Biological Conservation 186 (2015) 176–186
Contents lists available at ScienceDirect
Biological Conservation
journal homepage: www.elsevier.com/locate/biocon
Witnessing extinction – Cumulative impacts across landscapes and the
future loss of an evolutionarily significant unit of woodland caribou in
Canada
Chris J. Johnson a,⇑, Libby P.W. Ehlers b, Dale R. Seip c
a
b
c
Natural Resources and Environmental Studies Institute, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia V2N 4Z9, Canada
Natural Resources and Environmental Studies Graduate Program, University of Northern British Columbia, 3333 University Way, Prince George, British Columbia V2N 4Z9, Canada
Ecosystem Protection and Sustainability Branch, British Columbia Ministry of Environment, Prince George, British Columbia V2L 3H9, Canada
a r t i c l e
i n f o
Article history:
Received 7 November 2014
Received in revised form 5 March 2015
Accepted 10 March 2015
Keywords:
Cumulative impacts
Extinction
Habitat
Population decline
Rangifer
Resource selection
a b s t r a c t
Habitat change is a major driver of species distribution and persistence, but there have been few recorded
extinction events for terrestrial mammals across Canada. Currently, we are observing the decline, extirpation, and perhaps extinction of several evolutionarily significant units of woodland caribou (Rangifer
tarandus caribou), an iconic and cultural keystone species. We used an extensive set of caribou locations
(5 subpopulations, 102 animals, 270,808 GPS-collar locations) collected over 11 years within the Central
Mountain Designatable Unit to develop species distribution models that quantified avoidance by caribou
of anthropogenic and natural disturbance features. Those empirical relationships allowed us to measure
the loss of habitat over a 22-year period and correlate habitat change with measured population decline.
The disturbance responses for caribou were complex and varied by season and subpopulation. We modelled a zone of influence for roads (1.75 km), seismic and pipelines (2.5 km), oil and gas features
(4.25 km), cutblocks (5.5 km), burns (8.0 km), and coal mines (3.0 km). When the distribution models
for each subpopulation were applied to the respective seasonal ranges, we measured a maximum loss
in high-quality habitat of 65.9%. The reduction in habitat was strongly correlated with the annual
multiplicative growth rate of 5 subpopulations of caribou. At current rates of habitat loss and population
decline, these caribou, a significant component of Canada’s biodiversity, are unlikely to persist. Although
the factors leading to extinction are complex, the cumulative impacts of industrial development are a correlative if not causative factor.
Ó 2015 Elsevier Ltd. All rights reserved.
1. Introduction
Habitat change is cited as one of the most influential causes of
the deepening biodiversity crisis (Pimm et al., 2014). Although a
logical outcome, there are few contemporary extinction events
for mammalian fauna that can be empirically related to this process (Loehle and Eschenbach, 2012). This is especially the case
when considering broadly distributed terrestrial mammals found
in the developed nations. Canada is a case in point. A country rich
in both economic wealth and capacity for conserving and managing biological diversity, Canada has recorded the extinction of only
three mammalian species or other evolutionary unit (SAR Public
Registry, 2014). Dawson caribou (Rangifer tarandus dawsoni), an
⇑ Corresponding author at: Ecosystem Science and Management Program,
University of Northern British Columbia, 3333 University Way, Prince George,
British Columbia V2N 4Z9, Canada. Tel.: +1 250 960 5357.
E-mail address: johnsoch@unbc.ca (C.J. Johnson).
http://dx.doi.org/10.1016/j.biocon.2015.03.012
0006-3207/Ó 2015 Elsevier Ltd. All rights reserved.
island endemic, was the last subspecies declared extinct; however,
habitat loss from human activities was likely not a leading causal
factor.
Approximately 80 years after the extinction of the Dawson caribou, we are witnessing the loss of a second subspecies of Rangifer,
woodland caribou (R.t. caribou; Festa-Bianchet et al., 2011).
Significant contractions in distribution across southern Canada
can be traced to the late 19th and early 20th centuries following
human expansion and ecosystem change (Spalding, 2000;
Schaefer, 2003; Santomauro et al., 2012). Currently, rapid industrial development across much of the boreal and subboreal forest
has precipitated new concerns for the loss of this cultural keystone
subspecies. Caribou receive much conservation attention from federal, provincial and territorial agencies as well as from industry,
Aboriginal communities and non-governmental organisations.
