RESPONSE OF SOIL GEOCHEMICAL PROPERTIES AND MICROBIAL
COMMUNITIES TO LONG-TERM STORAGE IN TWO MINE OPERATIONS IN THE
INTERIOR OF BRITISH COLUMBIA: IMPLICATIONS FOR RESTORATION PRACTICES
by
Ashley Fischer
B.Sc. (Hons) University of Waterloo, 2017
A thesis submitted in partial fulfillment of the requirements for the degree of:
MASTER OF SCIENCE IN ENVIRONMENTAL SCIENCE

THOMPSON RIVERS UNIVERSITY
Kamloops, British Columbia
January 2023
Thesis Examining Committee:
Lauchlan Fraser (PhD), Professor and Thesis Supervisor,
Department of Natural Resource Sciences, Thompson Rivers University
Dr. Jonathan van Hamme (PhD), Professor and Committee Member,
Department of Biological Sciences, Thompson Rivers University
Dr. Wendy Gardner (PhD), Professor and Committee Member,
Department of Natural Resource Sciences, Thompson Rivers University
© Ashley Fischer, 2023

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ABSTRACT
Mining activities are often severely disruptive to the landscape and a major barrier to
reclamation after mining is lack of quality topsoil. This project addresses knowledge gaps in the
industry by exploring the compositional nature of topsoil stockpiles and their ability to facilitate
post-mining revegetation after long-term storage. To do this, we conducted an extensive profile
characterization of two topsoil stockpiles in the interior of British Columbia, where soil
geochemical properties and microbial communities with high-throughput sequencing were
investigated. Both stockpiles show depleted soil quality and significant changes compared to
reference soils. Importantly, there were declines in microbial diversity, major shifts in
community structure, and a reduction in soil nutrients with increasing stockpile depths in one of
the stockpiles. These results highlight the important influence of topsoil-stockpile height on
geochemical properties and microbial communities in the soil, which ultimately influences the
success of restoration. This research can help industry to optimize restoration and expediate
recovery in their mine-closure practices and provides insights into the general structure of the
microbiome existing across a gradient in severely disturbed mining soils.

Keywords: Mine reclamation, soil microbial ecology, ecosystem restoration, soil response to
disturbance, high-throughput sequencing

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ACKNOWLEDGEMENTS
Firstly, I would like to thank my supervisor Dr. Lauchlan Fraser, for providing me with the
opportunity to conduct this research as well as giving me support and guidance; Dr. Jonathan
Van Hamme and Dr. Eric Bottos for their generous support and expertise throughout my project,
particularly throughout sequencing and data analysis; Dr. Jayprakash Singh for editing and
providing microbial data analysis expertise; and Dr. Wendy Gardner for taking the time to be on
my committee.
Many thanks to the team at New Gold’s New Afton Mine, Scott Davidson, Luke Holdstock,
and Crystal Simon for their support in this project, helping with sampling and allowing us access
to the site; and to Steve Jennings, Lloyd Thomas, and Jodie Oakley at Barkerville Gold Mine’s
QR Mill for their support in this project, hosting us on site, and helping with sampling.
Financial support for this project was provided by New Afton Mine, Barkerville Gold Mine,
Geoscience BC, and through a Natural Sciences and Engineering Research Council of Canada
Industrial Research Chair to L Fraser in partnership with Greater Vancouver Sewerage and
Drainage District, Genome BC, Geoscience BC, New Gold Inc, Teck, Real Estate Foundation of
BC, Arrow Transportation, Trans Mountain Corp, BC Cattlemen’s Association. Furthermore, I
would like to thank the Graduate Studies Department at Thompson Rivers University and the BC
Technical and Research Committee on Reclamation for recognizing my research and rewarding
me through additional scholarships.
I would like to specially thank Breanne McAmmond for her exceptional support and patience
while guiding me in the TRUGen lab; Chantalle Gervan for her friendship and assisting me in
the lab, in the field, and with analysis; Matthew Coghill and Solenn Vogel for their help in the
lab or in the field. I am grateful to everyone in the Fraser Lab and everyone in the MSc
Environmental Science program for their friendship and support throughout my project and
making my time at Thompson Rivers University an incredible experience.
Finally, my deepest gratitude to my friends and family for their encouragement and
assistance throughout my academic journey; and most of all to my husband, Devin Fischer for
his unwavering support and companionship for the past 10 years.

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LIST OF FIGURES
Figure 2.1 Aerial image of the New Afton mine site, including a close-up of the topsoil
stockpile of interest that shows the locations of four soil core samples. Map generated in QGIS®
with Bing VirtualEarth background. ............................................................................................. 23
Figure 2.2 Photos showing a) sampling of the topsoil stockpile in New Afton via auger driller
and b) soil collection set up where soil was placed into Whirl Pak© bags and Microcentrifuge
tubes. ............................................................................................................................................. 23
Figure 2.3 Pictures showing a) sampling of the topsoil stockpile in QR mill via mechanical
digger and b) a sample hole exposing part of the topsoil profile .................................................. 24
Figure 2.4 Aerial image of the QR mill, including a close-up of the topsoil stockpile of
interest that shows the locations of three soil pit samples. Map generated in QGIS® with Bing
VirtualEarth background. .............................................................................................................. 24
Figure 2.5 PCA plots showing differences in soil chemical properties at New Afton with
changing stockpile depth. PC1 accounts for 34.7% and PC2 accounts for 19.7% of variation
observed between soil samples. .................................................................................................... 28
Figure 2.6 PCA plots showing differences in soil chemical properties at QR mill with
changing stockpile depth. PC1 accounts for 32.9% and PC2 accounts for 18.5% of variation
observed between soil samples. .................................................................................................... 29
Figure 2.7 Boxplots showing differences in geochemical variables with stockpile depth at the
New Afton site. ............................................................................................................................. 34
Figure 2.8 Linear regression plots showing changes in geochemical variables with stockpile
depth at the QR mill site. The blue lines represent a linear model and the shaded area in grey
represents the 95% confidence intervals. ...................................................................................... 35
Figure 3.1 Coloured barplots showing relative proportion of a) bacterial and b) fungal phyla
by stockpile depth in New Afton. Phyla categorized in the “Other” group are composed of
combined phyla with <1% relative abundance. ............................................................................ 55

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Figure 3.2 Coloured barplots showing relative proportion of the top 99% a) bacterial and b)
fungal phyla by stockpile depth in QR mill. Phyla categorized in the “Other” group are
composed of combined phyla with <1% relative abundance........................................................ 56
Figure 3.3 Bar plots showing observed and Shannon diversity measures of a) bacterial and b)
fungal communities from varying stockpile depth intervals and reference soil from New Afton.
....................................................................................................................................................... 57
Figure 3.4 Regression plots showing observed and Shannon diversity measures of a) bacterial
and b) fungal communities from varying stockpile depth intervals and reference soil from QR
mill. The blue lines represent linear models and the shaded area in grey represents a 95%
confidence interval. ....................................................................................................................... 58
Figure 3.5 Non-metric multidimensional scaling (NMDS) analysis for New Afton. NMDS
scatterplot of samples representing the OTU community composition of soil a) bacterial and b)
fungal biota from a reference soil and at four depths from four soil core samples from the New
Afton stockpile. The distance between points indicated the degree of difference based on
Unweighted Unifrac similarities of OTU composition in each sample. ....................................... 59
Figure 3.6 Non-metric multidimensional scaling (NMDS) analysis for QR mill. NMDS
scatterplot of samples representing the OTU community composition of soil a) bacterial and b)
fungal biota from a reference soil and at varying depths from three soil core samples from the
QR mill stockpile. The distance between points indicated the degree of difference based on
Unweighted Unifrac similarities of OTU composition in each sample. NMDS calculations based
on significant soil chemical parameters and the ordination scores on each NMDS axis. ............ 60

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LIST OF TABLES
Table 1 Reclamation suitability ratings for the topsoil stockpiles in grassland (New Afton) and
forested (QR mill) ecosystems. G = good; F = fair; P = poor; U = unsuitable. ............................ 32

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TABLE OF CONTENTS
ABSTRACT…… .................................................................................................................................................II
ACKNOWLEDGEMENTS .......................................................................................................................... III
LIST OF FIGURES ...................................................................................................................................... IV
LIST OF TABLES ........................................................................................................................................ VI
TABLE OF CONTENTS .............................................................................................................................VII
1.0

GENERAL INTRODUCTION ................................................................................................. 1

1.1

OVERVIEW OF ECOSYSTEM RESTORATION .............................................................................................1

1.2

SOIL AND RESTORATION .........................................................................................................................3

1.2.1

Soil Health and Function .............................................................................................................3

1.2.2

Role of Key Soil Geochemical Properties ....................................................................................4

1.2.1

Role of Soil Fungal and Bacterial Communities ..........................................................................5

1.3

OVERVIEW OF DISTURBANCE FROM MINING ACTIVITIES .......................................................................6

1.3.1

Environmental Impacts ................................................................................................................6

1.3.2

Regulatory Framework in British Columbia ................................................................................7

1.3.3

Salvaging and Stockpiling Topsoil ...............................................................................................8

1.4

RESEARCH OBJECTIVES ..........................................................................................................................9

1.5

LITERATURE CITED ..............................................................................................................................10

2.0

INVESTIGATING IMPACTS FROM TOPSOIL STOCKPILE HEIGHT ON SOIL
GEOCHEMICAL PROPERTIES .......................................................................................... 18

2.1

INTRODUCTION .....................................................................................................................................18

2.2

METHODS .............................................................................................................................................19

2.2.1
2.2.1.1

New Afton Mine ................................................................................................................................... 19

2.2.1.2

QR Mill................................................................................................................................................. 20

2.2.2

Soil Sampling ............................................................................................................................. 21

2.2.2.1

New Afton Mine ................................................................................................................................... 21

2.2.2.2

QR Mill................................................................................................................................................. 21

2.2.3
2.3

Study Sites .................................................................................................................................. 19

Experimental Design and Statistical Analysis ............................................................................ 25

RESULTS ...............................................................................................................................................26

2.3.1

General Trends........................................................................................................................... 26

2.3.2

Macronutrients (N, P, K, S, Mg, Ca) .......................................................................................... 26

2.3.3

Micronutrients (Fe, Mn, Zn, Cu, B)............................................................................................ 30

2.3.4

Salt Content ................................................................................................................................ 32

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2.3.5

pH ............................................................................................................................................... 33

2.3.6

Organic Matter........................................................................................................................... 33

2.4

DISCUSSION ..........................................................................................................................................36

2.4.1

Macronutrients (N, P, K, S, Mg, Ca) .......................................................................................... 36

2.4.2

Micronutrients (Fe, Mn, Zn, Cu, B)............................................................................................ 38

2.4.3

Salt Content ................................................................................................................................ 38

2.4.4

pH ............................................................................................................................................... 39

2.4.5

Organic Matter........................................................................................................................... 40

2.4.6

Conclusions ................................................................................................................................ 41

2.5

LITERATURE CITED ..............................................................................................................................42

3.0

INVESTIGATING IMPACTS FROM TOPSOIL STOCKPILE HEIGHT ON SOIL
MICROBIAL COMMUNITIES ............................................................................................. 47

3.1

INTRODUCTION .....................................................................................................................................47

3.2

METHODS .............................................................................................................................................50

3.2.1

Soil Sampling and Study Sites. ................................................................................................... 50

3.2.2

Metabarcoding Microbial Communities .................................................................................... 50

3.2.3

Processing and Statistical Analysis ............................................................................................ 51

3.3

RESULTS ...............................................................................................................................................52

3.3.1

Community Composition ............................................................................................................ 52

3.3.2

Alpha Diversity........................................................................................................................... 53

3.3.3

Beta Diversity and Geochemical Properties .............................................................................. 54

3.4

DISCUSSION ..........................................................................................................................................61

3.4.1

Community Composition ............................................................................................................ 61

3.4.2

Alpha Diversity........................................................................................................................... 63

3.4.1

Beta Diversity and Geochemical Properties .............................................................................. 63

3.5

CONCLUSIONS ......................................................................................................................................64

3.6

LITERATURE CITED ..............................................................................................................................65

4.0

GENERAL CONCLUSIONS.................................................................................................. 70

4.1

KEY FINDINGS ......................................................................................................................................70

4.2

TOPSOIL MANAGEMENT IMPLICATIONS ................................................................................................71

4.2.1

Stockpile Height ......................................................................................................................... 71

4.2.2

New Afton ................................................................................................................................... 72

4.2.3

QR mill ....................................................................................................................................... 73

4.3

LIMITATIONS AND FUTURE RESEARCH .................................................................................................73

4.4

LITERATURE CITED ..............................................................................................................................76

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1.0

GENERAL INTRODUCTION

1.1 Overview of Ecosystem Restoration
Widespread environmental degradation through human activities is leading to biodiversity
loss and declines in ecosystem services (Cardinale et al., 2012; Hooper et al., 2005). Ecosystem
services including carbon storage, climate regulation, nutrient cycling, soil fertility, provision of
medicines and cultural association are intrinsically linked to human livelihood (Catovsky et al.,
2002; Douglas, 2017). For instance, it has been suggested that up to 60% of the world’s arable
land is nutrient deficient or polluted causing major hindrances in food production (Fageria et al.,
2008). These losses to the natural environment can be mitigated by ecosystem restoration efforts,
which focuses on expediting the recovery of damaged ecosystems to a pre-disturbance state
(Bullock et al., 2011). Alternatively, the system can recover on its own through natural processes
without human intervention much more slowly (Suding et al., 2016).
Restoration efforts are often designed to expedite natural succession so that the desired
endpoint is reached quicker (Palmer et al., 2017). For instance, tree planting in tropical systems
can accelerate soil recovery by promoting microbial biomass and carbon inputs (Moreno-Mateos
et al. 2015). The Society for Ecological Restoration (SER) defines ecological restoration as the
“process of assisting the recovery of an ecosystem that has been degraded, damaged or
destroyed” (SER International Science & Policy Working Group, 2004) . In general, the goal of a
fully restored ecosystem is to be self-sustaining and resilient. The significance of restoration
efforts is highlighted in a meta-analysis of 89 restoration projects around the world showing a
substantial increase of biodiversity and ecosystem services in restored sites compared to nonrestored sites (Rey Benayas et al., 2009). Additionally, the 2020 Aichi Biodiversity Targets
released by the Convention of Biological Diversity (CBD) aim for the restoration of ecosystems
that provide essential services, as well as the restoration of at least 15% of degraded ecosystems
(CBD, 2017). However, the restoration of a site greatly depends on the practitioner and their
specific goals.
The outcomes and goals for restoration are highly variable. Goals typically include reaching
certain diversity indices, vegetation structures, and/or ecological processes (Ruiz-Jaen & Aide,
2005). However, restoration outcomes and timescale depend on disturbance level, fragmentation,
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and landscape conditions. Additionally, restoration goals and targets are largely shaped by
cultural values, indigenous knowledge, economics, and policy that are specific to the landscape
of interest. Understanding restoration and ecosystem ecology is increasingly important during the
growing prevalence of anthropogenic disturbances and climate crises (Food and Agriculture
Organization of the United Nations, 2020).
The practice and theory of ecological restoration has developed rapidly over the past few
decades as well as the integration of ecological concepts to restoration projects (Suding et al.,
2016), particularly community assembly and succession theory (Weiher et al., 1998; Weiher and
Keddy 1999; Wainwright et al., 2018). Community assembly builds off of succession theory by
emphasizing the random chance that results in very different outcomes of community succession
(Weiher and Keddy 1999). Community assembly theory aims to describe how species
composition change over time and suggests that the first species to establish in an ecosystem has
priority, and this priority effect can determine which species are successful later on. The order in
which species establish is important because they can have positive, neutral, or negative effects
on each other (Suding et al., 2016). For instance, Young et al. (2014) and Ploughe et al. (2020)
found that an early introduction of native grasses significantly reduced the negative effects to the
plant community from invasive exotics. Additionally, it is common practice in disturbed areas to
introduce legumes first as a nurse plant to create favourable soil conditions for other species to
establish (Suding et al., 2016). The theory particularly focuses on the importance of three filters
in determining local community composition: dispersal, physical environment, and biotic
interactions. Therefore, we can use community assembly principles to reach restoration goals by
purposefully introducing certain species into a disturbed landscape. Succession theory assumes
the continuous and gradual recovery after a discrete disturbance and landscapes are believed to
return to their historical state or a known trajectory to a point of equilibrium (Suding et al.,
2016). For example, seeding of later-successional species can expedite restoration to forests in
degraded pastures (Cole et al., 2011). This suggests that human interventions post-disturbance
can improve restoration by accelerating the rate of succession towards a later stage by
introducing components of a later successional stage (Mcclain et al., 2011). Additionally,
succession models assume that ecosystem development will occur through natural processes over
time (Christensen, 2014), suggesting restoration without human intervention can occur, but the

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process is slower. These frameworks and others are used to understand the natural world around
us and are particularly useful tools in restoration projects (Heneghan et al., 2008).

1.2 Soil and Restoration
1.2.1

Soil Health and Function

Soil is a complex ecosystem comprising of abiotic components (organic matter, water, air,
minerals) and biotic components (bacteria, fungi, protists, and animals). Soil health or
functionality can be defined as the capacity of a soil to sustain certain ecosystem functions such
as plant productivity, water and air quality, and provide habitats for biodiversity (Brussard, 1997;
Fitter et al., 2005). Traditional landscape restoration science has put the emphasis on
aboveground communities, such as plants and insects, but recently the importance of
belowground processes and biota have become increasingly recognized (Heneghan et al., 2008;
Kardol & Wardle, 2010). For instance, soil nutrient manipulations have been increasingly
applied to manage invasive species in restoration (Davis et al., 2000; Knauf et al., 2021).
Soils are the major terrestrial supply of nutrients supporting plants and other organisms. Soil
communities are highly complex and diverse ranging from larger organisms, such as earthworms
through to microscopic organisms, such as bacteria and fungi; however, the majority of this
diversity comes from the unseen microbial communities which are not well understood
(Delgado-Baquerizo, 2019; Maron et al., 2018). Bacterial and fungal communities are essential
to vital terrestrial processes including nutrient cycling, climate regulation and pollution
degradation (Bardgett & van der Putten, 2014). Additionally, soil microbes shape terrestrial
ecosystem dynamics through their close relationships with plants (Bauer et al., 2015; Kardol &
Wardle, 2010; Kulmatiski et al., 2008). Despite this, there is a lack of data on soil biodiversity
and soil physical and chemical indicators and their interconnectedness with overall ecosystem
components is poorly understood (Food and Agriculture Organization of the United Nations,
2020). Consequently, there has been an increasing focus in restoration and soil research aiming
to understand how belowground geochemical properties and microbial communities influence
plant dynamics and overall restoration success (Garris et al., 2016; Harris et al., 1989; Heneghan
et al., 2008; van der Heijden et al., 2008; Wagg et al., 2014).

