BIO4004 – Honours Research Project

Impact of Proposed Mining Infrastructure Elements

on Species at Risk in Northern Ontario’s Ring of Fire

Author: Evan Burns – 6065535 (eburn040@uottawa.ca)

Supervisor: C. Scott Findlay (findlay@uottawa.ca)

Faculty of Science

University of Ottawa

Abstract The Ring of Fire multi-metal mineral deposit is contained within the Hudson Bay

Lowlands and boreal forest regions of Ontario’s Far North. This region is considered to be important

habitat to many species at risk. Noront Resources is currently undertaking environmental

assessment procedures to begin development of this deposit. Proposed mine site and regional

infrastructure elements pose threats to species at risk in the area. This study aims to quantify three

direct and indirect adverse effects on species at risk associated with this development – road

mortality, habitat loss, and fragmentation – and to locate geographic areas of particular concern for

species at risk based environmental assessment procedures. Through the use of literary survey,

ArcGIS mapping, quantification of habitat modifications, and qualitative assessment of species

sensitivity to development, this study ranked species in terms of their conservation priority, from 1

(high conservation priority) to 7 (low conservation priority). It is possible that two avian species will,

conditionally, gain some benefit from this development. Remaining avian and mammalian species

will suffer detrimental effects associated with road kills, habitat loss, and fragmentation.

Conservation priority rankings suggest that woodland caribou (Rangifer tarandus caribou) and

wolverine (Gulo gulo) should be considered a focal conservation species system, with conservation

efforts focused in the geographic area immediately north of Ontario’s Far North boundary. The

relatively untouched landscape in this region of Ontario sustains significant compound adverse effects resulting from a few seemingly discrete developments.

Introduction

The Hudson Bay Lowlands and boreal forest regions of Ontario’s Far North are considered to be

important habitat to many species listed as at risk in Ontario and Canada (Simpson and Dyczko

2012, Abraham and McKinnon 2011, Ministry of Natural Resources and Forestry [MNR] 2015a,

2015b). This region contains the Ring of Fire mineral deposit, a multi-metal mineral deposit

containing valuable quantities of chromite, copper, zinc, nickel, platinum, vanadium, and gold. The

development of the Ring of Fire is expected to generate $9.4 billion in GDP within the first 10 years

of its operation (Hjartarson et al 2014). Currently, Noront Resources has staked a claim to develop

this area and extract nickel, copper, platinum, and palladium from an underground mining facility

located near McFaulds Lake (Knight Piésold Consulting [KPC] 2013). The environmental

assessment for this project – Noront Resources’ Eagle’s Nest – is currently underway and will be

completed to Provincial and Federal environmental assessment standards (Ministry of

Environment and Climate Change 2014). The project timeline estimates that 11.1 million tonnes of

nickel, copper, platinum, and palladium will be extracted within the 16-year operation of the mine

site (KPC 2013).

Mining and resources extraction developments require roads, and often rail and power

transmission lines, all of which function as linear corridors (Chetkiewicz and Linter 2014). The

effects of linear corridors on wildlife can be direct, such as increased mortality due to road kills, or

indirect, resulting from changes in wildlife behaviour and habitat use (Trombulak and Frissell

2000). Linear corridors, such as roads, fragment landscapes (Simpson and Dyczko 2012) resulting

in habitat loss through reduced habitat patch size and suitability (Fahrig 2003). Linear corridors

are also known to facilitate predator movement through fragmented landscapes (James and Stuart-

Smith 2000), increasing the risk of mortality due to predation for prey species. Wildlife response to

linear corridors and localized area of human activity (i.e active mine sites) is largely dependent on a

population’s particular sensitivity to effects associated with fragmentation (Jaegar et al. 2005). The

development of Eagle’s Nest mine site infrastructure, and construction of 282 km all-season access

road (KPC 2013) are expected to demonstrate similar effects on the landscape and species at risk in Ontario’s Far North.

