Effects of Restoration on Carbon Storage in Smelter ......2010/01/06 · part, for scholarly...

Effects of Restoration on Carbon Storage in Smelter-

impacted Industrial Barrens

by

Robyn Rumney

A thesis submitted in partial fulfillment

of the requirements for the degree of

Master of Science (MSc) in Biology

The Faculty of Graduate Studies

Laurentian University

Sudbury, Ontario, Canada

© Robyn Rumney, 2019

ii

THESIS DEFENCE COMMITTEE/COMITÉ DE SOUTENANCE DE THÈSE

Laurentian Université/Université Laurentienne

Faculty of Graduate Studies/Faculté des études supérieures

Title of Thesis

Titre de la thèse Effects of Restoration on Carbon Storage in Smelter-impacted Industrial Barrens

Name of Candidate

Nom du candidat Rumney, Robyn

Degree

Diplôme Master of Science

Department/Program Date of Defence

Département/Programme Biology Date de la soutenance December 17, 2019

APPROVED/APPROUVÉ

Thesis Examiners/Examinateurs de thèse:

Dr. John Gunn

(Supervisor/Directeur de thèse)

Dr. Nathan Basiliko

(Co-supervisor/Co-directeur de thèse)

Dr. Peter Beckett

(Committee member/Membre du comité)

Dr. Erik Emilson

(Committee member/Membre du comité)

Approved for the Faculty of Graduate Studies

Approuvé pour la Faculté des études supérieures

Dr. David Lesbarrères

Monsieur David Lesbarrères

Dr. Brian Amiro Dean, Faculty of Graduate Studies

(External Examiner/Examinateur externe) Doyen, Faculté des études supérieures

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I, Robyn Rumney, hereby grant to Laurentian University and/or its agents the non-exclusive license to archive and

make accessible my thesis, dissertation, or project report in whole or in part in all forms of media, now or for the

duration of my copyright ownership. I retain all other ownership rights to the copyright of the thesis, dissertation or

project report. I also reserve the right to use in future works (such as articles or books) all or part of this thesis,

dissertation, or project report. I further agree that permission for copying of this thesis in any manner, in whole or in

part, for scholarly purposes may be granted by the professor or professors who supervised my thesis work or, in their

absence, by the Head of the Department in which my thesis work was done. It is understood that any copying or

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owner solely for the purpose of private study and research and may not be copied or reproduced except as permitted

by the copyright laws without written authority from the copyright owner.

iii

ABSTRACT

Landscape carbon (C) storage is a key component of climate change mitigation. Globally,

industrial barrens cover large areas and their restoration can facilitate C storage in otherwise

under-utilized sites, while concomitantly enhancing numerous other ecosystem services. I

assessed how restoration of smelter impacted barren land enhanced C storage by studying a site

in Sudbury, Ontario near a former Ni and Cu metal smelter that ceased operation in 1972. The

site was treated by aerial liming, fertilizing, and grass and legume seeding in 1994-1997,

followed by jack pine (Pinus banksiana) planting in the upland areas in 1997-2001. Forty-five

0.1 ha size plots were selected across restored and untreated adjoining areas, 32 in exposed

upland industrial barrens and 13 in sheltered lowland valleys. The focus of my study was on

upland industrial barrens, which exhibited severe site conditions and little natural regrowth. I

measured the amount of C in coarse woody debris, fine woody debris, herbs, mineral soil,

organic soil (LFH layers), shrubs, and trees in each plot. Measures of wetness index, plant

species richness, soil metal concentrations, soil pH, distance from smelter, and elevation were

then used to assess factors affecting total ecosystem C storage.

In lowland valleys where no active tree planting occurred (only natural regeneration) the

treatments with lime, fertilizer, and grass and legume seed showed a 38% increase in C storage

(101.1 ± 5.5 Mg C ha-1 (mean ± S.E.)) compared to untreated lowland plots (73.3 ± 5.9 Mg C ha-

1). In upland areas where growing conditions were more severe (i.e., thin soils, low moisture),

tree C increased from 0.5 ± 0.4 Mg C ha-1 in areas of natural regeneration to 19.3 ± 1.4 Mg C ha-1

following liming, fertilizing, seeding, and tree-planting. There was no significant difference in

total C storage in untreated reference plots (36.1 ± 8.4 Mg C ha-1) compared to limed, fertilized,

seeded, and tree-planted plots (58.2 ± 4.4 Mg C ha-1), likely due to variable site conditions across

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the landscape. Wetness index, plant species richness, and soil bioavailable metal concentrations

were the best predictors of C storage in upland industrial barrens, with the best model explaining

64% of variance in C storage. Overall, mineral soil remained the largest C pool in both the

uplands (53%) and the lowlands (40%). The forests in my study were not mature, so C storage is

expected to continue to increase in the future. My findings demonstrate that soil amendments and

tree planting can increase tree C storage in industrial barrens, but site characteristics, particularly

wetness, are key to the rate of total C accumulation. C storage in less disturbed lowland valleys

also benefitted from restoration. Well-designed restoration efforts that optimize C storage in

globally extensive industrial barrens can therefore sequester C and may in turn assist in climate

change mitigation.

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ACKNOWLEDGMENTS

Thank you to my supervisors Dr. John Gunn and Dr. Nathan Basiliko for the opportunity

to work with them on this project and for guidance along the way. Thank you to my committee

members Dr. Peter Beckett and Dr. Erik Emilson for feedback and direction throughout my

research. Thanks to the Ministry of Environment, Conservation and Parks for allowing continued

research at Daisy Lake Uplands Provincial Park. A big thanks to Emily Smenderovac for going

above and beyond during my fieldwork, calculations, and R crises. Thank you to Dr. Trevor

Jones for sharing your expertise of carbon pool calculations and your field equipment. Thank

you to Dr. Brie Edwards for statistics help that added the finishing touches to my linear

regression. Thanks to Brittany Rantala-Sykes for your extraordinary plant identification. This

project would not have been possible without the help of my fearless field crew: Meghan Ward,

Kalpana KC, James Bull, and Aaron Rysdale who went out rain or shine. Thanks to everyone

else at the Cooperative Freshwater Ecology Unit for their advice, support, friendship, and

entertaining lunch conversations. I would also like to extend the warmest thanks to all my family

members and friends for supporting me throughout this process.

Financial support was provided by L-CARE (Landscape Carbon Accumulation through

Reductions in Emissions), the Ecolac program, the Tom Peters Memorial Mine Reclamation

Award, and the Fisheries and Oceans Habitat and Restoration Scholarship.

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TABLE OF CONTENTS

Effects of Restoration on Carbon Storage in Smelter-impacted Industrial Barrens ............................. i

ABSTRACT ................................................................................................................................................ iii

ACKNOWLEDGMENTS .......................................................................................................................... v

LIST OF FIGURES ................................................................................................................................. viii

LIST OF TABLES ..................................................................................................................................... ix

LIST OF ABBREVIATONS ...................................................................................................................... x

1.0 Introduction ......................................................................................................................................... 1

2.0 Methods ................................................................................................................................................ 7

Site description and plot-setup ...................................................................................................................... 7

Coarse woody debris C storage .................................................................................................................... 9

Fine woody debris C storage ...................................................................................................................... 10

Understory: Herb and shrub C storage ...................................................................................................... 11

Tree C storage............................................................................................................................................. 11

Plant species richness ................................................................................................................................. 13

Soil chemistry and C storage ...................................................................................................................... 13

Wetness index .............................................................................................................................................. 15

Distance from smelter ................................................................................................................................. 16

Data analysis .............................................................................................................................................. 16

3.0 Results ................................................................................................................................................ 20

Effect of treatment on C storage ................................................................................................................. 20

C pools: Upland industrial barrens ............................................................................................................ 20

Predictors of C storage: Upland industrial barrens ................................................................................... 21

Mineral soil pH ........................................................................................................................................... 22

4.0 Discussion........................................................................................................................................... 29

C storage: Upland industrial barrens ......................................................................................................... 29

Predictors of C storage ............................................................................................................................... 32

C storage: Lowland valleys ........................................................................................................................ 33

Restoration recommendations .................................................................................................................... 34

Limitations of study ..................................................................................................................................... 35

Conclusion .................................................................................................................................................. 36

Future studies.............................................................................................................................................. 36

vii

REFERENCES .......................................................................................................................................... 38

APPENDIX ................................................................................................................................................ 44

viii

LIST OF FIGURES

Figure 2.1 Map of Daisy Lake watershed plots in upland industrial barrens (> 280 m centre

elevation) (n = 32) and lowland valleys (≤ 280 m centre elevation) (n = 13). Shading indicates

tree-planted (T), limed, fertilized, and seeded (L), and limed, fertilized, and seeded + tree-planted

(L+T) treatments. Untreated (U) areas are not shaded. Daisy catchments H, I, and J are labelled.

Arrow indicates direction of historic Coniston smelter.

Figure 2.2 Layout of sampling points at each 0.1 ha plot. FWD-1 through FWD-4 were LIS

locations for FWD survey. Soil depth was measured at 1-16, while soil samples were taken at

even numbers only. Understory sampling occurred in 1 m2 square quadrats located at numbers 4,

8, 12, and 16. All other sampling occurred throughout entire plot.

Figure 2.3 Soil sampling protocols for organic and mineral soil layers. Mineral soil was divided

into 3 depths (0-5 cm, 5-10 cm, 10-20 cm) and sampled with a1.5 cm diameter soil corer and

organic soil was collected using a 5 cm circular frame.

Figure 3.1 Total ecosystem C storage (Mg C ha-1 ± SE) in upland industrial barrens among

untreated (U), limed, fertilized, seeded (L), tree-planted (T), and limed, fertilized, seeded + tree-

planted (L+T) plots (n = 32 plots). Different letter designations above bars indicate significant

differences at p < 0.05.

Figure 3.2 C storage (Mg C ha-1 ± SE) in each C pool (CWD, FWD, herb, mineral soil, organic

soil, shrub, and tree) in upland industrial barrens among untreated (U), limed, fertilized, seeded

(L), tree-planted (T), and limed, fertilized, seeded + tree-planted (L+T) plots (n = 32 plots).

Different letter designations in tree C indicate significant differences at p < 0.05. Only tree C

differed significantly by treatment.

Figure 3.3 Wetness index (z-scores) in upland industrial barrens among untreated (U), limed,

fertilized, seeded (L), tree-planted (T), and limed, fertilized, seeded + tree-planted (L+T) plots (n

= 32 plots). Different letter designations above plots indicate significant differences at p < 0.05.