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1.2.2

Role of Key Soil Geochemical Properties

The geochemical properties play a large role in the success of plant establishment and other
high level ecosystem components including water retention and microbial functionality. Most
plants take up nutrients needed for their metabolism through their roots within the soil medium.
Essential plant nutrients are categorized as macronutrients (primary nutrients and secondary
nutrients) and micronutrients. Key primary plant nutrients are nitrogen (N) mainly in the form of
ammonium (NH4+) and nitrate (NO3-), phosphorus (P) in the form of phosphates (HPO4-2, H2PO4, and PO4-3), and potassium (K+). Primary nutrients are required in a relatively large amount for
plant metabolism and growth and are frequently applied to soils in fertilizers. Secondary
nutrients are calcium (Ca2+), magnesium (Mg2+), and sulfur (S) in the form of sulphate (SO42-).
Secondary nutrients are typically present in soils and are not often required to be applied
artificially. Micronutrients include iron (Fe) in the form of iron (III) oxide-hydroxide (Fe(OH)3)
precipitate, boron (B) in the form of H3BO3, zinc (Zn2+), copper (Cu2+), manganese (Mn2+) and
molybdenum (Mo) in the form of (MoO4-2, HMoO4-, and H2MoO4), chloride (Cl-), and nickel
(Ni2+). Very small amounts of micronutrients are required for plant growth, however they
become toxic beyond a threshold concentration (Naeem et al., 2017)
In addition to the nutrient composition of soil, soil properties including pH, electrical
conductivity (EC), and organic matter (OM) are responsible for driving ecosystem functions and
are sensitive to disturbances (Baer & Birgé, 2018; Brevik et al., 2015). Soil pH is a measure of
the activity of hydrogen ions (H+) and affects plant growth through its regulation of nutrient
solubility and metal toxicity. Most plant nutrients are available between pH 6.0 and 7.5 and the
optimum range for microbes is pH 5.0 to 8.0 (Smith & Doran, 1996). Acidic soils (low pH) are
common in the mining industry due to waste materials from heavy metal extractions (Costigan et
al., 1981). EC is an important soil quality indicator as a proxy of salinity, which influences
toxicity and nutrient availability in soils (Jurinak et al., 1987; Muñoz-Rojas, 2018). Salinity
typically reduces the availability of essential nutrients N, P, and K due to the competition from
high amounts of Ca, Na, Cl, and S ions (Jurinak et al., 1987; Naeem et al., 2017). OM is an
important source of nutrients and improves soil structure. Importantly, OM slowly releases
nutrients through decomposition by soil organisms, providing a reservoir of plant nutrients. Low
levels of organic matter are typical in degraded soils, which can result in poor nutrient supply to
plants (Baer, 2016).(Baer, 2016).
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Nutrient deficiency is one of the most common factors limiting plant growth and
establishment of sites impacted by mining (Baer, 2016; Sheoran et al., 2008, 2010) causing a
hinderance to restoration success (Chen et al., 1998). Soil degradation post-mining may be
amended with the addition of soil nutrients in the form of organic inputs or fertilizer. For
example, Silva et al. (2013, 2015) demonstrated a single application of nutrients and organic
matter in the form of biosolids led to the rapid and spontaneous revegetation of abandoned mines
in central Brazil that had been disturbed for several decades. In addition to improving plant
establishment in disturbed lands, soil geochemical properties can shape the plant community
trajectory during restoration. For instance, two studies demonstrated that the manipulation of N
levels in soil optimal for native species, led to an increase in native grass diversity (Baer et al.,
2004; Perry et al., 2010). Thus, the characterization of key geochemical properties in the soil
profile is critical in post-mining reclamation planning (Baer, 2016).
1.2.1

Role of Soil Fungal and Bacterial Communities

Plants heavily depend on soil microbes for various provisions, including the bioavailability
of nutrients in the soil and for the delivery of these nutrients. The root surface area often does not
adequately provide proper uptake of all needed nutrients in the soil, therefore, associations with
bacteria and fungi are essential to establishment and nutrient uptake, especially in nutrient poor
environments (Miransari & Omidi, 2011). In addition to this, it is well documented that soil
bacteria and fungi directly influence plant productivity and health through various mechanisms
such as suppression of disease and altered biotic interactions between plants and other organisms
(Bever et al., 2010; van der Heijden et al., 2008; Wang, 2017). Furthermore, it has been
estimated that 80-90% of terrestrial plants form symbiotic arbuscular mycorrhizal fungi (AMF)
associations (Smith & Read, 2008) and plants in mine-impacted sites often reach a higher
percentage of mycorrhizal associations, indicating their importance to plant establishment in
disturbed ecosystems (Wang, 2017). Although recent sequencing technologies have allowed a
substantial improvement in the understanding of soil microbial composition and diversity, the
ecological role and identity of microbial taxa are still not well understood (Delgado-Baquerizo,
2019).
Soil microbes can assist plants in nutrient uptake partly because they are major drivers in
geochemical fluxes. For instance, symbiotic and free-living nitrogen-fixing bacteria convert N
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from the atmosphere to bioavailable NO3-, promoting plant productivity. Furthermore, bacteria
and fungi decompose dead OM such as leaf litter, releasing bioavailable nutrients for plants.
Importantly, mycorrhizal fungi and nitrogen-fixing bacteria are responsible for providing 5-20%
of all N and up to 75% of P in grasslands and savannahs (van der Heijden et al., 2008). Another
way soil bacteria and fungi can increase nutrient uptake is through upregulation of genes during
nutrient deficiencies (Bisis & Kumar, 2016). Additionally, fungi can modify resources through
fungal symbionts, called common mycorrhizal networks (CMNs), where nutrients are captured
and transferred between plants (Bever et al., 2010).
Because of this resource partitioning, and other mechanisms such as disease suppression and
shifting biotic interactions, manipulations of soil microbes can significantly promote plant
growth and alter plant community structure (Bardgett & van der Putten, 2014; Bauer et al., 2015;
Bever et al., 2010; Reynolds et al., 2003; van der Heijden et al., 2008). For example, the addition
of AMF can promote ecological restoration of mine sites by improving nutrient uptake and
tolerance of plants, improving soil structure, and quality thereby helping maintain ecosystem
functions (Wang, 2017). Consequently, integrating soil ecological knowledge including soil
microorganisms and chemical properties is an important area of research and is essential to the
restoration of natural systems in mine disturbed landscapes (Birnbaum et al., 2017; Callaham et
al., 2008; Sheoran et al., 2010; van der Heijden et al., 2008; Wall, 2012).

1.3 Overview of Disturbance from Mining Activities
1.3.1

Environmental Impacts

Mining operations often require the complete removal of soil from a site. Heavy equipment
is used to clear vegetation and soils from landscapes, reducing biodiversity, soil nutrients, and
soil structure. These processes are severely disruptive to landscapes, however necessary to meet
growing energy and material demands. As of 2017, Canada produces 60 minerals and metals at
200 active mines and 7 000 pits and quarries. Canada’s mining industry is a key contributor to
the economy; in 2017 the industry contributed 5% to Canada’s Gross Domestic Product. It has
provided over 634 000 jobs and is the industry with the highest Indigenous representation after
fishing in the private sector (Mines Canada, 2019). Left to natural processes, these disturbed
ecosystems take decades to restore (Bradshaw, 1997). While necessary, mining activities are

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destructive to the environment (Cooke & Johnson, 2002). New scientific knowledge and
technologies are needed to inform and guide reclamation policies and practices that will enhance
reclamation, such as wildlife habitat, commercial forestry, or indigenous forage grounds.
Gold along with copper and silver were the first metals known to be used by humans in the
Stone Ages, typically in the form of nuggets found in riverbeds. Significant natural gold deposits
can be found in quartz veins in rock formations, alluvial deposits, and within copper and lead
deposits (Gasparrini, 1993). Gold can be mined using surface mining including quarries, open
pits, and mounting top removal or using underground mining. The activities associated with gold
mining and other types of mining affect most components of the environmental – atmosphere,
hydrosphere, pedosphere, biosphere, and lithosphere (Matschullat & Gutzmer, 2012; Miao &
Marrs, 2000). Arsenic minerals associated with gold are dispersed in the environment. Gold like
many other mined products generate a large amount to mine waste such as overburden, barren
rocks, tailings, heap leach, and mine water causing environmental damage. Sulphide tailings
from gold ore processing can be a major source of acid mine drainage. In addition to pollution
and waste by-products from processing, large-scale land degradation is a consequence of gold
mining and most other types of mining (Gasparrini, 1993; Miao & Marrs, 2000). In open-cast
mining, the area must be completely stripped of vegetation to remove the overburden covering
the mineral deposits. Underground mining can cause 2-11 times less land disturbance compared
to surface mining (Miao & Marrs, 2000), however still causes significant soil disturbances.
1.3.2

Regulatory Framework in British Columbia

The Mines Act (1996) and the Health, Safety and Reclamation Code for Mines in British
Columbia (2017) require mining operations to conduct an environmental protection and
reclamation program to ensure that land, watercourses, and cultural heritage resources will be
returned to a safe and environmentally sound state and to an appropriate land use. Under the BC
Mines Act, mining companies are required to conduct the reclamation of lands prior to mine
closure (1996). Furthermore, Part 10 of the Code (Mine Plan and Reclamation Program
Information), focuses on reclamation and closure (2017). For example, Section 10.7.6 (Longterm Stability) states that “Land, watercourses and access roads shall be left in a manner that
ensures long-term stability” and Section 10.7.8 (Growth Medium) states that “all surficial soil
materials removed for mining purposes shall be saved for use in reclamation programs…”.
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Although somewhat vague, these frameworks in BC highlight the specific need for proper soil
management and successful soil restoration on mine affected lands.
1.3.3

Salvaging and Stockpiling Topsoil

Soil destruction, particularly topsoil, is one of the most important environmental impacts of
mining activities. Topsoil is defined as the surface “A” (organo-mineral) horizons of the soil
profile and lies above the subsoil or “B” horizon containing the upper portion of parent rock
material (Alberta Soil Advisory Committee). Healthy topsoil is a critical component in severely
disturbed landscapes such as mine sites because it supports faster plant establishment, higher
plant survival rates, and protects against invasive species by providing a strong native seed bank.
Stockpiling topsoil is a common restoration strategy in mining that includes the removal and
storage of topsoil, which is re-spread at the time of mine closure for reclamation. When topsoil is
re-applied to the disturbed area, it creates similar pre-disturbance conditions for plants to
establish in (Strohmayer, 1999). However, storage practices can alter and damage the soils
physio-chemical properties and biota hindering its ability to support ecosystem restoration
(Mummey et al., 2002b).
The process of topsoil deterioration often begins with stripping and relocation with heavy
machinery. Although not well understood, the severity of the loss in soil quality has been shown
to impacted by the length of time and the depth of the stockpile (Abdul-Kareem & McRae, 1984;
Birnbaum et al., 2017; Ghose & Kundu, 2004; Golos et al., 2016; Mummey et al., 2002b;
Sheoran et al., 2010). The storage period for stockpiled soil can range from months to decades
and stockpiles are often meters deep (Strohmayer 1999). There is limited research on long-term
topsoil storage, but existing literature has shown a decrease in soil nutrients, soil structure, and
microbial biomass (Ghose & Kundu, 2004; Mummey et al., 2002a). For instance, (Harris &
Birch, 1989) found a dramatic reduction in the presence of bacteria below 2 meters of a topsoil
stockpile. As the duration and size of storage increase stockpiles can become stratified in
content, particularly when below 1-3 meters (Boyer et al., 2011; Mackenzie, 2013), resulting in a
reduction in seed viability and an increase in soil compaction and anaerobic conditions (AbdulKareem & McRae, 1984; Harris et al., 1989). However, properly managed stockpiles have the
potential to minimize soil loss, preserve quality, and improve plant establishment upon
respreading.
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It is often recommended in soil best management practices that stockpiles remain under 1
meter and less than 1 year old to maintain topsoil quality (The City of Calgary Parks, 2018).
Furthermore, mixing topsoil and subsoils will typically degrade topsoil quality and should be
stored in separate piles onsite, or at least placed in the same order that they were removed (BC
Ministry of Energy & Mines, 2002). Stockpiles should be vegetated within 30 days with native
seed suited to the area (BC Ministry of Energy & Mines, 2002; Mackenzie & Renkema, 2013),
reducing the potential for surface erosion and promoting stability (The City of Calgary Parks,
2018). Once created, stockpiles need to be monitored for stability, surface erosion, vegetation
establishment, and presence of invasive species (BC Ministry of Energy & Mines, 2002).

1.4 Research Objectives
In the context of restoration ecology, it is key to understand how the management of longterm topsoil stockpiling alters soil chemical and microbial composition, which heavily influences
plant establishment and community dynamics. There is very known about how topsoil quality is
impacted by management practices during storage, particularly from storage height. This project
explores the chemical and biological compositional nature of topsoil stockpiles to better
understand their viability to support post-mining restoration after storage. I predicted that the
topsoil stockpiling practices at New Afton and QR mill in British Columbia (B.C.) have had
adverse effects on soil quality and therefore, restoration viability. To address this hypothesis, I
conducted a two-part study; Chapter 2.0 investigates how soil geochemical properties are
impacted by topsoil storage and Chapter 3.0 investigates how soil microbial community
composition is impacted by topsoil storage. Both chapters set out to investigate the topsoil
stockpile restoration suitability from two gold operations in the interior of B.C. and examine the
impacts from stockpile height on soil quality. Chapter 4.0 contains general conclusions of this
research including key findings, management implications, limitations, and future research.
Lastly, Appendix A shows findings from a supplementary experiment assessing the impacts of
soil depth on plant establishment in a greenhouse trial. The results from this project contribute
knowledge regarding soils response to topsoil stockpiling and will be useful to restoration
ecology practitioners and researchers. Broadly, it provides new information on the approaches of
ecological restoration post-mining with an emphasis on aboveground-belowground linkages and
helps to better understand how soil responds to severe ecological disturbance.
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Garris HW, Baldwin SA, van Hamme JD, Gardner WC, Fraser LH. 2016. Genomics to assist
mine reclamation: A review. Restoration Ecology. 24(2):165–173. doi:10.1111/rec.12322.
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Golos PJ, Dixon KW, Erickson TE. 2016. Plant recruitment from the soil seed bank depends on
topsoil stockpile age, height, and storage history in an arid environment. Restoration
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Harris JA, Birch P. 1989. Soil microbial activity in opencast coal mine restorations.
Harris JA, Birch P, Short KC. 1989. Changes in the microbial community and physico‐chemical
characteristics of topsoils stockpiled during opencast mining. Soil Use and Management.
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van der Heijden MGA, Bardgett RD, van Straalen NM. 2008. The unseen majority: Soil
microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecology
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Heneghan L, Miller SP, Baer S, Callaham MA, Montgomery J, Pavao-Zuckerman M, Rhoades
CC, Richardson S. 2008. Integrating soil ecological knowledge into restoration management.
Restoration Ecology. 16(4):608–617. doi:10.1111/j.1526-100X.2008.00477.x.
Hooper DU, Chapin FS, Ewel JJ, Hector A, Inchausti P, Lavorel S, Lawton JH, Lodge DM,
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Knauf AE, Litton CM, Cole RJ, Sparks JP, Giardina CP, Gerow KG, Quiñones-Santiago M.
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2.0

INVESTIGATING IMPACTS FROM TOPSOIL STOCKPILE
HEIGHT ON SOIL GEOCHEMICAL PROPERTIES

2.1 Introduction
In British Columbia (B.C.), disturbed landscapes from mining activities are required by law
to reclaim land to a self-sustaining state. British Columbia was one of the first provinces in
Canada to enact that mining companies must reclaim disturbed land caused by mining activities
through the Mines Act (Mines Act, 1996). Additionally, mining companies are required to
produce annual reclamation reports outlining a summary of all mining activities as well as all
research and monitoring for the Environmental Protection Reclamation Program. There are a
wide variety of potential restoration targets for any given mining operation depending on various
factors such as historical use, culture, physical and biological characteristics, and mine type
(Mborah et al., 2015), but ultimately the goal is to create a self-sustaining ecosystem that is
resilient and requires no further human interventions (SER International Science & Policy
Working Group, 2004).
Under the provincial regulations, salvaged topsoil is stored and re-spread post-mining to help
expedite ecological succession and return disturbed sites to a historic state. Native topsoil is a
critical source of seeds and propagules and provides beneficial physical, chemical, and microbial
properties for restoration and plant establishment. Successful plant community recovery has been
reported from re-spreading topsoil stockpiles post-mining. For example, Hall et al. (2010) found
approximately 66% plant species recovery for a forest ecosystem in the Appalachian Mountains
and Holmes (2001) found that plant species recovered by 66% in a shrubland in South Africa.
The deterioration of topsoil quality due to disturbance is one of the greatest hindrances to
restoration success during mining operations. Topsoil salvage, storage, and replacement during
mining operations can have adverse effects on soil quality (Abdul-Kareem & McRae, 1984;
Ghose & Kundu, 2004; Golos et al., 2016; Harris et al., 1989; Stahl et al., 2002; Thurber
Consultants Ltd. et al., 1990; Wick et al., 2009), resulting in long-term consequences for
restoration (Mummey et al., 2002b). Topsoil buried deep in a storage pile may become
anaerobic, which alters physical, chemical, and biological components of the soil. Additionally,
stripping and relocating topsoil often results in severe compaction from heavy machinery.

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Moreover, the equipment often causes admixing topsoil with lower quality subsoil and parent
materials during stripping, further degrading topsoil quality in stockpiles. Severely compacted
soils are known to have lower oxygen levels, restricted root growth, poor drainage, and nitrogen
loss from denitrification (Abdul-Kareem & McRae, 1984; Boyer et al., 2011; Buresh & Patrick,
1978). For example, Birnbaum et al. (2017) found that 10-year-old stockpiles resulted in
significantly lower plant biomass compared to plants grown on younger stockpiles.
Nutrient availability and suitable geochemical conditions (including salinity, electrical
conductivity (EC) and pH) are critical for the establishment and sustainment of plant
communities. Macronutrients (nitrogen (N), phosphorus (P), potassium (K), sulphur (S),
magnesium (Mg), and calcium (Ca)) and micronutrients (iron (Fe), manganese (Mn), zinc (Zn),
copper (Cu), boron (B)) in soil are required for plant health and growth, and the concentration of
these major nutrients are commonly manipulated to achieve restoration goals. For example,
organic amendments such as manure, biosolids, and wood chips are widely used to provide
nutrients and to improve soil quality for restoration (Larney & Angers, 2012). Additionally,
Gardner et al. (2012a) found that the application of biosolids on copper mine tailings
significantly improved plant establishment by increasing nutrient availability. Because soil
geochemical properties and nutrient levels impact restoration outcomes (Baer, 2016; Knauf et al.,
2021), understanding the content of salvaged topsoil for restoration purposes is critical.
This chapter investigated the geochemical changes occurring within stockpiles at two mines
in British Columbia in an effort to understand how management impacts soil stockpile viability
for reclamation. The primary value of this work is assisting industrial operators to optimize
topsoil stockpile assets, and for the development of tools for assessing soil health that are directly
relevant to reclamation practices.