Of particular concern are the potential impacts of the proposed development on species at risk. The

provincial districts of Cochrane and Kenora contain the Ring of Fire, and some 22 species at risk in

Canada (MNR 2015). Dominating this region is the boreal forest ecozone (Simpson and Dyczko

2012), within which development of mineral resources has been identified as a potential risk for a

number of species including wolverine (Gulo gulo), the boreal population of woodland caribou

(Rangifer tarandus caribou), short-eared owl (Asio flammeus), Canada warbler (Wilsonia

Canadensis), common nighthawk (Chordeiles minor), olive-sided flycatcher (Contopus cooperi), and

rusty blackbird (Euphagus carolinus) (COSEWIC 2002-2014). These risks include increased road

mortality (Forman and Alexander 1998), habitat loss and isolation (Chetkiewicz and Lintner 2014),

modified predator-prey dynamics (COSEWIC 2002, 2014, Dyer et al. 2001), and reduced

reproductive success (Trombulak and Frissell 2000).

This study focuses on an assessment of the risk posed to species at risk by the proposed Eagle’s

Nest development. The assessment aims to (a) provide information that can be used to rank species

at risk in terms of the threats posed by Ring of Fire developments; and (b) identify geographical

areas of particular concern, that should be of particular focus in the environmental assessment process.

Method

The analysis proceeded in 8 steps. First, a list of species at risk that could be potentially impacted

by the proposed development was derived. For each species, digital range maps were either used as

provided, or generated from existing raster map formats. Formatted species range maps were

overlain with vector data representing proposed Eagle’s Nest mine site infrastructure and the

regional access road. Infrastructure areas, including roads, were then buffered dependent on

species reported avoidance distances to represent each species’ habitat losses. These losses and the

proportion of linear corridors and fragmentation in the species range were quantified using ArcGIS.

Species ranges were overlain to illustrate geographic areas of particular concern. A sensitivity index

was developed based on five qualitative criteria, which would denote the species threat score.

These criteria were derived from common threats shared between study species, as reported in

COSEWIC status reports. As well, data on annual road kills per daily traffic volume per year were

assembled to calculate the estimated road mortality for each wildlife type – aves, ungulates, and

mustelids. Species were then prioritized with regard to appropriate conservation attention

according to (1) estimated annual road mortality, (2) species qualitative threat score, and (3)

proportional increase in habitat fragmentation.

Selection of Suitable Study Species

Of the 22 species at risk in the study area, two mammalian and five avian species have habitat

ranges which overlap proposed Eagle’s Nest mine site or regional infrastructure. These species

include the wolverine (Gulo gulo), the boreal population of woodland caribou (Rangifer tarandus

caribou), short-eared owl (Asio flammeus), Canada warbler (Wilsonia Canadensis), common

nighthawk (Chordeiles minor), olive-sided flycatcher (Contopus cooperi), and rusty blackbird

(Euphagus carolinus). These species were considered for assessment over the remaining species to

better focus conservation effort on species directly impacted by this development. While other

study species may in fact feel some indirect effects as a result of this development, more immediate conservation attention should be directed to those directly-affected species.

Digital Species Range Maps

Range map shapefiles, derived from Environment Canada and COSEWIC status reports and

recovery strategies were provided by Sue McKee at the Institute of the Environment at the

University of Ottawa for all the species studied except the wolverine. Wolverine range

representation was generated through georeferencing of a map hosted by the Royal Ontario

Museum, and creation of a usable, vector polygon shapefile. These range maps were then clipped to

restrict habitat area to that contained within the study area – Ontario’s Far North. Digital maps of

Native Canadian Settlement locations, Ontario rivers, roads, and rail lines, as well as relevant

provincial boundaries were downloaded from the Scholars Geoportal hosted at the University of

Ottawa (http://geo2.scholarsportal.info/#_lang=en). These additional shapefiles were used as

georeferencing aids, as well as to provide spatial reference in finalized maps (Appendix SMRI: 1-7).

