soil and vegetation change on a coal mine 15 years …
TRANSCRIPT
SOIL AND VEGETATION CHANGE ON A COAL MINE 15 YEARS AFTER
RECLAMATION IN THE ASPEN PARKLAND OF ALBERTA
A Thesis
Presented to
The Faculty of Graduate Studies
of
The University of Guelph
by
TREMAYNE SITHEAN STANTON-KENNEDY
In partial fulfilment of requirements
for the degree of
Master of Landscape Architecture
April, 2008
© Tremayne Sithean Stanton-Kennedy, 2008
ABSTRACT
SOIL AND VEGETATION CHANGE ON A COAL MINE 15 YEARS AFTER
RECLAMATION IN THE ASPEN PARKLAND OF ALBERTA
Tremayne Sithean Stanton-Kennedy Advisor: University of Guelph, 2008
Professor Karen Landman Professor M. Anne Naeth
To evaluate the outcomes of reclamation design, soil and plant community changes on an
unmanaged, 15-year-old certified-reclaimed site were analysed and compared with an
undisturbed reference location. Patterns were analysed using MRPP while change was
measured with rmANOVA. Plant species were poor predictors of selected soil properties.
Percent soil organic carbon increased (p = 0.032), while soil pH did not change. Overall plant
community composition did not change in proportion of cover between a priori groups of
seeded/unseeded species or between native/introduced species. Individual species did vary in
amount of cover change between 1993 and 2007. A linear regression of richness versus area
covered by native species determined that the Shannon index is not a suitable measurement
for monitoring plant community change towards the reference ecosystem. These findings
highlight the importance of initial design, and the potential additive role of landscape
architects as part of reclamation planning.
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Acknowledgments I always rather liked the bit in Alice through the Looking Glass where Alice receives a lesson in planting design from cranky vegetation. Soft beds make for sleepy flowers, while hard beds put them in a snit. Conversational etiquette dictates that flowers must be addressed first, and will only reply should they deem the inquirer to have much sense. I think this speaks volumes to those who study plants, for if you know how to listen you’ll learn a great deal. I would like to thank my co-supervisors for their constant enthusiasm, patience and great personalities. Karen Landman (Guelph) showed no concern with my atypical approach to thesising, and her zen-like knowledge of planting design and indefatigable editing skills will perpetually amaze me. Anne Naeth (Alberta) showed her gambling side by taking on a random student, sharing precious resources and not minding while I distracted the rest of the lab. Karl Cottenie stepped up last minute to the committee plate with good humour, and demonstrated even statisticians have artistic flair. Without them, I wouldn’t have this thesis. Financial support was gratefully received from the Latornell Foundation, Ivarson-AIC Scholarship, Ontario Graduate Scholarship and various awards from the University of Guelph. Thank you to Margwyn Zacaruk (TransAlta), Alberta SRD and the Stony Plain chapter of AFGA for information and permission to use my study sites. Thank you to the most marvellous Edmonton posse: Mae Elsinger, Anayansi Cohen-Fernandez, Mallory Jackson and Ingrid Hallin, without whom I would still be out there trying to collect enough soil for lab samples. Robyn Brown and Paul MacDougall missed the milkshakes, but provided entertainment back at the camp. To you I toss the Frisbee. MLA 2008 are incredible folks to work with, and always made re-entry easier. Thank you to my family for nodding and smiling, then attempting to casually inquire whether I was yet finished. Also for the chocolate and cheese. Thanks as well to the administrative angels who work with paper wings – navigating bureaucracy and keeping accounts in order. When I think of the amount of paper generated by my peregrinations I feel thankful individuals more organised than I are there to shepherd the sheaves. Lastly, I wish to thank my stubborn streak, which I feel played a significant role in getting me from A to B, despite the occasional dalliance at C.
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Table of Contents 1.0 Introduction......................................................................................................................... 1
1.1 Thesis Structure Overview.............................................................................................. 3 2.0 Literature Review................................................................................................................ 4
2.1 Soils and Mine Reclamation ........................................................................................... 4 2.2 Plant Communities and Mine Reclamation .................................................................... 7 2.3 Mine Reclamation in Alberta........................................................................................ 10 2.4 Landscape Architecture and Natural Environments ..................................................... 11 2.5 Summary ....................................................................................................................... 15
3.0 Research Goal, Questions and Rationale .......................................................................... 16
3.1 Research Goal ............................................................................................................... 16 3.2 Research Questions and Rationale................................................................................ 16
3.2.1 Soil pH and Percent Organic Carbon Change........................................................ 17 3.2.2 Patterns of Soil Properties Beneath Dominant Species ......................................... 17 3.2.3 Vegetation Cover Composition Change ................................................................ 18 3.2.4 Ability of Diversity Indices to Capture Vegetation Dynamics for Conservation.. 19
3.3 Summary ....................................................................................................................... 20 4.0 Methods and Results ......................................................................................................... 21
4.1 Study Site Selection ...................................................................................................... 21 4.1.2 Site Details ............................................................................................................. 21
4.2 Reference Site Selection ............................................................................................... 26 4.3 Field Work: Vegetation and Soil Sampling .................................................................. 27
4.3.1 Vegetation Sampling.............................................................................................. 27 4.3.2 Soil Sampling......................................................................................................... 34
4.4 Laboratory Analyses: Soil Sample Analysis Methods.................................................. 35 4.5 Statistical Work: Analysis and Results ......................................................................... 36
4.5.1 Soil pH and Percent Organic Carbon Change........................................................ 36 4.5.2 Patterns of Soil Properties Beneath Dominant Species ......................................... 39 4.5.3 Vegetation Cover Composition Change ................................................................ 43 4.5.4 Ability of Diversity Indices to Capture Vegetation Dynamics for Conservation.. 48
4.6 Summary ....................................................................................................................... 50 5.0 Discussion ......................................................................................................................... 51
5.1 Soil pH and Percent Soil Organic Carbon .................................................................... 51 5.2 Patterns of Soil Properties Beneath Dominant Species ................................................ 53 5.3 Vegetation Cover Composition Change ....................................................................... 54 5.4 Ability of Diversity Indices to Capture Vegetation Dynamics for Conservation......... 57 5.5 The Landscape Architecture Perspective...................................................................... 58 5.6 Summary ....................................................................................................................... 59
6.0 Landscape Architecture and Reclamation ........................................................................ 60
6.1 Summary ....................................................................................................................... 66
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7.0 Conclusions....................................................................................................................... 67 8.0 References......................................................................................................................... 69 Appendix: Data Tables ........................................................................................................... 81
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List of Tables Table 1: Robinson's (2004) categories of plant analysis and design for an
individual or assemblage. 14
Table 2: Top five species in 1993 and 2007 for the sweet clover-creeping red fescue community based on proportion of area cover.
47
Table 3: Top five species in 1993 and 2007 for the alfalfa-creeping red fescue community based on proportion of area cover.
47
Table 4: Linear regression of Shannon’s H versus proportion of total area comprising native plants.
49
Table 5: Linear regression of Shannon’s E versus proportion of total area comprising native plants.
50
Table 6: EPL soil sample laboratory analyses data. 82
Table 7: List of plant symbols and associated species information recorded during vegetation assessments.
87
Table 8: EPL square quadrat averaged vegetation assessment data. 89
Table 9: Reference forest site square quadrat averaged vegetation assessment data.
92
Table 10: EPL circular quadrat vegetation assessment data. 93
Table 11: Location of EPL and reference sites used for square quadrat assessments. Site numbers reflect those locations sampled.
94
Table 12: Location of EPL sites used for circular quadrat assessments. 95
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List of Figures Figure 1: The multiple scales at which landscape architects analyse landscape pattern. 12
Figure 2: Location of East Pit Lake and property ownership map. 22
Figure 3: Current landscape context of East Pit Lake. 25
Figure 4: Location of sampling sites used in assessment of soil properties (square quadrat sampling protocol).
30
Figure 5: Diagram of sampling design for square quadrat vegetation assessments. 33
Figure 6: Location of sampling sites used in assessment of vegetation change (circular quadrat sampling protocol).
33
Figure 7: Diagram of sampling design for circular quadrat vegetation assessments. 33
Figure 8: Change in measured soil pH (saturated paste) between 1992 and 2007. 38
Figure 9: Change in measured soil percent organic carbon between 1992 and 2007. 38
Figure 10: Change in proportion of cover for native and introduced plant species in the sweet clover-creeping red fescue community between 1993 and 2007.
45
Figure 11: Change in proportion of cover for seeded and unseeded plant species in the sweet clover-creeping red fescue community between 1993 and 2007.
45
Figure 12: Change in proportion of cover for native and introduced plant species in the alfalfa-creeping red fescue community between 1993 and 2007.
46
Figure 13: Change in proportion of cover for seeded and unseeded plant species in the alfalfa-creeping red fescue community between 1993 and 2007.
46
Figure 14: Horizontal structure diagram of transition areas, showing a gradient of vegetation mixing between two community types. For example, the transition from forest to grassland.
62
Figure 15: Vertical transition structure diagram of transition areas (based on Robinson 2004).
63
Figure 16: Typical native grassland structure of the Aspen Parkland, photographed in the Rumsey Block, Alberta.
64
Figure 17: Photographs and vegetation analysis images at EPL. 65
Figure 18: Vegetation clusters are not contiguous on EPL, with relatively linear gaps. 66
1
1.0 Introduction
Natural resource extraction can irrevocably change a landscape and its inherent functions.
Our continuing need for these resources means that previously undisturbed areas will be
affected. Mitigating the loss of such areas has ramifications for both the site and its
surrounding environment. A site is a function of processes acting upon its physical, chemical,
biological and social attributes. The site can originate and facilitate the continuance of
processes, such as hydrology or species movement. Hence, every action we take upon a site
has the opportunity to affect other locations, at a variety of scales.
Various disciplines focus on the study of physically repairing landscapes changed during
resource extraction. Each approaches this task from a different perspective, but maintains
focus on enabling successful function of all disturbed ecosystem components. Landscape
ecology is the “study of spatial variation in landscapes at multiple of scales, (including) the
biophysical and societal causes and consequences of landscape heterogeneity” (IALE 2007).
Reclamation science studies the process of repairing land disturbed, commonly due to human
activity, back to its pre-disturbance state or a state of equivalent functionality (Naeth 2008).
Restoration science is closely related to reclamation, with the emphasis placed on facilitating
the establishment of an ecosystem similar to that which existed prior to disturbance, or to an
ecosystem type which is native to the area of disturbance (SER 2004). Landscape
architecture is defined as encompassing “the analysis, planning, design, management, and
stewardship of the natural and built environments” (ASLA 2008). All of these disciplines
should be combined for the best mitigation of damaged landscapes.
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One caveat of landscape science is that no two situations under study will ever be identical
due to landscape complexity at multiple scales. As such, landscape research draws
conclusions from a variety of studies in a form of meta-analysis for best practice. A wide
variety of studies at multiple scales, both temporal and physical, are essential for developing
the best approaches for landscape repair.
Reclamation approaches landscape repair from a biophysical perspective, while landscape
architecture includes an additional component: the critical ecological aesthestic. Combining
art and science provides an enhanced framework for analysing projects, since a landscape is
defined both by its patterns and its processes. As previously undisturbed landscapes become
subject to disturbance, we potentially lose part of our landscape history as the processes
leading to a site’s formation can never be replicated in their entirety. The value in replicating
as near as possible the pre-disturbance ecosystem may be considered an ethical judgement;
however, there may be knowledge and use of that ecosystem beyond anthropogenic
consideration. If we cannot determine what we are losing, the difficulty in its replacement is
compounded. Understanding how our actions affect outcomes is essential.
This research explores soil and plant community changes on a non-topsoiled, mine site
fifteen years after reclamation from both a reclamation and landscape architecture
perspective. Since reclamation, the site was subject to unmanaged, natural environmental
processes. Understanding how reclamation design affects outcome will enhance future
project design, evaluation and our appreciation of the intricacies of natural site evolution
within the landscape.
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1.1 Thesis Structure Overview
Following the introduction in Chapter 1, Chapter 2 provides a literature review focussing on
effects upon soils and plant communities during mine reclamation, the mine reclamation
situation in Alberta and the landscape architecture approach to design of natural
environments. Chapter 3 presents the research goal and questions, while Chapter 4 details site
selection, sampling methods, followed by combined statistical analyses and results. Chapter 5
provides a discussion of research question results, which is supported by further discussion in
Chapter 6 of a landscape architecture perspective of reclamation at East Pit Lake. Brief
conclusions are presented in Chapter 7. The appendix includes raw data and sampling site
locations, should further research be conducted at East Pit Lake.
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2.0 Literature Review
Reclamation can affect both soil properties and plant communities. Determining which soil
properties are most influenced by reclamation activities assists with an understanding of the
corresponding effects upon plant communities. Plant communities experience their own
dynamics both amongst plants and upon soil. This interplay between plant and soil is an
integral component of reclamation plan design. Landscape architecture includes pattern and
process analysis in its approach to landscape design. Through this vehicle landscape
architects provide an added dimension to reclamation schemes.
2.1 Soils and Mine Reclamation
Soil is a thin layer of the lithosphere capable of supporting life. Formed through interacting
natural processes, it is a function of its environment: topography, parent material, climate,
time and biota (Jenny 1941). Soil comprises both mineral and organic content, differentiating
it from other terrestrial media.
As a medium for life, soil supports biogeochemical cycling, habitat for microbiota and
facilitates plant development (Brady and Weil 2002). The proportions of sand, silt and clay
dictate soil texture properties and mediate the efficacy of related processes. Particle surface
area provides microhabitats and ion exchange capacity. Particle size influences pore space,
which affects soil solution holding capacity and availability, microbiotic habitat quality, gas
exchange and buffering ability. For example, soils with very high amounts of sand (particle
size 2.0 to 0.05 mm) typically have large pore spaces and high drainage rates but poor
fertility. Soil clay particles and organic matter aid in aggregate stability, creating a
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macrostructure within the soil profile and enhancing chemical and biological activity (Lado
and Ben-Hur 2004).
Coal mining activities disturb the soil profile its functions. First, topsoil, the most organic-
enriched fraction of the soil profile, is removed and stored until replaced. Subsequently,
subsoil horizons are removed and stored. Finally, overburden, comprising bedrock and
surficial material, is broken and removed to expose the coal seam, which is mined and taken
for processing. Removal and storage mix the soil, disrupting microbial activity, including
enzyme production and mycorrhizae (Machulla et al. 2005, Kiss et al. 1998, Fresquez et al.
1997, Insam and Domsch 1988, Stroo and Jenks 1982). Storage of topsoil reduces its quality
as a growing medium and seed bank (Ghose 2001, Rokich et al. 2000, Johnson and West
1989). Area strip mining moves across a landscape systematically, with overburden material
from one trench deposited into the previously mined trench. The resulting landscape is a
series of aligned spoil mounds which are contoured and stockpiled soil is replaced during
reclamation.
Mine spoils are what remain after high quality minerals, such as gravel and coal, are removed.