2.2 Methods
2.2.1

Study Sites

2.2.1.1

New Afton Mine

New Gold’s New Afton copper-gold mine was located approximately 10 kilometers west of
Kamloops in British Columbia’s Southern Interior. It is situated within the historic Afton Mine
(Figure 2.1), formerly owned by Afton operating Corp. New Afton began commercial production
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in July 2012 and is the largest underground hardrock mine in Canada. It comprises of
underground workings, historic support facilities, a historic open pit, a concentrator, and a
tailings facility. The end land use objective is to return the ecosystem to native grasslands that
support wildlife and traditional hunting opportunities by First Nations (New Gold, 2017). New
Afton is located within the traditional territories of the Tk’emlúps and Skeetchestn Bands. These
bands are part of the larger cultural group known as the Secwépemc or Shuswap First Nation.
Additionally, New Afton is in the Bunchgrass (BGxw1) biogeoclimatic zone at approximately
700 m in elevation. The Biogeoclimatic Ecosystem Classification (BEC) system in British
Columbia incorporates information on climate, soils, and vegetation to provide a framework for
management practices (Meidinger & Pojar, 1991). The BGxw1, commonly known as the
“middle grasslands” is dominated by bluebunch wheatgrass, Junegrass, big sagebrush and rabbit
brush (Lloyd et al., 1990). The primary soil orders within the mine site include Tranquille, Trapp
Lake, Godey, and Timber. These soils are moderately saline and calcareous and are dominated
by Orthic Brown and Dark Brown Chernozems with one occurrence of an Eluviated Eutric
Brunisol soil (Government of British Columbia, 2018). The tailings in the New Afton mine are
alkaline (pH >8.5) and are high in copper and molybdenum.
2.2.1.2

QR Mill

Barkerville Gold Mines Ltd. (BGM) (formally International Wayside Gold Mines) is a
Canadian company headquartered in Toronto, Ontario. Barkerville Gold Mines Ltd. BGM has
various gold mines around the Barkerville-Wells area. Barkerville Gold Mines’ Quesnel River
(QR) mill in Cariboo is located approximately 80 km east of the city of Quesnel in the southern
interior of B.C. Ore, concentrate, and waste rock from the Bonanza Ledge Mine and Cariboo
Gold Mine are transported, stored, and processed in the QR mill. The current reclamation goals
set for QR mill are to restore the landscape so that it does not require further human intervention
(Barkerville Gold Mines Ltd., 2019). QR mill is situated in the traditional territories of the
Secwépemc or Shuswap First Nation and lies within the moist, warm Sub-Boreal Spruce
(SBSmw) BEC zone based on the BC provincial BEC system. The SBSmw is part of the
Canadian Boreal Forest Region and Quesnel Highland (mean annual precipitation ranges from
440-900 mm) and is dominated by Douglas-fir, red-stemmed feathermoss, knights plume, hybrid
white spruce, subalpine fir, and electrified cat’s-tail moss (Annas & Coupe, 1979). The QR mill
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and surrounding area is dominated by Bedenesti, Deserters, and Dominion soil orders. These
soils consist primarily of Brunisolic Gray Luvisols and Luvisolic Humo-Ferric Podzols that were
deposited by glacial ice (Government of British Columbia, 2018).
2.2.2

Soil Sampling

2.2.2.1

New Afton Mine

New Afton has a 6-year-old, 25-meter-deep topsoil stockpile (50.654442, -120.509320) with
approximately 250 600 m3 of topsoil materials. Because additional topsoil materials have been
added throughout the mine life, the oldest soil is at the bottom of the stockpile and the youngest
soil is at the surface. Four soil cores were extracted via solid stem auger drilling by Geotech
Drilling Ltd provided by New Afton. during September 26th and 27th of 2018, with each core
being approximately 3 meters apart. The first 1.53 m was sampled in 0.3 m increments, then
once every 0.3 m until the bottom at 13.7 m. Thus sampling depths were at 0.3 m, 0.6 m, 0.9 m,
1.2 m, 1.5 m, 3.0 m, 4.6 m, 6.1 m, 7.6 m, 9.1 m, 10.7 m, 12.2 m, and 13.7 m. The outer 1 cm of
the soil core was dis-carded to ensure that collected soil was not contaminated by upper layers.
Soil was placed into two 1 L Whirl-Pak® bags and Falcon® tubes as they were pulled up soil
from the stockpile. Post-collection, samples were combined by depth intervals of the stockpile
are as follows; 0.0-0.6 m, 0.6-1.5 m, 1.5-6.1.0 m, and 6.1-13.7 m. The soil stockpile at the time
of sampling was sparsely vegetated with grasses and weedy species, likely from natural
regeneration. A nearby grassland site was sampled as a reference site, where approximately 6 kg
of soil from the top 10 cm was collected using a trowel. The Whirl-Pak® samples were stored in
a -20oC freezer at the Research Greenhouse and the Falcon® tube samples were stored in a -80oC
freezer in the TRUGen laboratory at TRU until analysis.
2.2.2.2

QR Mill

QR mill has a 20-year-old, 6-meter-deep topsoil stockpile (52.670306, -121.783556). It is a
combination of organic soil and general till soil stripped form the surface layers. The stockpile
was intended to cover and re-contour during post-mining reclamation. Sampling at the QR mill
of Barkerville Gold Mines Ltd. was completed in May 2019. Three soil pits approximately 100
m apart were dug using an excavator in May 2019 to access various layers of the stockpile from
the surface to the bottom at 575 cm. In the field, soil was placed into two 1 L Whirl-Pak® bags
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and Falcon® tubes as soil was removed from the stockpile. The stockpile at the time of sampling
had vegetation cover, including cottonwood stands in the northwest corner. An adjacent
undisturbed forested site was sampled as a reference site, where approximately 16 kg of soil
from the top 10 cm was collected The Whirl-Pak® samples were stored in a -20oC freezer at the
Research Greenhouse and the Falcon® tube samples were stored in a -80oC freezer in the
TRUGen laboratory at TRU until analysis.

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Figure 2.1 Aerial image of the New Afton mine site, including a close-up of the topsoil stockpile of
interest that shows the locations of four soil core samples. Map generated in QGIS® with Bing
VirtualEarth background.

a)

b)

Figure 2.2 Photos showing a) sampling of the topsoil stockpile in New Afton via auger driller and b) soil
collection set up where soil was placed into Whirl Pak© bags and Microcentrifuge tubes.

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Figure 2.4 Aerial image of the QR mill, including a close-up of the topsoil stockpile of interest that shows the
locations of three soil pit samples. Map generated in QGIS® with Bing VirtualEarth background.

a)

b)

Figure 2.3 Pictures showing a) sampling of the topsoil stockpile in QR mill via mechanical
digger and b) a sample hole exposing part of the topsoil profile

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2.2.3

Experimental Design and Statistical Analysis

For analyzing stockpile depth effects on soil geochemical properties, a linear mixed effects
regression was used that included depth as a fixed factor and soil cores or soil pits as a random
factor (lme4 and lmertest in R). The reference soil characteristics were included in figures and
analysis primarily as a benchmark and were not included in statistical testing. Residual plots of
geochemical variables were used to determine if a log(x+1) transformation was necessary. Al,
Cu, Fe, Mg, Mn, Na, P, NH4, and H for the New Afton dataset, and Cu, S, Zn, NH4, NO3, OM,
and C/N variables for the QR mill dataset, were log(x+1) transformed. Multicollinearities
between variables were tested using Spearman’s rank correlation in ‘varclus’ function in the
nmle R package. Highly correlated (Spearman’s p2>0.7) data were excluded, specifically soil Ca
in the QR mill dataset (Land, 2015). The composition of all measured soil properties with depth
were summarized using principal component analysis (PCA) on scaled geochemical variables
(ggfortify in R). Values of variables below detection limits were set to have the value of the
detection limit.
After sampling New Afton soil samples were combined to meet the minimum requirement of
sample amount for testing. Smaller soil sample intervals were purposely formed near the surface
of the stockpile because the most changes and activity were likely to occur at the surface level.
However, the exact depths sampled were a function of the sampling ability of the auger drill
used. Given the large depth range in each pooled sample used for geochemical analysis, stockpile
depth was treated as categorical variable in the New Afton data analysis. Conversely, QR mill
soil samples were collected at a point depth for each soil pit. More soil samples were purposely
taken near the surface of the stockpile, however, the exact depths sampled were largely driven by
the sampling ability of the excavator. Because samples more closely represented a single depth,
stockpile depth was treated as a numerical variable in the QR mill data analysis.
The elemental composition of the soil samples was measured at the Analytical Laboratory at
the Ministry of Environment and Climate Change Strategy in Victoria, B.C. The samples were
prepared by heating soil samples at 70oC for 24 hours, followed by sieving through a 1 mm pan.
Analyses composed of a profile of major elements; total Al, B, Ca, Cu, Fe, Mg, Mn, P, K, S, and
Zn via acid, microwave digestion followed by inductively coupled plasma–optical emission
spectrometry (Sandroni et al., 2003) , available P via Bray P-1 extraction ultraviolet analysis
(Bray & Kurtz, 1945), and available NH4-N and NO3-N via potassium chloride extraction
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(Kachurina et al., 2000). Organic matter and moisture content were measured in-house via Losson-ignition (LacCore, 2013), and soil-water pH (H2O) and EC were determined using a
Palintest® 800 meter. Loss-on-ignition was calculated by weighing approximately 1.5 g of soil
into pre-weighed tins and then heating in succession at 105oC then 500oC for 12 and 5 hours
respectively, until constant weights were achieved. After each succession, the dried soil was
weighed to calculate water content and organic content of the soil. Total C, S, and N amounts
was measured with a ThermoScientific CHNS Elemental Analyzer. These samples were
prepared by drying in an oven at 70oC for 24 hours followed by sieving through a 1 mm pan and
grinding with a mortar and pestle.

2.3 Results
2.3.1

General Trends

In order to explore the effect of soil stockpile depth on geochemical properties, PCA plots
were drawn (Figure 1 and Figure 2), illustrating that stockpile depth led to significant changes in
soil properties at both New Afton (R2 = 0.15, P < 0.01) and QR mill (R2 = 0.28, P < 0.01). In
New Afton, PC1 explains 34.7% of variation and PC2 explains 19.7% of variation observed
between samples (Figure 2.5). In QR mill, PC1 explains 32.9% and PC2 explains 18.5% (Figure
2.6). The PCA clearly showed variations among different stockpile depths at QR mill, but not
New Afton. Additionally, PCA clearly showed geochemical variations among the soil samples
from stockpiles and reference soils at both sites. The corresponding reference soil properties
from both sites were included on the PCA plots as a benchmark, and not included in the principal
component calculations (Figure 2.5; Figure 2.6).
2.3.2

Macronutrients (N, P, K, S, Mg, Ca)

Soil NH4-N showed a notable increase below the 152-610 cm depth interval in the New
Afton stockpile (Figure 2.7, P = 0.08) from an average of 0.27 mg/kg to 2.5 mg/kg at the bottom
610-1372 cm interval. Ammonium also increased significantly with depth in the QR mill topsoil
stockpile (Figure 2.8, R2 = 0.18, P = 0.05) and ranged from 3.6 mg/kg to 18.8 mg/kg.
Soil NO3-N decreased with depth in the New Afton stockpile (Figure 2.7; P = 0.04), from an
average of 27 mg/kg and 30.5 mg/kg in the top 152 cm to 13.5 mg/kg at the bottom 610-1372
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cm. The corresponding reference soil had a lower soil NO3-N content at 5.6 mg/kg. Conversely,
at QR mill there were no significant differences in soil NO3-N with depth (Figure 2.8, R2 = 0.02,
P = 0.38), and the average range was between 7.5 mg/kg and 1.4 mg/kg. The reference soil for
QR mill had a much higher level of soil NO3-N at 29.1 mg/kg.
There was no evidence that the carbon/nitrogen (C/N) ratio changed significantly with
stockpile depth in New Afton (Figure 2.7, P = 0.11) ranging from 6.6 to 10.6, and similar to the
reference soil. There was evidence that C/N increased with stockpile depth in the QR mill topsoil
stockpile (Figure 2.8, R2 = 0.35, P < 0.01), ranging from 7.8 to 23.4. Generally, the reference
soil (average C/N = 15.2) was most like the bottom half of the topsoil stockpile at the QR mill
site.

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Figure 2.5 PCA plots showing differences in soil chemical properties at New Afton with changing
stockpile depth. PC1 accounts for 34.7% and PC2 accounts for 19.7% of variation observed between soil
samples.

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Figure 2.6 PCA plots showing differences in soil chemical properties at QR mill with changing
stockpile depth. PC1 accounts for 32.9% and PC2 accounts for 18.5% of variation observed between
soil samples.

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There was no evidence that total P content or available P content changed with stockpile
depth in New Afton (Figure 2.7, P = 0.63, P = 0.82, respectively). Total soil P content ranged
between 0.1 % and 0.13 % and available P content range from 1.1 mg/kg to 10 mg/kg in the New
Afton stockpile. The corresponding reference soil for New Afton had an average total P content
of 0.08% and available P content of 27.7 mg/kg. There was no evidence that total P content
changes significantly with depth (Figure 2.7, R2 = 0.076, P = 0.24), however available P
decreased steadily with increased stockpile depth (Figure 2.8, R2 = 0.37, P = 0.01). QR mill total
soil P content range between 0.07% and 0.12% and available P content range from 0.41 mg/kg to
18 mg/kg. The corresponding reference soil for New Afton had an average total soil P content of
0.13% and an average available P content of 145 mg/kg (not shown in figure).
There was no evidence that K levels changed significantly with stockpile depth in the New
Afton (Figure 2.7, P = 0.39). There was some evidence that K levels changes significantly with
depth in the QR mill topsoil stockpile (Figure 2.8, R2 = 0.16, P = 0.08). Neither stockpile
appeared to be notably different than their reference soils.
There was some evidence that soil S levels increased with depth in the New Afton stockpile
(Figure 2.7, P = 0.06). After 0-61 cm depth, S increased by approximately 45% in the upper
depth intervals. Soil S was notably higher in the New Afton stockpile compared to the reference
soil. There was also some evidence that S increased with depth in the QR mill stockpile (Figure,
R2 = 0.16, P = 0.07). The QR mill stockpile generally has similar S levels to the reference soil.
There was no evidence that Mg levels changed significantly with stockpile depth in the New
Afton (Figure 2.7, P = 0.13) and QR mill topsoil stockpile (Figure 2.8, R2 = 0.16, P = 0.1). Both
stockpiles showed elevated levels of Mg compared to their respective reference soil.
There was no evidence that calcium (Ca) levels changed significantly with stockpile depth in
the New Afton (Figure 2.7, P = 0.41) and appeared to have elevated levels of Ca compared to the
reference soil.
2.3.3

Micronutrients (Fe, Mn, Zn, Cu, B)

There was some evidence that Fe levels increased significantly with depth in New Afton
(Figure 2.7, P = 0.07), where average Fe levels increased by 7% from the top of the stockpile (061 cm) to the bottom (610-1372 cm). Soil Fe was notably higher in the stockpile soil (ranging
from 39 000 mg/kg to 44 000 mg/kg) compared to the reference soil (average = 30 666 mg/kg) in
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New Afton. There was little to no evidence that Fe levels increased significantly with depth in
QR mill samples (Figure 2.7, R2 = 0.11, P = 0.14), ranging from 40 000 mg/kg to 63 000 mg/kg.
Soil Fe was higher in the stockpile soil compared to the reference soil (average = 33 000 mg/kg)
for QR mill.
There was very little to no evidence that soil Mn changed significantly with stockpile depth
in New Afton (Figure 2.7, P = 0.29). Soil Mn ranged from 870 mg/kg to 1000 mg/kg and the
corresponding reference soil for New Afton had an average Mn content of 660 mg/kg. There was
evidence that soil Mn increased steadily with stockpile depth in QR mill (Figure 2.8, R2 = 0.34, P
< 0.01) despite one outlier with 840 mg/kg Mn at 565 cm. Mn values ranged from 680 mg/kg to
1400 mg/ kg and the corresponding reference soil for QR mill had an average Mn content of
1133 mg/kg.
There was evidence that soil Zn changed significantly with stockpile depth in New Afton
(Figure 2.7, P < 0.01), increasing from an average of 65.3 mg/kg at the surface to 71.0 mg/kg at
the bottom of the stockpile, with a range from 58 mg/kg to 80.0 mg/kg. The corresponding
reference soil for New Afton had an average Zn content of 79.7 mg/kg. There was also possible
evidence that Zn increased with stockpile depth in QR mill (Figure 2.7, R2 = 0.18, P = 0.05). Soil
Zn ranged from 58.0 mg/kg to 140.0 mg/kg and the corresponding reference soil for QR mill had
an average Zn content of 154 mg/kg.
There was a spike of copper (Cu) at the 152-610 cm sample interval with an average of 495
mg/kg (up to 840 mg/kg was detected) in the New Afton stockpile compared to the rest of the
stockpile (ranging from 120.0 mg/kg to 160.0 mg/kg, Figure 2.7, P < 0.01). There was little to no
evidence Cu levels changed significantly with QR mill stockpile depth (Figure, R2 < 0.01, P =
0.9) and Cu content ranged from 83 mg/kg to 250 mg/kg.
There was very little to no evidence that boron (B) changed significantly with stockpile depth
in New Afton (Figure 2.7, P = 0.27). B ranged from 12.0 g/kg to 27.0 mg/kg and the
corresponding reference soil for New Afton had an average B content of 12.3 mg/kg. There was
very little to no evidence that soil Mn changed significantly with stockpile depth in QR mill
(Figure 2.8, R2 = 0.04, P = 0.38), ranging from 7.0 mg/kg to 13.0 mg/kg and the corresponding
reference soil for QR mill having an average of 11.6 mg/kg.

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2.3.4

Salt Content

After a 16% decrease in soil EC at the surface to 61-152 cm to 1190.6 µm/S, EC increased to
an average of 1445.8 µm/S (Figure 2.7, P = 0.01) in the New Afton stockpile. The New Afton
stockpile had notably higher EC levels than the reference soil (average 67.3 µS/ cm). There was
evidence that soil EC increased significantly with stockpile depth at QR mill (Figure 2.8, R2 =
0.23, P = 0.03), ranging from 39.6 µS/ cm to 217.0 µS/ cm. The QR mill stockpile had similar
EC content to the reference soil (average 119.4 µS/ cm). Based on the reclamation suitability
ratings for EC, the New Afton stockpile was rated as fair at all depths and the QR mill stockpile
was rated as good at all depths (Table 1)
Despite a 20% decrease at the 152-610 cm depth interval, Na appeared to increase steadily
with depth, from 2200 mg/kg average at the surface (0-60 cm) to 3025 mg/kg average at the
bottom (610-1372 cm) interval (Figure 2.7, P = 0.01) for the New Afton Site. Its corresponding
reference soil had lower Na content at an average of 766.7 mg/kg. There were no significant
changes in soil Na observed in the QR mill stockpile with values ranging from 570 mg/kg to 810
mg/kg and the reference soil had an average soil Na of 840 mg/kg (Figure 2.8, P = 0.13). Based
on the reclamation suitability ratings for SAR, the New Afton stockpile was rated as poor or
unsuitable at all depths and the QR mill stockpile was rated as fair at all depths (Table 1).
Table 1 Reclamation suitability ratings for the topsoil stockpiles in grassland (New Afton) and forested (QR mill)
ecosystems. G = good; F = fair; P = poor; U = unsuitable.
Source: (Macyk et al., 2004)

EC = Electrical Conductivity
a = Sodium Absorption Ratio; see Section 2.4.4 and Appendix B. for more information.
b = May be characterized as poor or unsuitable based on soil texture and moisture content.

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2.3.5

pH

There was evidence of significant differences in pH with stockpile depth at New Afton
(Figure 2.7, P = 0.03). The topsoil stockpile at New Afton was slightly alkaline, with pH values
ranging from 8.0 to 8.3, the corresponding reference soil had an average pH of 7.3. Average pH
was lowest at the bottom of the stockpile (pH 8) and highest in the 152-610 cm depth interval
(pH 8.18). The QR mill stockpile soil was acidic to neutral and ranged from pH 5.4 to 7.3, with
no evidence of significant changes in pH with soil depth (Figure, R2 = 0.02, P = 0.52), the
corresponding reference soil had an average pH of 5.63. Based on the reclamation suitability
ratings for pH, the New Afton stockpile was rated as fair at all depths and the QR mill stockpile
was rated as good or fair at varying depths (Table 1).
2.3.6

Organic Matter

There was no evidence of significant changes in soil organic matter (OM) content with depth
at the New Afton stockpile (Figure 2.7, P = 0.43). Soil OM ranged from 1.6% to 4.0% and the
corresponding reference soil had an average soil OM at 3.3%. Conversely, there was an
immediate decrease in soil OM moving away from the surface of the QR mill stockpile after 20
cm in the (Figure 2.8, R2 = 0.28, P = 0.02). Soil OM values ranged between 2.7% and 11.1% and
the reference soil had an average soil OM content of 10.2%.

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Figure 2.7 Boxplots showing differences in geochemical variables with stockpile depth at the New Afton
site.

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Figure 2.8 Linear regression plots showing changes in geochemical variables with stockpile depth at the
QR mill site. The blue lines represent a linear model and the shaded area in grey represents the 95%
confidence intervals.
Note: the reference samples are not shown in the available P plot.