Figure 3.4 Plant species richness (± SE) in upland industrial barrens among untreated (U),

limed, fertilized, seeded (L), tree-planted (T), and limed, fertilized, seeded + tree-planted (L+T)

plots (n = 32 plots). Different letter designations above bars indicate significant differences at p

< 0.05.

Figure 3.5 Total ecosystem C storage (Mg C ha-1 ± SE) at lowland valley untreated (U) and

limed, fertilized, seeded (L) plots (n = 13). Different letter designations above bars indicate

significant differences at p < 0.01.

ix

LIST OF TABLES

Table 3.1 Linear regression relating total ecosystem C storage (Mg C ha-1) in upland industrial

barren plots (n = 32 plots) to wetness index, plant species richness, PC1 (first axis of principal

component of soil bioavailable [As], [Cd], [Co], [Cu], [Ni], [Pb]), soil pH, jack pine frequency,

distance from smelter, and elevation (MASL). Model was generated using backward selection

with second order Akaike Information Criterion (AICc).

x

LIST OF ABBREVIATONS

COC- chemical(s) of concern

CWD- coarse woody debris

DCRF- decay-class reduction factor

DOC- dissolved organic carbon

FWD- fine woody debris

ICP-MS- inductively coupled plasma mass spectrometry

L- limed, fertilized, seeded plots

L+T- tree-planted + limed, fertilized, seeded plots

LIS- line intersect sampling

PC- principal component

QAQC- quality assurance and quality control

SOC- soil organic carbon

SOM- soil organic matter

SRF- structural reduction factor

T- tree-planted plots

U- untreated plots

1

1.0 Introduction

Climate change is a challenge faced by society that requires multi-faceted solutions. One

natural climate solution is to decrease atmospheric CO2 concentrations by storing more carbon

(C) in forest ecosystems (Griscom et al., 2017). Boreal forests alone store approximately 550 Gt

C, which is 30% of Earth’s total terrestrial C pool (Bradshaw et al., 2009). Therefore, forest C

dynamics are a key component of global C budgets (Kurz et al., 2013).

Forest net C balance is determined by C uptake during net primary production and C loss

during autotrophic respiration, decomposition (heterotrophic respiration), and oxidation in forest

fires (Kurz et al., 2013). When forests have net C uptake they are a C sink (Kurz et al., 2013) and

store C in coarse woody debris (CWD), fine woody debris (FWD), herbs, mineral soil, organic

soil (LFH layers), shrubs, and trees (Lambert et al., 2005). Boreal forests store around 60% of C

in soil, 20% in biomass (Pan et al., 2011), and 20% in downed woody debris (CWD and FWD)

(Russell et al., 2015).

Boreal forests have been estimated to sequester approximately 2.1 Mg C ha-1 y-1 (Amiro

et al., 2010). Boreal forest deciduous trees are larger C sinks than boreal conifers, with pure

black spruce (Picea mariana) and jack pine (Pinus banksiana) stands inputting 1.3 Mg C ha-1 y-1

(Gower et al., 2001). Boreal forest C storage is highly variable and dependent on tree age,

species composition, density, site characteristics, and disturbance. Gao et al. (2017) found boreal

forest C storage to be reflective of stand age, increasing from 96.5 ± 5.1 Mg C ha-1 in 8-year-old

stands to 327.9 ± 11.9 Mg C ha-1 in 147-year-old stands, and then decreasing to 271.1 ± 13.6 Mg

C ha-1 in 210-year-old stands. Hazlett et al. (2005) found highly variable C storage that differed

by forest position, with 95-year-old upland stands storing 28.3 to 135.08 Mg C ha-1 and 95-year-

2

old lowland riparian stands storing 29.3 to 269.1 Mg C ha-1, possibly because of disparity in

species compositions. Boreal forests experience C loss after stand-replacing disturbances, but

become C sinks again within 20 years of revegetation (Amiro et al., 2010). Young forests that

have grown back after a disturbance are net C sinks, while recently disturbed forests (i.e., fire,

insects, etc.) that have not recovered are net C sources (DeLuca & Boisvenue, 2012). Climate

change is expected to exacerbate boreal forest fire disturbance by increasing the annual number

of fires and area burned (Flannigan et al., 2006), which could turn larger areas into temporary C

sources (Amiro et al., 2010). More severe and frequent forest fires could also burn deep, legacy

soil C that escaped past fires (Walker et al., 2019). Minor insect pest disturbances can benefit C

storage, as they promote tree diversity and growth and therefore increase C influx (Gao et al.,

2017). Major stand-replacing insect disturbances, however, release C, and with climate change

these events could become more prolific (Kurz et al., 2013). Anthropogenic CO2 fertilization and

N deposition could also increase forest productivity, with uncertain net effects on C storage

(DeLuca & Boisvenue, 2012).

Large areas of the world have been severely damaged by mining and smelting (Kozlov &

Zvereva, 2007; Moore & Luoma, 1990) creating extensive industrial barrens where C storage is

currently minimal (Kozlov & Zvereva, 2015; Sherman and Beckett, 2003) because of vegetation

and soil loss (Kozlov & Zvereva, 2007). Forest regrowth after disturbance creates a C sink that,

while not enough to solely reverse climate change, is expected to positively affect the global

climate system (Pugh et al., 2019). Increasing Earth’s total forested area, such as by reforesting

industrial barrens and other large areas of disturbance, may then help limit climate warming from

exceeding the 1.5°C above pre-industrial levels that is one of the stated goals of the most recent

ICPP reports (Lewis et al., 2019). The extensive industrial barrens (> 80,000 ha) that surrounded

3

the smelters in Sudbury, Canada, are among the best studied examples of large-scale industrial

barren disturbance and restoration, and its barrens that have been subjected to decades of

restoration represent a potentially large C sink for this study (City of Greater Sudbury, 2018;

Kozlov & Zvereva, 2007; Sherman & Beckett, 2003).

Sudbury lies on the ecotone of the Great Lakes – St. Lawrence forest region and the

boreal forest (Amiro & Courtin, 1981), therefore Sudbury contains elements of both forest types

and can be described as a mixed boreal forest (Hutchinson & Whitby, 1977). In the 1970s,

reductions in Sudbury’s atmospheric pollution began with closure of the Coniston smelter and an

ore-sintering plant near Copper Cliff, approximately 50% initial decrease in overall SO2 and

metal emissions, and the erection of a 381 m “super stack” (then the tallest in the world) with an

electrostatic precipitator (Freedman & Hutchinson, 1980). These measures greatly reduced local

pollution, especially ground level fumigation events that were devastating to vegetation (Gunn et

al., 1995). Emission reductions facilitated some natural re-growth of mainly white birch (Betula

papyrifera) but soil pH remained low (2.0- 4.5), so dolomitic limestone (Ca and Mg) was used as

a soil amendment when a large-scale land reclamation program began in 1978 (Beckett &

Negusanti, 1990). A fertilizer and seed mixture was also applied as part of the re-greening

strategy (Winterhalder, 1983). Between 1978 and 2018 the program limed 3,478 ha, fertilized

3,252 ha, seeded 3,179 ha, and planted 10,214,559 trees and shrubs (City of Greater Sudbury,

2018). Sudbury’s restoration was focused mainly on rocky barren uplands with little natural

regeneration. Emission reductions have also continued and current emissions of sulphur and

metal particulates are now approaching 1% of historic highs in the 1960s (Carmichael, 2018).

Sudbury’s regreening program was initially focused on creating an ecosystem, reducing

soil erosion, providing vegetation cover for aesthetic reasons (i.e., improving the image of the

4

city, providing recreation areas, etc.), and for economic improvement of the city (Beckett &

Negusanti, 1990; City of Greater Sudbury, 2018; Szkokan-Emilson et al., 2011). As the program

evolved it took on more ecological challenges, such as increasing terrestrial biodiversity and

improving water quality in local lakes (City of Greater Sudbury, 2018; Dixit et al., 1996). To

date, however, apart from the work by Sherman and Beckett (2003), relatively little attention has

been paid to the role of restoration treatments in sequestering of atmospheric C.

The Sherman and Beckett (2003) study did show considerable C sequestration shortly

after liming and tree planting, but the work included sand plains and other relatively

homogeneous measurement plots and did not address the extreme variability that could occur

across the topography of the damage zone. Exposed hills, for example, appeared the most

damaged and most difficult to restore, while low-lying valleys were more protected and

maintained more of the original natural vegetation and deeper soils (Kozlov & Zvereva, 2007;

Winterhalder, 1995). Valleys also continued to collect soil as the hilltops eroded, adding to

lowland soil depth (Winterhalder, 1995). Therefore, it was clear going into this study that

microscale and catena-level topography would likely be important controls on C storage in

Sudbury’s industrial barrens.

Other site characteristics were also expected to affect revegetation and C storage in

Sudbury’s barrens. Distance from historic smelters affected the disturbance intensity of

atmospheric pollution, as Ni, Cu, and SO4 pollution increased with proximity to the smelters

(Freedman & Hutchinson, 1980). In Sudbury, limited moisture in barren uplands also clearly

impeded natural recolonization and ecosystem C sequestration following emission reductions

(McCall et al., 1995; Mensah et al., 2016). Plant species richness and diversity may also affect C

storage in Sudbury’s restored barrens by increasing vegetative niche portioning, facilitating soil

5

nutrients, and increasing the chances of having more productive and higher biomass yielding

species (Lewis et al., 2019; Mensah et al., 2016). Finally, soil metal concentrations were

expected to impact C storage by affecting plant growth and soil microbial processes (Bolan et al.,

2003). An extensive study of soil metals on their potential toxicity to plants identified seven

metals (As, Cd, Co, Cu, Ni, Pb, Se) that were considered the principal chemicals of concern

(COC) in the Sudbury area (SARA Group, 2009).

I went into this study with the prediction that tree-planting Sudbury’s industrial barrens

could create large C sinks, a practice that if carried out across global brownfield environments,

could contribute to climate change mitigation. Lewis et al. (2019) found reforestation to be the

largest natural pathway to hold global climate warming at below 1.5˚C. Reforestation also

benefits biodiversity, habitat, air quality, water quality, flood control, and soil quality (Griscom

et al., 2017). Tree-planting has net positive effects on long-term tree and downed woody debris C

storage (Griscom et al., 2017; Keith et al., 2009). Although tree-planting increases aboveground

C, many temperate and boreal terrestrial ecosystems store most of their C in soils, and the

relationship between tree-planting and soil C storage is less clear (DeLuca & Boisvenue, 2012;

Nave et al., 2013). Soil C is generally the largest temperate and boreal C pool because it is

relatively stable across time and disturbance regimes (DeLuca & Boisvenue, 2012). However,

Nave et al. (2013) found that tree-planting on industrial barrens causes an initial decline in

mineral soil C followed by a significant increase 15 years post-planting. Long-term organic soil

C also increases after tree-planting due to the addition of litter and woody debris (Nave et al.,

2013). Keith et al. (2009) agreed that forests continue to sequester C in soils even after forests

reach maturity. Tree species also affects soil C storage, with coniferous trees having shallow

roots that accumulate more soil C in the organic than mineral soil horizons (Jandl et al., 2007).