Low to non-existent levels of organic material, large particle sizes and potentially toxic
leachate create difficult growing conditions (Barnhisel and Hower 1997, Kent 1981). Mine
soils are spoils which are re-contoured, followed by soil replacement if available. Treatment
with amendments to improve the mine soil as a growing medium is common. These mine
soils then become the new parent material for future soil development.
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Heavy equipment moving spoil and the creation of new topography can result in compacted
soils, especially when the soil is above field capacity (Page-Dumroese et al. 2006, Taylor et
al. 1966). Soils with coarse textures recover more rapidly; however, compaction effects can
persist indefinitely (Page-Dumroese 2006, Atwell 1993). Compaction reduces pore space,
limiting gas exchange and restricting root growth, nutrient availability and water transport
(Sinnett et al. 2006, Vaz and Hopmans 2001, Indorante et al. 1981). Consequences include
slower plant growth rates and smaller plants (Passioura 2001, Beemster et al. 1996).
Reclamation aims for pre-disturbance site conditions; in Alberta this is defined under Section
1(e) Alberta Regulation 115/93 as ‘equivalent land capability’ (Government of Alberta 2005,
Jansen 1981). Creating stable relief and minimizing erosion are initial concerns in
reclamation, as physical stability is essential for long-term soil and plant community
development (Puigdefabregas 2005, Barthes and Roose 2002). Compaction can be alleviated
in part through selection of equipment and by timing activities to coincide with dry soil
conditions, and through deep ripping (Moffat and McNeill 1994). Topsoil should be
conserved and replaced, as it is the ideal source of site-specific soil microorganisms and plant
propagules (Bruland and Richardson 2004, Vecren and Muller 2003). However, topsoil
quantity may be insufficient, of poor quality or unavailable, the latter a common occurrence
in mine sites pre-dating reclamation management (Stahl et al. 2002, Verhagen et al. 2001).
Generally, a cover crop of plants is seeded to protect soil from erosion by water and wind,
and to provide initial organic inputs. Fertilisers may be used to enhance soil fertility.
Depending on the initial site vegetation community, it may be advisable to use alternate
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seeding and fertilisation approaches (Native Plant Working Group 2001). Reclaimed land can
be used for agriculture or left to develop into unmanaged natural habitat.
2.2 Plant Communities and Mine Reclamation
The mechanics of plant community assembly are a diverse area of study within plant ecology.
Essentially, any given assemblage of plants is a function of site, landscape and species. Site
conditions informs the growing conditions in which a plant could establish, such as weather,
existing vegetation and soil (MacDougall et al. 2008, Stohlgren et al. 1999, Burke et al. 1998,
Philippi et al. 1998). Landscape comprises seed source and transport, as well as greater
landscape processes and how they may affect specific site conditions (Meiners and Pickett
1999, Czaran and Bartha 1989). Lastly, any given species will have characteristics that
enables it to survive and propagate (Walker et al. 2006). Hence, any grouping of plants
naturally occurring in the environment represents those individuals that can access and
survive within a given context (Holdaway and Sparrow 2006). Assemblies are not necessarily
static as environmental conditions are in constant flux, changing niche availability for a given
location. However, relative stability on large scales gives rise to ecoregions and ecotypes,
where the likelihood of finding certain plant species together is increased.
Plant succession is the change in plant community composition and structure over time. Early
theorists proposed that succession is an ordered process towards a climax community (e.g.
Odum 1969, Clements 1916) while later work emphasises mechanisms of perpetual change
(e.g. Pickett et al. 1987, Connell and Slayter 1977). Furthermore, succession is divided into
two types: primary and secondary. Primary succession is generally considered as the
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establishment of a plant community where no soil or prior seedbank is present. Secondary
succession more closely relates to invasion, whereby disturbance or lack thereof results in a
changed environmental context. An existing vegetation community is or was present and may
remain viable.
Reclamation of mined land falls at the interface of primary and secondary succession, as
generally some soil-like medium is available for plant growth. Succession can be directed or
spontaneous depending on site treatment after extraction is complete. Directed succession is
managed intervention to achieve a particular plant community, such as burning to maintain
grasslands. Seeding, planting and topsoil replacement are also forms of directed succession.
Natural revegetation by volunteer species from the environs or animal activity can be
categorized as spontaneous, also known as natural recovery (Aide et al. 2000, Robinson and
Handel 1993).
Succession on mine soil can be enhanced through management (Bradshaw 2000). Plant
growth is important for soil development, enrichment and protection. Post-disturbance
vegetated sites display improved soil conditions sooner than unvegetated sites (Carter and
Ungar 2002, Panagopoulos and Hatzistathis 1995). For nutrient-poor mine soils, plants are
particularly important for increasing mineralization and microbial activity (Berendse 1998,
Van Breemen and Finzi 1998). Furthermore, vegetation affects soil structure and aggregate
stability through the rhizosphere, organic matter accumulation and water management
(Angers and Caron 1998).
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Initial seeding to protect or stabilise the ground may slow vegetation recovery to reference
conditions in the long-term (Holl 2002, Holl and Cairns 1994). Species selected for
reclamation can potentially suppress native colonising vegetation (Leps et al. 2007,
Hodacova and Prach 2003, Ninot et al. 2001, Sydnor and Redente 2000, Wade 1989).
Replacing or using topsoil from target ecosystems provides a ready native seedbank, and can
result in higher species diversity, ground cover and rates of colonisation (Hodacova and
Prach 2003, Smyth 1997). However, spontaneous succession may be insufficient to provide
the desired amount and type of cover (Martinez-Ruiz and Marrs 2007, Ash et al. 1994). In
primary succession, environmentally-limiting circumstances create nutrient islands that then
facilitate vegetation expansion and change (Campbell et al. 1990, Yarranton and Morrison
1974). Plant litter can also suppress recruitment through shade, microclimate effects, creation
of a physical barrier to the soil as well as biochemical influences on soil (Troumbis et al.
2002, Berendse 1998, Facelli and Pickett 1991).
Human disturbance can affect succession pathways (Bernhardt-Romermann et al. 2006, Hill
and Pickering 2006). Increasing stress on existing vegetation, changing soil conditions and an
excess of nutrients create opportunities for invasion or exclusion based on competitive ability
(Bartuszevige et al. 2007, Barthram et al. 2005, Burke and Grime 1996). Invasion by non-
native species can influence below-ground activity. Arbuscular mycorrhizae and other soil
microorganisms are important for biogeochemical cycling and establishment success in some
plant species (Enkhtuya et al. 2005, Francis and Read 1994). Disturbed soil decreases or
eliminates the ability of arbuscular mycorrhizae to survive and infect host plants (Jasper et al.
1989). Studies in different environments find that non-native plants can introduce foreign
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microorganisms to the host soil, shifting types and decreasing amounts of native
microorganisms (Hawkes et al. 2006, Belnap et al. 2005). This shift can create a competitive
advantage for non-native species and preclude successful re-establishment of native plants
(Batten et al. 2008).
Ecosystem invasibility is a function of opportunity. Resource-poor ecosystems experience the
greatest competition, such that a change in the site conditions creates more opportunity for
other species to invade (Renne et al. 2006, Goldberg et al. 1999). Individual plant species
characteristics influence ability to invade or resist invasion (Fargione et al. 2003, Callaway
and Aschehoug 2000). Increased diversity is generally considered a successful barrier to
invasion, through elimination of niches or opportunity for other species to establish (Kennedy
et al. 2002, Lavorel et al. 1999). MacDougall et al. (2008) found removal of non-native
species and systematic re-introduction of native plants was necessary to restore grassland
ecosystems that could then resist re-invasion. Rather than focusing on large foci of invasive
species, controlling small outbreaks is more effective in the long-term for plant control
(Moody and Mack 1988).
2.3 Mine Reclamation in Alberta
The scale and extent of resource extraction in Alberta is large, as are the concomitant
potential effects. The semi-arid climate and winter season create a unique situation for
reclamation work. Low-cost approaches that meet defined goals within set time frames are
essential. Reclamation in Alberta is generally defined as “the process of reconverting
disturbed land to its former or other productive uses. All practicable and reasonable methods
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of designing and conducting an activity to ensure . . . stable, non-hazardous, non-erodible,
favourably-drained soil conditions and equivalent land capability” (Powter 2002, p. 59). In
Alberta, research into coal mine reclamation practice and outcome began in the 1970s, with
the greatest focus on solonetzic soils and their saline-sodic conditions (Edwards and
Schumacher 1989, Macyk 1986, Powter and Sims 1982). During the 1960s, the United States
also began research programs into surface coal mine reclamation in the Midwest (Schafer et
al. 1979). Studies varied in scale and length of time; however, the primary interest was the
ability of soil to support agronomic plant growth.
Under Alberta’s Environmental Protection and Enhancement Act (2000), once the provincial
ministry of the environment, Alberta Environment, issues a reclamation certificate, a site is
considered reclaimed and no further work is required (Government of Alberta 2008).
However, a site remains open to appeal and any contaminants are the responsibility of the
company for the duration of their presence. In Alberta, reclaimed land is commonly used for
agriculture, agro-forestry or left to develop into unmanaged natural habitat.
2.4 Landscape Architecture and Natural Environments
Landscape architecture is defined as encompassing “the analysis, planning, design,
management, and stewardship of the natural and built environments” (ASLA 2008).
Landscape architects can use planting design to achieve an end goal (Landman 2008).
Landscape architects follow a design process which includes observation, inventory and
analysis, which then lead to design solutions. To facilitate this process, landscape architects
are trained to observe pattern in the landscape, interpret the components that form the pattern
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and then determine how to re-create the pattern. Pattern exists at multiple scales: scape, scene
and site (Figure 1). The interpretation of pattern might result with its complete re-creation,
such as in environmental restoration, or an impressionistic re-creation, to evoke the feeling of
a place. Evoking the spirit of place is known as genius loci.
Figure 1: The multiple scales at which landscape architects analyse landscape pattern.
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Landscape architects are trained to be aware of what they observe and why outcomes exist as
they do – their application of interpretations are not basic visual mimicry but underlie a
fundamental understanding of why a community exists as it does. Research into visual
preference and concept of wilderness shows that critical awareness of what is natural and the
reasons why is essential – the casual observer rarely recognises actual, native vegetation
assemblage and pattern for a given location (Ryan 2005, Stamps 2005, Nassauer 2004, Hands
and Brown 2002, Misgav and Amir 2001, Tahvanainen et al. 2001, Herzog et al. 2000,
Habron 1998, Kliskey 1998, Ribe 1994, Kaplan et al. 1989, Kaplan and Herbert 1987,
Kaplan et al. 1972). However, the casual observer is capable of feeling what is natural
(Crowe 1988, Appleton 1975).
In natural environments, plants, landform and their placement are the primary tools used by a
landscape architect to re-create pattern. Robinson’s (2004) classic guide on planting design
outlines planting as a tool for analysis and design. He considers both the importance of the
individual and assemblies of plants from multiple perspectives: visual, temporal and scale
(Table 1). His scheme can be used to analyse a landscape and its components in order to
facilitate their recreation. Robinson believes assemblages can act as “plant signatures”,
whereby the “use of a carefully chosen grouping of plants (can) refer to, or signif(y), a
distinctive plant association or community” (Robinson 2004, p 128). A similar idea also
forms the basis of ecoregions used in landscape assessment for other disciplines, such as
forestry. The underlying concept is that pattern is a response to landscape, and that by seeing
the pattern we can also infer knowledge about the landscape.
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Table 1: Robinson's (2004) categories of plant analysis and design for an individual or
assemblage.
Individual • Form
Arching Oval Upright Conical Palm Erect or Ascending Prostrate and Carpeting Fastigiate and Columnar Succulents and Sculptural Hummock, Dome and Tussock Tabulate and Level Spreading Open Irregular Trained
• Line and Pattern
Ascending Pendulous Diagonal Quality (degree pronunciation) Horizontal
• Texture
Fine Medium Coarse
• Colour
Hue Colour Perception Value Colour Effects Saturation
Assemblage • Canopy
Single-layer Two-layer Three-layer
• Transition • Mode of Spread
Seed Vegetative
• Seasonality • Structure
Clump/Copse Hedge/Rows Forest/Belt Edge/Centre Open/Closed
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Re-creating and facilitating natural ecological function is a part of good design in landscape
architecture and reclamation. Initial seed mix, seeding location, use and location of woody
species as well as site topographical design should be informed by reference ecosystems. The
site should be designed to facilitate natural succession and resilience to invasion.
Consideration of edge, seed dispersal, pattern of plant expansion, climate, hydrology and
functional scale are essential components of site design and process (De Miguel et al. 2005,
Parker 2004, Oosterhorn and Kappelle 2000, Robinson and Handel 1993, Moody and Mack
1988).
2.5 Summary
Reclamation affects soil and plant community properties. Changes in soil texture, structure
and chemical properties create new growing conditions that may differ significantly from the
original soil state. Seeded plants may inhibit vegetation dynamics through competitive
exclusion or competitive advantage. A reclaimed site should support an equivalent land
capability to the undisturbed condition. Land reclamation in a natural area must consider how
the site will fit into the greater landscape context to design the most suitable reclamation plan.
Landscape architecture analyses landscape at multiple scales, and can use this information to
re-create natural vegetation and landform patterns.
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3.0 Research Goal, Questions and Rationale
Variation in goal and method of reclamation can produce different outcomes. As such,
understanding how a selected reclamation approach affects land and plant communities
beyond certification is important to enable a site along a trajectory towards desired plant and
soil communities, particularly when the land will not be managed. Measuring patterns and
composition change over time is a good gauge of how an ecosystem responds in natural
circumstances. Methods selected should help characterise the type and magnitude of change
or pattern measured.
Not all parameters to characterise soil and plant communities can be measured due to time,
ease and financial constraints. Selecting particular indicator tests can capture ecosystem
dynamics, as multiple characteristics can be inferred from specific measurements.
3.1 Research Goal
To study soil and plant community changes on an unmanaged, 15-year-old certified-
reclaimed site to determine effects of past reclamation practice from a landscape architecture
perspective.
3.2 Research Questions and Rationale
Sections 3.2.1, 3.2.2, 3.2.3 and 3.2.4 present the four research questions and associated
rationale investigated.
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3.2.1 Soil pH and Percent Organic Carbon Change
3.2.1.1 Rationale
Soil pH and organic carbon content can significantly influence plant species composition and
growth on post-mined land, although no study directly addresses whether changes in either
property are cause for patterning or its consequence (Rehounkova and Prach 2006, Bonet
2004, Wiegleb and Felinks 2001, Brenner et al. 1984). Both pH and soil organic carbon are
measured for inclusion in a reclamation certificate application. As such, these variables are
useful for long-term comparison.
3.2.1.2 Research Question
Between similarly textured soils, is there a significant difference in soil pH and percent
organic carbon content measured in 1992 versus 2007?