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2.4 Discussion
To assess the effect of stockpile storage height on topsoil quality, macronutrients,
micronutrients, salt content, pH, and organic matter were measured at varying depths from the
topsoil stockpiles at New Afton and QR mill
2.4.1

Macronutrients (N, P, K, S, Mg, Ca)

Nitrogen is one of the most limiting nutrients for plant growth and productivity. Plants
acquire nitrogen from the soil, mainly in the form of NH4-N and NO3-N, although NO3-N is the
preferable form for take-up by plants. Microbial decomposition converts organic nitrogen into
bioavailable NH4-N (mineralization) and, furthermore, through a two-step process, nitrifying
bacteria can then oxidize NH4-N into bioavailable NO3- (nitrification). Nitrate often dominates in
aerobic soils, while NH4-N tends to be more prevalent in acidic and anaerobic soils (Hachiya &
Sakakibara, 2017). The soil N results in this study are generally consistent with other research;
for example, two soil restoration studies at coal mine sites (Harris and Birch, 1989; Williamson
and Johnson, 1990) found that when soil was stockpiled in piles that were more than 100 cm
deep, there was a large accumulation of NH4-N (up to 70 mg/ kg) in the topsoil. Conversely
Abdul-Kareem 1984 saw little variation in the amount of NO3-N and NH4-N. Our results showed
some evidence of NH4-N accumulation in both the New Afton (at and below 610 cm) and QR
mill (at and below 100 cm) topsoil stockpiles, which may indicate anoxic conditions at these
depths. The increasing C/N ratio in the QR mill stockpile supports this as this indicates lower
rates of microbial decomposition (Ghose & Kundu, 2004). Because of the high pH levels in the
New Afton stockpile, the accumulation of NH4-N from anaerobic conditions may have been
lessened and resulted in retention of NO3-N. In contrast NH4-N was dominant in QR mill topsoil
stockpile, especially below 100 cm. This difference in stockpiles may be a function of site age;
the QR mill stockpile was approximately 14 years older than the New Afton stockpile, allowing
more time for denitrification of NO3-N to NH4-N. Alternatively, the differences may be a result
of site factors including soil pH, climate, and geological history. In general, NO3-N content for
New Afton were quite high (often higher than 10 mg/kg) compared to the reference levels,
whereas NH4-N content was mostly below reference levels.

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Phosphorus is an essential nutrient for plant structures and is necessary for various
biochemical reactions and is typically taken up as H2PO4-. Only the QR mill topsoil stockpile
showed a decreasing trend in available P with depth, perhaps due to stockpile age (20 years).
This was likely a result of immobilization by soil microbes in anoxic conditions within the
stockpile, corresponding with the decrease in OM. This finding was consistent with other
research; for example, one study found that soil N and P decreased with depth in claypan soils
(Hsiao et al., 2018). According to the Interpretations for Soil Test Phosphorus and Potassium
Guidelines for Southern British Columbia (BC Ministry of Agriculture, 2010) document, both
topsoil stockpiles characterised here are classified to having low amounts of available P
concentrations (5-19 ppm). The New Afton reference soil had a medium (20-39 ppm)
concentration, and the QR mill reference soil had high (>100 ppm) levels of available P. Because
P availability is pH-dependent, P reduction in the New Afton topsoil may be linked to the
alkaline soil conditions. In alkaline soils, (pH > 7), P precipitates with Ca, reducing availability.
Deficiency in soil P generally results in stunted plant growth and poor establishment.
The majority of S in soil is found in OM and was released through microbial mineralization
processes. Our results indicated that total S likely increased with stockpile depth in both
stockpiles. For example, S reached 0.29% at 61-152 cm depth, increasing 45% from the top of
the stockpile (0-61 cm) in New Afton. The slight accumulation with stockpile depth may be a
result of S leaching throughout the piles (Baer, 2016). Both stockpiles had elevated levels of total
S (up to 0.29%) above the recommended guideline (0.05%) for agricultural land (CCME, 1999)
and higher than the corresponding reference soils; however, the ranges present in the stockpiles
are considered typical for organic soils (K. A. Brown, 1982). It is possible that anaerobic
conditions allowed sulphate-reducing bacteria to increase the amount of hydrogen sulfide acid
(H2S) and decrease the amount of H2SO4.
Secondary macronutrients and primary cations, such as Ca, K and Mg are critical for
photosynthesis, signal transduction and structure in plants (Yan & Hou, 2018). These nutrients
do not change throughout the stockpile profiles examined here. In general, these nutrients are
likely sufficient in both stockpiles for revegetation upon re-spreading. Although, Ca levels in the
New Afton stockpile samples (up to 4.5%) were higher than the reference soil (up to 1.1%), the
stockpile may benefit from additional Ca inputs due to the high Na levels (Table 1; Figure 3)
(Naeem et al., 2017).
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2.4.2

Micronutrients (Fe, Mn, Zn, Cu, B)

Necessary micronutrients (e.g., Cu, Fe, Mn, and Zn) are typically found in sufficient levels in
soil (Horneck et al. 2011). Mn tended to increase with depth in QR mill stockpile only and Fe
increased with depth in New Afton only, whereas Zn increased with depth in both stockpiles.
The accumulation of Fe and Zn compared to reference soils may indicate anaerobic conditions
within the piles. In general, there was an accumulation of Fe throughout both stockpiles
compared to their reference soils. There was a spike in Cu concentration at the New Afton
stockpile at 152-610 cm depth, reaching up to 840 mg/kg, so this section of the stockpile should
be avoided when respreading. Additionally, the QR mill stockpile suffered from elevated Cu
levels at all depths. The Cu may be present at high amounts at this depth due to contamination
from natural sources while salvaging. The majority of samples from both stockpiles had levels
above the recommended concentration for agricultural and residential lands (63 mg/kg) and
commercial and industrial (91 mg/kg) (CCME, 1999). Additionally, some samples in both piles
had Cu levels above the thresholds for livestock/ plant/ invertebrate toxicity (150 mg/kg) and
microbial impairment (350 mg/kg ). Impacts from local mineralogy or admixing with Cucontaining bedrock during topsoil stripping could explain the high levels of Cu in these
stockpiles.
Soil B is an important nutrient for cell wall structure. Soils typically have between 10 and 80
mg/kg of elemental B, although most of which is not available to plants (Naeem et al., 2017).
The results show that soil B does not change within the pile profiles at either site and will be
sufficient to support plant growth (ranging between 15 to 27 mg/kg in New Afton and 7 to 13 in
QR mill).
2.4.3

Salt Content

Electrical conductivity (EC) is a measure of cations or anions of a solution and was
associated with soil salinity and soluble nutrients (Smith & Doran, 1996). Because of this, EC is
important for understanding soil quality. The statistically significant p-values for pH and EC
measured with the Palintest® 800 meter in the New Afton stockpile are unlikely to have
biological or environmental importance (mean pH and EC ranging from 8.1 to 8.2 and 1191
µS/cm to 1446 µS/cm, respectively). Low p-values were likely due to the minimal variation

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(maximum standard error for pH was 0.04 and EC was 90.5) seen between soil samples (Figure
2.5). This is supported by Table 1 where sustainability ratings for EC and pH are classified with
the same ratings throughout varying stockpile depths. Soil EC was much higher than the
reference soil (average 67.3 µS/cm), which was likely the result of elevated Na content in the
stockpile. The EC levels in the QR mill soil tended to increase with stockpile depth, indicating an
increase in a soluble salt, perhaps Mg or Na. Nevertheless, both piles are characterized as good
in terms of their EC levels for reclamation suitability (Table 1) and are not considered saline
(Smith & Doran, 1996).
Soil Na is not necessary for plant growth and high levels of Na can damage soil structure and
plant growth. Soil Na content in New Afton increased 38% from the surface of the pile to the
bottom of the pile and was approximately 187% higher than the reference soil. While there are
no known studies measuring soil Na levels in topsoil stockpiles, the increase in Na within the
stockpile depths may have occurred due to leaching of salts down the pile or chemical diffusion
which can move salts where there are differences in concentration (Thurber Consultants Ltd. et
al., 1990). The general elevated Na levels in the stockpile may be a result of the semi-arid
conditions in the Kamloops region, where high temperatures and low precipitation are common.
Here, the rates of evaporation may have caused an accumulation of Na in the pile. High salt
concentrations can cause an ionic imbalance, reducing K and Ca availability and creating
drought conditions for plants by reducing the water potential (Naeem et al., 2017). According to
the guidelines from the Soil Quality Criteria Relative to Disturbance and Reclamation, the New
Afton stockpile was poor or unsuitable for reclamation at all depths (depending on soil texture),
due to the high SAR content (Table 1). Additionally, the SAR and EC levels in the New Afton
stockpile indicate the soil as sodic. Sodic soils are often alkaline (typically greater than pH 8.5)
as a result from the hydrolysis of sodium carbonate (Na2CO3), releasing hydroxyl groups (OH-)
(Kumaragamage et al., 2021).
2.4.4

pH

Soil pH is an important measure of quality for plant growth as it heavily influences microbial
community composition and the availability of soil nutrients and toxic elements (Smith & Doran,
1996). The soil pH at New Afton was alkaline, often above pH 8.0 at all depths. Using the
guidelines from the Soil Quality Criteria Relative to Disturbance and Reclamation (Macyk et al.,
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2004), the New Afton topsoil stockpile was classified as fair (Table 1), having moderate soil
limitations due to high soil pH (>8.0). Conversely, the QR mill topsoil stockpile was slightly
acidic and had none to slight limitations for revegetation (Macyk et al., 2004; Smith & Doran,
1996).
High pH in soils can negatively impact nutrient availability to plants. A rise in pH can
increase mineralization, reducing the presence of N and could indicate the presence of free
carbonates. Additionally, the availability of P and K can be significantly reduced (Smith &
Doran, 1996). Alkalinization of soil can occur in arid and semi-arid ecosystems when there was
minimal rainfall such as in the New Afton site. These effects are likely compounded by the fact
that the stockpile was likely highly compacted, creating an impenetrable barrier that would
negate any rainfall and allow salts to accumulate beneath the surface. Alternatively, an increase
in pH can occur due to admixing with calcareous subsoils (Thurber Consultants Ltd. et al.,
1990). In support of this, the parent materials of the dominant soils in the New Afton mine area
(Tranquille, Timber, and Trapp Lake) are calcareous and saline. Thus, in typical soil profiles of
this area the pH rises considerably in the C-horizon (approximately pH 8.5) compared to the Ahorizon (approximately pH 7.1) (Government of Canada, 2019). It is likely that incorporation of
the alkaline subsoils and parent materials occurred resulting in the alkaline and sodic conditions
in the topsoil stockpile at New Afton.
2.4.5

Organic Matter

Soil OM includes decomposing plant and animal residues and is an important source of plant
nutrients and soil structure (Salehi et al., 2011). Organic matter in the New Afton stockpile did
not fluctuate with depth, but the stockpile had approximately 15% to 39% less OM content
compared to the undisturbed soil. Although considerably higher than New Afton soils, organic
matter in the QR mill stockpile decreased sharply after the first 10 cm (OM content was
approximately halved). This roughly corresponding to the decline in available P and rise in C/N
ratio, indicating microbial decomposition in QR mill. Organic matter is degraded through
respiration (mineralization); this process is much slower under anaerobic conditions compared
to aerobic (Kumaragamage et al., 2021). Therefore, the relative decline in OM in these piles
likely happened relatively quickly during oxygenic conditions and has not changed substantially
during the anoxic phase. A drop in OM content with topsoil stripping and storage has been
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generally recognized in the few available studies (Ghose & Kundu, 2004). For example, one 3year study found that OM and soil structure declined with increased storage time (Wick et al.,
2009) and another study found that OM declined with stockpile depth (Abdul-Kareem & McRae,
1984). Alternatively, OM content in these piles may have decreased OM content due to mixing
with subsoils while stripping. Further, higher levels of OM at the surface of QR mill stockpile
may be due to litter and vegetation establishment. Despite, the losses of OM content in these
piles, it was likely to be adequate to support plant establishment post-mining.
2.4.6

Conclusions

Our results show that stockpile height was a key factor to some soil geochemical properties,
which ultimately impacts restoration success on mine sites. However, the impact of soil depth on
nutrient deterioration was not as great as some other studies suggest (Ezeokoli et al., 2019;
Harris et al., 1989; Thurber Consultants Ltd. et al., 1990). There was not a substantial decline in
nutrients with stockpile depth in the taller, younger pile (New Afton), except for fluxes between
NO3-N and NH4-N. However, in the shorter, older pile (QR mill), OM and available P appeared
to deteriorate with increasing stockpile depths. This may indicate stockpile height was not as
influential as stockpile age (Golos et al., 2016). While most nutrient levels of the New Afton
soils were within an acceptable range for reclamation, many were depleted compared to the
reference soil. Additionally, we found an accumulation of NH4-N, Mn, and Zn with stockpile
depth, suggesting anaerobic conditions. Both stockpiles suffered from copper levels above the
recommended CCME concentration for agricultural/ residential and commercial/ industrial land
at most depths. Additionally, some samples were above the threshold levels of Cu set by the
Contaminated Sites Regulation for toxicity to livestock, invertebrates, plants, and microbial
activity. Further, the New Afton stockpile was rated as unsuitable or poor restoration soil due to
a high sodium absorption ratio. While some key nutrients do not change with depth and are
likely able to sustain revegetation, we found significant deviations in the overall stockpile soil,
especially deeper soils, compared to the native undisturbed soil. The stockpile soil conditions
suggest there may be challenges for native vegetation establishment during restoration efforts.

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Holmes PM. 2001. Shrubland restoration following woody alien invasion and mining:
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Exposure to Traffic: A Case Study on In situ Uranium Wellfields. Restoration Ecology.
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3.0

INVESTIGATING IMPACTS FROM TOPSOIL STOCKPILE
HEIGHT ON SOIL MICROBIAL COMMUNITIES

3.1 Introduction
The mining industry in British Columbia is an essential source of resources and is a key
contributor to the economy. As of 2020, there were 16 major metal and coal mines, and during
2020, the mining industry produced approximately $7.3 billion dollars and provided more than
30 000 jobs (Ministry of Energy, Mines, and Low Carbon Innovation, 2020). Mining activities
typically include mineral exploration, mineral development, mine production, and mineral
processing. These disturbance events cause lasting negative environmental impacts such as
ecosystem degradation, habitat destruction, pollution, and loss of soil carbon (Tripathi et al.,
2016). Federal and provincial regulations ensure that the restoration or reclamation of landscapes
are conducted at mine sites to repair disturbances and return the land to a sustainable ecosystem
with a historical level of productivity. Mining is particularly damaging to ecosystems because
soils are stripped from landscapes and require reconstruction, which take hundreds or thousands
of years if left to natural processes (Bradshaw, 1997).
Topsoil is a valuable bioactive substrate hosting a wide variety of organisms including plants,
microorganisms, animals, viruses, and protists. Specifically, topsoil is defined as the uppermost
part of the soil profile, typically ranging in depth from 7 to 25 cm and contains the highest
concentration of organic matter and microorganisms (Soil Science Society of America, 2008).
Moreover, topsoil which has been disturbed by human activity such as tillage or mining, is
referred to an Ap horizon. To preserve valuable topsoil in mining operations, it is common
practice to store stripped topsoil on site as a topsoil stockpile for ecosystem rehabilitation.
Stockpiling topsoil can be used post-mining to provide nutrients, structure, seeds, and amend
waste materials on site. However, long-term storage of topsoil has shown to deteriorate soil
health by altering its geochemical properties and microbial communities (Ghose & Kundu, 2004;
Gorzelak et al., 2020; Harris et al., 1989; Mummey et al., 2002b). For example, Boyer et al.
(2011) observed that topsoil stockpiles contained compacted and anaerobic soil below one meter,
resulting in a low abundance and diversity of earthworms. Moreover, a laboratory study found

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that stockpiling topsoil caused a decrease in soil organic matter degradation rate implying
diminished microbial activity (Felton & Taraba, 1994).
Soil health is key in restoration success and ecosystem functioning; soil organic matter, soil
nutrients and soil microorganisms play major roles in maintaining healthy sustainable
ecosystems. Although living soil microorganisms are less than 1% of the total soil volume
(Adhikari & Hartemink, 2016), a single gram of soil can contain about 104 species (Roesch et al.,
2007). Currently, only a small proportion of microbial diversity has been identified (DelgadoBaquerizo, 2019; Delgado-Baquerizo, Oliverio, Brewer, Benavent-González, et al., 2018;
Tedersoo et al., 2014). Soil microorganisms, particularly bacteria and fungi provide many
ecosystem functions including roles as biological regulators, chemical cyclers, and ecosystem
engineers (Saccá et al., 2017), but are extremely sensitive to disturbances and may take decades
for recovery (Bastida et al., 2008; Costantini et al., 2016; Mummey et al., 2002b). Therefore, soil
microorganisms are a key component to steering the recovery of disturbed ecosystems and
research on soil microbial functions and communities in various ecosystems and scales are
important.
Special attention should be paid to soil microbial communities in revegetation of mine soil,
where severe, large-scale disturbance occurs, particularly because soil microbes are large drivers
in organic matter decomposition and nutrient cycling of macro- and micronutrients critical for
plant growth and establishment (Gruber, 2015). Furthermore, soil bacteria and fungi form close
relationships with plants, providing services such as nutrient acquisition and protection against
environmental stress and pathogens. For example, Chen et al. (2007) found that mycorrhizal
colonization significantly increased plant growth in copper (Cu) contaminated soils. This was
thought to be a result of an increased phosphorus (P) acquisition and decreased Cu
concentrations in plant roots. Using their long, root-like hyphae network, arbuscular mycorrhizal
fungi (AMF) can acquire P in areas that are difficult to access. Additionally, AM fungi secrete
organic acid activating unavailable P sources and can increase expression of phosphate
transporter genes in plants (Wang, 2017). Therefore, soil bacteria and fungi are paramount to the
restoration process of a landscape as well as maintaining a sustainable and resilient ecosystem.
The inclusion of microbial community composition data when monitoring soil quality provides
crucial insights and a more accurate understanding of how land management impacts the soil
ecosystem.
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Microorganisms cultured in the lab for study and characterization has been occurring since
1870s. However, the use of sequencing in the 2000s highlighted that the number of observable,
culturable bacteria and fungi are just a tiny fraction of those that are present but unseen (Nilsson
et al., 2018). Therefore, molecular techniques including high-throughput DNA sequencing, have
been used as an alternative to culturing and characterization of microorganisms. Although this
sequencing technology has been around since the early-mid 2000s, the ability for accurate and
easy large-scale identification of microbial communities in the environment has not been
available until the last decade with improvements to third-generation sequencing (Land et al.,
2015). Characterizing soil microbial genomics in mining operations has the potential to aid our
ability to restore degraded sites by providing insights and improving our understanding of how
land management impacts the soil ecosystem and determining the responses of soils to
disturbances and to assess ecosystem sustainability.
Understanding how microbial communities respond to disturbance and how they recover is
critical for optimizing restoration practices. Additionally, at a time when high-throughput
sequencing technology has provided novel information about microbial taxa, we are able to
characterize the microbial communities throughout the topsoil stockpile depths, which will
provide new information to the field of ecological restoration. Although, it is well understood
that soil microbial communities are important for many ecosystem functions, it is less well
known how soil microbial activities and composition are impacted from mining disturbance.
Furthermore, very little is known of the impacts to soil microbial communities from depth
gradient in soil stockpiles. Examining soil microbial community composition across a known
environmental gradient from the top to the bottom of topsoil stockpiles has the potential to
provide insights into factors that shape the microbiome in stored soils and address knowledge
gaps. This study aims to improve our understanding of how bacterial and fungal communities in
topsoil respond to soil disturbance from stripping, piling, and long-term storage on mine sites by
providing soil microbial community composition across a depth gradient. Research investigating
soil responses over varying disturbance events and across a range of ecosystem types is critical
to developing a unifying theory of ecosystem resilience and recovery. This is especially critical
at a time when anthropogenic forces including climate change are causing increasingly extreme
changes to the environment.