Ring of Fire Infrastructure Overlay

Shapefiles were generated for all infrastructure elements by first georeferencing raster maps found

in Noront’s Eagle’s Nest reports and proposals, then creating corresponding vector datasets and

polygons to represent each infrastructure element. Coordinate systems used in generated vector

data were either the NAD_1983 or GCS_1984 geographic coordinate systems (geographically

compatible). The NAD_1983_UTM_17N projected coordinate system was used for final

representation of map images, and in the calculation of total areas of species ranges and

infrastructure avoidance distances. These geographic and projected coordinate systems were

identical to those used in maps used in Eagle’s Nest project descriptions, and shapefiles

downloaded from the Scholars Geoportal hosted at University of Ottawa

(http://geo2.scholarsportal.info/#_lang=en) to maintain accuracy of assessment. Species

infrastructure avoidance distances were calculated as the average of avoidance distances reported

in the literature surveyed. These distances were used to calculate a buffer region around

infrastructure elements in which species discontinued or significantly reduced their use. These

buffered areas were considered to be habitat area removed from the species range.

Determination of Geographic Areas of Concern

To illustrate areas which should be of particular focus during species at risk based environmental

assessment processes areas of most species overlap had to be quantified. Vector species ranges

were converted to raster, and each cell was assigned a value of 1 if it contained species habitat area,

and 0 if it did not. Raster layers were overlain, and cell values were summed to calculate the

number of species ranges in each particular cell. Cells were then assigned a colour code according

to their species range count. Colours ranged from dark red, presence of all (seven) species, to dark

green, presence of only one species – illustrated in Figure 1.

Quantifying Habitat Modification

Three parameters were calculated using spatial data obtained from analyses in ArcGIS. The change

in habitat area, change in linear corridor length, and change in fragmentation (ratio of linear

corridor length: habitat area).

Change in available habitat area was calculated as the difference between habitat area after

development (AD) and habitat area before development (BD), proportionate to the habitat area before development:

𝐴𝑙𝑜𝑠𝑡 = (𝐴𝐴𝐷 − 𝐴𝐵𝐷)/𝐴𝐵𝐷 × 100

Increase in linear corridors were similarly calculated as proportionate to the total length of linear

corridors before development:

𝐿𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒 = (𝐿𝐴𝐷 − 𝐿𝐵𝐷)/𝐿𝐵𝐷 × 100

Increase in habitat fragmentation considered the difference between the ratio of linear corridor

length to habitat area before and after development, proportionate to the ratio before development:

𝐹𝑟𝑎𝑔𝑚𝑒𝑛𝑡𝑎𝑡𝑖𝑜𝑛𝑖𝑛𝑐𝑟𝑒𝑎𝑠𝑒 =(𝐿𝐴𝐷

𝐴𝐴𝐷⁄ −

𝐿𝐵𝐷𝐴𝐵𝐷⁄ )

𝐿𝐵𝐷𝐴𝐵𝐷⁄

× 100

Proportional changes in these three parameters are considered in this study – reported in Table 2.

Threat Scoring

Species’ sensitivity to development were considered based on five common criteria: whether the

species (1) had a large individual territory, (2) had a low population density, (3) suffered a

reduction in a significant food source following development, (4) is known to poorly adapt to

human activity, or (5) had its preferred breeding habitat converted or reduced by development.

Species were scored on a binary system either as (1) meeting the criteria or (0) not meeting the

criteria. The final threat score for a species – its sensitivity to development based on the five criteria

– was the sum of the individual criterion scores. Species threat scores are shown in Table 1.

Estimation of Annual Road Mortality

Annual road mortality for each wildlife type – aves (birds), ungulates (caribou), and mustelids

(wolverine) – were calculated using data collected from studies quantifying road mortality. Road

mortalities were reported as annual road kills per average daily traffic volume (ADTV) per 732km,

the length of road in the study area (Case 1978). These values were converted to annual road kills

per average daily traffic volume (ADTV) per 1 km, and plotted for each wildlife type (Appendix

EARM:1-3). Using the equation of the linear regression line the estimated annual road kills for

predicted traffic volumes of the Ring of Fire access road. The product of these values and the total

length of the Ring of Fire access road (282km) is the estimated annual road mortality for the Ring of

Fire access road for each wildlife type. Each species belonging to a wildlife type is expected to

sustain the estimated annual road mortality as a result of mine operations.