3.2.2 Patterns of Soil Properties Beneath Dominant Species
3.2.2.1 Rationale
Plant species differentially affect soil, which in turn can alter soil suitability for other species
(Hobbie et al. 2007, Binkley and Giardina 1998). From an identical starting environment,
Mitchell et al. (1997) found soil nutrient concentrations differed depending on plant
composition trajectories, which then affects potential for restoration. Introduced species,
including agronomic species, are commonly used for reclamation work given their proven
fast establishment and hardiness. Species may also be selected for soil-improving properties,
such as nitrogen fixation. Understanding if species selection affects soil properties and
development in the longer term could influence seed selection.
18
Soil properties include physical, chemical and biological aspects. Plant-soil relationships can
be characterised through soil properties directly influencing plant growth and establishment.
3.2.2.2 Research Question
Is it possible to predict a pattern of soil properties based on dominant vegetation cover?
3.2.3 Vegetation Cover Composition Change
3.2.3.1 Rationale
Common invasion theory states that species have the ability to exploit shared niches in their
environment, leading to competition or invasion based on opportunity (White and Houlahan
2007, Kennedy et al. 2002, Stachowicz et al. 1999, Tilman et al. 1997, Case 1991).
Competitive ability is a function of the plant species, regardless of its status as introduced or
native. Vegetation community dynamics can have repercussions for invasion as new
opportunities arise, such as through facilitation or exclusion. In natural systems, change is not
managed through human intervention. Since reclaimed land can be used for less-managed
conservation or habitat purposes, understanding the process or result of change permits
enhanced reclamation design. While integral for promoting target communities through
design, knowledge of vegetation dynamics is important since a reclaimed site can act as both
potential source and sink of plant species, whether desired or problematic.
Characterising change in plant community can provide information on vegetation trajectories.
Assessing cover vegetation is an effective measurement for comparison across time periods.
19
3.2.3.2 Research Question
Between 1993 and 2007, is there a change in average vegetation cover composition based on
seeded versus unseeded species or native versus introduced species?
3.2.4 Ability of Diversity Indices to Capture Vegetation Dynamics for
Conservation
3.2.4.1 Rationale
Diversity is often considered a measure of success in reclamation or restoration activities
(Martin et al. 2005). The quality of a given habitat will determine its richness in species
diversity as opportunities to exploit different niches arise (Davis 2003). However, richness
alone is not necessarily an indicator of desired diversity. Particularly in unmanaged systems,
high richness may indicate that an environment is heavily invaded by undesired species, as
individual species may exploit resources in a similar fashion (Meiners 2007, Meiners et al.
2004, Lonsdale 1999, Stohlgren et al. 1999, Stohlgren et al. 1998, Wiser et al. 1998). One
measurement commonly used to determine diversity in research and reclamation is the
Shannon diversity index (H), and its associated measurement of evenness (E). The frequent
use of the Shannon index permits comparison with a many studies, making it a good test for
critically examining the use of mathematical measurements for capturing both richness and
target species composition in analysis.
The area covered by a plant species is included as a weighting within the index. The area of
target species in a community can be more important than the number of individual species,
20
as it may take many smaller plants to obtain an equivalent amount of vegetation cover of a
few, larger species. In situations where the ideal vegetation cover would comprise particular
species, such as native plants, a simple count may give an inaccurate picture of their
colonisation or spread. Hence, examining how area and diversity are related is beneficial for
determining the use of indices as tools for evaluation of reclamation project outcomes.
3.2.4.2 Research Question
Is Shannon’s index of diversity (H) and evenness (E) a suitable measurement to qualify
desired vegetation changes on unmanaged reclaimed sites in natural areas? Does amount of
area covered by native vegetation directly relate to calculated Shannon’s H and E?
3.3 Summary
This chapter introduced the research goal, questions and their associated rationale. The next
chapter details the methods selected to characterise the type and magnitude of change or
pattern on EPL.
21
4.0 Methods and Results
The study of change necessitates comparing measurements over time and an understanding
of shifting patterns. Pattern analysis requires the ability to compare the likelihood of sample
co-occurrence for specific variables to draw clear conclusions. As such, selected tests must
be sensitive to the dependent nature of linked cases and the variance of data.
To facilitate reading, methods and results are combined in this chapter with each result being
presented after its associated protocol description.
4.1 Study Site Selection
Studying the longer term effect of reclamation and non-intervention on soil and plant
communities requires a location with available baseline data including information about site
history, reclamation procedure and site management. East Pit Lake (Wabamun, Alberta,
Canada) was certified reclaimed in 1994 by the Province of Alberta Environmental
Protection Branch, which ensures the location will meet minimum regulatory standards of
what is considered successful reclamation for certification purposes. Permission to use EPL
as a study site was obtained from TransAlta and the Province of Alberta.
4.1.2 Site Details
4.1.2.1 History – Legal
East Pit Lake (EPL) forms part of a network of reclaimed coal strip mining lands and is
intended as a replacement for local recreational lakes that were drained during mining. The
site is situated in the Central Aspen Parkland bioregion, and is located one hour west of
22
Figure 2: Location of East Pit Lake and property ownership map. Property ownership
information based on 2007 Parkland County Land Ownership Map.
Edmonton near Wabamun, Alberta. EPL is an award-winning reclaimed site (Figure 2)
(TransAlta Utilities Corporation 1998). Once certified reclaimed, TransAlta transferred
23
ownership of the property to the Province of Alberta, who in turn leases the land to the
Alberta Fish and Game Association. The local Association chapter uses EPL as the central
component in their wildlife conservation efforts, and acquires or leases land in the site’s
vicinity as part of the Whitewood Conservation Properties (WWCP). Officially opened in
2004, the area covers over 400 ha and contains a patchwork of undisturbed and human-
modified land. The WWCP are intended for wildlife and habitat conservation, education and
recreation (TransAlta Utilities Corporation 2004).
4.1.2.2 History – Landscape and Land Use
The surrounding land uses have changed in area extent but not purpose, with most land being
used for mining, agriculture (crop and grazing) or forest. Pre-mining site topography was
undulating with boggy areas to the north while the surrounding land was gently rolling to flat.
Pre-disturbance soils formed upon surficial Quaternary deposits of till and lacustrine
sediment over Cretaceous and Tertiary sandstone bedrock and poorly consolidated Tertiary
gravels (TransAlta Utilities Corporation 1993). Peat and other organic deposits were
common on the surface, with mudstone and siltstone occurring between coal seams
(TransAlta Utilities Corporation 1993). Poor drainage was notable in some locations,
resulting in boggy areas (TansAlta Utilities Corporation 1993). The site formed part of the
luvisolic Cooking Lake and regosolic Codesa Soil Series, with organic Eaglesham soils
dominant in low-lying areas (Lindsay et al. 1968). Aspen (Populus tremuloides Michx.)
stands dominated upland while balsam poplar (Populus balsamifera L.) and black spruce
(Picea mariana (P. Mill.) B.S.P.) were found on imperfectly-drained locations (TransAlta
Utilities Corporation 1993). The average annual low temperature is -16 oC and the average
annual high temperature is 24 oC, with an average annual precipitation of 520 mm, most
24
falling as rain (Environment Canada 2007). Winds blow predominantly from the west and
north-west (TransAlta Utilities Corporation 1993).
4.1.2.3 History – Reclamation
Mining activities ceased in the early 1980s, followed by decommissioning and site
reclamation. Recontouring of the existing subsoil and spoil piles transformed topography
adjacent to the lake into strongly rolling hills, with elevation gains approximating ten metres.
No topsoil was replaced, only available subsoil and overburden (TransAlta Utilities
Corporation 1993). The dragline used during mining to remove soil above the coal was
employed to move soil and spoil around the site (TransAlta Utilities Corporation 1999).
Belly dump trucks and dozers were used for fine-scale grading and earth works (Leskiw 2007,
Sumer et al. 1995, TransAlta Utilities Corporation 1993). Lake development studies from the
mid to late 1980s resulted in a water body design permanently fed by groundwater and
seepage (TransAlta Utilities Corporation 1993).
Agronomic species and other introduced grasses were seeded at 56 kg/ha in the early 1990s
followed by fertiliser application (TransAlta Utilities Corporation 1993). An oats (Avena
sativa L.) cover crop was also seeded. Seed mixes were based on shoreline or slope position
(TransAlta Utilities Corporation 1993). Shoreline species comprised 40% orchard grass
(Dactylis glomerata L.), 25% smooth brome (Bromus inermis Leyss.), 25% meadow fescue
(Festuca pratensis (Huds.) Beauv.) and 10% white clover (Trifolium repens L.) by weight
(TransAlta Utilities Corporation 1993). Species mix for the remainder of the site comprised
40% creeping red fescue (Festuca rubra L.), 40% crested wheat grass (Agropyron cristatum
(L.) Gaertn.) and 20% white clover (Trifolium repens L.) by weight (TransAlta Utilities
25
Corporation 1993). Willow (Salix spp) plantings were used in riparian areas, while further
reforestation was undertaken the following years, meeting various levels of success. A large
tree spade was employed to install Norway spruce (Picea abies (L.) Karst.) and balsam fir
(Abies balsamea (L.) P. Mill.) near the public site entrance on the south-east side (TransAlta
Utilities Corporation 1993). In 1993, patches of relatively bare ground still remained,
predominantly in the southwest corner of the lake (TransAlta Utilities Corporation 1993).
Figure 3: Current landscape context of East Pit Lake.
26
4.1.2.4 Current Situation of East Pit Lake
East Pit Lake sits at the intersection of extensive agricultural and undisturbed forest/marsh
land uses (Figure 3). The ground-fed, stocked lake is a popular fishing location. Beaten trails
lead to the lake from the parking lot; however, the rest of the site displays limited usage by
walking or hiking. Most visitors follow a beaten trail along the shoreline directly adjacent to
the water to circumnavigate the lake. Bird houses are installed along fence lines and on tall
posts in various locations. Two benches and memorial markers are installed, although no
evidence of site maintenance or mowing is present based on vegetation heights around these
objects. There are no permanent lake-based features other than two duck boxes at the north-
west end of the lake. Animal use is evident in droppings, dens, trails, browse and sightings.
An active family of beavers (Castor canadensis) lives in the lake, and evidence of willow
harvesting is prevalent. No motorised activity is permitted onsite, although vegetated ruts are
visible in the soil which may be remnants of previous reclamation work. Numerous
introduced and agronomic plant species persist, with limited colonisation by native species.
Various willow and aspen species are the dominant trees forming patches onsite. Grass litter
(thatch) of varying depths is observable across the entire site. Surface stones embedded in the
soil are noticeable in areas; however, bare ground is limited to sites of animal disturbance.
Vegetation is distinctly patchy in distribution, although patchiness is less prevalent at the
western end of the lake.
4.2 Reference Site Selection
As a control comparison, a reference site was selected near EPL to limit effect of climate and
ecoregion. An upland wooded knoll was identified within one kilometre of the study site.
27
Aerial imagery was employed to determine that the reference was undisturbed forest since
1948. The reference site comprises luvisolic Cooking Lake soils (Lindsay et al. 1968). The
land forms part of the Wabamun-Whitewood Conservation Properties. Permission to use the
land was obtained from the Stony Plain Fish and Game Association.
4.3 Field Work: Vegetation and Soil Sampling
Two types of vegetation assessment were used to collect data for different research questions.
A square quadrat method was used to assess vegetation in distinct patches along the length of
the lake. These data were used with soil analyses. A second vegetation assessment was
carried out using a circular quadrat sampling design, spaced at 100 metre intervals. The
second assessment was necessary to maintain a similar sampling strategy as used in 1993,
and is used to assess vegetation change and diversity. Site reconnaissance was performed in
June 2007, while all detailed vegetation assessment was conducted during August 2007. Soil
sampling occurred during early September 2007. Reclamation certificate vegetation data are
from 1993.
4.3.1 Vegetation Sampling
Section 4.3.1.1 provides a detailed overview of site selection and methods of soil and
vegetation sampling used at EPL and the reference site. Section 4.3.1.2 presents the second
type of vegetation assessment used at EPL to assess plant community change, and the site
selection methods.
28
4.3.1.1 Square Quadrat Sampling
4.3.1.1.1 EPL: Sample Site Selection
A reconnaissance site assessment was conducted in June 2007 to assess the extent and types
of vegetation patches at East Pit Lake by walking its full circumference. To limit
confounding effects, only vegetation on the south-facing side and located mid-slope was
considered. Sites were selected for similar slope position and percent values. All 9 m2
patches with upwards of 90% cover by a single species were recorded with a GPS device.
Selecting high cover values for a plant permits analysis of species effect upon soil as most
root mass will be attributable to above ground vegetation. From the June reconnaissance, six
types of cover vegetation were identified within these criteria which resulted in a total count
of 91 patches. To check accuracy of GPS points, the memorial marker was selected as a
reference point. This point was recorded first and last to examine any shift in distance caused
by the GPS device.
Points were transferred to an ArcMap project file (ESRI ArcGIS 9.1) for selection of sample
sites. GPS points were labelled according to vegetation cover species, termed cover type. For
each type, five patches were selected across the extent of the lake. For even sampling, the
north slope area was divided into three regions along the length of the lake as measured from
the road: near, mid and far. At least one sample point per cover type was located in each of
the sections. A random sequence generator was used to select points corresponding with the
GPS label created in the field (Haarh 2007). When using the number list, if more than two
points of a single vegetation type occurred within a single section, the third point would be
discarded. Sites were selected until 30 points representing six cover types were identified.
Later, a seventh cover type comprising mixed vegetation of no species exhibiting greater than
29
90% dominance was identified in the field. These five sites were also spaced across the lake
extent.
In total, seven cover types were examined at EPL at five sites each (Figure 4). Each cover
type is named for the area of ground covered (upwards of 90%) by a single plant species.
Surface cover is used as a corollary for extent of upper level rooting zone influence.
• Aspen (Populus balsamifera L., Populus tremuloides L. and hybrids): Aspen are both
planted and naturally returning to the site, and/or demonstrating reproduction. An aspen
site is characterised by full canopy cover of aspen leaves and branches.
• Planted Willow (Salix spp.): Willows were originally planted for stabilisation and habitat
that may be undergoing natural expansion. The sites are characterised by full canopy
cover of willow leaves and branches. Exact species identification was unsuccessful using
available plant keys.
• Creeping Red Fescue (Festuca rubra L.): Areas with this persistent plant initially seeded
in 1988.
• Smooth Brome (Bromus inermis Leyss.): Areas with this persistent plant initially seeded
in 1988.
• Alfalfa (Medicago sativa L.): This vegetation type is characterised by full canopy cover
and 0.2 m spacing between plants such that upper soil level root zones of individual
plants would be touching.
• Common Horsetail (Equisetum arvense L.): Areas with dense populations of this early
successional native species (hereafter referred to as equisetum).
30
• Mixed Species: Sites where no single species dominates with upwards of 90% coverage.
Figure 4: Location of sampling sites used in assessment of soil properties (square quadrat
sampling protocol).