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3.2 Methods
3.2.1

Soil Sampling and Study Sites.

Information about the two study sites, New Gold’s New Afton and Barkerville Gold Mines’
Quesnel River (QR) mill and details regarding soil sampling are covered in Section 2.2.1 and
Section 2.2.1. Additionally, information about how the soil geochemical data was analysed is
covered in Section 2.2.3.
3.2.2

Metabarcoding Microbial Communities

The soil microbial community composition in each sample was characterised for both fungal
and bacterial OTUs in the Applied Genomics Laboratory at TRU. Deoxyribose nucleic acids
(DNA) from the soil samples was extracted using the MagAttract PowerSoil DNA Kit (Qiagen
Inc.). Extractions were followed by DNA quantification using Qubit™ dsDNA HS Assay Kit
(Thermo Scientific). The set of primers: 341R and 806F were used to amplify the 16S rRNA
gene and the set of primers: ITS86F and ITS4R were used to amplify the internal transcribed
spacer (ITS) region between the 5.86S rRNA gene and the 28S rRNA genes (Vancov & Keen,
2009). Samples were run through a second round of polymerase chain rection (PCR) including
the addition of barcoded primers. The thermocycler program for first round bacterial and fungal
amplicon generation consisted of: 95°C for 4 min followed by 25 cycles of 95°C for 30 seconds,
53.4°C for 45 seconds and 72°C for 2 minutes, with a final extension at 72°C for 5 minutes. For
generating sequencing barcode adapted bacterial and fungal amplicons, the thermocycler
program was the same except that 20 cycles were used with an annealing temperature of 65°C.
After the first and second round of PCR, the amplicons were purified using the Inovant and
AgenCourt AMPure (Beckman Coulter Inc.) magnetic beads prior to sequencing. Barcoded
amplicons were quantified using a Qubit dsDNA HS Assay Kit (Thermo Fisher Scientific,
Mississauga, ON) and visualized on an agarose gel. This was followed by pooling samples in
equimolar amounts and purified following agarose gel electrophoresis using an E.Z.N.A Gel
Extraction Kit (Omega Bio-Tek) and on an Ion Torrent PGM (Life Technologies Inc., Carlsbad,
CA), where the library dilution factor was determined using an Ion Library Quantitation Kit and
sequenced them on an Ion S5XL (ThermoFisher Scientific) (Fantini et al., 2015).

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3.2.3

Processing and Statistical Analysis
Data were processed using AMPtk version 1.5.1 (Palmer et al., 2018) for quality filtering,

OTU clustering at 97% sequence identity, and to assign taxonomies. Kept reads, above 10 000,
were compared for taxonomy-based analysis. For both bacteria and fungi, amplicons were
analyzed using the Quantitative Insights Into Microbial Ecology (QIIME) v. 1.9.0 (Caporaso et
al., 2010) workflow. Reads were filtered to remove reads below a quality filter (Q20). Chimeras
were filtered using “USEARCH” and taxonomy was assigned using the assign_taxonomy.py
QIIME script with the GreenGenes (DeSantis et al., 2006) database using “UCLUST”. Detection
limits will vary between samples due to differences in yields between sequencing runs and
unequal representation of samples in pooled sequencing libraries (Hugerth & Andersson, 2017).
Thus, to compare data sets efficiently and to avoid bias, the reads were rarified as suggested by
McKnight (2019). Rarefaction curves of the observed richness were calculated in R using 1000fold resampling without replacement using the Vegan package (Appendix D). Read numbers
were standardized to 10 000 reads per sample to reduce bias (McKnight, 2019). Operational
Taxonomic Units (OTUs) were normalized using the “rarefy_even_depth” function in the
Phyloseq package without subsampling. To test for significant differences in the composition
and/or relative abundances of bacteria and fungi in samples from different stockpile depths, nonparametric PERMANOVA tests were performed using the “Adonis” function from the Vegan
package in R with 999 permutations (Anderson, 2001).
After processing the sequencing data, our analysis classified 11 828 bacterial and 27 074
fungal read counts in New Afton and 10 496 bacterial and 17 273 fungal read counts in QR mill.
Additionally, our results identified 7 627 bacterial and 2 856 fungal OTUs in New Afton and 6
894 bacterial and 1 797 fungal OTUs in QR mill. A sampling depth of 10 000 reads per sample
appeared to be sufficient OTU richness according to rarefaction curves (Appendix D).
To determine if the mean relative proportion of taxonomic orders varied significantly
between stockpile depth, rare OTUs were removed (less than 1% of total abundance per sample)
and were then collapsed OTUs at the Phylum level. Mixed effects linear regression model was
used to test for significant differences among stockpile depths. PIME was used in conjunction
with Phyloseq to find the OTUs in the bacterial and fungal taxa that changed the most over
stockpile depths (set to a prevalence level of 70%). PIME builds randomized decision trees,
where each tree gives a vote for the prediction of the target variable (Dobbler & Roesch, n.d.;
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Roesch et al., 2020). To measure differences in OTU richness in bacterial and fungal
communities between stockpile depths, Shannon Diversity Index was calculated for each depth.
Because the data violated assumptions of normal distribution even after transformations,
Kruskal-Wallis Rank Sum test was used to determine if there were any significant differences
between depth intervals. Non-metric multidimensional scaling (NMDS) ordinations
(“metaMDS” function in vegan, try = 500) was used to describe patterns in microbial community
composition (based on unweighted UniFrac distance measures) between stockpile depths.
Permutation-based significance tests (n = 999) with the “envfit” function were used to fit
geochemical and microbial variables to the NMDS ordination using soil pits as set blocks.

3.3 Results
3.3.1

Community Composition

Results showed that the topsoil stockpiles were dominated by the following bacterial phyla:
Acidobacteria, Actinobacteria, Bacteroidetes, Firmicutes, and Proteobacteria; and Acidobacteria
in New Afton (Figure 3.1) and Actinobacteria, Bacteroidetes, Candidatus_Saccharibacteria,
Chloroflexi, Firmicutes, Proteobacteria, Spirobahaetes, and Verrucomicrobiota in QR mill
(Figure 3.2). The identified soil fungi were mainly composed of Ascomycota, Basidiomycota,
and Mortierellomycota in New Afton (Figure 3.1), and Ascomycota, Basidiomycota,
Mortierellomycota, and Rozellomycota phyla in QR mill (Figure 3.2).
Both bacterial and fungal phyla were generally consistent between different stockpile depths
in New Afton (Figure 3.1, Appendix E), except for the decrease in Mortierellomycota with
depth, whereas several of the bacterial and fungal phyla fluctuated significantly with stockpile
depth in QR mill (Figure 3.2, Appendix E). Of the five bacterial phyla in New Afton and nine
bacterial phyla in QR mill, all were present in the stockpile soil and reference soil, except for
Spirochaetes in QR mill. Of the three fungal phyla in New Afton and four fungal phyla in QR
mill, all were present in the stockpile soil and reference soil. The proportion of Firmicutes tended
to increase at the bottom of the New Afton stockpile (610-1372 cm) and had much lower
presence in the reference soil (Figure 3.1, Appendix E, P = 0.06) proportion of
Mortierellomycota decreases with stockpile depth (Appendix E, P = 0.012). The proportion of
Bacteroidetes, Chloroflexi, Firmicutes, and Spirochaetes increased substantially or were present

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exclusively in the bottom of the QR mill stockpile below 60-120 cm (Figure 3.2, Appendix E, P
< 0.05). The proportion of Acidobacteria, Actinobacteria, Proteobacteria, and Verrucomicrobia
decreased substantially or were present exclusively in the surface of the QR mill stockpile above
60-120 cm (Figure 3.2, Appendix E, P < 0.05). Congruent with the bacteria results, the
proportion of Rozellomycota significantly increased at the bottom of the QR mill stockpile after
60-120 cm (Figure 3.2, Appendix E, P = 0.01). There was approximately 10-19% unknown
bacterial phyla and 8-35% unknown fungal phyla identified in New Afton and 12-15% unknown
bacterial and 1-6% unknown fungal phyla identified in QR mill. The relative proportion within a
sample also composed of approximately 3-5% bacterial phyla and 1% fungal phyla identified in
New Afton and 1-3% bacterial phyla and 1-3% fungal phyla in QR mill that had less than 1%
relative proportion within a depth and were combined and categorized in as an ‘others’ group.
The PIME analysis identified the top ten OTUs that showed significantly different proportion
between stockpile depths. Six bacteria genera were identified in the New Afton stockpile:
Fibrobacter, GP4, Inquilinus, Nocardiodes, Pedobacter, and Subdivision3 Genera Incertae Sedis
(Appendix F). Seven fungi classes were identified in the New Afton stockpile: Alternia,
Cladophialophora, Gamsia, Geomyces, Mortierella, Phialemonium, and Preussia. Six bacteria
genera were identified in the QR mill stockpile: Anaeromyxobacter, GP1, Mycobacterium,
Rhizobium, Roseiarcus, and Subdivision3 Genera Incertae Sedis (Appendix F). Six fungi genera
were identified in the QR mill stockpile: Chaetomium, Conicochaeta, Exophiala, Leohumicola,
Penicillium, and Pseudeurotium. There was a large proportion of bacteria and fungi that were
unclassified at the genus level at both sites.
3.3.2

Alpha Diversity

Our results showed no evidence of significant changes in alpha diversity measures for
bacterial or fungal communities (observed and Shannon index) with stockpile depth in New
Afton (P > 0.1, Figure 3.3). The bacterial and fungal alpha diversity in the New Afton stockpile
was more similar to the reference soil than the deep topsoil samples. In contrast, the observed (R2
= 0.39, P < 0.01) and Shannon index (R2 = 0.34, P = 0.02) diversity for bacterial communities
and the observed diversity for fungal (R2 = 0.44, P < 0.01) communities in the QR mill stockpile
significantly decreased with depth (Figure 3.4). There was no evidence that the Shannon index
decreased with depth in fungal communities in the QR mill stockpile (Figure 3.4, R2 = 0.08, P =
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0.2). The bacterial and fungal alpha diversity of the surface of the QR mill stockpile was most
like the reference soil.
3.3.3

Beta Diversity and Geochemical Properties

Operation taxonomic units obtained from NMDS showed a weak, but statistically significant
correlation between stockpile depths and bacterial (R2 = 0.11, P < 0.01) and fungal (R2 = 0.10, P
< 0.01) community composition (Figure 3.5) in New Afton. The results may suggest weak
spatial structure for fungal communities across the four depth intervals. Additionally, the
bacterial and fungal communities in the reference soil was relatively distinct from the stockpile
soil in New Afton (Figure 3.5). Operation taxonomic unit communities obtained from NMDS
showed a weak, but statistically significant correlation between stockpile depths and bacterial (R2
= 0.25, P < 0.01) and fungal (R2 = 0.14, P < 0.01) community composition (Figure 3.6) in QR
mill. The results suggest spatial structure for bacterial and fungal communities across stockpile
depths. Additionally, the bacterial and fungal reference soil was relatively similar to the surface
of the stockpile soil and distinct from the bottom depths (Figure 3.6).
Envfit analysis shows that the majority of variation in beta-diversity of New Afton bacterial
communities was attributed to available Zn, Fe, and Mn (P < 0.05), while fungal communities
was attributed to available NO3-N and C/N (Figure 3.5, P < 0.05). Additionally, the majority of
variation in beta-diversity of QR mill was attributed to available P, organic matter (OM),
potassium (K), ammonium (NH4-N), manganese (Mn), electrical conductivity (EC), P, sulphur
(S), iron (Fe), and magnesium (Mg) (P < 0.05) for bacteria and available P, OM, NH4-N, Mn, P,
sodium (Na), and pH (P < 0.05) for fungi (Figure 3.6).

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Figure 3.1 Coloured barplots showing relative proportion of a) bacterial and b) fungal phyla by stockpile depth in New Afton. Phyla categorized in the “Other”
group are composed of combined phyla with <1% relative abundance.

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Figure 3.2 Coloured barplots showing relative proportion of the top 99% a) bacterial and b) fungal phyla by stockpile depth in QR mill. Phyla categorized in the
“Other” group are composed of combined phyla with <1% relative abundance.

a)
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Figure 3.3 Bar plots showing observed and Shannon diversity measures of a) bacterial and b) fungal communities from varying stockpile depth
intervals and reference soil from New Afton.

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a)

b)

Figure 3.4 Regression plots showing observed and Shannon diversity measures of a) bacterial and b) fungal communities from varying stockpile
depth intervals and reference soil from QR mill. The blue lines represent linear models and the shaded area in grey represents a 95% confidence
interval.

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Figure 3.5 Non-metric multidimensional scaling (NMDS) analysis for New Afton. NMDS scatterplot of samples representing the OTU community
composition of soil a) bacterial and b) fungal biota from a reference soil and at four depths from four soil core samples from the New Afton
stockpile. The distance between points indicated the degree of difference based on Unweighted Unifrac similarities of OTU composition in each
sample.
Note: Chemical parameters ending in “.1” have been normalized by log transformation.

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a)

b)

Figure 3.6 Non-metric multidimensional scaling (NMDS) analysis for QR mill. NMDS scatterplot of samples representing the OTU community
composition of soil a) bacterial and b) fungal biota from a reference soil and at varying depths from three soil core samples from the QR mill
stockpile. The distance between points indicated the degree of difference based on Unweighted Unifrac similarities of OTU composition in each
sample. NMDS calculations based on significant soil chemical parameters and the ordination scores on each NMDS axis.

Note: Chemical parameters ending in “.1” have been normalized by log transformation

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3.4 Discussion
3.4.1

Community Composition

Through our molecular analysis, we provided an assessment of response of soil microbial
communities caused by long term storage of topsoil. The bacterial and fungal community
structure at New Afton and QR mill were generally inconsistent with those described by global
trends (Figure 3.2) (Delgado-Baquerizo, Oliverio, Brewer, Benavent-gonzález, et al., 2018;
Tedersoo et al., 2014). Similar to global trends in soil bacterial communities (DelgadoBaquerizo, Oliverio, Brewer, Benavent-González, et al., 2018), Actinobacteria and
Proteobacteria were the dominant bacteria phyla identified throughout the New Afton stockpile
and the surface of the QR mill stockpile, however, the proportions were less than expected.
Additionally, proportions of Planctomyctes, Verrucomicrobia, and Chloroflexi in New Afton
were much lower than projected in this study; they were either not present or have a relative
proportion less than 1%; only the presence of Planctomycetes was missing from the QR mill
samples. Comparing to global trends and other profiles, the QR mill samples contained a
relatively high proportion of rare phyla at the bottom of the stockpile, which started around 60120 cm, including Spirochaetes, Firmicutes, Candidatus Saccharibacteria, and Rozellomycota
(Delgado-Baquerizo, Oliverio, Brewer, Benavent-gonzález, et al., 2018; Eilers et al., 2012;
Gorzelak et al., 2020; Hansel et al., 2008; Tedersoo et al., 2014). Our results also differed from
the study by Gorzelak et al. (2020) where more than half of the bacterial sequences found in
topsoil stockpiles were Chloroflexi. While the soil microbial communities do not typically
follow patterns shown in other studies, the New Afton stockpile samples were generally
consistent with the reference soil. In contrast, the microbial communities in the QR mill stockpile
shifted considerably, and the reference soil most resembles soils in the surface samples.
Despite several large geochemical alterations with stockpile depths in New Afton, such as the
large influx of Cu levels in the middle of the stockpile (Figure 2.7), the microbial community
remained relatively consistent over depth. This was contrary to the understanding that microbial
communities are highly sensitive and reactive to environmental changes. One explanation was
that the major factors known to influence soil microbial community structures (for example pH
and OM) remained relatively stable through stockpile depths in New Afton (Figure 2.7). The

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most pronounced changes found within the topsoil stockpile profiles occurred in QR mill. The
change in the relative proportions of Firmicutes, Bacteroides, Spirochaetes, and Candidatus
Saccharibacteria along the stockpile depths was apparent.
Coniochaeta, a known plant pathogen (Delgado-Baquerizo et al., 2020) was found
throughout the QR mill stockpile depths and had notable increased with depth until 390 cm,
where its relative proportion was more than 50%. Additionally, the plant pathogen, Alternaria
(Delgado-Baquerizo et al., 2020), was found in all stockpile depths in New Afton, but not the
reference soil. These pathogens may have negative impacts on plant productivity and health
during post-mining reclamation. A higher-than-normal fungal plant pathogen load are found in
other topsoil stockpile samples as well, namely Phoma, Fusarium, and Alternaria (Ezeokoli et
al., 2019b; Gorzelak et al., 2020).
Anaeromyxobacter was present from 60-120 cm depth to the bottom 500-575 cm depth in
QR mill, potentially decreasing with depth. This supports the understanding that anaerobic
conditions in topsoil stockpiles generally become apparent around 100-200 cm (Abdul-Kareem
& McRae, 1984; Boyer et al., 2011; Harris et al., 1989; Williamson & Johnson, 1990), which
was also shown in our geochemical data for QR mill (Figure 2.8). It is possible that anaerobic
taxa existed around 100 cm in New Afton, but was not captured by our analysis.
Unexpectedly, we found a higher proportion of Rhizobium, a nitrogen-fixing bacteria
(Zahran, 1999) in the middle of the QR mill stockpile, from 60-120 down to 350-390 cm
(Appendix F). Rhizobia are known to be resilient to stockpile soil disturbance (Jasper, 2007)and
may have thrived at these depths due to reduced biota competition along with NH4-N
accumulation at these depths. Similarly, Leohumicola, typically heat-tolerant, mycorrhizal soil
fungus (Hambleton et al., 2005) increased at the same depths. Birnbaum et al. (2017) found a
higher presence of soil arbuscular mycorrhizal fungi, but not Rhizobia, in 10-year-old topsoil
stockpiles compared to younger stockpiles, suggesting that mycorrhizal communities in
stockpiles re-establish over time. However, only the top 10 cm of the stockpiles were sampled in
this study. Both Leohumicola and Rhizobia are highest around 60-120 cm of the QR mill
stockpile, likely having a positive impact on plant growth and health during respreading.
The differences in these soil microbes in QR mill corresponded to similar changes in soil
OM, C/N, EC, available P, and K changes with stockpile depth (Figure 3.6). Thus, soil resource
availability is likely a key factor responsible for the observed changes throughout the profiles.
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Additionally, communities from depths below 20 cm or 60 cm were relatively similar compared
to the surface samples, likely due to the decreased edaphic factors deep in the stockpile.
3.4.2

Alpha Diversity

Alpha diversity was measured to capture the diversity within samples by estimating the
observed OTUs (richness) and Shannon’s diversity index. Decreases in microbial diversity
occurred in QR mill only, with the most pronounced changes occurring below approximately 60
cm for bacteria and below 20 cm for fungi. A decrease in microbial diversity with depth has been
observed in other studies, anticipated to be from depleted C levels (Eilers et al., 2012; Hansel et
al., 2008). In support of this theory, the observed trend of microbial diversity depletion with
stockpile depth in QR mill generally corresponded to the decrease in OM and increase in C/N,
especially when soil was deeper than 20 cm. Additionally, Gorzelak et al. (2020) found that
bacterial richness tended to decrease with stockpile age. Overall, this may indicate that less
surface microorganisms, typical for topsoil, are not thriving below the surface in large stockpiles
stored for long periods. Furthermore, a lower microbial diversity could have negative impacts on
plant communities during re-spreading. For instance, Maron et al. (2018) demonstrated that
lower microbial diversity negatively affected ecosystem functioning such as nutrient availability.
Furthermore, Tedersoo et al. (2014) found that a decrease in fungal richness was correlated with
a decrease in plant richness, however, likely a result from shared environmental variables as
opposed to direct effects from plant-fungal linkages.
3.4.1

Beta Diversity and Geochemical Properties

Beta diversity measures are estimates of similarity or dissimilarity between populations. The
NMDS plots showed that the bacterial and fungal communities present in the stockpile samples
are relatively similar and vary greatly compared to the reference soil in QR mill (Figure 3.5).
However, the surface bacterial and fungal communities in QR mill were more similar to the
reference soil compared to deep samples (Figure 3.6). This indicates that the storage of topsoil
can steer communities in the deeper soils were farther away from a historical structure,
potentially creating a greater barrier to restoring native ecosystems. For example, Mummey et al.
(2002) showed that re-applied stockpiled topsoil with depleted microbial activity had detrimental
effects on restoration, even 20 years after seeding of the reclaimed sites. Furthermore, the
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significant correlation observed between the soil microbial communities and geochemical
properties with varying stockpile depths may indicate a change in ecosystem nutrient cycling as
stockpile depth increases.
Current research has revealed pH to be the best predictor of microbial composition and
diversity (Xiao et al., 2021; Xue et al., 2018), but not over a depth gradient (Fierer et al., 2003).
The results showed soil pH as an important predictor for fungal beta diversity in QR mill, but not
bacteria. Additionally, pH has been strongly linked to AMF distribution (Davison et al., 2021),
therefore, it is likely that pH may be the cause to the increase in Leohumicola fungi in the middle
of the stockpile.

