes could benefit from the inclusion of natural sites that had been disturbed (burned, blown down or logged) within the last 20 years. Adding information on short to mid-term (say 0 – 20 year) natural ecosystem recovery would allow us to see whether ecosystem development on those sites mirrors that on reclaimed sites of the same age. The anticipated addition of further information, particularly on microbial diversity and function and hydrological function will refine and improve our ability to measure the success of reclamation treatments in the oil sands. NMS proved to be a useful technique for analysing data from this unbalanced experimental design and it is a rapid way of producing results that are visually informative and quick to interpret. It was impressive to find that reclamation treatments were still distinguishable after 20-25 years and that treatment differences did create changes that were consistent at the treatment level.  75  Recommendations 1. That where tailings sands, lean oil sand or saline-sodic materials are reclaimed, a P:M mix is salvaged and managed as the top capping layer as per Suncor specifications and repeated fertilisation is carried out in the first 5 years after planting 2. That a mineral subsoil (B and C horizon) is used as the capping and/or intermediate layer (i.e. under P:M) for reclamation of overburden but not tailings sand, receiving one application of fertiliser in the first year of planting 3. That a low or nil N, and high P, K fertiliser(s) with trace elements is formulated and tested and the sites monitored for change over a 15-year period 4. That shrubs are included in planting mixtures, or conditions for their natural colonisation are encouraged 5. That treatments with Clearwater shales or lean oil sand continue to be assessed for restoration potential 6. Subject to these implementations, a reclaimed site should be similar to a target ecotype after 25-30 years for P:M mix sites and 15 years for mineral capped sites.  It is further recommended that public access be avoided for 30 years on all tailings sand sites, and 15-20 years on non-saline overburden sites. Public access to any site should be permitted only subject to an ecological risk assessment since it remains uncertain exactly what environmental hazards may arise in the oil sands in the long term (Qualizza, pers. comm. 2005).  76  Bibliography Allen, E. B., Wallace Covington, W. & Falk, D. A.(1997): Developing the conceptual basis for restoration ecology. Restoration Ecology 5(4), pp 275 – 276  AMEC Earth & Environmental and Paragon Soil and Environmental Consulting Inc. (2005): Results from long term soil and vegetation plots established in the oil sands region. Submitted to Oil Sands Soil and Vegetation Working Group Attiwill, P. M. & Adams, M. A. (1993): Nutrient cycling in forests. New Phytologist 124, pp 561 – 582  Beckingham J. D. & Archibald J. H. (1996): Field guide to ecosites of northern Alberta. Canadian Forest Service, Edmonton.  Bell, S. S., Fonseca, M. S. & Motten, L. B. (1997): Linking restoration and landscape ecology. Restoration Ecology 5(4), pp 318 – 323  Brown, S. & Lugo, A. E. (1994): Rehabilitation of tropical lands: a key to sustaining development. Restoration Ecology 2 (2), pp 97-111  Castro, J., Zamora, R., Hódar, J. A., Gómez, J. M. & Gómez-Aparicio, L. (2004): Benefits of using shrubs as nurse plants for reforestation in Mediterranean mountains: a 4-year study. Restoration Ecology 12(3), pp 352 – 358  Cole, D.W. & Rapp, M. (1981): Elemental cycling in forest ecosystems. In Reichle, D.E. (Ed.), Dynamic Properties of Forest Ecosystems 23, pp 341 - 409. Cambridge University Press, Cambridge, England  Danielson R. M., Visser, S. & Parkinson, D. (1983): Plant growth in four overburden types used in the reclamation of extracted oil sands. Canadian Journal of Soil Science. 