4.3.1.1.2 Reference Site: Square Quadrat Sampling
Using aerial photography dating from 1948, potential undisturbed reference locations were
identified around EPL. Locations were reconnoitred on the ground visually. One site met the
criteria of being undisturbed since the earliest aerial photograph, was proximate to EPL,
represented vegetation characteristic of the region and presented an upland condition. This
location was within the Wabamun Whitewood Conservation Properties. Three sites were
selected for sampling as references, However, these sites would be characterised as mixed
vegetation as no species dominated. To match the site selection criteria used at EPL, all sites
were south-facing and located mid slope.
31
4.3.1.1.3 Vegetation Assessment Protocol
Vegetation assessments were performed in mid-August 2007. Woody vegetation was
assessed for species and percent cover within a 9 m2 quadrat (Figure 5). Species and percent
cover for herbaceous vegetation and litter were assessed in three 0.25 m2 quadrats nested
within the greater 9 m2 quadrat. The 0.25 m2 quadrats were positioned to maintain a 30 cm
buffer from the edge of the 9 m2 quadrat and maximise distance between each in a triangular
pattern.
Visual estimates of percent cover were made using the area of the quadrat to represent 100%.
For sites with vegetation that camouflaged a significant herbaceous understorey, hidden
species were also identified as a percent addition to the 100% herbaceous-level quadrat.
Hence, some quadrats can have cover values exceeding 100%. Where a species could not be
identified, a sample was taken for later identification and a proxy name was used. Authorities
Figure 5: Diagram of sampling design for square quadrat vegetation assessments.
32
for species immediately identified in the field are according to USDA PLANTS Database
(USDA 2007). Authorities for species not identified in the field are according to the plant
guide containing the plant key used for identification (Farrar 1995, Johnson et al. 1995). For
each 0.25 m2 quadrat, percent cover of litter and bare ground were assessed within the 100%,
and depth of litter in centimetres was measured at the centre. Vegetation data were averaged
for each 9 m2 site.
4.3.1.2 Circular Quadrat Sampling
4.3.1.2.1 EPL: Sample Site Selection
Not including the lake area, soil samples were taken in a grid at 100 m intervals in 1992 as
per the reclamation certificate application (TransAlta Utilities Corporation 1993). The same
sites were used in 2007 for continuity, to eliminate bias in site selection and to provide
comprehensive vegetation cover sampling (Figure 6). In ArcGIS, the sampling map diagram
used in the reclamation certificate application was georeferenced to the National Topographic
System Mapsheet 083G09’s National Topographic Data Base roads layer (Natural Resources
Canada 2007), then re-projected into UTM 11. Sampling points were digitised into a new
layer and coordinates transferred to a GPS device. In the field, the GPS was used to locate
sites. Two sites in the shoreline community were not sampled to limit effect of original seed
mix and hydrologic regime on comparisons.
33
Figure 6: Location of sampling sites used in assessment of vegetation change (circular quadrat
sampling protocol).
Figure 7: Diagram of sampling design for circular quadrat vegetation assessments.
34
4.3.1.2.2 Vegetation Assessment Protocol
Sampling occurred in late August. Each identified point was located and assessed for percent
cover within a circular quadrat of diameter 1 m (Figure 7). Grass litter was measured in five
evenly-spaced locations. When a species could not be identified, a sample was taken for later
identification and a proxy name was used. Authorities for species immediately identified in
the field are according to USDA PLANTS Database (USDA 2007). Authorities for species
not identified in the field are according to the plant guide containing the plant key used for
identification (Farrar 1995, Johnson et al. 1995).
4.3.2 Soil Sampling
Soil was sampled only within square quadrats. The centre of each 0.25 m2 quadrat was
flagged after vegetation assessment. Soil samples were taken as near to these flags as
possible during early September 2007. Reclamation certificate soil data are from 1992.
4.3.2.1 Penetration Resistance Measurement
A Fisher-Scientific proving ring, centre-cone penetrometer (cone size diameter 13 mm, area
1.33 cm2, maximum PSI 300) was used to infer soil compaction through measurement of
penetration resistance. Readings were performed at the end of August and beginning of
September 2007. Persons performing sampling practised to standardise the method in order
to limit the effect of strength on penetration measurements. No rain fell between the start of
measurements and completion, nor for several weeks prior to commencement. Readings were
obtained in each 0.25 m2 quadrat, and were recorded at 5 cm depth intervals commencing at
surface (0 cm) to a final depth of 15 cm. Commencing at the far end of the lake, attempts
continued until five full depth range readings were obtained in each quadrat. Each failure to
35
reach 15 cm was recorded. After ten sites, the procedure was modified, given time constraints.
For site label number T25 and lower, a maximum of five attempts were made in each 0.25 m2
quadrat to obtain three full readings. A failure to reach 15 cm was recorded as due to either a
rock or compaction.
4.3.2.2 Soil Samples
Soil sampling occurred in mid to late September 2007. Soil cores were extracted from each
0.25 m2 quadrat using a backsaver soil sampler. Cores were divided into two depths: 0 to 5
cm and 5 to 15 cm. Loose litter and debris were removed from each core before division,
while notes on appearance were recorded. Cores were taken evenly from all three 0.25 m2
quadrats at each site and combined until a 500 mL Ziploc bag was filled for each depth.
Where stoniness prevented extraction of samples to a depth of 15 cm, a pickaxe was used to
dig a hole and soil from the required depth was removed from a clean face. Samples were
stored in a cooler filled with ice packs within four hours of extraction. At the end of each day,
samples were stored in a walk-in refrigerator maintained at a constant 4˚C at Ellerslie
Research Farm, Edmonton, Alberta. All samples were submitted for laboratory analyses after
one month of storage at Ellerslie.
4.4 Laboratory Analyses: Soil Sample Analysis Methods
Specific soil properties were selected for analysis based on their ability to characterise soil
quality for plant growth, ease of acquisition and use for inferring related, unmeasured soil
properties. All soil samples were analysed by Bodycote Norwest Labs in Edmonton, Alberta.
Samples were analysed for plant available nitrogen (nitrate-N), phosphorus (P), potassium
36
(K) and sulphur (sulphate-S), total organic and inorganic carbon (TOC and TIC), cation
exchange capacity (CEC), pH and particle size analysis (PSA).
• Plant-available NPK was analysed using the Modified Kelowna Soil Test (0.015M NH4F,
1.0M ammonium acetate, 0.5M acetic acid) (Ashworth and Mrazek 1995).
• Plant-available S was analysed as sulphate extractable by 0.1M CaCl2 after protocol 4.47
in McKeague (1978).
• TOC and TIC were analysed according to Nelson and Sommers (2005).
• CEC was analysed according to protocol 3.32, CEC and Exchangeable Cations by
NH4OAc at pH 7 (McKeague 1978).
• pH was analysed in a saturated soil paste, protocol 3.14 (McKeague 1978).
• PSA was analysed according to Sheldrick and Wang (1993) using the hydrometer method,
protocol 47.3.
4.5 Statistical Work: Analysis and Results
Sections 4.5.1, 4.5.2, 4.5.3 and 4.5.4 present the statistical analysis used for each research
questions, followed by results.
4.5.1 Soil pH and Percent Organic Carbon Change
Between similarly textured soils, is there a significant difference in soil pH and percent
organic carbon content measured in 1992 versus 2007?
4.5.1.1 Analysis
37
Soil pH and percent organic carbon content data for the north slope of EPL were obtained
from the reclamation certificate, and grouped based on soil texture. Only three soil texture
types were available. Soil pH and percent organic carbon data measured in 2007 were
grouped and averaged based on soil texture. Only soil texture types identified in both 1992
and 2007 were used for analysis.
A repeated measures analysis of variance (rmANOVA) was performed using SPSS Release
15.0.1 for Windows (SPSS Inc. 2006). The rmANOVA measures whether observations from
two or more groups are significantly different over time, or before/after effects. The null
hypothesis is that no significant difference exists between the means of groups (all groups
have identical means). The rmANOVA is a two-way ANOVA which does not assume each
factor level under analysis is independent from another. As a parametric test, the assumptions
are that the residuals of the model should be continuous, normally distributed and have equal
variances for each factor. In practice, these assumptions are difficult to measure given the
reduced number of observations (Dytham 2003). Significance was considered at a p-value of
0.05.
4.5.1.2 Result
Sphericity was not assumed, so significance values from the Greenhouse-Geisser correction
were used for both pH and percent organic carbon analyses. No statistically-significant
change in soil pH occurred between 1992 and 2007 (Figure 8). Results indicate that there is a
significant (p = 0.032) change in measured soil percent organic carbon content between 1992
and 2007 (Figure 9).
38
6.5
6.6
6.7
6.8
6.9
7
7.1
7.2
7.3
7.4
1992 2007
Soil
pH
Figure 8: Change in measured soil pH (saturated paste) between 1992 and 2007.
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
1992 2007
Soil
Org
anic
Car
bon
(% d
ry w
eigh
t)
Figure 9: Change in measured soil percent organic carbon between 1992 and 2007.
rmANOVA F(1,2)=6.644, p=0.123
pH
rmANOVA F(1,2)=29.960, p=0.032
OC
39
4.5.2 Patterns of Soil Properties Beneath Dominant Species
Is it possible to predict a pattern of soil properties based on dominant vegetation cover?
4.5.2.1 Analysis
Soil data collected in 2007 were analysed using PCORD 5.10 for Windows (McCune and
Mefford 2006). Out of range variables for penetration resistance due to impenetrability were
‘Winsorized’ to enable multivariate analysis. Penetration resistance is measured at set depths
until the cone reaches the maximum selected depth or cannot penetrate further. ‘Winsorizing’
takes outlier data and brings it closer to the centre of the data distribution (Sokal and Rohlf
1981). The highest value measured by the penetrometer is 5.3 mPa, so the value of 6 mPa
was selected.
Ecological data are rarely normal in distribution, so the non-parametric multi-response
permutation procedure (MRPP) was used to test whether a difference existed between soil
properties associated with dominant plant cover types. MRPP tests the hypothesis of no
significant difference between a priori determined groups (Mielke and Berry 2001, Biondini
et al. 1985). MRPP measures between and within-group similarity based on a calculated
distance matrix. The test shows the degree of similarity of samples within a selected group,
and the degree to which the group may differ from other selected groups. A non-parametric
test does not require normality in data distribution. MRPP requires independent samples and
equal magnitudes of measurement for comparability in analysis (McCune and Grace 2002).
The soil data were in different units, necessitating standardisation for comparison (McCune
and Grace 2002). Data were adjusted to the standard deviate for each column (variable)
40
using: B = (xij – x̄i) / Si. Si is the standard deviation within column i. The transformation
results in all columns having a mean of 0 and variance of 1.
MRPP calculated a distance matrix using the Euclidean distance measure. The weighted
mean within-group distance used to calculate δ was: δ = Σ Ci xi with Ci = ni / Σni, where xi is
the average distance within each group i, Ci is the weight applied to each item in group i, ni is
the number of samples in group i (McCune and Grace 2002).
Cover Type was selected as the grouping variable, resulting in seven groups of five samples
for analysis.
The calculated p-value represents the likelihood of an observed δ being due to chance. An
MRPP also generates an estimate of effect size independent of sample size, the chance-
corrected within-group agreement (A). The agreement test statistic A represents effect size,
or calculated distance between group centres. A is calculated: A = 1 - (observed δ /expected
δ). The expected δ is an average of the δ calculated for all possible data permutations. When
all samples in a group are identical, A is 1. When all samples are heterogeneous as expected
by chance, A is 0. If A is less than 0 then more heterogeneity exists within groups than
expected by chance.
The MRPP was first run for all soil properties together. Subsequently, an MRPP was run for
each soil property using Cover Type as the grouping variable. The per property test was to
ensure no significant patterns were lost through an MRPP’s aggregating approach to
41
calculating differences. Significance was considered at a p-value of 0.05. Where significant
results were found, a sequential Holm-Bonferroni calculation was performed to reduce the
occurrence of Type II statistical errors (the chance of accepting an insignificant hypothesis as
significant). The Holm-Bonferroni calculates a more conservative p-value for determining
significance based on the number of pairs in comparison. A significant result from the MRPP
may not be such for all pairs compared, so using a correction enables determination of which
cases are more likely to be significant in order to identify real patterns.
Pairwise p-values calculated during the MRPP are ranked from smallest to largest. For the
smallest calculated p-value, the selected level of significance (p = 0.05) is divided by n, the
number of pairs compared. For subsequent corrections: pH = psig / n-x, where x is the number
of pairs with a smaller pairwise p-value than the pairwise p-value in question. The result is a
sequence of corrected levels of significance that are used to accept or reject the null
hypothesis.
4.5.2.2 Results
There is no significant difference for soil properties between groups based on dominant cover
species (p = 0.358, A = 0.006). The insignificant p-value means that the observed δ does not
significantly differ from the expected δ. The result A = 0.006 indicates that samples within
the same group are different, and could co-occur through chance.
The per soil property MRPP resulted with three significant overall variables: plant-available
sulphur 0-5 cm (p = 0.002, A = 0.243), plant-available nitrogen 0-5 cm (p = 0.029, A =
0.140) and compaction (mPa) 5-10 cm (p = 0.040, A = 0.123). The MRPP performed a total
42
of 21 pairwise comparisons for each soil property variable before determining an overall
significance. There were nine significant pairwise comparisons for sulphur, seven for
nitrogen and four for compaction. After the sequential correction, only one case for sulphur
and five cases for nitrogen were still statistically significant. However, it was decided to also
consider significant pre-correction ecological data as weak indicators of potential patterns
given inherent variability commonly found in ecological data. Raw environmental data was
used to support or reject the validity of any conclusions drawn.
Equisetum-dominant plots generally had the highest amount of plant-available sulphur in the
0-5 cm depth (x̄ = 51 mg/kg, SD = 1), while alfalfa had the lowest (x̄ = 6 mg/kg, SD = 3).
The only corrected statistically-significant case for sulphur content was equisetum compared
to alfalfa. However, this pattern did not hold for all comparisons of equisetum with other
cover species indicating weak predictive ability.
After correction, plant-available nitrogen was significant in five cases (p = 0.05). However,
raw data for those cases indicated that an outlier was responsible for each circumstance, as
nitrogen varied very little for all cover types. Thus, no particular pattern exists for nitrogen
beneath plant species.
Compaction data were ‘Winsorized’ before analysis, as previously explained. After
sequential p-value correction, no cases remained significant. However, original p-values for
pairwise comparisons indicated that alfalfa tended to have the highest levels of compaction at
the 5 to10 cm depth. Three of five alfalfa assessment plots were ‘Winsorized’. In this
43
instance, rocks prevented penetration measurements rather than soil compaction. As such,
alfalfa does not necessarily characterise compacted sites, rather sites which are rocky with
depth.
4.5.3 Vegetation Cover Composition Change
Between 1993 and 2007, is there a change in average vegetation cover composition based on
seeded versus unseeded species or native versus introduced species?