6

Generally, however, boreal forests tend to store more total soil C under coniferous than

deciduous trees (Vesterdal et al. 2013). Therefore, it is of note that conifers comprised 95% of

the trees planted in Sudbury’s regreening program between 1979 and 2018 (City of Greater

Sudbury, 2018).

To date, nearly 40 years after the large-scale land reclamation began in Sudbury, there is

little knowledge of how Sudbury’s soil amendment procedures and tree-planting have affected

ecosystem C storage. We know that treating acidic and nutrient-limited forests with dolomitic

limestone generally increases plant C storage by neutralizing pH, reducing the bioavailability of

metals, and providing nutrients (Bolan et al., 2003; Moore & Ouimet, 2006). Dolomitic liming

fertilizes the soil with Ca and Mg, which are limiting nutrients in northern hardwood trees

(Moore & Ouimet, 2006). Furthermore, lime mitigates the phytotoxic impacts of acidity-

dependent trace metals, including Al, which negatively impact plant growth and development

(Bengtsson et al., 1988). High soil concentrations of bioavailable metals can also decrease the

uptake of plant essential nutrients by competing for binding sites (Bengtsson et al., 1988). Al, for

example, can cause P, a plant essential nutrient, to adsorb and become biologically unavailable

(Giesler et al., 2004). Therefore, liming effectively promotes plant growth and C storage. Liming

also increases microbial decomposition, which could decrease soil C (Haynes & Naidu, 1998).

However, liming’s positive effect on plant growth also supplements litter inputs, increasing soil

C (Paradelo et al., 2015). Liming-induced microbial activity could also deposit extracellular

binding agents that increase soil aggregation, which reduces soil erosion and increases soil depth

(Haynes & Naidu, 1998). Jandl et al. (2007) found that liming and fertilizing decreased soil C

due to losses from microbial decomposition and dissolved organic C (DOC) leaching. In

Sudbury, liming, fertilizing, and seeding facilitated the recolonization of plant species

7

(Winterhalder, 1996). Establishing plant cover generally reduces soil erosion and seeding with

N-fixing legume species improves soil N availability, both of which should promote continued

plant growth (Winterhalder, 1996). Seeding the understory, however, could also create

competition with trees for light and nutrients (Winterhalder, 1996).

The objectives of my study were: i) to compare the C storage of smelter-impacted upland

industrial barrens and naturally revegetated lowlands across untreated plots (U), limed, fertilized,

and grass and legume seeded plots (L), tree-planted plots (T), and limed, fertilized, grass and

legume seeded + tree-planted plots (L+T), and ii) to determine how landscape characteristics

(wetness index, plant species richness, soil metal concentrations, soil pH, jack pine frequency,

distance from smelter, elevation) affect C storage following these treatments on upland industrial

barrens.

I hypothesized that: i) liming, fertilizing, and grass and legume seeding would increase C

storage both above and below ground in Sudbury’s upland industrial barrens and lowland

valleys; ii) planting conifer trees in Sudbury’s upland industrial barrens would increase C storage

over time, and iii) site characteristics (i.e., topography, wetness index, plant species richness, soil

metal concentrations, etc.) would affect C storage across all restoration treatments in upland

industrial barrens.

2.0 Methods

Site description and plot-setup

My study was conducted in small adjacent headwater catchments of the Daisy Lake

watershed in Sudbury, ON. Daisy Lake is a 36 ha lake located 3.5 km southwest of the

abandoned Coniston Ni and Cu smelter that operated until 1972 (Szkokan-Emilson et al., 2011).

8

The Daisy Lake watershed was severely contaminated and partially denuded of vegetation and

soil by atmospherically deposited metals in a disturbance gradient away from the Coniston

smelter (with no vegetation closest to the smelter and minimal impact further away); it primarily

consisted of industrial barrens on uplands and steep slopes and naturally regenerated forests in

lowland valleys (Dixit et al., 1996). White birch naturally regenerated in some of the Daisy Lake

watershed (Dixit et al., 1996). Additionally, the Daisy Lake watershed’s valleys contained

vegetative and soil refugia, with tree core data from the 1990s aging the oldest trees in the

watershed to 1908 (Szkokan-Emilson et al., 2011; Vanttinen, 1993).

The study was conducted in and around three adjoining catchment areas labelled J, I, and

H with increasing distance from the abandoned smelter site located approximately 3-5 km

northeast of the Daisy catchments (Fig. 2.1). Daisy J, the closest catchment to the smelter, is a 39

ha catchment of mostly rock barrens that was aerially limed with 410 tons of coarse dolomitic

limestone (53.9% CaCO3, 44.8% MgCO3) in 1994, and in 1997 fertilized (N-P-K of 6-24-24)

and seeded with five species of grasses and two species of legumes (City of Greater Sudbury,

2019; Gunn et al., 2001; Szkokan-Emilson et al., 2011). The next catchment, Daisy I is a 32 ha

mostly barren catchment that was predominately untreated (Gunn et al., 2001). Daisy H is a 29

ha catchment consisting of low-lying valleys with eroded soil inputs and both residual and

naturally regenerating vegetation; it was entirely untreated.

Three years (1997) and then again 7 years (2001) after the initial aerial liming, jack pine

seedlings were planted by municipal tree planting crews in much of Daisy J, a small section of

Daisy I, and just peripheral to the Daisy watershed (City of Greater Sudbury, 2019). Tree

planting only occurred in the upland areas, and crews were somewhat haphazard in tree

placement, varying tree densities based on local site conditions (i.e., apparent soil depth).

9

I established forty-five circular plots of 0.1 ha size in 2018, twenty-four years after the

initial aerial liming treatment, across Daisy J, Daisy I, Daisy H, and just peripheral to the

watershed in areas of limed, fertilized, and grass and legume seeded sites (L), tree-planted sites

(T), limed, fertilized, and grass and legume seeded + tree-planted sites (L+T), and untreated (U)

sites (Fig. 2.1). I chose plots based on historic sampling points when possible, and additional

plots were added to increase replication across treatments. Tree-planted plots were characterized

as those containing > 20 jack pine. It was impossible to distinguish between planted and second-

generation jack pine, as tree size was variable depending on site and shading. Final data were

collected from 45 plots, with 15 U plots, 10 L plots, 8 T plots, and 12 L+T plots. Plot coordinates

and elevation were measured using GNSS (ISXBlue GPS with terra4go collector app) placed on

a 2 m levelled pole, with measurements taken after sub-meter accuracy was attained. Thirty-two

of the established plots were classified as upland industrial barrens (> 280 m centre elevation),

with 5 U plots, 7 L plots, 8 T plots, and 12 L+T plots (Fig. 2.1). Upland industrial barren plots

were the focus of my study. A remaining 13 plots were described as lowland valleys (≤ 280 m

centre elevation), of which 10 were U and 3 were L plots (Fig. 2.1). Valley plots were not barren

prior to restoration, so C storage was compared but I did not pursue further analysis.

Coarse woody debris C storage

Coarse woody debris (CWD) was defined as any downed wood that was not self-

supporting with a midpoint located within the plot boundary and a midpoint diameter ≥ 10 cm.

All CWD was surveyed within each plot and the species (if identified), length, decay class at

midpoint, and dimensions at the top, middle, and bottom were recorded. A 5 level decay class

system was used to differentiate decomposition and derive densities (Harmon et al., 2011). I

10

calculated the volume (V) of each piece of CWD using the conic-paraboloid equation from

Fraver et al. (2007):

(1) V = L/12 (5Ab + 5Au + 2√𝐴𝑏𝐴𝑢)

where L is length of CWD, Ab is the area of base end of CWD, and Au is the area of the upper

end of CWD. I then calculated CWD C by multiplying volume (V) by density (S) (Harmon et al.,

2011) and a C ratio of 0.50 (Laiho & Prescott, 2011; Luyssaert et al., 2008; Smith et al., 2003):

(2) CWD C (g) = V * S * 0.50

The biomass to C ratio did not vary with decay stages (Lambert et al., 1980). Total CWD C for

each plot was calculated by summing the CWD C, converting to Mg, and dividing by plot area:

(3) CWD C (Mg C ha-1) = Σ(CWD C (g)) / 1x106 / 0.1 ha

Fine woody debris C storage

Fine woody debris (FWD) was defined as downed, non-self-supporting wood with a

diameter at line intersect sampling (LIS) between 1.0 cm and 9.9 cm. FWD was surveyed using

LIS along four 17.84 m transects that ran north-south and east-west through the centre point of

each plot (Fig. 2.2). FWD for each transect was tallied by diameter at transect and decay class. I

calculated FWD biomass (W) per transect area with an equation from Van Wagner (1968),

which used an average of decay class densities for downed wood in Harmon et al. (2011):

(4) W = 𝜋2 * S * Σd2/ 8L

where S is the mean density along each LIS, d is diameter of FWD, and L is LIS length.

Total FWD C (Mg C ha-1) for each plot was then calculated by scaling the biomass to per

hectare, multiplying it by 0.50, and finding the mean FWD C of the four LIS transects (Laiho &

Prescott, 2011; Luyssaert et al., 2008; Smith et al., 2003).

11

Understory: Herb and shrub C storage

I sampled understory C by establishing four 1 m2 quadrats per plot, located at the far edge

of each transect (Fig. 2.2). Herbs were defined as dead and living biomass ≤ 1 m high, including

vegetation leaning in but not rooted in the quadrat. The percent cover of each quadrat was

categorized into rock, organic soil, mineral soil, lichen, fungi, and plants (identified to species

when possible). The herb layer within each quadrat was harvested at the ground and bagged. The

shrub layer was defined as any biomass > 1 m high and ˂ 3 cm diameter at breast height (DBH).

Shrubs rooted in quadrats were harvested at the base (including any branches extending out of

quadrat) and bagged. Herbs and a subset of shrub samples (n = 53) were dried at 70˚C for 48

hours and then weighed. Many shrubs were too large to process in drying ovens and others were

disturbed by small mammals while in storage. The subset of shrubs was dried at 70˚C for 48

hours and had an average weight loss of 44.8 ± 1.5%, which was then applied to remaining

shrubs to calculate total dried weights. The aboveground biomass of herbs and shrubs for each

plot was calculated by dividing mean biomass by quadrat area. Root biomass was not measured,

so total herb biomass was calculated as 3 times herb aboveground biomass and total shrub

biomass as 2.5 times shrub aboveground biomass (Johnston et al., 2002; Sherman & Beckett,

2003). Herb C and shrub C pools were calculated by multiplying total herb biomass by 0.45

(Sherman & Beckett, 2003; Vogel & Gower, 1998) and total shrub biomass by 0.48 (Foster &

Morrison, 2002; Johnston et al., 2002; Sherman & Beckett, 2003).