63, pp 353 – 361  Dobson, A. P., Bradshaw, A. D., & Baker, A. J. M. (1997): Hopes for the future: restoration ecology and conservation biology. Science 277, pp 515 – 522  77  Fedkenheuer A. W., Heacock, H. M. & Lewis, D. L. (1980): Early performance of native shrubs and trees planted on amended Athabasca oil sand tailings. Reclamation Review 3, pp 47 – 55 Gayton, D.V. (2001): Ground Work: Basic Concepts of Ecological Restoration in British Columbia. Southern Interior Forest Extension and Research Partnership, Kamloops, B.C. SIFERP Series 3 Government of Alberta (1999): Conservation & Reclamation Information Letter Guidelines for Reclamation to Forest Vegetation in the Athabasca Oil Sands Region. C & R/IL/99-1  Hardy BBT Ltd (1990): Natural plant invasion into reclaimed oil sands mine sites. Alberta Land Conservation & Reclamation Council Report RRTAC 90-3.  HBT AGRA Ltd (1994): Oil sand tailings capping study. Alberta Land Conservation & Reclamation Council Report RRTAC 90-3  Hobbs, R. J. & Norton, D. A. (1996): Towards a conceptual framework for restoration ecology. Restoration Ecology 4(2), pp 93 – 110 Hodačová D. & Prach, K. (2003): Spoil heaps from brown coal mining: technical reclamation versus spontaneous revegetation. Restoration Ecology 11(3), pp 385 – 391  Lanoue, A (2003): Phosphorus Content and Accumulation of Carbon and Nitrogen in Boreal Forest Soils. Master of Science thesis, University of Alberta  Laughlin, D. C. & Abella, S. R. (2007): Abiotic and biotic factors explain independent gradients of plant community composition in ponderosa pine forests. Ecological Modelling 205, pp 231 – 240  Lesko, G. L. (1974): Preliminary re-vegetation trials on tar sands tailings at Fort MacMurray, Alberta. Northern Research Centre Information Report NOR-X-103, Edmonton  Li, X. & Fung, M. Y. P. (1998): Creating soil-like materials for plant growth using tailings sand and fine tails. Journal of Canadian Petroleum Technology 37 (11), pp 44 – 47  78  Lockwood, J. L. (1997): An alternative to succession: assembly rules offer guide to restoration efforts. Restoration & Management Notes 15(1), pp 45 – 50  Marshall, I. (1982): Mining, land use and the environment (I); a Canadian overview. Lands Directorate, Environment Canada, Ottawa  McCune, B. & Grace, J. B.(2002): Analysis of Ecological Communities. MJM Software, Oregon  Michener, W. K. (1997): Quantitatively evaluating restoration experiments: research design, statistical analysis, and data management considerations. Restoration Ecology 5(4), pp 324 – 337  Millar, C. S. (1974): Decomposition of coniferous leaf litter. In: Dickinson, C.H., Pugh, G.J.F. (Eds.) Biology of Plant Litter Decomposition, Vol. 1. pp. 105–128. Academic Press, London Miller, J. R. & Hobbs, R. J. (2007): Habitat restoration – do we know what we’re doing? Restoration Ecology 15(3), pp 382 – 390  Ministry of Forests & Ministry of Sustainable Resource Management, BC (2003): Estimating Historical Variability of Natural Disturbances in British Columbia. Land Management Handbook 53. Victoria, BC  Morrison, M. L. (1995): Wildlife conservation and restoration ecology: toward a new synthesis. Restoration & Management Notes 13(2), pp 203 – 208 Mossop, G. D. (1980): Geology of the Athabasca oil sands. Science 207, pp 145 – 152 Mulder, C. P. H., Bazeley-White, E., Dimitrakopoulos, P. G., Hector, A., Scherer-Lorenzen, M & Schmid, B. (2004): Species evenness and productivity in experimental plant communities. Oikos 107, pp 50 – 63  Mummey, D. L., Stahl, P. D. & Buyer, J. S. (2002): Soil microbiological properties 20 years after surface mine reclamation: spatial analysis of reclaimed and undisturbed sites. Soil Biology & Biochemistry 34, pp 1717 – 1725  79  National Research Council of Canada (1998): The Canadian system of soil classification (3rd Ed.). NRC Research Press, Ottawa  Nonaka, E. & Spies, T. A. (2005): Historical range of variability in landscape structure: a simulation study in Oregon, USA. Ecological Applications 15(5), pp 1727 – 1746  Norman, M. A., Koch, J. M., Grant C. D., Morald T. K. & Ward, S. C. (2006): Vegetation succession after bauxite mining in western Australia. Restoration Ecology 14(2), pp 278 – 288  The NOx & SO2 Management Working Group Science Colloquium (2000): Summary of acid deposition information for the Athabasca oil sands region. 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(2000): Reclamation of saline-sodic waste dumps associated with the oilsands industry. In: Global Land Reclamation/Remediation 2000 and Beyond. Proceedings of the Canadian Land Reclamation Association's 25th Annual Meeting, September 17-20th 2000, Edmonton Alberta.  