4.5.3.1 Analysis
Vegetation assessments for the 1993 reclamation certificate divided the northern slope, non-
shoreline area of EPL into two communities: sweet clover-creeping red fescue dominant and
alfalfa-creeping red fescue dominant. Species and percent area were provided for each
community. Data from the 2007 circular quadrat vegetation assessment sample sites were
divided geographically based on community boundaries identified in 1993. The 2007 data
were averaged for each species identified to obtain a mean for comparison. The 1993 and
2007 values were subjected to angular transformation to reduce heterogeneity of variance.
The angular transformation (arcsine square root transformation) is commonly used on
proportional data, and reduces the effect of differences in magnitude based on sample size.
Using SPSS Release 15.0.1 for Windows, data were transformed using (SPSS Inc. 2006):
xa = 57.295 * ARCSIN(SQRT(xyr)), where xa is the resulting transformed value, 57.295 is
the conversion factor of radians to degrees, ARCSIN(SQRT()) is the syntax for the arcsine
square root transformation and xyr is the value to be transformed.
44
An rmANOVA was then performed for each community type in SPSS. Two different
analyses were performed based on a selected grouping variable. First, changes in cover over
time of seeded and non-seeded species were calculated. Second, changes in cover over time
of native and non-native species were investigated. For each community, the top five species
comprising the most cover in 1993 and 2007 were listed to illustrate general, individual
trends. Significance was considered at a p-value of 0.05.
4.5.3.2 Results
Sphericity was not assumed, so significance values from the Greenhouse-Geisser correction
were used for all analyses. Results indicate there was no significant change in proportion of
native/introduced species cover composition in the sweet clover-creeping red fescue
community between 1993 and 2007 (Figure 10). There is a significant (p = 0.004) group
effect in proportion of cover composition for seeded/unseeded species (Figure 11). Seeded
species remain static in proportion of cover while the proportion of unseeded species
increases. For both the native/introduced and seeded/unseeded species, there was no
significant change in proportion of cover area in the alfalfa-creeping red fescue community
between 1993 and 2007 (Figure 12, Figure 13). For individual species, there are changes in
proportion of cover and species comprising the dominant cover vegetation for both SCCRF
and ACRF communities (Table 2, Table 3).
45
-5
0
5
10
15
20
25
1993 2007
Prop
ortio
n of
cov
er a
rea
Figure 10: Change in proportion of cover for native and introduced plant species in the
sweet clover-creeping red fescue community between 1993 and 2007.
-5
0
5
10
15
20
25
30
35
40
45
1993 2007
Prop
ortio
n of
cov
er a
rea
Figure 11: Change in proportion of cover for seeded and unseeded plant species in the
sweet clover-creeping red fescue community between 1993 and 2007.
rmANOVA Time p = 0.527 Group p = 0.261 Time x Group p = 0.860
rmANOVA Time p = 0.693 Group p = 0.004 Time x Group p = 0.700
In
Nt
Sd
Vo
46
-5
0
5
10
15
20
25
1993 2007
Prop
ortio
n of
cov
er a
rea
Figure 12: Change in proportion of cover for native and introduced plant species in the
alfalfa-creeping red fescue community between 1993 and 2007.
-5
0
5
10
15
20
25
30
35
1993 2007
Prop
ortio
n of
cov
er a
rea
Figure 13: Change in proportion of cover for seeded and unseeded plant species in the
alfalfa-creeping red fescue community between 1993 and 2007.
rmANOVA Time p = 0.746 Group p = 0.218 Time x Group p = 0.236
rmANOVA Time p = 0.556 Group p = 0.059 Time x Group p = 0.987
In
Nt
Sd
Vo
47
Table 2: Top five species in 1993 and 2007 for the sweet clover-creeping red fescue
community based on proportion of area cover.
Table 3: Top five species in 1993 and 2007 for the alfalfa-creeping red fescue
community based on proportion of area cover.
Alfalfa-Creeping Red Fescue Community
Common Name Species 1993 Common Name Species 2007
Alfalfa Medicago sativa 0.32 Creeping red fescue Festuca rubra 0.38
Creeping red fescue Festuca rubra 0.29 Alfalfa Medicago sativa 0.23
Smooth brome Bromus inermis 0.11 Smooth brome Bromus inermis 0.17
Orchard grass Dactylis glomerata 0.10 Canada thistle Cirsium arvense 0.08
Timothy grass Phleum pratense 0.05 Clover Trifolium spp 0.04
Sweet Clover-Creeping Red Fescue Community
Common Name Species 1993 Common Name Species 2007
Creeping red fescue Festuca rubra 0.60 Creeping red fescue Festuca rubra 0.50
Clover Trifolium spp 0.15 Alfalfa Medicago sativa 0.16
Sweet clover Melilotus spp 0.14 Reed canary grass Phalaris arundinacea 0.07
Sow thistle Sonchus arvensis 0.04 Sweet clover Melilotus spp 0.06
Alfalfa Medicago sativa 0.02 Canada thistle Cirsium arvense 0.06
48
4.5.4 Ability of Diversity Indices to Capture Vegetation Dynamics for
Conservation
Is Shannon’s index of diversity (H) and evenness (E) suitable measurement to qualify desired
vegetation change on unmanaged reclaimed sites in natural areas? Does amount of area
covered by native vegetation directly relate to calculated Shannon’s diversity (H) and
evenness (E) indices?
4.5.4.1 Analysis
Shannon’s H, E and area covered by native species as a proportion of total cover were
calculated for the circular quadrat vegetation assessments. The circular quadrat assessments
were used in preference to the quadrat assessments given their even spread across the site and
lack of bias based on species composition.
Shannon’s index of diversity is calculated as: where S is the
total number of species in the assessment and pi is the proportion of S made up of a particular
species. Shannon’s index of evenness is calculated as: E = H/S, where H is the calculated
Shannon’s H and S is the total number of species in the assessment quadrat.
Area of native species is calculated as: AreaNt = (total percent area covered by native species
/ total area covered by all species) / 100%. AreaNt is thus a proportional measurement for use
in comparison with Shannon’s H and E.
Using SPSS Release 15.0.1 for Windows, the calculated H, E and AreaNt were employed in
a linear regression (SPSS Inc. 2006). A linear regression tests the null hypothesis that the
H = (-1)* Σ pi*ln pi S
i=1
49
slope of the line of best fit is 0. A significant result is interpreted as the slope being
significantly different from 0, indicating a relationship between variables. The test carries
several assumptions about data distribution and measurement: there is no error in the
measurement of the independent variable; variation in the dependent variable is the same at
any one independent value; the dependent value is normally distributed for any independent
value; and that the relationship between the independent and dependent variable can be
described by a straight line (Dytham 2003). Significance was considered at a p-value of 0.05.
4.5.4.2 Results
There is no significant amount of variation explained in the regression line of best fit between
Shannon’s H and area covered by native species (F(1,18) = 0.627, p = 0.439) (Table 4).
Approximately 3% of the variation in vegetation coverage can be explained by H. Results
indicate that there is no significant amount of variation explained in the regression line of
best fit between Shannon’s E and area covered by native species (F(1,18) = 2.410, p = 0.138)
(Table 5). Approximately 12% of the variation in vegetation coverage can be explained by E.
Table 4: Linear regression of Shannon’s H versus proportion of total area comprising
native plants.
Source r r2 Adjusted r2 SE of Estimate
Regression_H 0.183 0.034 -0.020 0.038 SE standard error
50
Table 5: Linear regression of Shannon’s E versus proportion of total area comprising
native plants.
Source r r2 Adjusted r2 SE of Estimate
Regression_E 0.344 0.118 0.069 0.036 SE standard error
4.6 Summary
The previous sections detail the methods, analyses and results of the research questions
formulated to investigate soil and plant community change on EPL. The next chapter will
discuss these results and introduce a landscape architecture perspective.
51
5.0 Discussion
This research investigated change within soil and plant community development on a 15-
year-old reclaimed coal mine site. Vegetation and soil sampling were conducted on the
reclaimed site and in a nearby undisturbed forest reference location. Results will be useful for
future reclamation, design and administration of large land bases used in conservation
contexts. In this study a non-topsoiled reclaimed site was used, thus the results may differ
from a topsoiled site, which has a richer soil and seedbank present that could affect plant
species establishment. However, restrictions upon availability and quality of topsoil, and
many older sites requiring reclamation, translates into a need for an in-depth understanding
of reclamation activities and outcomes upon non-topsoiled sites.
5.1 Soil pH and Percent Soil Organic Carbon
Percent organic carbon and soil pH were selected assessing change in soil properties, as these
parameters were measured in both 1992 and 2007 using the same laboratory protocols. Both
properties are useful for measuring soil function over time, given their effects on plant
species establishment and community composition (Rehounkova and Prach 2006, Bonet
2004, Wiegleb and Felinks 2001, Brenner et al. 1984). However, the pattern of sampling and
amount of baseline data across the site could create limitations for the broad application of
conclusions. Soil was sampled midslope on the north shore of EPL; topography affects
drainage and potentially soil chemistry, hence results could be very different for shoreline
soil or top of slope soil. In 1992, few soil samples were taken on the north slope for
laboratory analysis. As such, only a subset of data from 2007 can be used for comparison
given the range of soil textural differences. Distinctions based on soil texture were not
52
possible given the limited number of 1992 samples, necessitating the combination of
chemistry data for use in statistical analysis. The 2007 data were averaged based on texture
and used for comparison with 1992. Hence, only a within-group effect can be analysed
statistically with a few cases. Inspection of raw data indicated that organic carbon and pH did
not range widely based on soil texture, such that overall statistical results are still valid for
interpretation.
Results indicate that limited change in soil and plant communities is occurring at EPL.
Percent soil organic carbon is increasing (F(1,2) = 29.960, p = 0.032), which could indicate
rising decomposition of plant litter and roots, and a greater amount of plant residue being
stored in the soil (Cadisch and Giller 1997). The increase could also represent decreased
microbial decomposition of organic matter (Brady and Weil 2002). Drier soil surfaces due to
plant cover and lack of ground disturbance contribute to reduced soil microbial activity. The
statistically non-significant change in soil pH (F(1,2) = 6.644, p = 0.123) could indicate to
adequate buffering ability and type of plant residue remaining. The soil pH at EPL is near
neutral, and would cause no barriers to successful plant growth. The pH at the forest
reference site was 6.2, which likely represents the type of vegetation present and the greatly
increased buffering capacity of the soil due to high silt content. Some plants have preferred
pH growing conditions, and the difference in soil pH between EPL and the reference site may
indicate that these species may not establish readily.
A further limitation of this study is that microbial communities were not measured or
characterised, nor was soil water measured. Further studies would benefit from focus on the
53
microbial communities given the amount of new research indicating their importance for
plant community composition. Soil water is known to affect microbial activity and some soil
physical properties (Lado and Ben-Hur 2004, Brady and Weil 2002, Fresquez et al. 1997).
However, experimental design and study budget limited the number and variety of soil
properties that could be measured and water was not considered as essential for
characterising sites. Soil texture and other chemical measurements employed were
considered sufficient proxy to determine what effect increasing soil water might have on a
sampled location. Nevertheless, any study that characterises microbiology should measure
soil water, as these two factors are intimately linked.
5.2 Patterns of Soil Properties Beneath Dominant Species
Soil properties do not vary consistently with selected dominant cover species, although weak
relationships exist for equisetum-dominated and alfalfa-dominated sites. Equisetum tends to
dominate where plant-available sulphur is highest in the upper five centimetres of soil, while
alfalfa dominates where sulphur is lowest. Post-hoc analysis with a sequential Bonferroni
results with only the case of equisetum versus alfalfa remaining significant (p = 0.0018, pH =
0.0024).
Before post-hoc corrections, alfalfa tended to be most significantly associated with
penetration resistance, likely due to higher raw data values compared to other cover types.
Three alfalfa assessment locations of five required ‘Winsorizing’ to run the MRPP. In the
case of alfalfa, values could not be obtained due to rockiness rather than soil resistance.
Rockiness was observed for many sites across EPL; however, the averaging of data between
54
fifteen attempts eliminated most of this information from subsequent analyses. While many
species grew on rocky sites, only alfalfa did so consistently which may indicate a competitive
advantage. However, post-hoc analyses did not return any significant results so the
relationship between alfalfa and high resistance measurements is statistically weak. Moreover,
rockiness encountered by a penetrometer is not necessarily a barrier to growth as roots grow
around obstructions (Atwell 1993).
There is no statistical consistency in soil properties for any one cover type. In 2007, plant
species locations do not seem dependent on soil properties. The extent of complete vegetative
cover and its patchy patterning in plant species is not the result nor accounts for differences
in soil properties. The amount of vegetation cover present supports that the soil is successful
as a growth medium that can support a variety of species.
5.3 Vegetation Cover Composition Change
Change in plant species cover composition at EPL since 1993 is limited. Grouped
rmANOVAs were performed to examine change in communities as outlined in 1993
compared with vegetation samples from similar geographic locations in 2007. Only seeded
versus unseeded species in the sweet clover-creeping red fescue (SCCRF) community
showed significant change with group differentiations. With time, both SCCRF seeded and
unseeded species became statistically similar (F(1,17) = 10.76, p = 0.004), as the seeded group
changed little in area proportion while the unseeded species increased. No significant
changes were measured for the SCCRF introduced versus native species grouping, or for
both alfalfa-creeping red fescue (ACRF) groupings. These results indicate that while changes
55
may be occurring, overall dynamics are statistically similar between designated groups. This
relative lack of change points to a degree of inertia within the system, whereby existing
vegetation is possibly filling all exploitable niches leading to competitive exclusion. The
number of unseeded species would be expected to increase over time as new niches or areas
for germination become available. However, the persistence of seeded vegetation could
indicate either lack of facilitation in ecosystem succession, or that the seeded vegetation is
most competitive. No significant ground disturbances at EPL since reclamation could mean
the opportunity for vegetation to shift is not arising (Renne et al. 2006). Use of EPL by
wildlife and its proximity to undisturbed forested land reduces the likelihood that isolation is
preventing recolonisation.
Since topsoil was not replaced during reclamation, it is possible that soil quality is retarding
community change (Martinez-Ruiz and Fernandez-Santos 2005). Reduced soil quality could
translate as fewer niches for exploitation and aggressive species persisting. However,
examining changes in community composition more closely, the top five species ranked by
proportion of area covered indicate that some shifts are occurring on an individual species
basis. These patterns are supported by other studies. For example, MacDougall et al. (2008)
found climate to be most influential on community composition, with change more prevalent
for individuals within a community than a shift towards newer species gaining competitive
advantage. The removal of undesired species and reintroduction of native plants was
necessary to restore invaded, abandoned agricultural fields in Saskatchewan. Community
dynamics appear to follow a similar pattern at EPL, whereby overall change is limited but
individual species may become more pronounced.