Despite those efforts, the subspecies continues to decline at an
increasing rate (e.g., St-Laurent et al., 2009; Hervieux et al., 2013;
COSEWIC, 2014).
C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
The cumulative impacts of multiple anthropogenic activities are
now recognized as one of the most pressing problems facing the
conservation and management of wildlife across North America
and beyond (Johnson et al., 2005; Vors et al., 2007; Krausman
and Harris, 2011). This issue is especially challenging for species,
such as woodland caribou, with broad distributions, slow life histories, and inherent sensitivity to human activities (Cardillo et al.,
2005). Habitat change resulting from cumulative human developments is well documented as having contributed to the decline
of caribou across much of their mountainous and boreal ranges
(St-Laurent et al., 2009; Environment Canada, 2012, 2014).
In Canada, federal conservation legislation (Species at Risk Act,
2002) recognizes the evolutionary importance of a collection of
individuals at a level below the taxonomic designation of species
(Green, 2005). These Designatable Units (DU) are identified as
irreplaceable components of Canada’s biodiversity and must constitute an evolutionarily significant and discrete population or collection of populations. This allows for the assignment of
conservation status for each DU. Currently, Rangifer tarandus are
classified as 4 extant subspecies and 11 DUs (COSEWIC, 2011).
The Central Mountain DU of woodland caribou consists of 9
extant and 2 extirpated subpopulations (i.e., herds) that are behaviourally distinct, generally separated by topography, and genetically dissimilar from neighbouring subpopulations found in
adjacent DUs (McDevitt et al., 2009; COSEWIC, 2011). Since the
1980s, the core range for these caribou subpopulations has undergone rapid land-use change (Nitschke, 2008). In particular, the 7
Central Mountain subpopulations located in British Columbia
(BC) have experienced a high rate of development from a large
number of resource sectors. Forestry and agriculture have a longhistory in the area, but in the last 25 years oil and gas exploration
and development has increased rapidly followed by coal mining,
and most recently wind energy. Consistent with other areas of
Canada, caribou populations in this region have shown steep declines in distribution and abundance with recent observations of population extirpation (COSEWIC, 2014).
Given the rapid rate of development and corresponding
decrease in Central Mountain caribou subpopulations, we
hypothesize that this generation of resource managers and conservation professionals will observe the extinction of this evolutionarily significant faunal group. If realized, this would be the
first empirically documented extinction of a mammalian DU in
Canada. Future population monitoring will allow a full test of
our hypothesis. In this paper, however, we provide early evidence
to support this assertion, namely a precipitous decline in the
abundance of caribou and a high rate of habitat loss. Although
contemporary declines and even extirpation are documented for
woodland caribou (Schaefer, 2003; Environment Canada, 2012,
2014; Hervieux et al., 2013) there have been few efforts to
quantify historical landscape change, relative to functional habitat, that might explain such patterns (but see Rudolph et al.,
2012).
We applied an extensive set of locations for monitored caribou
to a species distribution model and quantified the zones of influence that represented caribou avoidance of disturbance features.
We used those models to calculate the cumulative impacts of
habitat change over a 22-year interval for 5 subpopulations found
within the Central Mountain DU. We related those measures of
change in functional habitat to estimates of population decline.
We defined a loss of functional habitat as a reduction in the relative probability of use of an area adjacent to human or industrial
infrastructure. We assumed that caribou did not adjust their
behaviour in response to industrial development and that
measured avoidance resulted in a reduction in the quality of
habitat.
177
2. Study area
The study area is located on the eastern slopes of the Rocky
Mountains and encompasses approximately 41,000 km2 in the
South Peace region of eastern BC (Fig. 1). Williston Reservoir serves
as the northwest boundary of the study area, which then extends
southeast towards the town of Tumbler Ridge, and south along
the Alberta border. Topography is diverse across this region; the
Rocky Mountains are rugged in the west and transition to foothills
in the eastern portions of the study area. Heading north and east,
the low-elevation boreal forest becomes prominent.