Tree C storage

Trees were defined as any self-supporting, standing biomass with a DBH ≥ 3 cm. All

trees within each plot were surveyed for species, health status (living or decay class 1-5), and

DBH. The number of jack pine per plot was recorded to indicate jack pine frequency. CBM-

12

CFS3 DBH-based biomass equations from Lambert et al. (2005) were used for balsam poplar

(Populus balsamifera), eastern hemlock (Tsuga canadensis), eastern white pine (Pinus strobus),

largetooth aspen (Populus grandidentata), jack pine, red maple (Acer rubrum), red oak

(Quercus rubra), and red pine (Pinus resinosa). Some species were updated for CBM-CFS3 in

Ung et al. (2008), so these equations were used for trembling aspen (Populus tremuloides), white

birch, white spruce (Picea glauca), and unknown deciduous trees that were too decayed to ID.

Willow (Salix spp.) and cherry (Prunus spp.) trees were not identified to species, so unknown

deciduous tree equations were again used (Ung et al., 2008). DBH-based biomass equations were

used to calculate total aboveground tree biomass:

where yi is the dry mass (kg) of compartment i (wood, bark, stem, foliage, branches, or crown),

βi are parameters outlined in Lambert et al. (2005) and Ung et al. (2008), D is the tree DBH (cm),

Ȳi is the modeled value of yi, and ei is the error term for compartment i (Table A1, Table A2)

(Lambert et al., 2005; Ung et al., 2008). Tree C was initially calculated using both CBM-CFS3

equations and allometric equations from Sherman (2005), which included Sudbury-specific

equations for some tree species. Tree C values from both calculations were found to be similar,

so I used CBM-CFS3 equations in my final dataset to make my data more comparable to other

studies.

Belowground tree biomass was estimated at 20% of aboveground tree biomass, so total

tree C was calculated by multiplying aboveground biomass (WA ) by 1.2 (Sherman & Beckett,

13

2003), decay-class reduction factor (DCRF), structural reduction factor (SRF), and a C

concentration of 48% (Foster & Morrison, 2002; Russell et al., 2015; Sherman & Beckett, 2003):

(5) Tree C (Mg C ha-1) = 1.2 * WA * DCRF *SRF * 0.48

where DCRF values were from Harmon et al. (2011) and SRF values from Domke et al. (2011).

Martin et al. (2018) found tree C concentrations in temperate forests to be 46.5 ± 0.3% for

angiosperms and 50.1 ± 0.4% for conifers, and in boreal forests 49.2 ± 0.8% for angiosperms and

46.8 ± 0.6% for conifers. Therefore, a tree C concentration of 48% for angiosperms and conifers

in a boreal mixed forest was appropriate. I summed individual tree C values for each plot to

calculate final tree C.

Plant species richness

Species richness for trees, shrubs, and herbs was calculated by counting the number of

unique species in each plot. I calculated total plant species richness by summing the species

richness of the three vegetative layers.

Soil chemistry and C storage

Total depths of mineral and organic (LFH) soil were measured at 16 points along

transects, to a maximum depth of 20 cm (Fig. 2.2). Soil samples were collected at every other

sampling point (Fig. 2.2). A 10 cm diameter metal punch was used to sample and measure the

depth of the organic soil (Fig. 2.3). A 2.5 cm diameter soil auger was used to sample mineral soil

at 0-5 cm, 5-10 cm, and 10-20 cm depths (Fig. 2.3).

Soil samples were oven-dried at 60˚C for 16 hours and weighed. Soils were then

homogenized and passed through a 2 mm sieve. The pH of all homogenized soil samples was

taken with an Accumet AB150 pH meter with a 2:1 ratio of milli-Q to soil that was shaken and

14

then let settle. Inductively coupled plasma mass spectrometry (ICP-MS) was used to measure

total and bioavailable metal and nutrient concentrations in soil samples. All samples were

processed at the Elliott Lake Research Field Station laboratory at Laurentian University using a

multi-acid (HF/HCl/HNO3) 90 °C open-block digest for total metals and a weak (0.01M) LiNO3

extraction procedure for bioavailable metals (Abedin et al., 2012). Soil samples from the 0-5 cm

layer were pooled for each plot and sampled using cone-splitting (poured into a cone-shaped pile

and cut into even triangular slices). Bioavailable metal concentrations were sampled by cone-

splitting into 8 sections and weighing the section on the far-left of the pile to 3 g. Duplicates

were randomly chosen for every 10 samples, and a blank was run prior to every 20 samples.

Total metals were measured by further cone-splitting existing soil piles into 16 sections,

sampling the far-right section, and weighing out 0.5 g. Elements identified in the SARA Group

(2009) study as chemicals of concern (COC) for the Sudbury region (As, Co, Cu, Pb, Ni, Se, Cd)

were the focus of my study. Concentrations below detection limits were set at half of detection

limit. Total soil COC concentrations were compared to limits for uncontaminated soils for

residential, industrial, and park sites listed in Table 1 of Ontario Ministry of the Environment

Conservation and Parks (2011). Quality assurance and quality control (QAQC) of ICP-MS’s

analytical and method precision was analyzed by comparing the relative percent difference

between laboratory and sample duplicates for each element (Table A3). Known concentration

CRM TILL-1 data were compared to my samples, however my samples were digested with HF

while CRM standards were not, so they were not directly comparable.

Loss on ignition (LOI) was used to determine the soil organic matter (SOM) content of

all samples. Sub-samples of homogenized soils were weighed, combusted at 550˚C for 5 hours in

a muffle furnace, and re-weighed. Bulk density was calculated as the mass of the sieved soil

15

(excluding rocks that did not pass through the 2 mm sieve) divided by the volume of the soil core

for that layer.

Stored soil C was calculated by dividing SOM by the SOM:SOC ratio, multiplying it by

bulk density (BD) and each layer’s across-plot volume (V), and dividing it by 0.1 ha:

(6) Mineral soil C (Mg C ha-1) = SOM/ 2.114 * BD * V/ 0.1

(7) Organic soil C (Mg C ha-1) = SOM/ 2 * BD * V/ 0.1

I used a Sudbury-specific SOM:SOC ratio for mineral soils of 47.3% (conversion factor of

2.114) (Sherman, 2005) and a SOM:SOC ratio of 50% (conversion factor of 2) for organic soil

(Nelson & Sommers, 1996; Sherman, 2005; Smithwick et al, 2002). Organic C was presumed to

equal total C because in non-calcareous soil regions, such as Sudbury, soil contains negligible

non-organic C (Périé & Ouimet, 2011). My methods only measured soil C to a maximum depth

of 20 cm, which excluded deep mineral soil in some sites. However, Sudbury’s soils are

generally thin with large areas of surficial bedrock, so a maximum depth of 20 cm incorporated

most mineral soil.

Wetness index

Wetness index is the relative moisture of soil, vegetation, and other moist features that is

calculated by comparing the absorbance and reflectance of different wavelengths from satellite

imagery (Baig et al., 2014). I calculated wetness index using Landsat8 OLI data, with resolution

of around 30 m2, that I acquired from the USGS for July 17, 2018. The imagery was chosen

based on quality and the date coinciding with field work. R version 3.4.1 was used to plot and

crop the raster files. Upland industrial barren plot centre coordinates were overlaid onto the

Landsat cropped raster, and a 17.84 m buffer was superimposed around each point to indicate

16

plot boundaries (Fig. A1). Mean tasseled cap wetness index values were calculated for each plot

using the “tasseledCap” R package (Baig et al., 2014) and standardized with z-scores for

comparison.

Distance from smelter

ArcMap (10.6.1) was used to calculate the distance from the Coniston smelter to each

upland plot centre-point, with the smelter reference point set at half-way between the two

smokestacks.

Data analysis

Total plot C storage (Mg C ha-1) was calculated by summing the C stored in CWD, FWD,

herbs, organic soil, mineral soil (to a maximum depth of 20 cm), shrubs, and trees. Plots were

divided into upland industrial barrens (≥ 280 m) and lowland valleys (˂ 280 m) based on their

centre-point elevations. A cut-off of 280 m was chosen because tree-planting only occurred

above this altitude.

R 3.6.0 (64-bit) was used for all statistical tests. Data were tested for normality across all

treatments. A two-tailed t-test was used to compare limed, fertilized, and seeded plots (L) and

untreated plots (U) in lowland valley plots. An ANOVA with a significance level of 0.05 was

used to test differences in total C storage among treatments (U, L, T, L+T) in upland industrial

barrens. Tukey’s HSD was used as post-hoc test to determine what treatments were significantly

different from each other for upland industrial barrens.

Multiple linear regression was used to determine what site characteristics most affected

plot C storage in upland industrial barrens. A principal component analysis (PCA) for

bioavailable metals (chemicals of concern) in upland industrial barren sites was conducted,

17

including bioavailable concentrations of As, Cd, Co, Cu, Ni, and Pb (SARA Group, 2009). Se

was also listed as a Sudbury chemical of concern, but it was not included in my analysis because

all bioavailable concentrations were below the detection limit. (SARA Group, 2009). A broken-

stick analysis was used to determine how many principal components (PCs) to include in my

linear model. The broken-stick model designated that observed eigenvalues that exceeded

broken-stick model-generated eigenvalues were significant and should be included in the model

(Jackson, 1993). In my study only PC1 fit this criterion and was included in my linear regression

(Fig. A2, Table A6). I then conducted a Pearson correlation analysis to ensure that no predictor

variables in my linear regression were highly correlated to each other (correlation coefficient ≥

0.75) (Table A4). I also used a Pearson correlation matrix to see what predictors were

significantly correlated to C storage and each other (Table A5). Wetness index, plant species

richness, PC1 (soil bioavailable metals), soil pH (0-5 cm mineral soil), jack pine frequency,

distance from smelter, and elevation were all selected for potential inclusion in the linear

regression. Interactions between predictors were tested but not included in the final model

because they were not significant. Backwards linear regression was used to compare all possible

models and select the top model based on Second-order Akaike’s Information Criterion (AICc),

which was utilized because it was more robust with relatively small sample sizes and multiple

parameters. All variables were standardized using z-scores with the equation:

(8) y = (yi - ymean)/SD

where z-scores had a mean of 0 and a standard deviation (SD) of 1. Final variables included in

the linear regression were wetness index, plant species richness, PC1 (soil bioavailable metals),

soil pH, jack pine frequency, distance from smelter, and elevation.