Conservation Prioritization

Species were ranked according to their sensitivity to the development was calculated using three

main criteria: (1) estimated annual road mortality, (2) species qualitative threat score, and (3)

proportional increase in habitat fragmentation (Table 2). These rankings assigned each species a

conservation priority from 1 (highest conservation priority) to 7 (lowest conservation priority).

These priority rankings should be interpreted as relative to other study species, not empirical

absolutes.

Results

Estimated Annual Road Mortality

Annual expected road kills are summarized in Table 3. Values for each wildlife type are interpreted

as annual road kills for each species belonging to that wildlife type. Studies by Case (1978) and

Bishop and Brogan (2013) used two bird species (aves), deer (ungulates), and badger (mustelids).

These species were taken as surrogates for study species of similar wildlife type; aves for all avian

study species, ungulates for caribou, and mustelids for wolverine.

Table 1: Threat score of study species. Species were evaluated as either (1) meeting the criterion, or (0) not

meeting the criterion, and the score was recorded as the total value of criteria met.

Criteria

Species Large

individual territory

Low population

density

Reduction in significant

food source

Adapts poorly to human

activity

Breeding habitat converted by development

Threat Score

Woodland Caribou

1 1 0 1 1

4

Wolverine 1 1 1 1 0

4

Short-eared Owl

1 1 0 0 0

2

Rusty Blackbird

0 1 0 0 1

2

Common Nighthawk

0 1 0 0 0

1

Canada Warbler

0 0 0 0 0

0

Olive-sided Flycatcher

0 1 0 0 1

2

Habitat Modification and Threat Scoring

Negative values were obtained from calculations of change in habitat area, indicating that habitat

area was greater before development, and were interpreted as loss of habitat area. Percent change

in habitat area ranged between -0.010% (Common Nighthawk) and -0.280% (Woodland Caribou),

and are reported in Table 2 as positive values of habitat area lost.

Positive values of percent change in length of linear corridors ranged between 4.44% (Common

Nighthawk) and 34.94% (Rusty Blackbird), indicating that the total length of linear corridors had

indeed increased as a result of development. Similarly, positive values of percent fragmentation

increase ranged between 4.45% (Common Nighthawk) and 31.66% (Wolverine), indicating that

fragmentation had increased as a result of development. These values are reported as positive

values of proportional linear corridor length increase, and proportional fragmentation increase in

Table 2.

Table 2: Species range modifications following development, ordered by suggested conservation

priority ranking. Range modifications are presented as proportional to initial values, ([x - xinitial]/xinitial )*100.

Threat score is as follows from Table 1. Priority was determined according to (1) estimated annual road

mortality (EARM; values rounded to nearest whole number), (2) species qualitative threat score, and (3) percent

increase in habitat fragmentation.

Wildlife Type

Species % Habitat Loss

% Linear Corridor Increase

% Habitat Fragmentation

Increase

Threat Score*

EARM Priority

Mustelids Wolverine 0.080 31.55 31.66 4 30 1

Ungulates Woodland

Caribou 0.280 10.80 11.11 4 12 2

Aves

Rusty Blackbird 0.011 24.94 24.95 2

7

3

Olive-Sided Flycatcher 0.029 10.71 10.74 2 4

Short-eared Owl 0.022 9.11 9.13 1 5

Common Nighthawk 0.010 4.44 4.45 1 6

Canada Warbler 0.032 10.71 10.75 0 7

* From Table 1; total criteria met.

Fragmentation increases are similar, and related, to increases in linear corridor length, while

habitat losses did not follow the same trends. Habitat losses were more greatly affected by species

avoidance behaviour than solely by measurement of constructed infrastructure. The threat score

was developed as a metric by which species sensitivity to fragmentation could be measured, which

was not required for habitat losses because this already considered species’ behaviour (avoidance).