56
Creeping red fescue continues to dominate both SCCRF and ACRF communities. Alfalfa
area decreased in the ACRF community (0.32 in 1993 to 0.23 in 2007) but increased in the
SCCRF community (0.02 in 1993 to 0.16 in 2007), which lies directly east, suggesting that
alfalfa is moving across the extent of the EPL’s north shore. Alfalfa was used in the adjacent
western reclaimed agricultural and forage fields. Soil tests under alfalfa patches did not find
significantly different amounts of plant-available nitrogen, so this spread is not likely due to
competitive advantage from ability to fix nitrogen. Sweet clover became a less dominant
species (0.14 in 1993 to 0.06 in 2007) in the SCCRF community, although it is still common
in the area. Canada thistle forms part of the top five cover species in 2007, unlike 1993.
Visual inspection of the western portion of the north slope shows large patches of Canada
thistle, which is considered a noxious weed by Alberta Agriculture and Rural Development
(Bergstrom 2004). Native species still comprise limited area. The fact some noxious weeds
are persisting and increasing in area indicates that opportunity for invasion exists. These
plants’ presence in an unmanaged site is cause for concern, as they can spread further and
potentially invade locations adjacent to EPL.
One distinguishing characteristic of vegetation at EPL is its distinctly patchy distribution
over much of the site. Sampling design could affect results given that limited information
was available from 1993. Comparing means for relatively large geographical areas could
dilute species presence proportions. Many species exhibited focal spread on site such that
clusters of species could be missed, creating a distorted evaluation of cover. However,
general results indicate that, even after nearly fifteen years, plant communities are essentially
57
the same and visual inspection of EPL confirms the relative homogeneity of species
composition. Of the original six species used in the late 1980s seed mix, there were over
fifteen species identified in 1993 indicating, initially, colonisation occurred relatively quickly
compared to the amount of new species identified between 1993 and 2007. This finding
points to limitations of the site, likely due to competition of species already present.
Competition could be inhibiting colonisation by native species.
5.4 Ability of Diversity Indices to Capture Vegetation Dynamics for
Conservation
Diversity might be used to measure success in reclamation or restoration activities, such as
for the EPL reclamation certificate application (TransAlta Utilities Corporation 1993). The
application considers unseeded species an indication that succession is occurring and that
reclamation was successful. The document refers to ‘native invasion’, which means a species
volunteers onto the site and not that the species is a native (TransAlta Utilities Corporation
1993). The authors conclude the reproductive viability of the plant communities present in
1993. They imply that more species equates to a healthier, more successful reclaimed site.
In unmanaged, natural systems a high richness value may indicate an environment is invaded
by undesired species since individual species may exploit identical resources irrespective of
native or introduced status. (Meiners 2007, Meiners et al. 2004, Lonsdale 1999, Stohlgren et
al. 1999, Stohlgren et al. 1998, Wiser et al. 1998). Shannon’s index of diversity and evenness
is commonly used to evaluate ecosystem richness, and uses area covered by vegetation in its
calculation. Considering area makes it a more conservative measure of richness than a simple
58
count tool. The index does not weight species based on desirability in an ecosystem, which
can limit its utility for evaluating restoration efforts. However, in the instance of EPL, the
index is a useful means to measure trajectory towards an undisturbed reference. Over time, a
more diverse plant community might be expected after successful reclamation. Whether this
diversity actually captures success from a restoration ecosystem perspective is a different
argument worth considering.
Higher measurements of richness do not equate to more native species or greater area cover
of native species present on EPL. EPL is still significantly different in species composition
than the undisturbed reference site, which is comprised primarily of native species. These
findings support the weakness of using common measurements of richness for evaluating
ecosystems, and engender the question as to how to effectively and efficiently capture
vegetation dynamics through a transferable measurement.
5.5 The Landscape Architecture Perspective
Results suggest the soil on EPL is capable of supporting diverse plant species. However,
there appears to be limited change in the proportion of introduced versus native species or
seeded versus unseeded species, indicating a degree of inertia within plant community
dynamics. The result of initial reclamation design is a site that does not seem to be heading in
the direction of the reference. The original goal of reclamation may not have been the
restoration of the disturbed ecosystem. Provided there are no soil limitations, we might
expect that a successfully reclaimed site, left unmanaged and near appropriate seed sources
would eventually become compositionally similar to adjacent undisturbed locations.
59
Had a landscape architecture approach been employed during initial reclamation design, the
vegetation strategy might be more successful. Using a critical ecological aesthetic, landscape
architecture would incorporate scale, pattern and process into the design at both physical and
temporal scales. The next chapter details the critical aesthetic and the approach of landscape
architecture to site design.
5.6 Summary
This chapter presented a discussion of research results and potential limitations of the study.
The use of landscape architecture as part of reclamation design and analysis was introduced.
The next chapter provides greater detail on the potential contribution of landscape
architecture to reclamation design.
60
6.0 Landscape Architecture and Reclamation
How can landscape architecture enhance reclamation practice? A landscape architect brings a
unique perspective to reclamation design through the combination of art and science.
Through a strategy of observation and analysis, the landscape architect can perceive pattern
in the landscape and then re-create that same pattern in essence or in actuality. More than
simple cosmetic treatment, the landscape architect must understand the purpose for patterns
existing as they do – this cognisance is central to successful re-creation. This skill of
applying a critical ecological aesthetic is particularly suited to the interdisciplinary work of
landscape architecture.
Landscape can evoke place and feeling, through the visual association with the familiar or
unfamiliar. Change in large areas of land, such as through resource extraction, could lead to a
deepening feeling of loss in sense of place for our personal or collective landscape memory.
Reclamation may replace an ecosystem, or attempt to restore a native ecosystem after
disturbance. A replacement ecosystem may be a “false landscape” if it is ecologically
untenable with its surroundings (Crowe 1988). Even if all biophysical components of a native
ecosystem are replaced, it may fall short of restoration if the landscape pattern is lost. This
landscape pattern has both cultural and ecological significance: what we associate visually
with a landscape, and how a landscape facilitates process and flow of resources.
These new landscapes create a further level of complexity as part of landscape ecology; in
the short term, they will not approximate an extant, mature natural landscape disturbed
during extraction. As such, these patches become opportunity for both native species and
61
other species capable of exploiting available resources. Should undesired species take hold,
the site has potential to become a further source of spread. This situation is especially
problematic where minimal management will be performed post-certification. Both
reclamation and landscape architecture recognise that design is important for managing
outcome, and a successful design incorporates both biophysical and landscape patterning. Re-
creating landscape pattern should facilitate natural revegetation while re-establishing a sense
of place with the developing area.
EPL was reclaimed without the benefit of topsoil and its potential native seedbank. The
reclamation seed mix included persistent plant species such as creeping red fescue (Festuca
rubra) which remain dominant cover vegetation today. New species volunteered onsite from
adjacent areas and continue to dominate, such as alfalfa (Medicago sativa) from agriculture
fields and aspen (Populus spp) from undisturbed forest. This mix demonstrates the
susceptibility of a reclaimed site to its surroundings, where EPL sits at the confluence of
agricultural and undisturbed land uses. Problem species, such as Canada thistle (Cirsium
arvense) are also finding opportunities to spread onsite, and can pose future problems for
both agricultural and undisturbed lands. Current reclamation practice does emphasise
selecting the right plants for a location, often based on ecotype. It is the pattern of vegetation
across a landscape which should be of new focus for improving reclamation design.
Transition is a quick means of assessing landscape; it exists both horizontally and vertically
(Figure 14, Figure 15). Horizontal transitions can range between long and short, be distinct or
diffuse, and are characterised by species mixing. For example, dry forest may transition
62
gradually into wetter forest, which may transition rather abruptly into wetland vegetation
depending on water depth. Vertical transitions relate to canopy composition, and how that
composition varies over space. Again, a wet forest may contain tree to groundcover layers,
while it transitions to shrubs then herbaceous marsh vegetation as water level rises. It is this
mixing of transitions that gives depth and movement to the landscape.
Figure 14: Horizontal structure diagram of transition areas, showing a gradient of vegetation
mixing between two community types. For example, the transition from forest to grassland.
Community A Transition AB Community B
63
Figure 15: Vertical transition structure diagram of transition areas (based on Robinson 2004).
The new landform at EPL is more steeply rolling than its predecessor; however, it is
reminiscent of other rolling landscapes in the Aspen Parkland. Vegetation patterning on EPL
is less natural, in part due to species composition. Typical Aspen Parkland is characterised by
aspen forest copses and grassland areas without a significant shrub-like secondary canopy
(Figure 16). Aspen generally spreads through suckering, creating distinct plant transitions
with seedlings skirting larger, mature trees. As such, the transition into taller woody
vegetation is short but present, like a bell-shaped curve. Most aspen stands on EPL present
this pattern, while the willow stands do not. The willow stands are more characteristic of a
savannah landscape, where trees rise suddenly from an herbaceous understorey (Figure 17-c).
That this pattern is present with water-tolerant willow versus dryland oak is a particular
contrast. Some willows are planted in clusters, while others form a sentinel line around the
lake. Linear planting and spacing is still evident despite a fifteen year interlude, creating an
unnatural pattern which also does little for bank stability. Vegetation planted in lines with
64
gaps creates holes through which water can flow, destabilising soil surfaces on either side of
a plant, and redundancy in protection is lost should the plant not survive. Figure 17-b shows
an example of these willow plantings, with naturally spreading aspen further upslope. Figure
17-a shows the more gently rolling landscape looking westward from the lake. Although
naturalistic in appearance, a cluster of planted conifers sits mid-slope centre-photograph.
Confers would not likely be found at this type of location in the pre-disturbance Aspen
Parkland-Boreal transition site, nor are they especially characteristic of upland Aspen
Parkland. Alfalfa forms a patchy, secondary herbaceous canopy structure that is also not
characteristic of native grassland.
Figure 16: Typical native grassland structure of the Aspen Parkland, photographed in the
Rumsey Block, Alberta.
65
Figure 17: Photographs and vegetation analysis images at EPL.
Woody vegetation planted on EPL was concentrated mid to top of slope, and does not always
connect with adjacent forested areas (Figure 18). A landscape architect would endeavour to
focus planting near potential source vegetation, in order to facilitate movement of animals,
pollen and seed into preferred, like habitat. Increasing the diversity and structure of
a
b
c
66
vegetation in the riparian area would be a further goal, enabling a wider variety of landscape
patches and habitat types on one site.
Figure 18: Vegetation clusters are not contiguous on EPL, with relatively linear gaps.
6.1 Summary
Using the critical ecological aesthetic as part of design can improve reclamation plan
outcome. Added effort and analysis may lead to higher costs in the short-term reclamation
plan design; however, allowing nature to continue revegetation and ecosystem development
in the long-term is free. Landscape architects are best trained to apply a critical ecological
aesthetic to both site analysis and design. As such, a landscape architect is an excellent
addition to a reclamation team.
67
7.0 Conclusions
Reclamation design at EPL developed complete vegetative cover and soils capable of
supporting diverse species. However, measured soil and plant community change indicate
limited similarity with a reference, undisturbed ecosystem. These findings underscore the
importance of reclamation design for achieving desired ecosystem outcomes.
1. Between similarly textured soils, there is a significant difference in percent soil organic
carbon but not soil pH. Percent soil organic carbon increased significantly between
1992 and 2007 while soil pH did not change.
2. Overall, it is not possible to predict a pattern in soil properties beneath selected cover
species. Weak patterns in soil properties do exist beneath equisetum-dominated and
alfalfa-dominated cover types, although these are statistically inconsistent. Equisetum
areas tend to have higher plant-available sulphur in the upper five centimetres of soil
while alfalfa areas tend to have the lowest. Alfalfa also tends to occur more frequently
on rocky sites than other cover types.
3. Regardless of grouping variable selected for analysis, overall plant community
composition remained static on EPL since the 1993 assessments. There are fluctuations
in the proportion of cover by individual species. Native species comprise the least
amount of cover.
68
4. Shannon’s index of diversity and evenness are not suitable measurements for qualifying
desired vegetation change on unmanaged reclaimed sites in natural areas. Higher
richness does not capture extent of native vegetation presence, and as such does not
equate to EPL nearing compositional similarity of a reference location.
Landscape architecture provides a unique approach to site analysis and the use of design for
achieving a preferred outcome. Combining art and science, reclamation practice can be
enhanced through a landscape architecture perspective. Where site restoration is the goal,
reclaimed landscapes should approximate a native community in composition, pattern and
spirit.
69
8.0 References
Aide T.M., Zimmerman J.K., Pascarella J.B., Rivera L. and Marcano-Vega H. 2000. Forest regeneration in a chronosequence of tropical abandoned pastures: Implications for restoration ecology. Restoration Ecology 8:328-338.
Angers D.A. and Caron J. 1998. Plant-induced changes in soil structure: Processes and feedbacks. Biogeochemistry 42:55-72.