3.5 Conclusions
Our results showed that stockpile height is a key factor to soil microbial communities in one
of the two sites studied. The change in microbial communities ultimately impacts restoration
success on mine sites. Our results have demonstrated that bacterial and fungal communities show
comparable responses to stockpile depth in long-term topsoil storage. Relative proportions of
dominant phyla, alpha diversity, beta diversity of bacteria and fungi showed little to no changes
between stockpile depths in New Afton, whereas they both showed considerable alterations in
the QR mill stockpile samples, especially when deeper than approximately 60-120 cm. The
altered microbial community structure and diversity observed in the bottom of the QR mill
stockpile may have negative impacts for soil biological and chemical processes in the future,
such as nutrient cycling and vegetation establishment when re-applied (Gorzelak et al., 2020;
Mummey et al., 2002b). The potential positive influence from the influx of the beneficial
microorganisms Rhizobia and Leohumicola found below the surface of the QR mill stockpile
may be outweighed by the negative effects from plant pathogens (Gorzelak et al., 2020)
Coniochaeta and Alternaria. Additionally, the alterations observed in microbial communities in
both stockpiles compared to the reference soil may hinder post-mining restoration to a native
ecosystem. However, it is possible that soil microorganisms will recover over time in soil
stockpiles, especially if they are revegetated (Banning et al., 2011; Jasper et al., 1987; Sheoran et
al., 2010) . The relatively smaller changes with stockpile depth observed at New Afton compared
to QR mill may be a product of various site-specific influences, such as the difference in
stockpile age or the timing of additional of fresh topsoil.
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Vasco-Palacios, A. M., Thu, P. Q., Suija, A., Smith, M. E., Sharp, C., Saluveer, E., Saitta, A.,
Rosas, M., Riit, T., Ratkowsky, D., Pritsch, K., Põldmaa, K., … Abarenkov, K. (2014).
Global diversity and geography of soil fungi. Science, 346(6213).
https://doi.org/10.1126/science.1256688
Tripathi, Ni., Singh, R., & Hills, C. (2016). Reclamation of Mine-impacted Land for Ecosystem
Recovery. Wiley-Blackwell Publishers, UK.
van der Putten, W. H., Vet, L. E. M., Harvey, J. A., & Wäckers, F. L. (2001). Linking above- and
belowground multitrophic interactions of plants, herbivores, pathogens, and their antagonists.
Trends in Ecology and Evolution, 16(10), 547–554. https://doi.org/10.1016/S01695347(01)02265-0
Wang, F. (2017). Occurrence of arbuscular mycorrhizal fungi in mining-impacted sites and their
contribution to ecological restoration: Mechanisms and applications. Critical Reviews in
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Williamson, J. C., & Johnson, D. B. (1990). Mineralisation of organic matter in topsoils
subjected to stockpiling and restoration at opencast coal sites. Plant and Soil, 128(2), 241–
247. https://doi.org/10.1007/BF00011115
Xiao, E., Ning, Z., Xiao, T., Sun, W., & Jiang, S. (2021). Soil bacterial community functions and
distribution after mining disturbance. Soil Biology and Biochemistry, 157(March), 108232.
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Xue, P. P., Carrillo, Y., Pino, V., Minasny, B., & McBratney, A. B. (2018). Soil Properties Drive
Microbial Community Structure in a Large Scale Transect in South Eastern Australia.
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Zahran, H. H. (1999). Rhizobium -Legume Symbiosis and Nitrogen Fixation under Severe
Conditions and in an Arid Climate . Microbiology and Molecular Biology Reviews, 63(4),
968–989. https://doi.org/10.1128/mmbr.63.4.968-989.1999

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4.0

GENERAL CONCLUSIONS

4.1 Key Findings
Our results contribute to a new perspective for ecosystem restoration practices which
emphasizes belowground processes. The literature on soil impacts of stockpiling topsoil is
limited, especially those examining samples in large stockpiles below the first meter, thus not
accurately representing the whole stockpile. In this study, we captured soil quality changes
occurring with stockpile height by sampling and characterizing whole soil profiles. The results
confirmed the hypothesis that topsoil storage height has had adverse effects on topsoil quality,
but only at one of the sampled profiles, QR mill. However, there was evidence of depleted soil
quality and significant geochemical and microbial alterations with depth at both sites. The older,
shorter stockpile (QR mill) seemed to experience relatively more nutrient depletion and
microbial community shifts with depth compared to the younger, taller stockpile (New Afton).
Though, the soil quality at each mine operation will also be strongly influenced by site specific
differences that are not accounted for in this study such as stockpile management, subsoil
presence, soil texture, climate, and geology. For example, Abdul (1984) found that nutrient
fluctuations in stockpile profiles were influenced by soil texture.
At one or both of the sampled topsoil profiles, there was a decline in nutrients and microbial
diversity and an accumulation of metals, and beneficial, pathogenic, anaerobic, and rare
microbial genera with increasing stockpile depth. The geochemical properties and microbial
communities in the stockpiles, especially the deeper soils, varied from reference soils and global
trends for soil bacteria and fungi (Delgado-Baquerizo, Oliverio, Brewer, Benavent-González, et
al., 2018; Tedersoo et al., 2014), which may negatively affect the ability to restore the sites to a
historical state (Baer, 2016; Kumar & Gopal, 2015). The variations observed in bacterial or
fungal communities were best explained by pH, electrical conductivity (EC), organic matter
(OM), nitrate (NO3-N), ammonium (NH4-N), carbon/nitrogen (C/N) ratio, total and available
phosphorus (P), potassium (K), sulphur (S), magnesium (Mg), manganese (Mn), zinc (Zn), iron
(Fe), and sodium (Na). Many of these changes, including an increase in anaerobic bacteria and
an accumulation of metals became apparent when comparing to surface samples with those
below approximately 60-120 cm, likely indicating anaerobic conditions, which was generally

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consistent with other findings (Abdul-Kareem & McRae, 1984; Boyer et al., 2011; Harris et al.,
1989). Most nutrient levels in the stockpiles were likely able to sustain revegetation post-mining.
However, the New Afton stockpile suffered from copper levels above the recommended CCME
concentration for agricultural/ residential and commercial/ industrial land and all depths were
rated unsuitable or poor as a restoration soil due to a high sodium absorption ratio. While some
studies show a recovery in soil geochemical properties and microbial communities towards
reference conditions over long periods of time (Gasch et al., 2014; Wick et al., 2007), others
such as Mummey et al. (2002) showed long-lasting negative implications for the disturbed site.
Given the results detailed in this study, the potential of the topsoil after long-term storage is
likely not enough to fully restore a functional and structurally representative native habitat at
both mining operations without amendments. These stored soils, especially New Afton, will
require enhancement prior to placement for reclamation to achieve historical conditions.
Importantly, these results demonstrated the importance of interdependent ecological,
geochemical, and biological processes, and the role of belowground processes in determining the
success of restoration efforts.

4.2 Topsoil Management Implications
4.2.1

Stockpile Height

Findings from Golos et al. (2016) suggested that plant establishment is worse when grown in
stored subsurface topsoil. Similarly, our results showed that topsoil height was a key factor in
how topsoil retains its quality components and functions especially when soil depth reached
below approximately 60-120 cm depths. Particularly, at these depths, there was a decline in key
plant nutrients (available P, NO3-N, and OM) and microbial diversity and an accumulation of
metals (Cu, Fe, and Zn) in one or both of the stockpiles. These results generally aligns with other
findings (Abdul-Kareem & McRae, 1984; Boyer et al., 2011; Harris et al., 1989) and with
current best practice recommendations to keep topsoil stockpiles for less than 1 year and under
600 cm (Natural Resources Canada, 2017); although, some best management practices
recommend stockpiles below 130 cm (The City of Calgary Parks, 2018).

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4.2.2

New Afton

Apart from changes in Cu, Fe, Na, Zn, NO3-N, and Mortierellomycota, the New Afton soil
had relatively consistent geochemical properties and microbial communities with stockpile
depth. However, there was strong evidence of overall deterioration due to storage compared to
the reference soil compared to the reference soil which may hinder restoration efforts. A
previous Masters project found that roughening and loosening of the topsoil stockpile at New
Afton prior to seeding, improved the success of native plant establishment on the stockpile of
interest (Baethke, 2015). Although, the study only looked at the surface of the stockpile and a
greenhouse experiment showed poor plant health when grown in stockpile soil at varying depths
in New Afton (1Appendix A). Many samples in the stockpile contained copper levels above the
recommended CCME concentration for agricultural/ residential and commercial/ industrial land
(CCME, 1999) and all depths were alkaline and rated unsuitable or poor as a restoration soil due
to a high sodium absorption ratio (SAR) (Macyk et al., 2004). Although soil texture was not
measured in this study, there was a high frequency of coarse fragments found in the New Afton
stockpile. The high SAR, alkalinity, accumulations of metals, and large percentage of stones and
cobbles is likely to have a negative affect on revegetation and will require soil amendments.
Because Na was high and EC was relatively low, calcium (Ca) amendments are
recommended in New Afton to improve the sodium absorption ratio. Additionally, S or lime can
be added to neutralize pH (BC Ministry of Agriculture, 2015), although organic amendments
have been known to reduce pH as well. Organic amendments should be added to improve soil
nutrients, and decrease metal availability (Mendez & Maier, 2008; Ohsowski et al., 2012).
Biosolids and compost amendments have shown to reduce some metals and increase nutrients,
however Gardner et al. (2012) and Sidhu et al. (2016) found no improvement of soil Cu
concentrations. The addition of arbuscular mycorrhizal fungus (AMF), Glomus mossae,
improved plant establishment reduced Cu availability and increased nutrient availability in Cu
tailings (Chen et al., 2007). As discussed in Appendix A, recent research has suggested that
native soil inoculations can significantly improve soil quality and microbial communities
(Carbajo et al., 2011; Emam, 2016; Li et al., 2015; Middleton & Bever, 2012). Although,
potentially economically difficult, this would assist in steering restoration to a historical state.
Therefore, a combination native donor soil, Ca, S fertilizer or lime, biosolids or compost, and
AMF is suggested to ameliorate the topsoil conditions at New Afton.
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4.2.3

QR mill

Although the stockpile was rated as fair or good at all depths for reclamation suitability based
on salinity and pH (Macyk et al., 2004), many samples in the stockpile contained copper levels
above the recommended CCME concentration for agricultural/ residential and commercial/
industrial land (CCME, 1999), which may have negative impacts on revegetation success.
Additionally, the stockpile had depleted levels in nutrients OM, N, and P and microbial diversity.
Similar to New Afton, Organic sources such as biosolids or compost should be added to
restore soil microbial diversity, decrease metal availability, and improve soil nutrients (Mendez
& Maier, 2008; Ohsowski et al., 2012), namely OM, N, and P. Biosolids and compost
amendments have shown to reduce some metals and increase nutrients, however Gardner et al.
(2012) and Sidhu et al. (2016) found no improvement of soil Cu concentrations. The addition of
AMF, Glomus mossae, improved plant establishment reduced Cu availability and increased
nutrient availability in Cu tailings (Chen et al., 2007). As discussed in Appendix A, recent
research has suggested that native soil inoculations can significantly improve soil quality and
microbial communities (Carbajo et al., 2011; Emam, 2016; Li et al., 2015; Middleton & Bever,
2012). Although, potentially economically difficult, this would assist in steering restoration to a
historical state. Therefore, a combination native donor soil, biosolids or compost, and AMF is
suggested to ameliorate the topsoil conditions at QR mill.

4.3 Limitations and Future Research
The sequencing approach in this study allowed the detection of structural changes in
microbial community at individual microbial taxa that occurs with varying physical properties.
However, a limitation of some 16S and ITS high-throughput sequencing of taxonomic groups is
that most strains functionality cannot be predicted (Beiko, 2015). For instance, closely related
strains can differ significantly in metabolism (Douillard et al., 2013). Therefore, making
conclusions about microbial community functions at the genera level concisely is somewhat
challenging. Moreover, biases during polycyclic chain reaction (PCR) can occur, where shorter
fragments are preferentially amplified and mistakes such as chimaera formations (Nilsson et al.,
2018). Furthermore, rarefying samples to create equal sequencing depths discards some samples
resulting in lost information.

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This study assessed 20 major geochemical elements in the soil to measure soil quality,
however, because the results demonstrated high levels of Cu in both topsoil stockpiles, future
topsoil stockpile research should assess other contaminants of concern commonly associated
with copper mines including Cd, Co, Cr, Ni, and Pb. Moreover, not all nutrient sources for plants
were considered in this study. For example, in accordance with most research, we focused on
NO3-N and NH4-N nitrogen sources due to their dominance in agricultural soils. However, plants
also absorb NO2-2 and organic N sources including urea, amino acids, and peptides (Hachiya &
Sakakibara, 2017). Although, there may have been sources of nutrients that were not calculated
as part of our study; we believe it would not significantly change our analysis in this study.
Limitations of soil quality that may impact restoration success were uncovered during this
study. However, it is unknown how restoration and plant establishment may unfold once soil is
respread on site. A field or greenhouse study investigating how plants establish and respond to
soil from varying stockpile depths would provide critical information regarding restoration
success.
Results of this study are likely weakened from compounding factors of the potential of
subsoils and parent materials mixed with topsoil in the piles during salvage. This can unevenly
dilute the topsoil quality and cause differences within the stockpile and cannot be accounted for.
Future operational studies on topsoil stockpiles should try and obtain as detailed information as
possible to account for these variables. Additionally, this study did not assess soil texture content
which has major impacts on plant establishment and overall soil quality. Future research in this
area should include soil texture analysis (percent sand, silt, and clay) to improve the quality of
results as it is closely related to soil geochemistry and biological functions.
Additional analyses can be explored using the geochemical and molecular data collected in
this study. For example, further processing of the sequencing data can reveal and identify
important soil taxa occurring with stockpile depth and geochemical properties. This study
sampled two stockpiles at two sites; more samples at other Cu-Ag operations including multiple
stockpiles at a single site are needed to be able to observe key patterns in topsoil quality changes
from stockpile height and reduce influences from site specific factors (i.e., stockpile age,
ecosystem, climate, geology). The next phase should be to study more stockpiles and incorporate
field or greenhouse studies to test how native vegetation establishes at varying depths and over

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time. Researching the response of soil properties to disturbance will increase our knowledge in
the effects of stockpile storage on ecosystem restoration success.

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4.4 Literature Cited
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stockpiles. Plant and Soil, 76(1–3), 357–363. https://doi.org/10.1007/BF02205593
Baer, S. G. (2016). Nutrient Dynamics as Determinants and Outcomes of Restoration. In
Foundations of Restoration Ecology.
Baethke, K. A. (2015). Mine Restoration of a Native Grassland Plant Community in the British
Columbia Interior: the use of Biochar, Hydroseeding, and Raking. Thompson Rivers
University. MSc Thesis.
BC Ministry of Agriculture. (2015). Soil Factsheet: Soil pH. 637.100-1
Beiko, R. G. (2015). Microbial malaise: How can we classify the microbiome? Trends in
Microbiology, 23(11), 671–679. https://doi.org/10.1016/j.tim.2015.08.009
Boyer, S., Wratten, S., Pizey, M., & Weber, P. (2011). Impact of soil stockpiling and mining
rehabilitation on earthworm communities. Pedobiologia, 54(SUPPL.).
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Carbajo, V., den Braber, B., van der Putten, W. H., & de Deyn, G. B. (2011). Enhancement of
late successional plants on ex-arable land by soil inoculations. PLoS ONE, 6(7).
https://doi.org/10.1371/journal.pone.0021943
CCME. (1999). Canadian soil quality guidelines for the protection of environmental and human
health - Copper (1999). Canadian Environmental Quality Guidelines, 7.
Chen, B. D., Zhu, Y. G., Duan, J., Xiao, X. Y., & Smith, S. E. (2007). Effects of the arbuscular
mycorrhizal fungus Glomus mosseae on growth and metal uptake by four plant species in
copper mine tailings. Environmental Pollution, 147(2), 374–380.
https://doi.org/10.1016/j.envpol.2006.04.027
Delgado-Baquerizo, M., Oliverio, A. M., Brewer, T. E., Benavent-González, A., Eldridge, D. J.,
Bardgett, R. D., Maestre, F. T., Singh, B. K., & Fierer, N. (2018). A global atlas of the
dominant bacteria found in soil. Science, 359(6373), 320–325.
https://doi.org/10.1126/science.aap9516
Douillard, F. P., Ribbera, A., Kant, R., Pietilä, T. E., Järvinen, H. M., Messing, M., Randazzo, C.
L., Paulin, L., Laine, P., Ritari, J., Caggia, C., Lähteinen, T., Brouns, S. J. J., Satokari, R.,
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von Ossowski, I., Reunanen, J., Palva, A., & de Vos, W. M. (2013). Comparative Genomic
and Functional Analysis of 100 Lactobacillus rhamnosus Strains and Their Comparison with
Strain GG. PLoS Genetics, 9(8). https://doi.org/10.1371/journal.pgen.1003683
Emam, T. (2016). Local soil, but not commercial AMF inoculum, increases native and nonnative grass growth at a mine restoration site. Restoration Ecology, 24(1), 35–44.
https://doi.org/10.1111/rec.12287
Gardner, W. C., Anne Naeth, M., Broersma, K., Chanasyk, D. S., & Jobson, A. M. (2012).
Influence of biosolids and fertilizer amendments on element concentrations and revegetation
of copper mine tailings. Canadian Journal of Soil Science, 92(1), 89–102.
https://doi.org/10.4141/cjss2011-005
Gasch, C., Huzurbazar, S., & Stahl, P. (2014). Measuring soil disturbance effects and assessing
soil restoration success by examining distributions of soil properties. Applied Soil Ecology,
76, 102–111. https://doi.org/10.1016/j.apsoil.2013.12.012
Golos, P. J., Dixon, K. W., & Erickson, T. E. (2016). Plant recruitment from the soil seed bank
depends on topsoil stockpile age, height, and storage history in an arid environment.
Restoration Ecology, 24, S53–S61. https://doi.org/10.1111/rec.12389
Hachiya, T., & Sakakibara, H. (2017). Interactions between nitrate and ammonium in their
uptake, allocation, assimilation, and signaling in plants. Journal of Experimental Botany,
68(10), 2501–2512. https://doi.org/10.1093/jxb/erw449
Harris, J. A., Birch, P., & Short, K. C. (1989). Changes in the microbial community and physico‐
chemical characteristics of topsoils stockpiled during opencast mining. Soil Use and
Management, 5(4), 161–168. https://doi.org/10.1111/j.1475-2743.1989.tb00778.x
Kumar, B. L., & Gopal, D. V. R. S. (2015). Effective role of indigenous microorganisms for
sustainable environment. 3 Biotech, 5(6), 867–876. https://doi.org/10.1007/s13205-0150293-6
Li, X., You, F., Bond, P. L., & Huang, L. (2015). Establishing microbial diversity and functions
in weathered and neutral Cu-Pb-Zn tailings with native soil addition. Geoderma, 247–248,
108–116. https://doi.org/10.1016/j.geoderma.2015.02.010
Macyk, T. M., Brocke, L. K., Hermans, J. C., & McCoy, D. (2004). Soil Quality Criteria
Relative to Disturbance and Reclamation. In Alberta Agriculture, Food, and Rural
Development: Conservation and Devlopment Branch.
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http://www1.agric.gov.ab.ca/$department/deptdocs.nsf/all/sag9469/$FILE/sq_criteria_relativ
e_to_disturbance_reclamation.pdf
Mendez, M. O., & Maier, R. M. (2008). Phytoremediation of mine tailings in temperate and arid
environments. Reviews in Environmental Science and Biotechnology, 7(1), 47–59.
https://doi.org/10.1007/s11157-007-9125-4
Middleton, E. L., & Bever, J. D. (2012). Inoculation with a Native Soil Community Advances
Succession in a Grassland Restoration. Restoration Ecology, 20(2), 218–226.
https://doi.org/10.1111/j.1526-100X.2010.00752.x
Natural Resources Canada, Canadian Forest Service. (2017). A Guide to Soil Salvage. Ottowa. 4
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Nilsson, R. H., Anslan, S., Bahram, M., Wurzbacher, C., Baldrian, P., & Tedersoo, L. (2018).
Mycobiome diversity: high-throughput sequencing and identification of fungi. Nature
Reviews Microbiology, 17(2), 95–109. https://doi.org/10.1038/s41579-018-0116-y
Ohsowski, B. M., Klironomos, J. N., Dunfield, K. E., & Hart, M. M. (2012). The potential of soil
amendments for restoring severely disturbed grasslands. Applied Soil Ecology, 60, 77–83.
https://doi.org/10.1016/j.apsoil.2012.02.006
Sidhu, V., Sarkar, D., & Datta, R. (2016). Effects of biosolids and compost amendment on
chemistry of soils contaminated with copper from mining activities. Environmental
Monitoring and Assessment, 188(3), 1–9. https://doi.org/10.1007/s10661-016-5185-7
Tedersoo, L., Bahram, M., Põlme, S., Kõljalg, U., Yorou, N. S., Wijesundera, R., Ruiz, L. V.,
Vasco-Palacios, A. M., Thu, P. Q., Suija, A., Smith, M. E., Sharp, C., Saluveer, E., Saitta, A.,
Rosas, M., Riit, T., Ratkowsky, D., Pritsch, K., Põldmaa, K., … Abarenkov, K. (2014).
Global diversity and geography of soil fungi. Science, 346(6213).
https://doi.org/10.1126/science.1256688
The City of Calgary Parks. (2018). Soil Handling Recommendations: Best practices to improve
restoration work. https://www.calgary.ca/csps/parks/construction/park-developmentguidelines.html
Wick, A. F., Stahl, P. D., Rana, S., & Ingram, L. J. (2007). Recovery of Reclaimed Soil Structure
and Function in Relation To Plant Community Composition. Barnhisel RI, 307, 941–957.