Threats posed to species, and species’ corresponding threat scores are shown in Table 1.

Conservation Prioritization and Areas of Concern

In general, avian study species were determined to have lower conservation priority than

mammalian study species. While threat scores were equal for wolverine and caribou, wolverine

were assigned greater conservation priority due to greater estimated annual road mortality, and % fragmentation increase of its habitat.

Table 3: Estimated annual road mortality per average daily traffic volume (ADTV) per kilometer of road.

Data from two studies (Case 1978, Bishop and Brogan 2013), and as estimated for the Ring of Fire access road,

for aves, ungulates, and mustelids is shown. Estimation of Ring of Fire values for aves, ungulates, and mustelids

per km were calculated using the equations of linear regression lines in from the plots of reported data

(Appendix EARM:1-3).

Annual Road kills/km road

Wildlife Type

ADTV Aves Ungulates Mustelids

1068 0.101093 - -

1900 0.045082 - -

1930 0.069672 - -

2800 1.777322 - -

3108 -* - -

3404 0.045082 - -

5959 1.333333 0.215847 0.13388

6287 0.120219 - -

6525 2.107923 0.206284 0.110656

7192 1.297814 0.286885 0.094262

7494 0.893443 0.297814 0.129781

8011 1.901639 0.222678 0.102459

8011 0.483607 0.23224 0.107923

8031 1.811475 0.338798 0.161202

15** 0.0261 0.04325 0.10746

Annual road kills expected along 282km of Ring of Fire Access Road

7.3602 12.1965 30.30372

Values recalculated from (1) Case (1978), and (2) Bishop and Brogan (2013) to display road kills/ADTV/1 km of road.

* Not recorded

** Expected daily traffic volume for Ring of Fire access road (KPC 2013)

Overlain species ranges revealed the areas of most species overlap. This illustrates the geographic

area in which most species are coincident – the range which should be of particular focus during

the environmental assessment process (Figure 1). This focus area is indicated as a red band above

the Far North boundary. As colours shift from red to green less species are confined within that

geographic area, and so conservation of these areas would affect less species.

Discussion

The effects of calculated habitat losses appear negligible to most study species. The Eagle’s Nest

mine, processing facility, and tailings management and storage will all be underground (KPC 2013),

reducing its impact at the surface. The effects of habitat loss however are sometimes inaccurately

estimated by solely the quantity of habitat removed (Apps and McLellan 2006). Species can feel

compounded effects in particularly small habitat patches when habitat is removed through

fragmentation, as the reduction in use of habitat in the buffered area around linear corridors causes

an increase in use of habitat just beyond the buffered area (Joly et al 2006). Increased population

density can lead to a decrease in habitat suitability in species which have large individual ranges

(Andrén 1994), such as the woodland caribou, wolverine, and short-eared owl. This means that

while this study estimates low habitat losses and low severity of associated effects, compounding

effects associated with habitat loss have the potential to pose more significant threat to study

species.

The construction of linear corridors and associated increase in habitat fragmentation had

significant effects on species, and when considered with the species threat scores, can be used to

establish a useful conservation priority for the seven study species. Avian study species were

ranked lower than mammalian study species, as their estimated annual road mortality, threat

scores, and proportional fragmentation increase were consistently lower than those of the

mammalian study species. As well, Quesnelle et al (2013) notes that the main threat posed to

wetland birds is loss of wetland habitat. With relatively low quantified habitat loss following Eagle’s Nest developments, birds are estimated by this study to be less sensitive to these developments.

Two birds – the common nighthawk and olive-sided flycatcher – could potentially benefit from

these developments (COSEWIC 2007a, 2007b), similar to findings in a study by Rueda et al (2013)

in which a low proportion of bird species studied benefitted from development. The Common

Nighthawk is known to nest well in human developments including gravel roofs, roadsides, and

even mine tailings ponds (COSEWIC 2007a). As well, this species feeds on mosquitos, which are

attracted to areas of human presence (Russel and Hunter 2012). Thus, an increase in development

and human activity could possibly have some positive effect on populations of Common Nighthawk,

resulting from increased nest site availability, and prey availability. Calculated avian estimated

annual road mortality may underestimate this value for Common Nighthawk populations, as their

use of roadsides as suitable nest sites can increase their susceptibility to road kills (COSEWIC

2007a).