Sublette, K.L., Tapp, J. B., Fisher J. B., Jennings, E., Duncan, K., Thoma, G., Brokaw, J. & Todd, T.(2007): Lessons learned in remediation and restoration in the Oklahoma prairie: a review. Applied Geochemistry 22, pp 2225 – 2239  Swift, M. J., Heal, O. W. & Anderson, J. M. (1979): Decomposition processes in terrestrial ecosystems. Blackwell Scientific Publications, Oxford, England  Temperton, V. M.(2007) The recent double paradigm shift in restoration ecology. Restoration Ecology 15(2), pp 344 – 347  Vinton, M. A. & Burke, I. C. (1995): Interactions between individual plant species and soil nutrient status in shortgrass steppe. Ecology, 76 (4) pp 1116 – 1133  Visser, S. (1985): Management of microbial processes in surface mined land reclamation in western Canada. In Tate III, R. & Klein, D. A.(Eds). Soil Reclamation Processes: Microbial analyses and applications pp 203 - 241. Marcel Dekker, Inc.  Young, T. P. (2000): Restoration ecology and conservation biology. Biological Conservation 92, pp 73 – 83  81  Electronic citations www.environment.gov.ab.ca Government of Alberta – Environment website accessed January 2008 www.srd.gov.ab.ca Government of Alberta – Sustainable Resource Development website accessed February 2005  http://www.ser.org/content/ecological_restoration_primer.asp#8 Society of Ecological Restoration International, accessed January 2008  www.syncrude.com Syncrude Canada Ltd, accessed February 2005  http://www.devonian.ualberta.ca/peatland/peatinfo.htm University of Alberta – Devonian Botanic Garden, accessed February 2008  82  Addendum Shortly after defending this thesis I was advised the site age used for WA5 was incorrect. This site was established in 1976, so WA5 was 29 years old at the time of first sampling in 2005, making it the sole member of the 26-30 year age class.  The differences this new information made were with FH development over time and with ground-cover representation by age class.  Development of FH To achieve normality in distribution, age was transformed to log10 (x), with FH dry weight transformed as before to natural log ln(x+1). This new information strengthened the model for FH development over time on reclaimed sites, with a higher r2 value. FH development on reclaimed sites was best predicted with a model using site age and FH dry weight (p < 0.0001, r2 = 0.8282).  ln(dryFH+1) = - 0.25 + 3.46*log10(age) pH and C-content were also significant (p < 0.05) but each variable contributed just 1.5% to the model (r2= 0.8579). Moisture, FH depth and total N were not significant. Applying the model as a best guess (since 17% of the variability remains unexplained), the minimum time by which FH will be within the lower range of natural variability (i.e. 168 g) is 36 years. This was 10 years more than previously predicted.  Treatment effects were not altered significantly using log10(age). Treatment I maintained a very strong model (r2 = 0.9683) (Figure 10a and Table 6a).  Ground-cover by age class was altered, with WA5 representing the 26-30 age class and with mean values adjusted for the 21-25 age class (Figure 41a).  83  Figure 10a:  Treatment effect for FH mass with age  7  B A H  6  ln(dryFH + 1)  E 5 4 3  Treatment I r2 = 0.9683  2 1 0 0.0  0.2  0.4  0.6  0.8  1.0  1.2  1.4  1.6  1.8  log10(age) Treatment A ALBIAN Treatment B Treatment E Treatment F Treatment FF Treatment H Treatment I Treatment M Treatment WA5 Regression: log10(age) vs ln(dryFH+1) by treatment  Table 6a: Regressions describing FH development with age by treatments A, B, E, H & I Treatment  Slope  Model r2  p-value, log10Age  A  4.83(log10age)  0.74  0.0279  B  4.57(log10age)  0.35  0.2907  E  2.81(log10age)  0.53  0.4814  H  3.05(log10age)  0.56  0.1472  I  4.52(log10age)  0.9683  0.0024  84  Figure 41a:  Cumulative mean ground cover classes by ecotype or age class  Woody debris Pine  200  Spruce Broadleaf  150  Shrubs Forbs  100  Grass Lichen  50 Moss Bare  Ecotype  5  0  -3  31  5  -3  26  0  -2  21  5 16  -2  0  -1  -1  11  -5  6  1  d3  d2  d1  b3  b1  0  a1  Cumulative means, % cover  250  Age class  85  

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