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APPENDIX A. GROWTH RESPONSE TO
TOPSOIL STOCKPILE DEPTH AND NATIVE
SOIL INOCULATIONS
Introduction
To alleviate soil chemical deficiencies and altered microbial activities, thus enhancing
ecosystem recovery of disturbed sites, it is common practice to build degraded soils with the
addition soil amendments. Amendments can improve organic matter, nutrient content, microbial
activity, and soil structure. The addition of amendments such as manures, biosolids, manure
plant litter, biochar, and arbuscular mycorrhizal fungi (AMF) have demonstrated to successfully
increase health and productivity in degraded soils allowing for effective revegetation (S. Brown
et al., 2007; Gardner et al., 2012a; Larney & Angers, 2012; Ohsowski et al., 2012). However,
care must be taken to consider site-specific conditions and avoid promoting invasive or nontarget species (Laungani et al., 2016). Additionally, there was question as to whether the cost
applying amendments on a landscape level was economically viable. Therefore, research
investigating ecologically, and economically viable restoration practices was critical to optimize
mine-closure closure activities and accelerate ecological succession.
There has been a growing interest in research investigating local inoculum sources as soil
amendments to reduce the ecological impact of introducing foreign communities (Ohsowski et
al., 2012) as native field soil can be an important source of nutrients and local microorganisms.
Congruent with this suggestion, several studies have demonstrated that native AMF strains are
more effective than commercial strains and proposed that whole soil transfers exceed individual
strains in promoting restoration (Emam, 2016; Paluch et al., 2013b; Rowe et al., 2007). This has
been supported by recent studies investigating the use of local field soil additions to help restore
native vegetation. For example, Middleton and Bever (2012) demonstrated that 9% inoculations
with field soil significantly increased mid to late successional plant species. Another study found
that donor soil from local sites significantly increased plant community biomass in abandoned
arable soil in both 20% and 50% inoculations (Carbajo et al., 2011). Moreover, studies have
found that soil inocula not only significantly promoted ecosystem restoration, but also aided in
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steering plant community composition and development; where additions from late-successional
systems can enhance the growth of late-successional plant species (Bauer et al., 2015; Carbajo et
al., 2011; Kardol et al., 2006). This previous work and others (Emam, 2016; Li et al., 2015) have
indicated a potential for using local soil inoculations to expedite succession in restoration;
demonstrating that native soil can expedite the recovery of native plant re-establishment. A study
aiming to optimize topsoil in restoration further supports this phenomenon in finding that the
mixing of waste rock with healthy topsoil did not compromise the ability of topsoil to support
plant establishment (Merino-Martín et al., 2017). Because microorganisms are prolific and
ubiquitous, only small amounts of local soil addition may be required to observe positive results,
thus providing an ecological and economic sustainable option for native restoration.
The aim of this greenhouse study was two-fold. The first objective was to test the potential
soil property changes with stockpile depth on plant growth by growing plants in soil of
increasing stockpile depths. Secondly, this study aims to test the usage of undisturbed, native
field soil additions (5% and 10% by volume) in stockpiled topsoil to enhance plant growth.
Pseudoroegneria spicata (bluebunch wheatgrass) is a drought-tolerant species, native to B.C.’s
interior (Klinkenberg, 2020). Changes in soil properties occurring from stockpile depth and
native field soil additions was expected to cause differences in plant growth of bluebunch
wheatgrass. Important soil components that could influence plant growth are soil structure,
nutrients, or microbial activities. Assuming that long-term storage and stockpile height has
caused some type of degradation of the topsoil, we hypothesize that plants grown in deeper soil
intervals will have significantly lower plant growth; additionally, we hypothesize that the
addition of undisturbed local soil at 5% and 10% inoculations will enhance plant growth.

Methods
Soil Sampling and Preparation
Soil profiles were collected from two topsoil stockpiles, one 7 years old and 15 meters deep
from New Gold’s New Afton in September 2018, and the other 20 years old and 6 meters deep
from Barkerville Gold Mine’s Quesnel River (QR) mill in May 2019. The soil depth intervals
were grouped into roughly four and six depth categories for New Afton and QR mill,
respectively. Using a trowel, three replicates of reference/ native soil samples (30x30 cm) were

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collected from the upper 20 cm layer from a nearby undisturbed site for each mine operation.
Additional site and sampling information was outlined in Chapter 2.
Soil samples were stored in -20oC chest freezer at the Thompson Rivers university Research
Greenhouse and thawed at room temperature twelve hours before the greenhouse experiment
began. The sampling replicates (four cores in New Afton and three holes in QR mill) were
combined and thoroughly homogenized in large paper bags. Large rocks, roots, and other
materials were removed by hand prior to potting soil.
Germination Test
The germination rate of seeds of bluebunch wheatgrass were tested prior to experiment set up
to ensure seed viability. Four petri dishes (33 mm diameter x 18 mm deep) were lined with filter
paper. Each dish received 35 seeds of bluebunch wheatgrass. Petri dishes were placed in the
greenhouse under controlled conditions (temperature set to 20oC, and natural and artificial light,
day/night 16 hours/ 8 hours). The filter paper was kept saturated with tap water. The number of
germinated seeds was recorded at 2 and 4 days. Results of the germination test showed that the
germination rate of bluebunch wheatgrass seeds used were approximately 96%.
Experimental Design
In this greenhouse study, we aim to test the effects of increasing stockpile depth and the
addition of native soil inoculations on plant growth. A total of 100 mL of soil was added to 250
mL plastic pots using spoons and trowels. The trowels and spoons were sterilized with 75%
ethanol between treatments, but not between replicates Five bluebunch wheatgrass seeds were
placed in 100 mL of varying topsoil stockpile depth intervals with a plug of native soil at 0%,
5%, 10%, and 100% (by volume) depending on the treatment (Table 3.1). The native soil
treatments (5% and 10%) were compared to each other and the negative (0%) and positive
(100%) control.
If stockpile depth and/ or native soil inoculations influence plant growth, then there will be
an observed increase in shoot length, shoot weight, root length, and/or root weight. Each
treatment at each depth interval was replicated ten times. A random number generator and trays
were used to set up non-replicated randomized blocks in the greenhouse where each treatment
was present once in every tray (Figure A-1). Pots were placed in the greenhouse under controlled
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conditions (temperature set to maintain 21-25oC, natural and HID lighting, day/night 16 hours/ 8
hours, and 40-45% relative humidity). Once plants were established, they were pruned to two
plants per pot.

Figure A-1 View of QR mill pots set up in a randomized complete block design used for the
greenhouse growth experiment at the TRU Research Greenhouse.

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Table A-1 List of soil depth intervals sampled from New Afton and QR mill sites with varying native
soil inoculation percentages for the greenhouse study.

Data Collection and Analysis
Unfortunately, during this study, technical issues with the research greenhouse may have
impacted results. There was an instance where misters were constantly running for more than 24
hours, flooding several trays due to dysfunctional humidity sensors. Additionally, during a power
outage, the greenhouse was unable to regulate conditions and inside temperatures reached up to
40oC. These events may have contributed to the poor plant survival overall. However, due to the
blocking design of the greenhouse experiment, it was likely that these events effected all the pots
within a tray/ block equally, thus not altering effects between depths or treatments.
After four months, bluebunch wheatgrass were removed from pots and underwent three
rounds of rinsing in warm tap water to gently remove soil from the roots. The roots and shoots

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were separated then wet weight and length were measured. Shoot dry mass was measured and
recorded after drying for 48 hours at 60oC.
The experiment was set up as a two-factorial non-replicated randomized block design. We
were interested independently in the fixed effects from stockpile depth and the fixed effects from
the inoculation treatments. Random effects from blocks in the greenhouse were also considered.
Examining residual plots showed substantial violations of the assumption of homoscedasticity
and normality for linear regressions; furthermore, using Levene’s test for normality of variance
showed that the plant measurement variables had significant P-values (P < 0.05). The
distribution and residuals of the data were not improved with transformations. Therefore, we
tested differences in means with the non-parametric Friedman rank sum test to test mean
differences in the plant growth parameters between stockpile depths or inoculation treatments
with random effects from blocks. The Friedman rank sum test was followed by a pairwise
comparison Conover’s test for a two-way balanced complete block design with holm’s padjustment (PMCMR package in R).

Results
In the New Afton stockpile treatments, there was some evidence of differences in plant
growth between stockpile depths (P = 0.1). Specifically, it was observed that plants grown in the
bottom depth (610-1372 cm) had significantly higher shoot weight than those grown in the above
0-61 cm and the 152-610 cm depth intervals (Table A-2). All other New Afton treatments,
including the reference soil plants, were not significantly different than each other when
comparing between stockpile height (P > 0.05).
In the QR mill stockpile treatments, there was evidence that there were significant
differences in root length (P = 0.0012), root weight (P < 0.001), and shoot weight (P = 0.014)
between stockpile depths in the lowest soil depth interval (500-575 cm) for the 0% treatment.
Specifically, we observed that there were significantly lower root length, root weight, and shoot
weight in the bottom soil depth interval than those grown in some of the higher soil depth
intervals (Table A-3). In addition to the 0% treatment, there were significant differences
observed from the 5% treatment in root length (P<0.001), root weight (P < 0.01), and shoot
weight (P < 0.01). In this case however, plants grown in the bottom interval were significantly
higher than some of those in the higher depth intervals (Table A-3).

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There were no other significant differences in the QR mill treatments when comparing
between stockpile height (P > 0.05). However, plants grown in the reference soil showed to have
significantly larger root length, shoot length, root weight, and shoot weight compared to those
grown in most of the stockpile soil depths for all treatments (P < 0.05, Table A-3). For instance,
the reference soil plants showed to have an average of 69%, 49%, 90%, and 87% larger root
length, shoot length, root weight, and shoot weight compared to the largest of those grown in
varying topsoil stockpile depths at the 0% treatment.
In the New Afton stockpile treatments, there was little to no evidence that there were any
significant effects of 0%, 5%, 10%, or 100% native soil additions on plant growth for all
stockpile depths (Table A-4).
In the QR mill stockpile treatments, there were significant differences between native soil
additions for root length (P = 0.028), shoot length (P = 0.023), root weight (P = 0.019), and
shoot weight (P = 0.032) in the bottom depth interval (500-575 cm). Specifically, results show
that in the bottom depth interval, plants grown in 0% treatment had significantly lower root
length, shoot length, root weight, and shoot weight than those grown in the 5% and 100% native
soil addition treatments, but not 10% (Table A-5). Additionally, the 10% native soil addition
treatments had significantly lower root length, shoot length, root weight, and shoot weight than
the 100% treatments, but not 5%.

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Table A-2 Growth response of topsoil stockpile depth from the New Afton site (n=10, df=6).

Note: Columns followed by similar letters are not significantly different at the 5% probability level. Values in
brackets are standard errors. Bolded values indicate statistical significance in at least one of the groups at the 5%
probability level.

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A-9

Table A-3 Impact of topsoil stockpile depth on plant growth measurements from the QR mill site (n=10,

Note: Columns followed by similar letters are not significantly different at the 5% probability level. Values in
brackets are standard errors. Bolded values indicate statistical significance in at least one of the groups at the 5%
probability level.

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A-10
Table A-4. Impact of native soil inoculations on plant growth measurement for the new Afton site (n=10,
df=3).
Root length
(mm)

Shoot length
(mm)

Root weight
(mg)

Shoot weight
(mg)

Stockpile depth (cm)
61-152
152-610

Native soil treatment

0-61

610-1372

0%
5%
10%
100%
P value
Friedman chi-squared

0.94 (0.590)
a
0.32 (0.32)
a
3.91 (1.48)
a
4.93 (2.11)
0.1771
4.93

a

1.47 (0.617)
a
1.64 (1.11)
a
0.99 (0.99)
a
4.93 (2.11)
0.246
4.15

a

1.07 (0.621)
a
2.14 (1.62)
a
0.36 (0.36)
b
4.93 (2.11)
0.133
5.60

a

3.37 (0.96)
a
4.36 (1.72)
a
2.89 (2.44)
a
4.93 (2.11)
0.486
2.44

0%
5%
10%
100%
P value
Friedman chi-squared

7.61 (2.42)
a
4.24 (1.92)
a
7.62 (2.33)
a
7.44 (2.53)
0.7801
1.0875

a

21.5 (14.1)
a
5.13 (2.66)
a
4.39 (2.24)
a
7.44 (2.53)
0.35560
3.2432

a

5.06 (1.89)a
a
4.61 (2.42)
a
2.74 (1.9)
a
7.44 (2.53)
0.6312
1.726

11.9 (2.17)a
a
8.52 (2.48)
a
5.4 (2.85)
a
7.44 (2.53)
0.45990
2.5862

a

a
a
0%
0.104 (0.0443)a 0.246 (0.119)a 0.007 (0.00496) 0.784 (0.352)
a
a
a
1.1 (1.06)
0.928 (0.463)a
0.417 (0.31)
5%
0.1 (0.1)
a
a
a
a
10%
0.453 (0.453) 0.069 (0.069) 0.121 (0.0808)
1 (0.476)
a
a
a
a
100%
0.914 (0.459) 0.914 (0.459) 0.914 (0.459) 0.914 (0.459)
P value
0.3245
0.32730
0.26350
0.3131
Friedman chi-squared
3.4714
3.45
3.98
3.6
a

0.288 (0.112)a 0.477 (0.168)
0%
a
a
5%
0.375 (0.178) 0.59 (0.387)
a
10%
0.9 (0.402)a
0.406 (0.22)
a
1.34 (0.554)a
100%
1.34 (0.554)
P value
0.5007
0.76860
Friedman chi-squared
2.36
1.1

0.188 (0.0755)a
a
0.194 (0.129)
a
0.288 (0.212)
a
1.34 (0.554)
0.5282
2.2

1.06 (0.259)a
1.21 (0.66)a
a
1.06 (0.751)
a
1.34 (0.554)
0.4836
2.45

Note: Columns followed by similar letters are not significantly different at the 5% probability level. Values in brackets
are standard errors. Bolded values indicate statistical significance in at least one of the groups at the 5% probability level.
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A-11

Table A-5 Impact of native soil inoculations on plant growth measurement for the QR mill site (n=10, df=3).