Similarly, the Olive-sided Flycatcher uses forest openings for feeding (COSEWIC 2007b). While the

development of the Ring of Fire would indeed create more forest openings, it has been noted that

the reproductive rate of populations of Olive-sided Flycatcher associated with anthropogenic forest

openings is lower – often half – than that of those associated with natural forest openings

(COSEWIC 2007b). This means that populations of Olive-sided Flycatcher utilizing anthropogenic

forest openings are reproductive sink populations and must be supplied by source populations to

persist. While not considered in this study, if such a source population does not exist to supply the

potential population sink the effects of development could be significantly greater for the Olive-sided Flycatcher in Far North Ontario.

Figure 1. Geographic areas of particular concern. Seven study species ranges are overlain here to illustrate

geographic area which should be of particular focus during the environmental assessment process. Colours

range from dark red (presence of all species), to dark green (only one species present). Conservation efforts

and environmental assessment focus should be focused in this dark red band. Map scale: 1:4 500 000.

The proportional increase in fragmentation in caribou habitat was the second greatest of study

species, and proportional caribou habitat loss was the greatest of all study species. However the

effects on caribou are often underestimated by quantifying habitat loss, and fragmentation (Apps

and McLellan 2006), and the compound effects of fragmentation and habitat loss must be

considered. The use of estimated annual road mortality for ungulates along the Ring of Fire access

road helps to better illustrate the effects of development of linear corridors, other than

fragmentation. The greatest threat to caribou populations however is due to fragmentation

(Wittmer et al. 2007). Fragmentation poses particular threat to caribou, by increasing the risk and

rate of predation (Wittmer et al. 2005). The response of caribou to predation involves spreading

out from concurrent prey species to reduce their coincidence and thereby lower the risk of

predation (COSEWIC 2002). This increases the distance between predators and caribou, especially

calves and mothers. However, fragmentation reduces the area available to caribou to spread out,

decreasing the effectiveness of this anti-predator response (McCarthy et al 2011). Following this,

caribou mortalities attributed to predation typically are located closer to linear corridors than

would be expected at random (James and Stuart-Smith 2000). The spatial requirement for this

strategy means that the carrying capacity for caribou is often over-estimated as the forage capacity:

the capacity of an environment to provide desired resources (COSEWIC 2002). The ecological

carrying capacity of caribou ranges must consider the habitat required for anti-predator response

N

(Smith et al 2000), as well as significant buffer zones around anthropomorphic development (Vors

et al 2007), and is typically much smaller than an estimate of forage capacity.

Wolverine are very sensitive to human disturbance, and highly selective of habitat patches (May et

al 2010). As such, wolverine easily suffer habitat losses through decreased habitat suitability even

when habitat area is not directly removed through development (May et al 2006). As well, the

mustelid estimated annual road mortality was the highest of the wildlife types, suggesting

significant susceptibility to road kills. However, the data used in this calculation was surrogate

information from another mustelid species, the American Badger (Taxidea taxus). Likely the values

estimated for wolverine are somewhat over-estimated, due to their characteristic extremely

reclusive behaviour (COSEWIC 2014) and tendency to avoid less suitable habitat patches (May et al.

2006). The effects of fragmentation decrease habitat suitability and often induce Allee effects –

decreased fitness due to “undercrowding” – in solitary mustelid predators (Jager et al 2006), such

as wolverine. The inability to find a mate due to low population densities, often indicated by the

presence of unmated females, augur potential population decline in such mustelid species as

wolverine (Jager et al 2006).