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Appendix: Data Tables
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Table 6: EPL soil sample laboratory analyses data. Available Nutrients Classification
Nutrients in General Soil Carbon and Nitrogen in soil (FSJ) (CEC) -Ammonium
Nitrate - N Phosphorus Potassium Sulfate-S Carbon Carbon CEC Available Available Available Available Total Inorganic Total Organic mg/kg mg/kg mg/kg mg/kg % dry weight % dry weight meq/100g Detection Limit 1 5 10 1 0.05 0.05 0.007Site Cover Type Depth (cm) Result Result Result Result Result Result ResultRF1 Forest 0-05 6 160 660 22 0.3 11 43.1RF1 Forest 5-15 1 91 330 6 0.08 1.79 15.8RF2 Forest 0-05 1 110 480 22 0.27 6.86 33.9RF2 Forest 5-15 <1 110 260 5 0.08 1.72 13.5RF3 Forest 0-05 1 88 390 20 0.31 5.88 33.4RF3 Forest 5-15 <1 84 210 4 0.07 1.25 11.4T01 Equisetum 0-05 2 16 340 37 0.52 2.78 22.1T01 Equisetum 5-15 2 5 160 14 0.6 0.92 16.1T02 Alfalfa 0-05 1 <5 150 4 0.46 2.83 23.8T02 Alfalfa 5-15 1 <5 70 2 0.47 1.56 14.9T03 Aspen 0-05 1 <5 200 5 0.43 3.89 22.2T03 Aspen 5-15 1 <5 90 4 0.46 1.07 16.5T05 Equisetum 0-05 2 8 340 42 0.61 5.94 34.8T05 Equisetum 5-15 1 <5 200 46 0.59 3.38 30.3T07 Mix 0-05 1 6 300 10 0.46 5.41 36.1T07 Mix 5-15 1 <5 120 6 0.46 4.54 35.1T08 CRF 0-05 1 12 300 26 0.45 6.8 34.5T08 CRF 5-15 1 <5 160 16 0.52 4.78 36.4T09 Equisetum 0-05 2 7 340 50 0.5 5.07 37T09 Equisetum 5-15 3 <5 160 74 0.41 4.35 33T10 Willow 0-05 1 6 400 56 0.76 7.1 37.6
83
T10 Willow 5-15 1 <5 200 88 0.9 7.45 50.1T11 Willow 0-05 1 12 380 28 0.41 2.66 29T11 Willow 5-15 1 <5 230 19 0.26 1.06 23.9T12 CRF 0-05 1 <5 140 6 0.74 1.42 13.2T12 CRF 5-15 1 <5 100 3 0.92 1.13 11.7T13 Alfalfa 0-05 1 <5 90 3 0.76 0.9 8.38T13 Alfalfa 5-15 <1 <5 80 2 0.64 0.63 8.09T14 Mix 0-05 <1 <5 50 3 0.57 0.91 10.3T14 Mix 5-15 1 <5 40 2 0.46 0.78 10.4T15 Aspen 0-05 1 7 280 20 0.69 4.81 23.3T15 Aspen 5-15 1 <5 130 11 0.48 2.29 15.2T16 Willow 0-05 1 15 350 52 0.98 4.98 28.2T16 Willow 5-15 <1 <5 120 11 0.5 1.2 10.3T17 Aspen 0-05 2 17 570 31 0.59 4.76 30.4T17 Aspen 5-15 1 7 380 13 0.6 2.58 21.6T18 Willow 0-05 1 10 310 58 0.92 6.7 32.4T18 Willow 5-15 2 <5 130 30 0.82 3.48 24.8T19 CRF 0-05 1 12 290 78 0.94 4.99 31.2T19 CRF 5-15 1 <5 150 45 0.49 1.63 28.6T20 Mix 0-05 1 10 270 43 0.75 4.12 28.1T20 Mix 5-15 1 <5 150 34 0.47 2.21 21.2T21 Brome 0-05 3 16 310 27 0.78 6.36 36.8T21 Brome 5-15 2 6 210 26 0.6 4.35 29.9T22 Equisetum 0-05 1 18 340 96 0.59 5.15 27.8T22 Equisetum 5-15 1 <5 140 49 0.41 1.77 16T23 Alfalfa 0-05 7 11 320 6 0.51 4.57 28T23 Alfalfa 5-15 2 <5 100 2 0.47 3.31 23.6T24 Willow 0-05 1 16 340 30 1.09 4.32 25.4T24 Willow 5-15 1 5 160 9 0.32 1.02 18.6T25 CRF 0-05 1 20 370 25 0.45 4.96 29.3
84
T25 CRF 5-15 1 7 160 10 0.37 1.43 20.2T26 Brome 0-05 1 26 540 15 0.72 5.58 28.8T26 Brome 5-15 1 8 240 6 0.4 1.66 20.9T27 Aspen 0-05 2 51 450 42 0.42 3.9 27.5T27 Aspen 5-15 2 32 290 30 0.15 1.27 27.8T28 Alfalfa 0-05 9 6 350 6 0.54 2.38 23.7T28 Alfalfa 5-15 2 <5 200 3 0.49 0.77 17.6T29 Brome 0-05 1 36 380 13 0.28 3.73 15.5T29 Brome 5-15 1 29 220 10 0.29 1.82 14.1T30 CRF 0-05 2 16 310 5 0.16 2.6 16.7T30 CRF 5-15 1 9 160 3 0.11 0.92 8.61T31 Mix 0-05 1 19 720 26 0.83 8.55 33.2T31 Mix 5-15 1 9 300 10 0.33 3.36 20.2T33 Alfalfa 0-05 2 <5 570 10 1.86 8.4 44.8T33 Alfalfa 5-15 2 <5 390 7 4.15 6.67 49T34 Aspen 0-05 1 10 380 8 0.88 5.81 24.1T34 Aspen 5-15 1 <5 230 4 0.71 3.36 20.2T35 Brome 0-05 1 28 440 20 0.36 5.91 21.4T35 Brome 5-15 <1 15 210 6 0.16 1.66 15.7T36 Mix 0-05 1 24 340 8 0.28 3.59 22.4T36 Mix 5-15 1 11 180 3 0.18 1.16 19.8T37 Alfalfa 0-05 1 7 260 32 0.73 4.43 26.7T37 Alfalfa 5-15 1 <5 140 15 0.56 3 19.7T38 Brome 0-05 1 7 330 10 0.44 2.58 23.6T38 Brome 5-15 1 <5 220 4 0.45 1.3 17.4
85
Table 6 con’t. Physical and Aggregate Properties Soil Particle Size Analysis - GS pH Clay Sand Silt Texture v Saturated Paste % by weight pHSite Cover Type Depth (cm) Result Result Result Result ResultRF1 Forest 0-05 10 36 54 Silt Loam 6.6RF1 Forest 5-15 11 31.4 57.6 Silt Loam 6.2RF2 Forest 0-05 11.2 38.8 50 Silt Loam 6.2RF2 Forest 5-15 13 30.4 56.6 Silt Loam 6RF3 Forest 0-05 9 35 56 Silt Loam 6.1RF3 Forest 5-15 12 36.4 51.6 Silt Loam 5.9T01 Equisetum 0-05 11 69.4 19.6 Sandy Loam 7.2T01 Equisetum 5-15 10.4 76.4 13.2 Sandy Loam 7.4T02 Alfalfa 0-05 11 73.4 15.6 Sandy Loam 7.3T02 Alfalfa 5-15 12 75.4 12.6 Sandy Loam 7.5T03 Aspen 0-05 10 77.4 12.6 Sandy Loam 7.5T03 Aspen 5-15 12 79.4 8.6 Sandy Loam 7.6T05 Equisetum 0-05 25 53.4 21.6 Sandy Clay Loam 7.5T05 Equisetum 5-15 26.4 52.4 21.2 Sandy Clay Loam 7.3T07 Mix 0-05 16 63.4 20.6 Sandy Loam 7.4T07 Mix 5-15 17 68.4 14.6 Sandy Loam 7.4T08 CRF 0-05 19.6 57.4 23 Sandy Loam 7.2T08 CRF 5-15 24 56.7 19.3 Sandy Clay Loam 7.3T09 Equisetum 0-05 18.3 59 22.7 Sandy Loam 7.4T09 Equisetum 5-15 15 70.4 14.6 Sandy Loam 7.2T10 Willow 0-05 24 52.8 23.2 Sandy Clay Loam 7.4T10 Willow 5-15 19 62.4 18.6 Sandy Loam 7.1T11 Willow 0-05 30 45 25 Sandy Clay Loam 7.4T11 Willow 5-15 27.5 53.5 19 Sandy Clay Loam 7.4
86
T12 CRF 0-05 11 76.4 12.6 Sandy Loam 7.7T12 CRF 5-15 10 76.4 13.6 Sandy Loam 7.6T13 Alfalfa 0-05 7.6 83.4 9 Loamy Sand 7.8T13 Alfalfa 5-15 9.2 80 10.8 Loamy Sand 7.5T14 Mix 0-05 8.6 81.4 10 Loamy Sand 7.5T14 Mix 5-15 8.2 82.8 9 Loamy Sand 7.7T15 Aspen 0-05 16.8 66 17.2 Sandy Loam 7.4T15 Aspen 5-15 16 71.8 12.2 Sandy Loam 7.5T16 Willow 0-05 26.8 46.4 26.8 Sandy Clay Loam 7.4T16 Willow 5-15 15.2 73 11.8 Sandy Loam 7.5T17 Aspen 0-05 26.3 49 24.7 Sandy Clay Loam 7.4T17 Aspen 5-15 32 44 24 Clay Loam 7.4T18 Willow 0-05 22.8 57.2 20 Sandy Clay Loam 7.6T18 Willow 5-15 18 65.3 16.7 Sandy Loam 7.5T19 CRF 0-05 37 38.5 24.5 Clay Loam 7.3T19 CRF 5-15 34 42.7 23.3 Clay Loam 7.4T20 Mix 0-05 19.3 59.7 21 Sandy Loam 7.5T20 Mix 5-15 18.2 63 18.8 Sandy Loam 7.5T21 Brome 0-05 21.6 48.4 30 Loam 7.4T21 Brome 5-15 25.6 46.4 28 Sandy Clay Loam 7.4T22 Equisetum 0-05 22 52 26 Sandy Clay Loam 7.4T22 Equisetum 5-15 21.8 56.8 21.4 Sandy Clay Loam 7.6T23 Alfalfa 0-05 12.7 67.3 20 Sandy Loam 7.6T23 Alfalfa 5-15 14 69.4 16.6 Sandy Loam 7.7T24 Willow 0-05 23.5 39.5 37 Loam 7.8T24 Willow 5-15 28.8 40.6 30.6 Clay Loam 7.9T25 CRF 0-05 24 46.8 29.2 Loam 7.5T25 CRF 5-15 24.2 51.4 24.4 Sandy Clay Loam 7.6T26 Brome 0-05 24 40.5 35.5 Loam 7.4T26 Brome 5-15 27.2 42.8 30 Clay Loam 7.7
87
T27 Aspen 0-05 23.5 34.5 42 Loam 7.5T27 Aspen 5-15 27.4 29.2 43.4 Clay Loam 7.4T28 Alfalfa 0-05 23.7 48.3 28 Sandy Clay Loam 7.7T28 Alfalfa 5-15 30 41.6 28.4 Clay Loam 7.6T29 Brome 0-05 9.7 68.3 22 Sandy Loam 7.2T29 Brome 5-15 9.8 72 18.2 Sandy Loam 7.4T30 CRF 0-05 4.8 76.4 18.8 Loamy Sand 6.8T30 CRF 5-15 7.6 81.4 11 Loamy Sand 7.3T31 Mix 0-05 8.5 71 20.5 Sandy Loam 7T31 Mix 5-15 9.8 78 12.2 Sandy Loam 7T33 Alfalfa 0-05 18.3 63 18.7 Sandy Loam 7T33 Alfalfa 5-15 16.8 62.4 20.8 Sandy Loam 6.6T34 Aspen 0-05 9.7 71.7 18.7 Sandy Loam 7.4T34 Aspen 5-15 13.4 70.4 16.2 Sandy Loam 7.4T35 Brome 0-05 17 51 32 Loam 6.4T35 Brome 5-15 16.2 59.6 24.2 Sandy Loam 6.6T36 Mix 0-05 18.4 56.4 25.2 Sandy Loam 6.9T36 Mix 5-15 23.4 55.8 20.8 Sandy Clay Loam 7.2T37 Alfalfa 0-05 18.2 64.2 17.6 Sandy Loam 7.3T37 Alfalfa 5-15 20.4 63.6 16 Sandy Clay Loam 7.5T38 Brome 0-05 27.2 53.4 19.4 Sandy Clay Loam 7.3T38 Brome 5-15 24 58.4 17.6 Sandy Clay Loam 7.4
Table 7: List of plant symbols and associated species information recorded during vegetation assessments. Symbol Latin Name Authority USDA_PLANTS_Code Common Name AgrElo Agropyron elongatum (Host) Beauv. AGEL3 Tall Wheat Grass AgrSub Agropyron subsecundum (Link) A.S. Hitchc. AGSU Slender Wheat Grass AmeAln Amelanchier alnifolia (Nutt.) Nutt. ex M. Roemer AMAL2 Saskatoon Serviceberry AraNud Aralia nudicaulis L. ARNU2 Wild Sarsaparilla
88
As Populus sp. L. POPUL Aspen As_u Populus sp. L. POPUL Aspen AstCil Aster ciliolatus Lindl. ASCI Lindley's Aster AstCon Aster conspicuus Lindl. ASCO3 Western Showy Aster BetPap Betula papyrifera Marsh. BEPA Paper Birch BroIne Bromus inermis Leyss. BRIN2 Smooth Brome CalCan Calamagrostis canadensis (Michx.) Beauv. CACA4 Blue Joint Grass CanThi Cirsium arvense (L.) Scop. CIAR4 Canada Thistle CorCan Cornus canadensis L. COCA13 Bunchberry Dogwood CorCor Corylus cornuta Marsh. COCO6 Beaked Hazelnut CorSto Cornus stolonifera Michx. COST4 Red Osier Dogwood CRF Festuca rubra L. FERU2 Creeping Red Fescue CRF_u Festuca rubra L. FERU2 Creeping Red Fescue CWG Agropyron cristatum (L.) Gaertn. AGCR Crested Wheat Grass DacGlo Dactylis glomerata L. DAGL Orchard Grass EpiAng Epilobium angustifolium L. EPAN2 Fireweed EquArv Equisetum arvense L. EQAR Common Horsetail FesAru Festuca arundinacea Schreb. FEAR3 Tall Fescue FraVir Fragaria virginiana Duchesne FRVI Wild Strawberry GalBor Galium boreale L. GABO2 Northern Bedstraw GalTri Galium trifidum L. GATR2 Threepetal Bedstraw LatVen Lathyrus venosus Muhl. ex Willd. LAVE Veiny Peavine LinBor Linnaea borealis L. LIBO3 Twinflower LonDio Lonicera dioica L. LODI2 Twining Honeysuckle MaiCan Maianthemum canadense Desf. MACA4 Canada Mayflower MedSat Medicago sativa L. MESA Alfalfa MedSat_u Medicago sativa L. MESA Alfalfa Mel_sp Melilotus P. Mill. MELIL Sweet Clover MerPan Mertensia paniculata (Ait.) G. Don MEPA Tall Bluebells OryAsp Oryzopsis asperifolia Michx. ORAS Rough-leaved Rice Grass
89
OsmDul Osmorhiza dulcis Raf. n/a Sweet Sisely PetPal Petasites palmatus (Ait.) Gray PEPA31 Palmate Leaf Coltsfoot PhaAru Phalaris arundinacea L. PHAR3 Reed Canary Grass PhlPra Phleum pratense L. PHPR3 Timothy Grass PoaPra Poa pratensis L. POPR Kentucky Blue Grass PopTre Populus tremuloides Michx. POTR5 Termbling Aspen PruPen Prunus pensylvanica L. f. PRPE2 Pin Cherry PyrSec Pyrola secunda L. PYSE One-sided Wintergreen RosAci Rosa acicularis Lindl. ROAC Prickly Rose RubIda Rubus idaeus L. RUID American Red Raspberry RubPub Rubus pubescens Raf. RUPU Dewberry SheCan Shepherdia canadensis (L.) Nutt. SHCA Canada Buffaloberry SowThi Sonchus arvensis L. SOAR2 Field Sow Thistle SymAlb Symphoricarpos albus (L.) Blake SYAL Common Snowberry TarOff Taraxacum officinale G.H. Weber ex Wiggers TAOF Common Dandelion ThiInt Thinopyrum intermedium (Host) Barkworth & D.R. Dewey THIN6 Intermediate Wheat GrassTri_sp Trifolium L. TRIFO Clover Tri_sp_u Trifolium L. TRIFO Clover VibEdu Viburnum edule (Michx.) Raf. VIED High-Bush Cranberry VicAme Vicia americana Muhl. ex Willd. VIAM American Vetch VioCan Viola canadensis L. VICA4 Canada Violet Wi Salix L. SALIX Willow Wi (Bebb's) Salix bebbiana Sarg. SABE2 Bebb's Willow