18

Figure 2.1 Map of Daisy Lake watershed plots in upland industrial barrens (> 280 m centre

elevation) (n = 32) and lowland valleys (≤ 280 m centre elevation) (n = 13). Shading indicates

tree-planted (T), limed, fertilized, and seeded (L), and limed, fertilized, and seeded + tree-planted

(L+T) treatments. Untreated (U) areas are not shaded. Daisy catchments H, I, and J are labelled.

Arrow indicates direction of historic Coniston smelter.

19

Figure 2.2 Layout of sampling points at each 0.1 ha plot. FWD-1 through FWD-4 were LIS

locations for FWD survey. Soil depth was measured at 1-16, while soil samples were taken at

even numbers only. Understory sampling occurred in 1 m2 square quadrats located at numbers 4,

8, 12, and 16. All other sampling occurred throughout entire plot.

Figure 2.3 Soil sampling protocols for organic and mineral soil layers. Mineral soil was divided

into 3 depths (0-5 cm, 5-10 cm, 10-20 cm) and sampled with a1.5 cm diameter soil corer and

organic soil was collected using a 5 cm circular frame.

20

3.0 Results

Effect of treatment on C storage

In upland industrial barrens the mean total ecosystem C pool was 36.1 ± 8.4 Mg C ha-1

for U plots, 34.2 ± 8.6 Mg C ha-1 for L plots, 47.6 ± 4.2 Mg C ha-1 for T plots, and 58.2 ± 4.4 Mg

C ha-1 for L+T plots (Fig. 3.1). Total C storage among treatments was significantly different (F

(3,28) = 3.8, p < 0.05), with L+T plots storing significantly more C than L plots (p < 0.05) (Fig.

3.1).

In lowland valleys with deeper soils and more natural regeneration of tree cover, total

ecosystem C storage was significantly greater (t(21) = -4.95, p < 0.0001) than in the uplands area.

Liming in lowland valleys further increased (t(7) = 3.5, p < 0.01) the total C stores compared to

untreated plots from 73.3 ± 5.9 Mg C ha-1 to 101.1 ± 5.5 Mg C ha-1 (Fig. 3.5). Mineral soil C

(39.7%) and tree C (34.5%) were the largest contributors to total ecosystem C storage in lowland

valleys.

C pools: Upland industrial barrens

In upland industrial barrens, the main focus of my study, mineral soil was the largest C

pool in all treatments but did not differ significantly among treatments (p > 0.05) (Fig. 3.2).

Mineral soil C stored 69.6% of C in U plots, 64.4% in L plots, 51.1% in T plots, and 45.9% in

L+T plots. Tree C was significantly different among treatments (F (3,28) = 39.6, p < 0.001). Tree

C pools in T plots (13.0 ± 0.8 Mg C ha-1) and L+T plots (19.3 ± 1.4 Mg C ha-1) were

significantly larger than tree C pools in L plots (2.8 ± 1.8 Mg C ha-1) and U plots (0.5 ± 0.4 Mg

C ha-1) (p < 0.01), and L+T plots also had significantly more tree C than T plots (p < 0.01) (Fig.

3.2). Tree C stored 1.4% of total ecosystem C in U plots, 8.1% in L plots, 27.3% in T plots, and

21

33.1% in L+T plots. Organic soil C was not significantly different among treatments (p > 0.05)

(Fig. 3.2), contributing 18.8% of C storage in U plots, 16.9% in L plots, 12.1% in T plots, and

14.5% in L+T plots. Herb C was not significantly different among treatments (p > 0.05) (Fig.

3.2), storing 7.3% of total C in U plots, 7.2% in L plots, 3.8% in T plots, and 2.9% in L+T plots.

Shrub C was not significantly different among treatments (p > 0.05) (Fig. 3.2), storing 2.1% of

total C in U plots, 2.8% in L plots, 4.7% of C in T plots, and 3.0% in L+T plots. Neither FWD

nor CWD stored significantly different amounts of C among treatments (p > 0.05) (Fig. 3.2) and

both were relatively small C pools, storing < 1% of total C across all treatments.

Predictors of C storage: Upland industrial barrens

The wetness index was the strongest predictor of total C storage at the uplands sites,

followed by plant richness and a principal component index composed of the bioavailable

concentration of metals (As, Cd, Co, Cu, Ni, Pb) considered as chemicals of concern in the

Sudbury area. Together these metrics in a linear regression model explained 64.0% of the

variance in C storage (Table 3.1.)

No other model had ∆AICc < 2, meaning that the top model had considerable support.

Wetness index had the largest coefficient (11.27 ± 2.40) and explained 53.5% of the variance,

followed by plant richness (6.11 ± 2.58; 6.4%), and PC1 (-3.57 ± 1.45; 3.0%) (Table 3.1).

Wetness index (z-scores) increased significantly (p < 0.001) from -1.22 ± 0.42 in U plots

to 0.59 ± 0.17 in L+T plots (Fig. 3.3). T plots also had significantly higher wetness indices (p <

0.01; 0.30 ± 0.15) than U plots (Fig. 3.3). Furthermore, L+T plots also had significantly higher

wetness than L plots, which had a wetness index of -0.49 ± 0.45 (p < 0.05) (Fig. 3.3).

22

Plant species richness increased significantly (p < 0.001) from U plots (9.40 ± 1.17) and

T plots (10.50 ± 0.57) to L+T plots (18.42 ± 1.09) (Fig. 3.4). L plots also had significantly higher

(p < 0.05) plant species richness (15.71 ± 2.34) than U plots (Fig. 3.4).

Mineral soil bioavailable metal concentrations varied among restoration treatments,

however, there was no clear pattern between bioavailable metals and restoration treatment (Fig.

A4). Total concentration of these metals of concerns were used for comparison to provincial

standards, as there were no provincial standards for bioavailable soil concentrations. Total

mineral soil As, Cd, Cu, Ni, and Se concentrations for all treatments were above provincial

standards for soils in residential, parkland, institutional, commercial, or community property use

(Fig. A3) (Ontario Ministry of the Environment Conservation and Parks, 2011). Total mineral

soil Co and Pb concentrations for all treatments were below the provincial limits (Fig. A3).

Mineral soil pH

Soil pH varied by treatment in upland barrens; U plots had mean mineral soil pH values

that ranged between 4.1 – 4.5, L plots between 4.5 – 6.5, T plots between 4.1 – 4.5, and T+L

plots between 4.4 – 5.6 (Table A8). Lowland valley L plots had mean mineral soil pH values that

ranged between 4.5 – 5.4 and U plots between 3.7 – 4.6 (Table A8).

23

Figure 3.1 Total ecosystem C storage (Mg C ha-1 ± SE) in upland industrial barrens

among untreated (U), limed, fertilized, seeded (L), tree-planted (T), and limed, fertilized,

seeded + tree-planted (L+T) plots (n = 32 plots). Different letter designations above bars

indicate significant differences at p < 0.05.

24

Figure 3.2 C storage (Mg C ha-1 ± SE) in each C pool (CWD, FWD, herb, mineral soil, organic

soil, shrub, and tree) in upland industrial barrens among untreated (U), limed, fertilized, seeded

(L), tree-planted (T), and limed, fertilized, seeded + tree-planted (L+T) plots (n = 32 plots).

Different letter designations in tree C indicate significant differences at p < 0.05. Only tree C

differed significantly by treatment.

25

Figure 3.3 Wetness index (z-scores) in upland industrial barrens among untreated (U), limed,

fertilized, seeded (L), tree-planted (T), and limed, fertilized, seeded + tree-planted (L+T) plots (n

= 32 plots). Different letter designations above plots indicate significant differences at p < 0.05.

26

Figure 3.4 Plant species richness (± SE) in upland industrial barrens among untreated (U),

limed, fertilized, seeded (L), tree-planted (T), and limed, fertilized, seeded + tree-planted (L+T)

plots (n = 32 plots). Different letter designations above bars indicate significant differences at p

< 0.05.

27

Table 3.1 Linear regression relating total ecosystem C storage (Mg C ha-1) in upland industrial

barren plots (n = 32 plots) to wetness index, plant species richness, PC1 (first axis of principal

component of soil bioavailable [As], [Cd], [Co], [Cu], [Ni], [Pb]), soil pH, jack pine frequency,

distance from smelter, and elevation (MASL). Model was generated using backward selection

with second order Akaike Information Criterion (AICc).

Final model:

C storage ~ Wetness index + Plant richness + PC1

Wetness index

Plant richness

PC1

Estimate (SE)

11.27 (2.40)

6.11 (2.58)

-3.57 (1.45)

p value

<0.001

<0.05

<0.05

AICc= 257.83

Intercept= 46.86

R2= 0.64

p value < 0.001

28

Figure 3.5 Total ecosystem C storage (Mg C ha-1 ± SE) at lowland valley untreated (U) and

limed, fertilized, seeded (L) plots (n = 13). Different letter designations above bars indicate

significant differences at p < 0.01.

29

4.0 Discussion

C storage: Upland industrial barrens

Overall the restoration treatment in the uplands showed benefits in terms of tree C

accumulation in the time period of this study. The fully treated sites had 61% more C than the

untreated sites, but presumably because of the high variability among the sites, the differences

were not yet significantly different. This slow accumulation of C may be because the jack pine

were planted only 17-21 years prior to my study and jack pine continue to grow and reproduce

until 60-100 years of age (Wisconson Department of Natural Resources, 2016). This means that

the restored forests were not fully mature, but are expected to continue to grow, reproduce, and

sequester C in this industrial barren into the future. The rate of accumulation is uncertain because

of the very challenging soil and moisture conditions that exist in the upland barrens area.

A previous Sudbury study assessed C storage in restored barrens and found significantly

increased total ecosystem C storage in tree-planted + limed plots compared to untreated plots 23

years post-restoration (Sherman & Beckett, 2003). Sherman and Beckett (2003) found that

unrestored industrial barrens stored 35.5 ± 6.5 Mg C ha-1, which was very similar to the C

storage in my naturally regenerating upland barren sites (36.1 ± 8.4 Mg C ha-1). These results

indicate that over the 16 years between C quantification in Sherman and Beckett (2003) and my

study, negligible C was sequestered through natural regeneration of Sudbury’s industrial barrens.

Furthermore, Sherman and Beckett (2003) found that sites restored with liming and tree-planting

increased C storage by 200% (106.5 ± 6.2 Mg C ha-1), while I did not find a significant increase

in total C storage with restoration. Natural regeneration in untreated plots was limited by poor

site conditions that impeded plant and soil development. Some of these factors included drought

conditions, soil metals, wind cooling, direct solar radiation, and erosion (Winterhalder, 1995).