Crooks (2002) stated that selection of a focal species for conservation should consider high-trophic

carnivores as this focal species, to maximize the effect of conservation efforts. Independent of

Crooks’ recommendation, wolverine were assigned the highest conservation priority in this study,

being highly susceptible to road mortality, having a high threat score, and the largest proportional

fragmentation increase. Using these species as a focal species for conservation may serve to

increase the efficacy of conservation efforts in Far North Ontario. Caribou are an invaluable prey

species to wolverine, and wolverine population fluctuations often closely follow the abundance or

scarcity of caribou populations (COSEWIC 2012). Therefore conservation focused on wolverine

should include the preservation of their primary prey species. This sympatric occurrence makes

wolverine populations indirectly affected by fragmentation effects on caribou, following scarcity of

a significant prey source (May et al 2006). This relationship merits the consideration of wolverine

and caribou as a focal species system, in place of Crooks’ (2002) single focal species.

Effort and resources available for conservation are anything but infinite, and as such require finite

and manageable areas. While species ranges varied across the whole of Far North Ontario, specific

attention should be given to the geographic area (indicated in Figure 1) adjacent to the Far North

boundary. Here all seven species ranges overlap, and the maintenance of ecological integrity would

have the greatest effect for all species at risk in Ontario’s Far North. The red band in Figure 1

indicates the geographic area which should be of particular focus during environmental assessment processes.

Road construction in northern Ontario generally follows forestry, and Far North Ontario has low

(6%-7%) commercial potential for forestry (Chetkiewicz and Lintner 2014). As a result the Far

North is nearly roadless (Abraham and McKinnon 2011), and therefore fewer linear corridors than

more southern portions of boreal Ontario. Only shapefiles for highways and urban area roads, and

rail lines were found for Far North Ontario. Following this, only these corridor types were

considered when calculating the total length of linear corridors. There certainly exists more linear

corridors of various types in the study area (Pasher et al 2013) which were not considered. The

inclusion of these corridors would increase the length of linear corridors before development, and

therefore would decrease the % increase linear corridors, and by extension the % fragmentation

increase. While fragmentation and habitat loss should be considered and measured independently

of one another (Fahrig 2003), the relationship between the two cannot be ignored. An increase in

linear corridors would decrease habitat patch size and suitability, and therefore species’ use of

affectd habitat patches (Andrén 1994). This would, effectively, result in increased proportional

habitat losses for study species. The effects of development should not be considered in isolation

(Chetkiewicz and Lintner 2014), therefore combining effects of estimated annual road mortality

rates, proportional habitat fragmentation, and species sensitivity to development gives a much

better estimation of the actual threat posed to species, than any of these parameters could

individually.

This study considered only the effects of annual road mortality, habitat loss, and increased habitat

fragmentation, at a particular scale, on seven species at risk in Far North Ontario. It is nearly

impossible that these will be the only effects associated with this development, and the only species

affected. Pollution and chemical effects on species at risk, and regional vegetation and geology

certainly merit investigation. As well, the construction of all required infrastructure, and closure of

all facilities following the operational life of the mine will undoubtedly have additional and

compounding effects on this region of Ontario’s Far North. Natural reclamation of linear corridors

specifically is known to be a lengthy process, and can affect environments long after the closure of

their respective industrial developments (Dyer et al. 2001). Therefore, upset predator-prey

dynamics and edge effects associated with these corridors cannot be assumed to stop affecting species at the time of mine closure.

The linear corridor density in Far North Ontario is still below threshold values which cause changes

in wildlife behaviours (Dawson et al 2010). However, as demonstrated in this study, previously low

levels of fragmentation in this region prompts fragmentation increases of up to almost 32%

following the addition of only one all-season road. While this study considered the effects of

development of one mining project in this relatively untouched region, Chetkiewwicz and Lintner

(2014) warn that the compounding effects of multiple projects can substantially increase the severity of discrete effects.