u: denotes understorey vegetation hidden beneath a herbaceous layer. Table 8: EPL square quadrat averaged vegetation assessment data. Site Cover Type EquArv CanThi SowThi CRF CRF_u Tri_sp Tri_sp_u MedSat MedSat_u DacGlo Mel_sp TarOffT01 Equisetum 90.00 0.67 6.00 3.33 76.67 0.00 1.67 0.00 0.00 0.00 0.00 0.00T02 Alfalfa 0.00 0.00 0.00 5.67 81.67 0.00 0.00 94.33 11.67 0.00 0.00 0.00
90
T03 Aspen 0.00 0.00 0.00 13.33 0.00 1.67 0.00 15.67 0.00 0.00 5.33 0.00T05 Equisetum 94.33 0.00 0.00 3.00 97.00 0.00 0.00 2.67 3.00 0.00 0.00 0.00T07 Mix 7.67 16.00 3.00 58.67 0.00 0.00 0.00 3.00 0.00 0.00 0.00 11.67T08 CRF 2.67 0.00 0.00 95.67 0.00 0.00 0.00 0.67 0.00 0.00 0.00 0.00T09 Equisetum 99.67 0.00 0.00 0.33 13.33 0.00 0.00 0.00 2.33 0.00 0.00 0.00T10 Willow 86.67 0.00 0.00 1.67 10.00 0.00 0.00 1.67 0.00 0.00 0.00 0.00T11 Willow 58.33 0.00 0.00 3.33 5.00 0.00 0.00 1.67 0.00 0.00 0.00 10.67T12 CRF 0.00 0.00 0.00 95.00 0.00 0.00 0.00 3.00 0.00 0.00 0.00 0.00T13 Alfalfa 0.00 0.00 0.00 2.67 50.00 0.00 0.00 96.33 0.00 0.00 0.00 0.00T14 Mix 0.67 0.00 6.67 57.67 0.00 0.00 0.00 26.67 0.00 0.00 0.00 8.33T15 Aspen 28.33 0.00 0.00 38.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 6.67T16 Willow 31.00 0.33 0.00 59.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.00T17 Aspen 35.00 0.00 3.67 23.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00T18 Willow 53.33 0.00 0.00 41.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00T19 CRF 0.00 0.33 2.33 91.67 0.00 0.00 0.00 0.00 0.00 0.67 0.00 0.00T20 Mix 0.67 6.67 11.67 61.67 0.00 0.00 0.00 2.33 0.00 0.00 0.00 0.00T21 Brome 1.67 0.00 1.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33T22 Equisetum 91.67 1.00 1.67 5.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00T23 Alfalfa 0.00 1.33 0.00 1.67 81.67 0.00 0.00 97.00 0.00 0.00 0.00 0.00T24 Willow 1.33 0.33 0.00 89.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33T25 CRF 0.00 0.33 0.67 93.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00T26 Brome 0.00 0.00 2.33 7.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00T27 Aspen 0.00 0.67 0.67 96.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.33T28 Alfalfa 0.00 1.67 0.00 1.33 68.33 0.00 0.00 96.67 0.00 0.33 0.00 0.00T29 Brome 0.00 2.00 0.67 5.33 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00T30 CRF 0.00 0.33 0.00 93.33 0.00 0.00 0.00 0.50 0.00 0.00 0.33 0.67T31 Mix 12.67 0.00 0.00 35.00 0.00 0.00 0.00 11.33 0.00 0.00 1.67 5.00T33 Alfalfa 0.00 0.00 0.00 4.00 65.00 0.00 0.00 95.67 0.00 0.00 0.00 0.00T34 Aspen 0.00 0.00 0.00 56.67 20.00 0.00 0.00 38.33 0.00 0.00 0.00 0.00T35 Brome 0.00 0.00 0.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00 0.00
91
T36 Mix 0.00 0.00 0.00 73.33 0.00 9.33 0.00 0.00 0.00 0.00 0.00 5.00T37 Equisetum 92.00 0.67 5.00 1.67 0.00 0.00 0.00 0.33 0.00 0.00 0.00 0.00T38 Brome 0.33 1.17 0.17 5.00 0.00 3.00 0.00 0.00 0.00 0.00 0.00 0.00
Table 8 con’t Site Cover Type CWG VicAme EpiAng BroIne Wi As Litter Depth Thatch (cm)T01 Equisetum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.33T02 Alfalfa 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.83T03 Aspen 0.00 0.00 0.00 0.00 0.00 100.00 68.33 1.50T05 Equisetum 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.33T07 Mix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.00T08 CRF 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.33T09 Equisetum 0.00 0.00 0.00 0.00 0.00 0.00 15.00 1.17T10 Willow 0.00 0.00 0.00 0.00 90.00 0.00 51.67 1.00T11 Willow 0.00 0.00 0.00 0.00 90.00 0.00 35.00 2.00T12 CRF 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.33T13 Alfalfa 1.00 0.00 0.00 0.00 0.00 0.00 0.00 2.83T14 Mix 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.67T15 Aspen 0.00 0.00 0.00 0.00 0.00 95.00 50.00 2.00T16 Willow 0.00 0.00 0.00 0.00 100.00 0.00 55.00 1.33T17 Aspen 0.00 0.67 9.00 0.00 0.00 97.00 78.33 1.00T18 Willow 0.00 0.00 0.00 0.00 95.00 0.00 36.67 3.33T19 CRF 0.00 0.00 0.00 0.00 0.00 0.00 0.00 5.33T20 Mix 0.00 0.00 0.00 1.67 0.00 0.00 0.00 4.33T21 Brome 0.00 0.00 0.00 91.67 0.00 0.00 0.00 6.33T22 Equisetum 0.00 0.33 0.00 0.00 0.00 0.00 0.00 2.83T23 Alfalfa 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.33T24 Willow 0.00 0.00 0.00 0.00 97.00 0.00 46.67 2.00T25 CRF 0.00 0.00 0.00 0.00 0.00 0.00 0.00 2.17T26 Brome 0.00 0.00 0.00 90.00 0.00 0.00 0.00 4.83
92
T27 Aspen 0.00 0.00 0.00 1.67 0.00 95.00 45.00 1.17T28 Alfalfa 0.00 0.00 0.00 0.00 0.00 0.00 0.00 4.00T29 Brome 0.00 0.33 0.00 90.00 0.00 0.00 0.00 5.00T30 CRF 0.00 0.00 0.67 0.00 0.00 0.00 0.00 1.00T31 Mix 0.00 0.00 1.33 0.33 0.00 0.00 0.00 1.67T33 Alfalfa 0.33 0.00 0.00 0.00 0.00 0.00 0.00 1.83T34 Aspen 0.00 0.00 0.00 0.00 0.00 93.00 48.33 1.67T35 Brome 0.33 0.00 0.00 96.67 0.00 0.00 0.00 4.50T36 Mix 0.00 1.67 0.00 3.33 0.00 0.00 0.00 2.33T37 Equisetum 0.00 0.33 0.00 0.00 0.00 0.00 0.00 1.33T38 Brome 0.00 0.00 0.00 90.00 0.00 0.00 0.00 2.00
Table 9: Reference forest site square quadrat averaged vegetation assessment data. Site Cover Type CorCor Wi_BEBB RosAci SheCan SymAlb AmeAln RubIda PruPen PopTre VibEdu CorSto BetPapRF1 Forest 10.00 1.00 25.00 0.00 15.00 1.00 1.00 1.00 1.00 0.50 0.00 1.00RF2 Forest 5.00 0.00 1.00 0.00 0.00 1.00 0.00 20.00 20.00 0.50 5.00 20.00RF3 Forest 15.00 0.00 15.00 0.50 15.00 1.00 0.00 5.00 25.00 0.00 0.00 0.00
Table 9 con’t Site Cover Type LonDio FraVir GalBor MerPan PyrSec CorCan AgrSub OryAsp CalCan AstCil AstCon RubPubRF1 Forest 0.00 8.33 5.00 0.67 3.67 2.00 1.67 3.33 9.00 0.00 3.67 0.33RF2 Forest 0.50 6.33 5.67 2.33 2.67 8.33 0.00 0.00 1.00 3.33 0.00 1.67RF3 Forest 0.00 5.33 1.33 2.33 4.67 6.67 0.67 0.67 0.00 3.33 5.33 0.33
Table 9 con’t Site Cover Type PetPal VioCan OsmDul GalTri LatVen MaiCan AraNud LinBor VicAme Litter Depth Thatch (cm) BG RF1 Forest 0.33 1.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 60.00 4.33 40.00RF2 Forest 1.67 0.67 0.67 1.00 0.67 1.00 1.67 0.67 0.00 73.33 4.33 26.67RF3 Forest 1.67 0.00 0.00 0.00 0.00 0.00 0.00 0.00 1.33 63.33 2.33 36.67
BG: area bare ground
93
Table 10: EPL circular quadrat vegetation assessment data. Site Cover Type PopTre EquArv CanThi SowThi CRF Tri_sp MedSat DacGlo Mel_sp TarOff CWG VicAme BroIneDD9 GEN 0 1 1 0.01 20 0.01 20 0 2 1 0 0 0ODD GEN 0 0 0 0 3 2 25 0 0 0 0 0 0EE10 GEN 1 0.01 0.01 0 20 0.01 0 0 1 0.01 0 0.01 0EE9 GEN 0 0 0 0 10 0 10 0 0 0.01 0.01 0 0EE8 GEN 0 0 1 5 15 0 0 0 0.01 0 0.01 0 0EE7 GEN 0 0 0 0 15 0 0 0 0 0.01 0.01 0 0EE6 GEN 0 0 0 10 20 0 20 0 1 0 0 0 0EE5 GEN 0 0 0.01 0 5 0 15 0 2 0 0 0 0FF7 GEN 0 0 0 0.01 15 0 2 0 0.01 0.01 0.01 0 0FF6 GEN 0 2 0 1 15 0 0 0 1 0.01 1 0 0FF5 GEN 0 0 2 2 20 0 0 0 0.01 0 0 0 0FF4 GEN 0 0 0 0 25 0 0 1 0.01 0 0.01 0 0GG4 GEN 0 0 20 0 20 0 0 0 3 0 0.01 0 0GG3 GEN 0 0 1 0 20 10 0 1 1 1 1 5 0GG2 GEN 0 0 0.01 0 20 4 0.01 0 8 0 0 3 2HH5 GEN 0 0 1 0 20 0 0 0 3 3 0 1 0HH4 GEN 0 0 0 0 20 0 0 0 15 0 2 0 0HH3 GEN 0 5 1 0 75 10 5 0 5 0 0 0 0HH2 GEN 0 0 10 0 0 0 40 0 1 0 0 0 25HH1 GEN 0 0 15 0.01 5 0 30 0 0 0.01 0 1 30
Table 10 con’t Site Cover Type As As_u ThiInt AgrElo PhaAru FesAru PhlPra PoaPra Average Depth Thatch (cm) BG DD9 GEN 0 0 0 0 0 0 0 0 4.9 0ODD GEN 2 20 0 0 0 0 0 0 2.2 0EE10 GEN 0.01 0 0.01 0 0 0 0 0 4.6 0.01EE9 GEN 0 0 0 0 25 0 0 0 5.1 0EE8 GEN 0 0 0 0 3 0 0 0 7.1 0
94
EE7 GEN 0 0 0 3 3 0 0 0 6.1 0EE6 GEN 0 0 0 0 0 0 0 0 4.5 0EE5 GEN 0 0 0.01 0 0 0.01 0 0 3.1 0FF7 GEN 0 0 0 0 0 0 0 0 5.5 0.01FF6 GEN 0 0 0 0 0 0 0 0 4.9 0FF5 GEN 0 0 0 0 0 0 0 0 5.7 0FF4 GEN 0 0 0 0 0 0 0 0 6.6 0.01GG4 GEN 0 0 0 0 0 0 0 0 4.1 0GG3 GEN 0 0 0 0 0 0 0 0 3.9 0GG2 GEN 0 0 0 0 0.01 0 1 5 4.7 0HH5 GEN 0 0 0 0 0 0 0 0 4.8 0.01HH4 GEN 0 0 0 0 0 0 0 0 6.5 0HH3 GEN 0 0 0 0 0 0 0.01 0 2.7 0HH2 GEN 0 0 0 0 0 0 0 1 4 0HH1 GEN 0 0 0 0 0 0 0 5 5.8 0
BG: area bare ground Table 11: Location of EPL and reference sites used for square quadrat assessments. Site numbers reflect those locations sampled.
Site Position Altitude Cover Type Site Position Altitude Cover Type T01 11 U 667973 5940245 790 m Equisetum T21 11 U 667384 5940419 792 m Brome T02 11 U 667974 5940262 791 m Alfalfa T22 11 U 667298 5940439 789 m Equisetum T03 11 U 667981 5940274 791 m Aspen T23 11 U 667276 5940459 788 m Alfalfa T05 11 U 667920 5940364 791 m Equisetum T24 11 U 667265 5940457 789 m Willow T07 11 U 667863 5940355 791 m Mix T25 11 U 667254 5940456 788 m CRF T08 11 U 667859 5940363 792 m CRF T26 11 U 667248 5940449 782 m Brome T09 11 U 667842 5940337 788 m Equisetum T27 11 U 667116 5940461 787 m Aspen T10 11 U 667842 5940344 786 m Willow T28 11 U 667064 5940471 781 m Alfalfa T11 11 U 667820 5940326 785 m Willow T29 11 U 667066 5940480 786 m Brome T12 11 U 667810 5940349 789 m CRF T30 11 U 666770 5940558 785 m CRF T13 11 U 667803 5940352 793 m Alfalfa T31 11 U 666765 5940559 789 m Mix T14 11 U 667756 5940362 793 m Mix T33 11 U 666751 5940572 790 m Alfalfa
95
T15 11 U 667739 5940371 791 m Aspen T34 11 U 666742 5940580 789 m Aspen T16 11 U 667707 5940354 788 m Willow T35 11 U 666642 5940593 786 m Brome T17 11 U 667453 5940439 799 m Aspen T36 11 U 666647 5940598 786 m Mix T18 11 U 667455 5940406 792 m Willow T37 11 U 667734 5940369 793 m Equisetum T19 11 U 667417 5940407 793 m CRF T38 11 U 667949 5940221 780 m Brome T20 11 U 667335 5940422 787 m Mix RF1 11 U 665879 5941530 RF2 11 U 665873 5941543 RF3 11 U 665871 5941586
Table 12: Location of EPL sites used for circular quadrat assessments. Site Position Site Position II1 11 U 666641 5940700 EE5 11 U 667342 5940352 HH1 11 U 666462 5940573 EE6 11 U 667440 5940360 HH2 11 U 666562 5940592 EE7 11 U 667545 5940371 HH3 11 U 666659 5940602 EE8 11 U 667647 5940385 HH4 11 U 666760 5940614 EE9 11 U 667845 5940405 HH5 11 U 666861 5940624 EE10 11 U 667959 5940416 GG3 11 U 666996 5940537 ODD 11 U 667979 5940317 GG2 11 U 666887 5940525 DD9 11 U 667995 5940217 GG4 11 U 667107 5940548 FF3 11 U 667010 5940423 FF4 11 U 667119 5940440 FF5 11 U 667221 5940447 FF6 11 U 667329 5940457 FF7 11 U 667431 5940464