soil and vegetation change on a coal mine 15 years …

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.

i

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.

ii

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

iv

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

v

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.

2

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.

3

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.

4

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

5

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.

6

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

7

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

8

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).

9

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

10

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

11

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

12

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.

13

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.

14

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

15

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.

16

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.

17

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.


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


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.


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


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.



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.

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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.

Appleton J. 1975. The Experience of Landscape. Wiley, London, UK. 293 pp.

Ash H.J., Gemmell R.P. and Bradshaw A.D. 1994. The introduction of native plant-species on industrial-waste heaps - a test of immigration and other factors affecting primary succession. Journal of Applied Ecology 31:74-84.

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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

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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

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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