Root length (mm)

Shoot length (mm)

Root weight (mg)

Shoot weight (mg)

Stockpile depth (cm)
60-120
200-260

Native soil treatment

0-10

10-20

350-390

500-575

0%
5%
10%
100%
P value
Friedman chi-squared

2.62 (1.39)a
0.490 (0.490)a
1.77 (1.20)a
8.43 (2.02)b
0.0132
10.7

0.43 (0.30)
0.00 (0.00)a
a
1.53 (1.53)
b
8.43 (2.02)
0.00528
12.7

a

3.58 (1.85)a
0.00 (0.00)a
1.53 (1.53)a
8.43 (2.02)b
0.0254
9.31

0.850 (0.850)a 0.330 (0.330)a
0.00 (0.00)a
0.00 (0.00)a
a
a
2.79 (1.94)
0.00 (0.00)
b
b
8.43 (2.02)
8.43 (2.02)
0.00683
0.000707
12.2
17.0

0.00 (0.00)a
5.06 (2.11)bc
2.11 (1.53)ab
c
8.43 (2.02)
0.0275
9.14

0%
5%
10%
100%
P value
Friedman chi-squared

7.47 (2.55)a
3.73 (1.93)a
4.11 (2.85)a
14.6 (3.37)b
0.0522
7.72

4.22 (2.20)
0.00 (0.00)a
a
3.01 (2.01)
14.6 (3.37)b
0.00186
14.9

a

3.95 (2.08)a
2.90 (2.00)a
1.61 (1.61)a
14.6 (3.37)b
0.0119
11.0

3.17 (2.13)a
0.00 (0.00)a
3.33 (2.26)a
14.6 (3.37)b
0.00354
13.6

0.00 (0.00)a
7.55 (3.18)bc
4.25 (2.43)ab
14.6 (3.37)c
0.0234
9.49

a

4.15 (2.23)
0.00 (0.00)a
3.17 (2.12)a
14.6 (3.37)b
0.00530
12.7

0%
5%
10%
100%
P value
Friedman chi-squared

1.40 (1.30)a
0.036 (0.0318)a 1.38 (1.02)a 0.206 (0.206)a 0.083 (0.0766)a 0.00 (0.00)a
a
a
0.0570 (0.0570)
0.00 (0.00)a
1.02 (1.02)a
0.00 (0.00)
0.00 (0.00)a
5.76 (2.36)bc
a
a
a
a
a
ab
3.32 (2.88)
0.130 (0.130) 0.124 (0.124) 0.956 (0.664)
0.00 (0.00)
0.547 (0.505)
b
b
b
b
b
c
14.0 (4.95)
14.0 (4.95)
14.0 (4.95)
14.0 (4.95)
14.0 (4.95)
14.0(4.95)
0.0148
0.00144
0.00553
0.00255
0.000835
0.0187
10.5
15.5
12.62
14.3
16.6
10.0

0%
5%
10%
100%
P value
Friedman chi-squared

1.2 (0.622)ab
0.309 (0.167)a
0.711 (0.523)a
11.6 (8.69)b
0.0711
7.03

0.258 (0.134)a
0.00 (0.00)a
0.382 (0.345)a
11.6 (8.69)b
0.00515
12.8

0.421 (0.219)a 0.336 (0.227)a 0.347 (0.275)a 0.00 (0.00)a
a
a
a
bc
0.194 (0.156)
0.00 (0.00)
0.00 (0.00)
2.94 (1.24)
a
a
a
0.132 (0.132) 1.04 (0.741) 0.332 (0.266) 0.539 (0.382)ab
11.6 (8.69)b
11.6 (8.69)b
11.6 (8.69)b
11.6 (8.69)c
0.0171
0.0248
0.0140
0.0324
10.2
9.37
10.6
8.78

Note: Columns followed by similar letters are not significantly different at the 5% probability level. Values in brackets are standard errors.
Bolded values indicate statistical significance in at least one of the groups at the 5% probability level.
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Furthermore, there were no other significant differences in the QR mill treatments when
comparing between native soil addition treatments (P>0.05). However, plants grown in the
reference soil showed to have significantly larger root length, shoot length, root weight, and
shoot weight compared to those grown in most of the stockpile soil depths for all treatments (P <
0.05, Table A-5). For instance, the reference soil plants showed to have an average of 68.9%,
48.8%, 76.3%, and 89.7% larger root length, shoot length, root weight, and shoot weight
compared to the largest of those grown in varying native soil treatments at the 0-10 cm depth
interval.

Discussion
Growth Response to Topsoil Stockpile Height
While there was significant response in shoot weight to stockpile depth at the 152-610 cm
depth, there were no other observed differences with stockpile depth in the New Afton stockpile.
The similar of growth response to stockpile depth in the New Afton samples are expected given
the microbial and chemical results; where ordination analyses demonstrated a very high
similarity between all depths of the New Afton Stockpile. The similarity between samples
throughout depth was likely due to the mixing of the stockpile in New Afton mine. While there
were more changes in plant growth observed with stockpile depth compared to New Afton, there
was still little evidence of consistent significant changes in plant growth in response to stockpile
depth. Here, there was evidence that plants grown in the bottom layer did worse in the 0%
treatment than those grown in the topsoil depth; however, plants grown in bottom layer were also
observed to do significantly better than those grown in some of the upper depths in the 5%
treatment. It was possible, but unlikely that the native soil additions caused positive results in the
bottom layer at 5% because the same improvement was not observed in the 10% treatment at the
same depth. Interestingly, plants grown in the reference soil from QR mill did much better than
those grown in the QR mill stockpile soil, for most of the treatments, however, this was not the
case for New Afton. Soil content as result of site-specific factors could explain the differences
observed between the soils sampled from the two reference sites. Although both sites are within
the southern interior of British Columbia, in the Montane Cordillera Ecozone (Marshall et al.
1999), New Afton was in a semi-arid grassland and QR mill was located 350 km north in a forest
ecosystem. We observed large differences in the nutrient content of these soils in Chapter 2. For
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A-13
example, the QR mill reference soil had an average of 68%, 72%, 81%, and 65% more OM (%),
NH4-N (mg/kg), NO3-N (mg/kg), and C (%) than New Afton’s reference soil, likely driving the
large difference in plant growth for the two sites in the greenhouse experiment.
Growth Response from Native Soil Addition
While there was no evidence of native soil treatments having a significant impact on plant
growth in New Afton stockpile, there was some evidence that it improved plant growth in the
QR mill stockpile soil at the bottom depth interval (500-575 cm). Plant growth was observed to
improve in QR mill stockpile soil with the 5% native soil addition, compared to those grown in
just stockpile soil (0% native soil addition). However, there was not an increase in plant growth
seen in the 10% treatment, thus indicating that it was unlikely that the significant increase seen in
plant growth in the 5% treatment was a result of the soil addition.
There was little to no evidence that there were any further significant differences in plant
growth between native soil treatments in the QR mill soil or the New Afton soil. A possible
reason for this it that the addition was too low to have a substantial impact plant growth.
Previous studies have shown native soil additions to have positive impacts on plant growth in
concentrations as low as 9% in a field study (Middleton & Bever, 2012) and positive impacts on
soil health with additions as low as 12.5% and as high as 50% (Li et al., 2015). This would likely
be the case for the QR mill plants, where reference soil plants (100% native soil addition) were
significantly larger than plants grown in QR mill stockpile soil treatments. However, reference
soil plants and stockpile soil plants in the New Afton samples were not significantly different
from each other.
Another reason that the native soil additions may not have performed well in this study, was
because the additions on were likely on partially degraded soil instead of completely degraded
soils. Various studies with positive results have looked at adding native soils to highly
contaminated or deficient materials such as mineral soils, sterile medium, tailings, and waste
rock (Emam, 2016; Kardol & Wardle, 2010; Li et al., 2015). Thus, it was possible that the
topsoil stockpiles, while having some deficiencies, are not degraded so severely that small
additions of healthy native field soil causes a significant growth response.
Furthermore, the lack of positive impacts observed from native soil additions could be
limited by the length of this experiment. There may be long term impacts that were not captured
in this study; for example, Wubs et al. (2016) showed an ability to steer and promote plant
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A-14
community structure with soil additions in a 6-year field study. Additionally, soil
microorganisms and nutrient status might have been significantly altered by the native soil
additions, but this was not measured in our analyses. Moreover, soil microorganisms may not
have had long enough to proliferate in the pots. Here, we attempted to observe immediate growth
responses from native soil additions, however, over time, effects of soil biota may have become
more impactful.
We created a plug (hot spot) around the seeds to test the approach of introducing the native
material locally to plants so to increase feasibility of field applications. This approach was
applied with success by Middleton and Bever (2012). However, it may not have been effective in
this study due to some of the reasons listed in the previous paragraphs, or due to competition
from biota in the stockpile samples.

Conclusions
The results show that the native grass bluebunch wheatgrass grew relatively poorly in soils
from a 7-year-old, 15-meter-deep and a 20-year-old, 6-meter-deep topsoil stockpile, regardless of
the depth or treatment, compared to a reference field soil from one of the sites. Additionally,
despite promising results of native soil additions in other studies (Emam, 2016; Li et al., 2015;
Middleton & Bever, 2012; Paluch et al., 2013a; Wubs et al., 2016), there was no evidence that
the addition of native field soil at 5% or 10% applications enhanced immediate plant growth in
topsoil stockpiles. Discretion should be used when interpreting results from this study because of
the inconsistent greenhouse conditions described in the methods section; inaccuracy was a
potential concern. If results were more reliable, we may conclude that stockpile depth or native
field soil additions has no substantial observed impact on plant growth, and that further
manipulations of stockpiled topsoils are required to expedite ecosystem restoration in mine
closure procedures. Future studies, in addition to long-term and large-scale research, should
investigate effects on other native plant species (early and late-successional) and test the effects
of higher ratios of native field soil additions. Furthermore, this study should be repeated due to
the greenhouse complications faced in this study.

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Literature Cited
Bauer, J. T., Mack, K. M. L., & Bever, J. D. (2015). Plant-soil feedbacks as drivers of
succession: Evidence from remnant and restored tallgrass prairies. Ecosphere, 6(9).
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Brown, S., DeVolder, P., Compton, H., & Henry, C. (2007). Effect of amendment C:N ratio on
plant richness, cover and metal content for acidic Pb and Zn mine tailings in Leadville,
Colorado. Environmental Pollution, 149(2), 165–172.
https://doi.org/10.1016/j.envpol.2007.01.008
Carbajo, V., den Braber, B., van der Putten, W. H., & de Deyn, G. B. (2011). Enhancement of
late successional plants on ex-arable land by soil inoculations. PLoS ONE, 6(7).
https://doi.org/10.1371/journal.pone.0021943
Emam, T. (2016). Local soil, but not commercial AMF inoculum, increases native and nonnative grass growth at a mine restoration site. Restoration Ecology, 24(1), 35–44.
https://doi.org/10.1111/rec.12287
Gardner, W. C., Anne Naeth, M., Broersma, K., Chanasyk, D. S., & Jobson, A. M. (2012).
Influence of biosolids and fertilizer amendments on element concentrations and revegetation
of copper mine tailings. Canadian Journal of Soil Science, 92(1), 89–102.
https://doi.org/10.4141/CJSS2011-005
Kardol, P., Martijn Bezemer, T., & van der Putten, W. H. (2006). Temporal variation in plantsoil feedback controls succession. Ecology Letters, 9(9), 1080–1088.
https://doi.org/10.1111/j.1461-0248.2006.00953.x
Kardol, P., & Wardle, D. A. (2010). How understanding aboveground-belowground linkages can
assist restoration ecology. In Trends in Ecology and Evolution (Vol. 25, Issue 11, pp. 670–
679). https://doi.org/10.1016/j.tree.2010.09.001
Klinkenberg, B. (2020). E-FLora BC: Electronic Atlas of the Plants of British Columbia. Lab for
Advanced Spatial Analysis, Department of Geography, University of British Columbia,
Vancouver. http://linnet.geog.ubc.ca/Atlas/Atlas.aspx?sciname=Pseudoroegneria spicata
Larney, F. J., & Angers, D. A. (2012). The role of organic amendments in soil reclamation: A
review. Canadian Journal of Soil Science, 92(1), 19–38. https://doi.org/10.4141/cjss2010-064

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Laungani, R., Elgersma, K., McElligott, K., Juarez, M., & Kuhfahl, T. (2016). Biochar
amendment of grassland soil may promote woody encroachment by eastern red cedar.
Journal of Soil Science and Plant Nutrition, 16(4), 941–954. https://doi.org/10.4067/S071895162016005000067
Li, X., You, F., Bond, P. L., & Huang, L. (2015). Establishing microbial diversity and functions
in weathered and neutral Cu-Pb-Zn tailings with native soil addition. Geoderma, 247–248,
108–116. https://doi.org/10.1016/j.geoderma.2015.02.010
Merino-Martín, L., Commander, L., Mao, Z., Stevens, J. C., Miller, B. P., Golos, P. J., Mayence,
C. E., & Dixon, K. (2017). Overcoming topsoil deficits in restoration of semiarid lands:
Designing hydrologically favourable soil covers for seedling emergence. Ecological
Engineering, 105, 102–117. https://doi.org/10.1016/j.ecoleng.2017.04.033
Middleton, E. L., & Bever, J. D. (2012). Inoculation with a Native Soil Community Advances
Succession in a Grassland Restoration. Restoration Ecology, 20(2), 218–226.
https://doi.org/10.1111/j.1526-100X.2010.00752.x
Ohsowski, B. M., Klironomos, J. N., Dunfield, K. E., & Hart, M. M. (2012). The potential of soil
amendments for restoring severely disturbed grasslands. Applied Soil Ecology, 60, 77–83.
https://doi.org/10.1016/j.apsoil.2012.02.006
Paluch, E. C., Thomsen, M. A., & Volk, T. J. (2013a). Effects of Resident Soil Fungi and Land
Use History Outweigh Those of Commercial Mycorrhizal Inocula: Testing a Restoration
Strategy in Unsterilized Soil. Restoration Ecology, 21(3), 380–389.
https://doi.org/10.1111/j.1526-100X.2012.00894.x
Paluch, E. C., Thomsen, M. A., & Volk, T. J. (2013b). Effects of Resident Soil Fungi and Land
Use History Outweigh Those of Commercial Mycorrhizal Inocula: Testing a Restoration
Strategy in Unsterilized Soil. Restoration Ecology, 21(3), 380–389.
https://doi.org/10.1111/j.1526-100X.2012.00894.x
Rowe, H. I., Brown, C. S., & Claassen, V. P. (2007). Comparisons of mycorrhizal
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Wubs, E. R. J., van der Putten, W. H., Bosch, M., & Bezemer, T. M. (2016). Soil inoculation
steers restoration of terrestrial Ecosystems. Nature Plants, 2(8), 1–5.
https://doi.org/10.1038/NPLANTS.2016.107

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B-1

APPENDIX B. RAW GEOCHEMICAL DATA FOR NEW AFTON AND
QR MILL
Table B-1 Raw geochemical data for QR mill (QR) and New Afton (NA) stockpile and reference soil.
Site

Depth
(cm)

NA
NA
NA
NA
NA
QR
QR
QR
QR
QR
QR
QR

Ref
0-61
61-152
152-610
610-1372
Ref
0-10
10-20
60-120
200-260
350-390
500-575

C/N
Avg
8.91
7.61
9
8.81
8.45
15.6
10.7
10.1
22.8
12.1
49.7
69.9

SE
1.2
0.38
0.62
0.55
0.19
1.3
0.9
0.7
12.3
1.1
33.7
28.7

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Ca (%)
Avg
1.01
3.18
3.7
3.3
3.3
1.3
1.47
1.5
2.38
2.4
3.37
2.7

SE
0.05
0.31
0.29
0.23
0.09
0.15
0.07
0.2
0.42
0.46
1.38
0.7

EC (µS/cm)

K (%)

Mg (%)

Avg SE Avg SE Avg
67.4 1.9 0.4 0.01 1.37
1378 55 0.38 0.01 1.68
1191 90 0.38 0.02 1.8
1245 78 0.41 0.03 1.95
1446 31
0.4 0.02 1.78
119 0.5 0.4 0.02 0.82
123 5.7 0.42 0.04 1.03
61
3.6 0.39 0.06 1.02
131 29.3 0.33 0.01 1.14
158 9.1 0.35 0.03 1.13
130 11.1 0.32 0.05 1.19
172 32.4 0.33
0
1.23

SE
0.07
0.09
0.04
0.1
0.03
0.18
0.03
0.04
0.08
0.12
0.18
0.12

Na (mg/kg) NO3- (mg/g) NH4 (mg/kg)

OM (%)

Avg SE Avg SE Avg SE Avg
767 35 4.63 0.97 5.6 2.1 3.26
2200 82 0.73 0.3
27
7
2.62
2500 58 0.27 0.15 30.5 4.7 2.82
2000 191 0.63 0.37 21
7.2 2.35
3025 354 2.58 0.59 13.5 0.9 2.84
840 44 16.67 2.19 29.07 14.04 10.2
683 52
3.6 0.4 7.47 6.27 10
647 34 3.57 0.88 1.3 0.1 6.6
692 37 4.49 3.64 1.48 0.58 4.34
683 66 18.73 11.2 1.08 0.49 5.9
670 15 18.77 8.72 1.69 0.76 4.38
760 51
18 13.53 2.43 1.91 3.84

SE
0.1
0.56
0.46
0.45
0.46
0.29
0.63
1.14
0.45
1.27
1.14
0.17

P.avail
pH
(mg/kg)
Avg SE Avg SE
27.7 2.8 7.28 0.09
4.83 0.59 8.11 0.04
4.43 1.63 8.07 0.02
5.68 2.38 8.18 0.04
6.18 0.53 8.05 0.02
145 57.95 5.63 0.11
14 2.08 6.85 0.07
12 1.15 6.24 0.25
3.54 1.25 6.6 0.35
4.07 2.58 6.57 0.33
5.07 2.36 6.56 0.32
2.73 0.62 6.81 0.19

S (%)
Avg
0.03
0.2
0.29
0.24
0.26
0.04
0.04
0.03
0.07
0.1
0.12
0.08

SE
0.01
0.03
0.02
0.02
0.02
0.01
0
0
0.02
0.04
0.06
0.04

January 2023

C-1

APPENDIX C. SODIUM ADSORPTION RATIO
Sodium adsorption ratio (SAR) is a measure of the concentration of sodium relative to Ca
and Mg concentrations. The following calculation was used:
Equation C-1

𝑆𝑆𝑆𝑆𝑆𝑆 =

[𝑁𝑁𝑁𝑁 + ] 𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝐿𝐿−1
1

(0.5 𝑥𝑥 [𝐶𝐶𝐶𝐶+ + 𝑀𝑀𝑀𝑀+ ])2 (𝑚𝑚𝑚𝑚𝑚𝑚𝑚𝑚 𝐿𝐿−1 )

Table C-1 SAR values at each stockpile depth.

Figure C-1 Chart taken from Kumaragamage et al. (2021).

Thompson Rivers University

January 2023

D-1

APPENDIX D. RAREFACTION CURVES

Figure D-1 Rarefaction curve for bacterial OTUs in New Afton
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January 2023

D-2

Figure D-2 Rarefaction curve for fungal OTUs in New Afton

Thompson Rivers University

January 2023

D-3

Figure D-3 Rarefaction curve for bacterial OTUs in QR mill

Thompson Rivers University

January 2023

D-4

Figure D-4 Rarefaction curve for fungal OTUs in QR mill

Thompson Rivers University

January 2023

E-1

APPENDIX E. RELATIVE PROPORTION TABLE AND ANALYSIS
FOR MICROBIAL DATA
Table E-1 Relative proportions of bacteria and fungi phylum present above 1% identified in New Afton. P-value was calculated using Type III
Analysis of Variance Table with Satterthwaite's method in each bacteria or fungi phylum by stockpile depth in New Afton, excluding the reference
soil.
0-61 cm
Bacteria
Acidobacteria
Actinobacteria
Bacteroidetes
Firmicutes
Proteobacteria
Unknown
Fungi
Ascomycota
Basidiomycota
Mortierellomycota
Unknown

Thompson Rivers University

61-152
cm

152-610
cm

610-1372
cm

Average

Standard
Deviation

P value

Reference

0.123
0.432
0.032
0.017
0.177
0.190

0.126
0.418
0.039
0.014
0.181
0.190

0.119
0.422
0.040
0.015
0.227
0.151

0.128
0.414
0.031
0.044
0.186
0.173

0.124
0.422
0.036
0.023
0.193
0.176

0.004
0.008
0.005
0.014
0.023
0.018

0.916
0.959
0.338
0.064
0.060
0.096

0.151
0.300
0.053
0.003
0.314
0.133

0.662
0.128
0.054
0.146

0.724
0.145
0.047
0.078

0.672
0.183
0.029
0.103

0.676
0.214
0.021
0.077

0.684
0.168
0.038
0.101

0.028
0.039
0.015
0.032

0.747
0.416
0.012
0.349

0.344
0.539
0.024
0.079

January 2023

E-2
Table E-2 Relative proportions of bacteria and fungi phyla present above 1% identified in QR mill. P-value was calculated using Type III Analysis
of Variance Table with Satterthwaite's method in each bacteria or fungi phylum by stockpile depth in QR mill, excluding the reference soil.

Thompson Rivers University

January 2023

F-1

APPENDIX F. STACKED BARPLOTS FOR THE TOP 10 OTUS (PIME
ANALYSIS)

Figure F-1 Coloured barplots showing relative proportion of the top ten a) bacterial and b) fungal genera that are most impacted by
stockpile depth in New Afton.

Thompson Rivers University

January 2023

F-2

Figure F-2 Coloured barplots showing relative proportion of the top ten a) bacterial and b) fungal genera that are most explained by
stockpile depth in QR mill.

Thompson Rivers University

January 2023