30

Planting trees and adding soil amendments somewhat mitigated these issues, facilitating tree

growth and C storage (Winterhalder, 1996). My restored sites’ total C storage values were

relatively low, but within the range given in Hazlett et al. (2005) (28.3 – 135.1 Mg C ha-1) for

upland boreal forests. Forest recovery and C storage was constrained on my site by the relatively

harsh growing conditions even following restoration treatments. I do, however, expect that my

restored sites will sequester C at a faster rate than untreated sites, perhaps eventually finding a

significant difference in total C storage between untreated and fully restored sites and reaching

the C storage of restored sites in Sherman and Beckett (2003). With almost 15,000 – 20,000 ha

of Sudbury’s industrial barrens still to be restored (P. Beckett personal communication, Dec. 19,

2019), Sudbury therefore still has significant potential to increase the C storage of these barrens

across all C pools. Restoration treatments and site conditions will determine the rate of C

sequestration and the magnitude of total C storage.

Tree C pools were larger in restored than untreated plots. Tree C was largest in plots that

were limed, fertilized, seeded, and tree-planted, demonstrating the benefit of reforestation paired

with soil amendments in acidified and metal-polluted soils. Reforestation directly increases tree

C by increasing tree biomass (Griscom et al., 2017). Liming increases soil pH and reduces the

bioavailability of acidity-dependent trace metals, while fertilizing provides plant nutrients (Bolan

et al., 2003; Lal, 2005). Therefore, a combination of liming, fertilizing, seeding, and tree-

planting increased tree C storage on my plots. Restoration was also expected to increase herb and

shrub biomass, but this was not the case in my study. It is possible that the increased tree canopy

subjected understory plants to light competition, which decreased their biomass (Burton et al.,

2013).

31

I expected that mineral soil C storage would increase in restored sites compared to

untreated sites, however mineral soil C was not affected by restoration treatments. My study

found that mineral soil was the largest C pool in all treatments. Most boreal forests store the

majority of C in mineral soil, so this C pattern was expected (DeLuca & Boisvenue, 2012).

Mineral soil is relatively stable and well protected from disturbance-derived decomposition, so

sequestering C in mineral soil is beneficial for long term C storage (Vesterdal et al., 2013). I also

expected that organic soil C storage would be affected by restoration, however, this did not occur

in my study. Other studies have found that soil liming caused a net loss in soil C by increasing

pH and facilitating microbial decomposition of SOM (Haynes & Naidu, 1998). Lime was applied

24 years prior to my study, so lime-induced decomposition could have affected both the mineral

and organic soil layers in earlier years. Furthermore, physical soil movement during tree-planting

or continued erosion could have decreased soil aggregation and bulk density, reducing soil C

(Nave et al., 2013). Afforestation could have also affected soil C through soil disturbance,

increased SOM mineralization, and increased litterfall inputs (Jandl et al., 2007). Therefore,

liming likely increased soil turnover rates, resulting in unchanged soil C pools between restored

and untreated plots. Other studies have found that mine-impacted soils started to accumulate

significant soil C 15-25 years post-planting (Nave et al., 2013). My study looked at forests

planted 17-24 years prior and limed, fertilized, and seeded 21-24 years prior, so we may just now

be entering a period where increasing soil C sequestration is expected. However, thin, rocky

soils, high metal concentrations, and limited moisture may continue to limit C storage in these

particular industrial sites for many years to come.

Additionally, I expected that restoration would increase downed woody debris C storage

in upland industrial barrens, but I found no significant difference in CWD or FWD C storage

32

across treatments. Downed woody debris usually comprises around 20% of total C storage in

mature forest ecosystems (Russell et al., 2015). In contrast my study found that CWD and FWD

only represented < 1% of total C. Downed woody debris C pools increase with forest age (Keith

et al., 2009), so depauperate downed woody debris C pools might have been due to the forest’s

young age. Furthermore, liming could have reduced downed woody debris C pools by

stimulating microbial decomposition of downed wood (Jandl et al., 2007). Afforestation also

increased the canopy cover, which could have affected downed woody debris decomposition by

increasing site moisture and reducing temperature extremes (Forrester et al., 2012).

Predictors of C storage

Wetness index was the best predictor of total ecosystem C storage in my study.

Sudbury’s landscapes had variable microtopography (i.e., elevation, aspect, and slope) that

created spatially varied soil wetness (Moeslund et al., 2013; Winterhalder, 1996). Soil wetness

controls vegetative growth by buffering soil temperatures, fulfilling water requirements, and

affecting nutrient availability (Moeslund et al., 2013). Tree-planting, liming, fertilizing, and

seeding likely increased soil wetness, as restoration protects against wind desiccation, prevents

drought-inducing temperature extremes, and increases soil moisture retention (Winterhalder,

1996). Areas with high wetness index values were in depressions and likely received soil erosion

inputs, were protected from wind erosion, were buffered from temperature extremes, and had

higher seed accumulation (Moeslund et al., 2013). Areas with low wetness index values were

mainly flat ridgetops with extreme temperatures, drought conditions, frost stress, high erosion

potential, and little seed accumulation, which resulted in reduced C storage (Moeslund et al.,

2013; Winterhalder, 1996).

33

Plant species richness was also a significant predictor of total ecosystem C storage.

Limed plots had significantly more plant species than un-limed plots. Forest diversity generally

increases plant C storage through niche portioning, increased stem density and production, and

enhanced soil fertility (Mensah et al., 2016).

I found that legacy metals from the smelter had a negative effect on total ecosystem C

storage. Most total metal concentrations more than 40 years after the smelter closed were still

above provincial soil standards (Fig. A3, Fig. A4). High concentrations of trace metals

negatively impact plant growth (Barrett & Watmough, 2015). Metal bioavailability is affected by

soil acidity, nutrient availability, and SOM content (Bolan et al., 2003; Meadows & Watmough,

2012). Therefore, I expected that tree-planting, liming, seeding, and fertilizing would impact

metal bioavailability. Restoration treatments, however, did not consistently affect bioavailable

metal concentrations. This could be because metals had differing adsorption acidity thresholds,

responses to SOM inputs, and interactions with Ca and Mg in dolomitic limestone (Bolan et al.,

2003).

C storage: Lowland valleys

Although it was not my initial focus because the full restoration efforts were confined to

the uplands, it was clear that topography had a very large effect on C storage. Lowland sites

showed improved C storage with liming, but even the untreated lowland sites had higher C

storage than the fully treated upland sites. This could be because valleys contained refugial

vegetation, were protected from smelting pollution, and received erosion inputs (Vanttinen,

1993; Winterhalder, 1995). Lowland valley limed, fertilized, and seeded plots had significantly

increased C storage compared to untreated plots. Soil amendments and seed distribution might

have bolstered productivity and increased C storage. Furthermore, an uneven sample size (L = 3,

34

U = 10) could have exasperated site differences among treatments. Two lowland valley limed,

fertilized, and seeded plots were at the base of steep cliffs with ephemeral standing water, which

could have enhanced plant growth, reduced decomposition, and increased C storage.

Restoration recommendations

I recommend that industrial barrens continue to be treated with tree-planting, liming,

fertilizing, and seeding, as this increased tree C storage in my study. Industrial barrens have poor

plant growth conditions, so natural regeneration is slow (Ketchledge et al., 1985; Winterhalder,

1996). Restoring industrial barrens promotes regeneration by reducing acidity and trace metal

bioavailability, preventing temperature extremes, buffering wind, and binding soil (Winterhalder,

1996). Therefore, liming, fertilizing, seeding, and tree-planting industrial barrens is expected to

create slow but still effective C accumulation and storage.

The addition of an organic amendment (i.e., mulch) to the restoration protocol might

further increase C storage by increasing soil moisture. Additionally, I would recommend a

diverse planting regime rather than a monoculture plantation. Monocultures store less C than

mixed forests (Jandl et al., 2007). I would also recommend planting N-fixing trees species, such

as alder (Alnus sp.) and black locust (Robinia pseudoacacia L.), as they increase soil C

concentrations by 20-100% (Johnson, 1992; Vesterdal et al., 2013). Boreal forests generally have

higher organic and mineral soil C stocks under conifers than deciduous trees, so planting

coniferous trees would also promote C storage (Vesterdal et al., 2013).

I further recommend that naturally regenerated lowland valleys also be treated with

limestone, fertilizer, and seed to further enhance plant development and C storage.

35

Limitations of study

In my study soils were generally very thin and my mineral soil C was only measured to a

maximum depth of 20 cm, while other studies indicate that boreal forests can store C in mineral

soil up to 1 m deep (DeLuca & Boisvenue, 2012). Many of my industrial barren plots had in total

less than 20 cm of mineral soil, however. Forests generally sequester C over time, so repeating

my study in another 10 years could find significant C sequestration trends. In addition to the soil

limitations, drought during the 2018 growing season also could have impacted C storage results.

Some vegetation prematurely senesced during my study, which could have impacted understory

biomass and litterfall rates. Finally, some white birch, red maple, and red oak in my study were

stunted with coppiced growth forms, while the DBH-based biomass equations were designed for

single-stemmed, healthy trees. However, I did initially compare my tree C calculations to the

allometric equations in Sherman (2005) and both sets of equations produced very similar values,

so the discrepancies would be minimal. I am uncertain if these possible errors would have had

any substantial effects.

Furthermore, I have used the boreal forest for comparisons throughout, while Sudbury is

on the ecotone of the Great Lakes – St. Lawrence and boreal forests. The restored sites I assessed

contained more boreal species (i.e., jack pine, red pine, white spruce, trembling aspen, white

birch) than Great Lakes – St. Lawrence species (i.e., red oak, red maple), so the boreal forest was

more comparable. Additionally, boreal forest literature is commonly used for comparison in

other Sudbury studies. However, with climate change, future Sudbury forest composition might

be better compared to Great Lakes – St. Lawrence forest types.