Conclusion

While avian species suffer some losses, mammalian study species are most at risk by the

development of the Ring of Fire. Wolverine and caribou should be considered a focal species system

for conservation efforts pertaining to the Ring of Fire development in Far North Ontario. These

efforts should be concentrated in area of greatest species overlap, an area just north of Ontario’s

Far North boundary. While a high-trophic carnivore would typically be considered as the focal

species, the wolverine’s dependence and association with caribou merits their concomitant

conservation. This would accommodate efficient and effective preservation of the ecosystem as a

result, and provide trickle-down conservation effects for other species at risk in the study area.

Acknowledgements This research was submitted as a thesis for the BIO4004 Honours Research course at the University of

Ottawa. Thank you, first and foremost, to my tireless research supervisor Professor C. Scott Findlay, whose

assistance I could not have completed this paper without. Thank you very much for your support and

understand this last year. Thank you to Mrs. Sue McKee, from whom I received a significant part of my

mapping data. As well, thank you to Professor M. Sawada for your patience with my questions about GIS

systems and mapping. Finally, thank you to my friends and family who edited or reviewed this paper or its

parts throughout its construction, and especially to anyone who allowed me to continuously explain my

reasoning, speed bumps, and finally, conclusion.

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Appendix: SRMI: Species Range Maps with Infrastructure

SRMI 1: Wolverine (Gulo gulo) range map with proposed Eagle’s Nest and existing regional

infrastructure. Map scale: 1:4 500 000.

SRMI 2: Woodland caribou (Rangifer tarandus caribou) range map with proposed Eagle’s Nest and existing regional infrastructure. Map scale: 1:4 500 000.

N

N

SRMI 3: Short-eared owl (Asio flammeus) range map with proposed Eagle’s Nest and existing regional infrastructure. Map scale: 1:4 500 000.

SRMI 4: Canada warbler (Wilsonia Canadensis) range map with proposed Eagle’s Nest and existing regional infrastructure. Map scale: 1:4 500 000.

N

N

SRMI 5: Common nighthawk (Chordeiles minor) range map with proposed Eagle’s Nest and existing regional infrastructure. Map scale: 1:4 500 000.

SRMI 6: Olive-sided flycatcher (Contopus cooperi) range map with proposed Eagle’s Nest and existing regional infrastructure. Map scale: 1:4 500 000.

N

N

SRMI 7: Rusty Blackbird (Euphagus carolinus) range map with proposed Eagle’s Nest and existing regional infrastructure. Map scale: 1:4 500 000.

Appendix: EARM: Estimated Annual Road Mortality

EARM1: Annual avian road kills per year per average daily traffic volume as plotted from

values in Table 3. Data from Case (1978) and Bishop and Brogan (2013) are represented as hollow

circles, while the estimated Ring of Fire access road value is represented as a solid circle, with value

shown. The linear regression line equation (y = 0.0002x + 0.0231) was used to estimate the expected annual road kills on the Ring of Fire access road.

0.0261

0

0.5

1

1.5

2

2.5

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

An

nu

al A

vian

Ro

ad K

ills/

year

Average Daily Traffic Volume

N

EARM 2: Annual ungulate road kills per year per average daily traffic volume as plotted from

values in Table 3. Data from Case (1978) are represented as hollow circles, while the estimated

Ring of Fire access road value is represented as a solid circle with value shown. The linear

regression line equation (y = 0.00003x + 0.0428) was used to estimate the expected annual road kills on the Ring of Fire access road.

EARM 3: Annual mustelid road kills per year per average daily traffic volume as plotted from

values in Table 3. Data from Case (1978) are represented as hollow circles, while the estimated

Ring of Fire access road value is represented as a solid circle with value shown. The linear

regression line equation (y = 0.000004x + 0.1074) was used to estimate the expected annual road kills on the Ring of Fire access road.

0.04325

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

0 1000 2000 3000 4000 5000 6000 7000 8000 9000

An

nu

al R

oad

Kill

s

Average Daily Traffic

0.10746

0

0.02

0.04

0.06

0.08

0.1

0.12

0.14

0.16

0.18

0 1000 2000 3000 4000 5000 6000 7000

An

nu

al R

oad

Kill

s

Average Daily Traffic