36

Conclusion

Restoration of industrial barrens on upland sites with a combination of tree-planting,

liming, fertilizing, and seeding increased tree C storage during a 24-year period, but total C

storage was not yet significantly higher than in untreated sites. C accumulation is a slow process

in such challenging sites, resulting in slower C sequestration rates and C storage than in most

boreal forest sites. Mineral soil was the largest C pool in all treatments but was unaffected by

restoration. Tree C increased significantly in the restored plots, while CWD, FWD, herbs,

organic soil, and shrubs were all unaffected by restoration. Wetness index, plant species richness,

and soil metal contamination all appeared to affect C storage, with wetness index the most

important factor. Therefore, it is important to consider site characteristics when planning

restoration to maximize C storage in industrial barrens. For example, I found that C storage in

naturally regenerated lowland valleys was significantly increased with liming, fertilizing, and

seeding. From a global perspective, it is important to recognize that industrial barrens are

estimated to cover millions of hectares (Gunn et al., 1995; Kozlov & Zvereva, 2015). Restoration

of industrial barrens in my study was found to increase tree C storage on a site with poor

growing conditions. Continued growth and C accumulation across all C pools in these restored

forests is expected, which suggests that largescale restoration practices in industrial barrens

could add to terrestrial C pools and contribute to climate change mitigation.

Future studies

I suggest that future studies assessing C storage in restored industrial barrens include

several local reference plots with comparable site characteristics (i.e., elevation, slope, geology,

aspect, climate) in areas that were not industrially-disturbed. This would allow a comparison

between similar restored and undisturbed areas to determine the difference in C storage at a local

37

scale. I would also suggest increasing the sample size for each treatment and having an equal

number of replicates per treatment. Repeated sampling over time of my current study sites is also

important. Future studies should also incorporate detailed microtopography data as potential

drivers of total ecosystem C storage. Heterogeneous landscapes, such as Sudbury, are difficult to

characterize at large scales, so remote sensing, such as LiDAR, would be useful for site

characterization. Finally, I recommend that location-specific biomass equations be constructed

for each tree species, which would ensure that tree C storage is accurately measured based on

local tree growth forms.

38

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Winterhalder, K. (1995). Early history of human activities in the Sudbury area and ecological

damage to the landscape. In John Maxwell Gunn (Ed.), Restoration and recovery of an

industrial region (pp. 17–31). Springer, New York.

Winterhalder, K. (1996). Environmental degradation and rehabilitation of the landscape around

Sudbury, a major mining and smelting area. Environmental Reviews, 4(3), 185–224.

https://doi.org/10.1139/a96-011

Wisconson Department of Natural Resources. (2016). Jack Pine. In Silvicultural Handbook (pp.

1–73).

44

APPENDIX

SUPPLEMENTARY TABLES AND FIGURES

Table A1 CBM-CFS3 species-specific DBH-biomass equations from Lambert et al. (2005) were

used for balsam poplar, eastern hemlock, eastern white pine, large-tooth aspen, jack pine, red

maple, red oak, and red pine.

Species Parameter Estimate SE

Balsam poplar (Populus balsamifera)

βwood1 0.0510 0.0033

βwood2 2.4529 0.0205

βbark1 0.0297 0.0035

βbark2 2.1131 0.0375

βbranches1 0.0120 0.0014

βbranches2 2.4165 0.0376

βfoliage1 0.0276 0.0018

βfoliage2 1.6215 0.0240

Eastern hemlock (Tsuga canadensis)

βwood1 0.0619 0.0030

βwood2 2.3821 0.0150

βbark1 0.0139 0.0010

βbark2 2.3282 0.0210

βbranches1 0.0217 0.0031

βbranches2 2.2653 0.0420

βfoliage1 0.0776 0.0069

βfoliage2 1.6995 0.0292

Eastern white pine (Pinus strobus)

βwood1 0.0997 0.0129

βwood2 2.2709 0.0350

βbark1 0.0192 0.0016

βbark2 2.2038 0.0237

βbranches1 0.0056 0.0008

βbranches2 2.6011 0.0381

βfoliage1 0.0284 0.0024

βfoliage2 1.9375 0.0248

Large-tooth aspen (Populus grandidentata)

βwood1 0.0959 0.0144

βwood2 2.3430 0.0483

βbark1 0.0308 0.0035

βbark2 2.2240 0.0388

βbranches1 0.0047 0.0006

βbranches2 2.6530 0.0435

βfoliage1 0.0080 0.0016

βfoliage2 2.0149 0.0629

Jack pine (Pinus banksiana)

βwood1 0.0804 0.0042

βwood2 2.4041 0.0168

βbark1 0.0184 0.0009

45

βbark2 2.0703 0.0165

βbranches1 0.0079 0.0011

βbranches2 2.4155 0.0486

βfoliage1 0.0389 0.0029

βfoliage2 1.7290 0.0251

Red maple (Acer rubrum)

βwood1 0.1014 0.0052

βwood2 2.3448 0.0173

βbark1 0.0291 0.0027

βbark2 2.0893 0.0320

βbranches1 0.0175 0.0034

βbranches2 2.4846 0.0603

βfoliage1 0.0515 0.0065

βfoliage2 1.5198 0.0400

Red oak (Quercus rubra)

βwood1 0.1754 0.0149

βwood2 2.1616 0.0267

βbark1 0.0381 0.0051

βbark2 2.0991 0.0403

βbranches1 0.0085 0.0028

βbranches2 2.7790 0.0979

βfoliage1 0.0373 0.0029

βfoliage2 1.6740 0.0268

Red pine (Pinus resinosa)

βwood1 0.0564 0.0034

βwood2 2.4465 0.0192

βbark1 0.0188 0.0006

βbark2 2.0527 0.0102

βbranches1 0.0033 0.0005

βbranches2 2.7515 0.0455

βfoliage1 0.0212 0.0014

βfoliage2 2.0690 0.0204

Table A2 CBM-CFS3 species-specific DBH-biomass equations updated for CBM-CFS3 in Ung

et al. (2008). These equations were used for trembling aspen, unknown deciduous (too decayed

to ID), white birch, and white spruce. Willow and cherry trees were not identified to species, so

“All hardwoods” tree equations were used (Ung et al., 2008).

Species Parameter Estimate SE

Trembling aspen (Populus tremuloides)

βwood1 0.0608 0.0029

βwood2 2.4735 0.0153

βbark1 0.0159 0.0006

βbark2 2.4123 0.0131

βbranches1 0.0082 0.0008

βbranches2 2.5139 0.0327

βfoliage1 0.0235 0.0032

βfoliage2 1.6656 0.0440

Unknown deciduous βwood1 0.0864 0.0015

46

βwood2 2.3715 0.0053

βbark1 0.0226 0.0008

βbark2 2.2151 0.0112

βbranches1 0.0186 0.0007

βbranches2 2.4462 0.0127

βfoliage1 0.0385 0.0010

βfoliage2 1.6255 0.0089

White birch (Betula papyrifera)

βwood1 0.0604 0.0016

βwood2 2.4959 0.0090

βbark1 0.0140 0.0008

βbark2 2.3923 0.0195

βbranches1 0.0147 0.0009

βbranches2 2.5227 0.0217

βfoliage1 0.0591 0.0026

βfoliage2 1.6036 0.0167

White spruce (Picea glauca)

βwood1 0.0334 0.0008

βwood2 2.5980 0.0086

βbark1 0.0114 0.0004

βbark2 2.3057 0.0115

βbranches1 0.0302 0.0019

βbranches2 2.0927 0.0227

βfoliage1 0.1515 0.0079

βfoliage2 1.5012 0.0182

Table A3 Method accuracy for comparing CRMs to my total metals data. NAs for analytical

computation indicate impossible comparisons because concentrations are <DL, NAs for method

accuracy represent missing data. Method accuracy is for total metals only.

Method Accuracy Analytical Precision

TILL- 1 CRM

% recovery compared

to certified values

Total metals: Relative

percent difference

Bioavailable

metals: Relative

percent difference

As 287.59% 3.19% 59.82%

Cd NA 0.99% 22.40%

Co 90.56% 0.62% 0.14%

Cu 309.57% 0.41% 0.58%

Ni 95.83% 0.14% 0.91%

Pb 90.38% 0.53% NA

Se NA 8.03% NA

47

Figure A1 Map of wetness index in upland industrial barren plots, calculated using tasseled cap.

Z-scores were used for final data use. More positive wetness index values indicate wetter soil,

more negative values indicate drier soil.

Figure A2 Broken-stick analysis showing how many PCs to include in linear model. PCs where

observed eigenvalues exceed broken-stick model eigenvalues were included. In this case just

PC1 met this criterion and was included.

48

Table A4 Pearson correlation coefficient (r) matrix for predictors and total C storage included in

linear regression. A limit of ≤ 0.75 between any two predictors was used for any two variables

being included in the linear model. No predictors reached this correlation limit.

C

storage

Wetness

index

Plant

richness

PC1

of

COC

pH Distance

from

smelter

Elevation Jack pine

frequency

C storage 1.00 0.74 0.42 -0.24 -0.08 0.076 0.022 0.51

Wetness

index

0.74 1.00 0.37 -0.08 -0.07 -0.11 -0.08 0.58

Plant

richness

0.42 0.37 1.00 0.39 0.63 -0.12 0.12 0.63

PC1 Of

COC

-0.24 -0.08 0.39 1.00 0.61 -0.27 0.07 -0.11

pH -0.08 -0.07 0.63 0.61 1.00 -0.11 -0.03 -0.08

Distance

from

smelter

0.076 -0.11 -0.12 -0.12 -0.11 1.00 0.34 0.03

Elevation 0.022 -0.08 0.12 0.07 -0.03 0.34 1.00 0.29

Jack pine

frequency

0.51 0.58 0.09 -0.11 0.29 0.03 0.29 1.00

Table A5 Pearson correlation p-values matrix for predictors and total C storage included in

linear regression.

C

storage

Wetness

index

Plant

richness

PC1

of

COC

pH Distance

from

smelter

Elevation Jack pine

frequency

C storage NA <0.0001 <0.05 0.19 0.67 0.68 0.90 <0.01

Wetness

index

<0.0001 NA <0.05 0.67 0.70 0.56 0.67 <0.001

Plant

richness

<0.05 <0.05 NA <0.05 <0.0001 0.53 0.52 0.61

PC1 Of

COC

0.19 0.67 <0.05 NA <0.001 0.14 0.70 0.55

pH 0.67 0.70 <0.0001 <0.001 NA 0.53 0.89 0.67

Distance

from

smelter

0.68 0.56 0.53 0.14 0.53 NA 0.05 0.87

Elevation 0.90 0.67 0.52 0.70 0.89 0.05 NA 0.11

Jack pine

frequency

<0.01 <0.001 0.61 0.55 0.67 0.87 0.11 NA

49

Table A6 Principal component loadings for COC (chemicals of concern) bioavailable

concentrations for PC1 (principal component 1) included as a predictor in the linear regression

for total C storage. Loadings represent the PC coefficients, which are the correlations between

each COC and PC1, therefore representing the contribution of each COC to PC1 and

subsequently the linear model.

Element Loading

As -0.238

Cd 0.466

Co -0.503

Cu -0.388

Ni -0.532

Pb 0.200

50

Figure A3 Total concentrations of Sudbury chemicals of concern (COC) found across untreated

plots (U), limed, fertilized, and seeded plots (L), tree-planted plots (T), and limed, fertilized, and

seeded + tree-planted plots (L+T) in upland industrial barrens (n = 32). Element concentrations

that were below detection limit were computed as half of detection limit. Dashed lines indicate

MECP standards for unpolluted soils (Ontario Ministry of the Environment Conservation and

Parks, 2011). Different letter designations above plots indicate significant differences at p < 0.05.

Only [Se] was significantly different among treatments.

51

Figure A4 Bioavailable concentrations of Sudbury chemicals of concern (COC) found across

untreated plots (U), limed, fertilized, and seeded plots (L), tree-planted plots (T), and limed,

fertilized, and seeded + tree-planted plots (L+T) in upland industrial barrens (n = 32). Element

concentrations that were below detection limit were computed as half of detection limit. All Se

concentrations were below the detection limit so it was not plotted. Different letter designations

above plots indicate significant differences at p < 0.05.

52

Table A7 Treatments and C storage for all study sites in untreated (U), limed, fertilized, and

seeded (L), tree-planted (T), and limed, fertilized, and seeded + tree-planted (L+T) treatments.

Elevation indicates whether sites were high (upland industrial barrens) or low (lowland valleys).

All C units are in Mg C ha-1.

Site Elevation

class

Treatment FWD

C

CWD

C

Shrub

C

Herb

C

Tree

C

Mineral

soil C

Organic

soil C

Σ C

storage

HP1N Low U 1.2 1.6 6.4 0.7 31.3 36.5 13.1 90.6

HP2N Low U 1.4 0.9 9.6 1.1 33.0 30.3 11.4 87.7

HP3N Low U 1.3 1.3 0.3 0.5 49.5 25.5 11.7 90.2

HP4N Low U 1.2 1.1 10.5 0.4 33.1 24.9 9.9 81.1

HP5N Low U 0.8 1.5 3.7 1.5 34.0 18.8 29.1 89.4

IP1MF High U 0.1 0.0 1.0 4.5 0.1 19.5 8.2 33.6

IP1N High L+T 0.1 0.2 0.8 2.3 26.0 46.6 11.2 87.3

IP2MF High U 0.3 0.0 0.7 1.5 0.2 17.2 3.2 23.0

IP2N High U 0.1 0.1 0.0 0.1 0.2 9.8 6.1 16.3

IP3M High U 0.3 0.1 0.0 4.1 2.1 46.9 10.9 64.3

IP3N Low U 0.4 0.4 2.7 0.8 10.5 29.3 15.2 59.3

IP4MF High U 0.2 0.1 2.0 3.1 0.0 32.1 5.6 43.2

IP4N Low U 0.7 1.2 2.7 0.6 36.0 23.1 12.1 76.4

IP5N Low U 0.9 0.2 2.3 1.6 15.5 38.3 10.0 68.8

IP6B Low U 1.2 0.1 0.0 2.1 6.5 31.2 10.7 51.6

IP6N High L+T 0.2 0.1 5.4 0.3 22.1 33.0 12.2 73.2

IP8R Low U 0.0 0.0 0.0 1.4 0.2 31.9 3.7 37.3

JP10R High L+T 0.5 0.0 3.8 1.1 15.4 22.0 6.5 49.3

JP11R High L 0.3 0.0 0.0 1.8 0.9 13.2 4.2 20.4

JP1MF High L+T 0.4 0.0 0.3 1.8 13.2 20.5 6.0 42.2

JP1N Low L 0.7 0.0 4.8 2.5 31.0 47.5 11.6 98.1

JP2M Low L 2.7 1.0 2.2 1.0 39.1 36.2 11.2 93.5

JP2N High L+T 0.2 0.0 0.1 3.4 18.1 33.6 4.3 59.7

JP2R High L 0.1 0.0 0.0 1.5 1.4 9.8 3.1 15.9

JP3MF High L+T 0.2 0.0 0.0 0.1 20.5 32.9 6.8 60.6

JP3N High L+T 0.1 0.2 2.6 3.5 28.4 34.8 13.5 83.2

JP4N High L+T 0.1 0.1 4.0 1.1 20.8 11.4 8.4 46.0

JP4R High L 0.0 0.0 0.0 0.7 0.9 16.6 4.0 22.2

JP5B High L 0.2 0.0 0.0 3.1 0.5 22.9 8.8 35.5

JP5N High L+T 0.5 0.1 0.9 0.7 16.6 19.2 7.9 45.9

JP5R High L 0.0 0.0 0.0 0.3 0.1 9.2 4.9 14.5

JP6M Low L 1.7 0.2 16.4 1.0 37.5 37.4 17.6 111.7

JP6N High L+T 0.6 0.0 2.5 0.9 20.0 17.8 11.2 53.1

JP7M High L 0.5 0.0 6.1 4.3 13.3 39.4 7.8 71.5

JP8B High L+T 0.5 0.1 0.0 0.6 10.9 27.4 7.3 46.7

JP9B High L+T 0.2 0.1 0.6 4.4 19.6 21.2 5.6 51.7

JP9M High L 0.1 0.0 0.6 5.7 2.2 43.2 7.6 59.4

OP1N High T 0.5 0.0 2.9 3.2 17.0 29.5 6.6 59.7

OP2N High T 0.1 0.0 0.0 1.9 12.9 24.7 8.3 47.9

OP3N High T 0.4 0.2 0.0 1.5 13.7 14.8 4.4 34.9

OP4N High T 0.1 0.0 0.0 0.6 9.1 19.0 3.6 32.5

OP5N High T 0.5 0.0 11.0 2.7 14.8 25.6 6.4 61.0

OP6N High T 0.6 0.2 0.0 0.6 11.9 26.8 2.7 42.8

OP7N High T 0.4 0.0 0.0 1.9 12.8 19.0 6.3 40.4

OP8N High T 0.5 0.1 3.8 2.1 12.0 35.2 7.7 61.6

53

Table A8 Summary of site characteristics for all study sites. Elevation is in meters above sea

level. Plant species richness is the total number of unique plant species (including trees, shrubs,

and herbs) identified per site. Smelter distance is the distance (m) from the historic Coniston Cu

and Ni smelter. Wetness index is the tasseled cap measurements of relative wetness among sites.

Soil pH is the mean pH of the 0-5 cm mineral soil layer. Jack pine count is the number of jack

pine per plot.

Site Latitude Longitude Elevation (MASL)

Plant species richness

Smelter distance

(m)

Wetness index

Soil pH

Jack pine

count HP1N 46.45446 -80.89144 257.9 21 4757 294.32 3.99 0

HP2N 46.45451 -80.89005 257.2 18 4661 1406.06 4.39 0

HP3N 46.45404 -80.88886 249.2 15 4640 1032.93 4.48 0

HP4N 46.45366 -80.88771 243.5 17 4583 -445.95 4.51 0

HP5N 46.45305 -80.88608 237.5 19 4540 -756.82 4.50 1

IP1MF 46.45874 -80.88594 303.8 11 4139 -5158.90 4.30 0

IP1N 46.46082 -80.88336 302.8 18 3841 -622.41 4.70 98

IP2MF 46.45957 -80.88473 305.5 13 4017 -5299.23 4.48 0

IP2N 46.45805 -80.88740 299.5 7 4280 -3764.56 4.10 0

IP3M 46.45815 -80.88466 280.5 7 4102 -1978.91 4.20 0

IP3N 46.45819 -80.88218 273.9 12 3947 -1220.14 4.22 0

IP4MF 46.46007 -80.88357 303.8 9 3900 -5144.05 4.37 0

IP4N 46.45691 -80.88188 256.8 23 4005 862.05 4.55 0

IP5N 46.46062 -80.88365 255.8 18 4325 -1059.49 4.15 1

IP6B 46.45665 -80.88474 273.6 13 4207 -2736.49 4.28 0

IP6N 46.46110 -80.88406 301.4 26 3867 -668.01 5.16 59

IP8R 46.45564 -80.88381 265.8 5 4220 -4306.37 3.70 0

JP10R 46.46192 -80.88290 303.8 19 3759 -2372.66 5.50 57

JP11R 46.46482 -80.87677 293.2 11 3180 -3873.57 4.72 1

JP1MF 46.45956 -80.88070 292.8 17 3752 -2758.58 5.30 61

JP1N 46.46313 -80.87595 278.2 23 3231 958.03 5.40 1

JP2M 46.45877 -80.87906 267.0 18 3715 -241.20 4.51 0

JP2N 46.46173 -80.88268 302.5 16 3740 -1701.99 5.12 74

JP2R 46.46140 -80.87875 292.5 13 3517 -3759.80 5.14 2

JP3MF 46.46076 -80.88135 301.9 13 3721 -2642.55 5.19 111

JP3N 46.46304 -80.88094 299.3 17 3567 -599.21 4.46 115

JP4N 46.46216 -80.88191 302.4 20 3679 -1310.16 4.87 73

JP4R 46.45996 -80.87919 287.6 20 3636 -5362.56 6.55 1

JP5B 46.46468 -80.87294 292.4 17 2943 -4556.48 4.51 0

JP5N 46.46132 -80.88208 302.8 17 3732 -1313.98 4.39 74

JP5R 46.46198 -80.87549 297.0 9 3274 -3094.33 4.96 0

JP6M 46.46129 -80.87642 278.5 26 3368 1027.13 5.11 0

JP6N 46.46139 -80.87992 296.2 25 3720 -985.40 5.61 56

JP7M 46.46271 -80.87844 288.6 27 3462 -925.02 4.88 0

JP8B 46.46032 -80.87942 292.0 15 3626 -2695.21 4.98 28

JP9B 46.46148 -80.88137 300.1 18 3674 -440.92 5.02 62

JP9M 46.46323 -80.87833 289.4 13 3371 -541.17 4.62 0

OP1N 46.46501 -80.88276 291.0 12 3573 -1621.47 4.26 70

54

OP2N 46.46396 -80.88177 296.7 9 3560 -2130.04 4.14 41

OP3N 46.46303 -80.88197 301.3 8 3631 -2533.72 4.29 117

OP4N 46.46454 -80.88415 294.4 10 3696 -3099.28 4.43 79

OP5N 46.46355 -80.88184 299.9 13 3602 -1430.90 4.42 89

OP6N 46.46563 -80.88241 284.5 10 3523 -1512.93 4.45 72

OP7N 46.46501 -80.88328 292.5 11 3613 -1236.79 4.36 87

OP8N 46.46456 -80.88263 290.4 11 3595 -2038.81 4.19 71

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