The gradient in topography, from the Rocky Mountains to the
boreal plains, results in a diversity of ecosystems. The Sub-Boreal
Spruce (SBS) bigeoclimatic zone in the western portion of the study
area and the Boreal White and Black Spruce (BWBS) zone in the
east occur at the lowest elevations (1300 m). Dominant tree species include hybrid white spruce (Picea engelmannii glauca), black
spruce (P. mariana) and tamarack (Larix laricana) on bog-type sites,
and drier stands of lodgepole pine (Pinus contorta). The Interior
Cedar Hemlock (ICH) is a low-elevation wet ecosystem found at
the southern boundary of the study area. The Engelmann SpruceSubalpine Fir (ESSF) zone occurs at mid-elevations. Dominant tree
species include white spruce (P. glauca), subalpine fir (Abies lasiocarpa), and trembling aspen (Populus tremuloides). At the highest
elevations (>2000 m), caribou are found across the Boreal Altai
Fescue Alpine (BAFA) ecosystem. Soils are generally poorly developed with conifers occurring in krummholz form at lower elevations. At higher elevations one finds a shrub layer of scrub birch
(Betula glandulosa) or open areas dominated by lichens
(Meidinger and Pojar, 1991).
2.1. Historical and current land use practices in the South Peace region
Dating back to the early 1990s, eastern BC has experienced
rapid land-use change from resource extraction activities, including the exploration and development of oil and gas reserves, in
addition to large-scale commercial forestry, agriculture, mining,
and most recently, wind power. The cumulative effects of these
activities have produced forests that are progressively younger
and more fragmented (Nitschke, 2008). In addition, forestry and
petroleum exploration and development results in a high density
of linear features such as roads and seismic lines. These disturbance types reduce the total availability of old forest, often used
by caribou as habitat. Also, landscape change creates early-seral
vegetation communities that provide forage for other ungulates,
resulting in a greater density and broader distribution of wolves
(Canis lupus) and bears (Ursus americanus), primary predators of
caribou (Serrouya et al., 2011).
Two open-pit coal mines that specialize in the extraction of
metallurgical coal are located within the core winter range of the
Quintette subpopulation of caribou. The development of infrastructure for wind energy is also increasing across the South
Peace region. Although not currently a direct threat to study subpopulations, the ridgeline locations and associated road networks
required for the construction of wind turbines can occur across
caribou habitat.
3. Methods
3.1. Study animals
The Central Mountain DU has 11 recognized subpopulations
(COSEWIC, 2011). We focused the analysis on 5 of the 7 subpopulations found in BC (Moberly, Burnt Pine, Quintette, Narraway,
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C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
Fig. 1. Locations of GPS collared woodland caribou representing their current distribution across the South Peace region of British Columbia, Canada.
Bearhole–Redwillow; Fig. 1) that represent approximately 50% of
the caribou in that DU (COSEWIC, 2014). Over the 11-year study
period (October 2000 – April 2011), a total of 102 female caribou
(Moberly/Burnt Pine = 17, Quintette = 31, Bearhole–Redwill
ow = 7, Narraway = 47) were captured via helicopter net-gun and
fitted with GPS collars. Animal capture and handling methods were
reviewed by the BC Animal Care Committee to assure compliance
with the BC Resources Inventory Committee Standards for Live
Animal Capture and Handling and the Canadian Council on
Animal Care guidelines (live-capture permit #FJ07-40107).
We deployed GPS collars primarily from two manufacturers:
Advanced Telemetry Systems (G2000 or remote-release collar;
C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
Minnesota, USA) and Lotek Wireless (Iridium; Newmarket,
Canada). The majority of collars (80%) recorded one location every
6–9 h (range 4–20 h). Although relocation intervals varied, given
the large sample size and the focus of these analyses on broadscale resource selection, not movement, it is unlikely that sampling
schedules influenced results (Johnson and Gillingham, 2005).
3.2. Distribution of caribou: resource selection function models
We used resource selection functions (RSF) to quantify the spatial relationships between GPS-collared caribou and a number of
variables that were hypothesized to influence caribou distribution
across both a 1990 (pre-disturbance) and a 2012 (current level of
disturbance) landscape. We defined two seasons for describing
the resource selection by caribou: summer (April 1 – October 31)
and winter (November 1 – March 31). Coefficients from RSFs represented selection for or avoidance of a resource (i.e., habitat or
industrial feature). Selection is assumed when an animal uses a
habitat type out of proportion to the availability of that habitat
across some defined area relative to a comparison set of random
locations.
We used a conditional fixed-effects (paired) logistic regression
to develop and generate the coefficients for seasonal RSFs for each
subpopulation (STATA V.11.0, StataCorp). Pairing of used and random locations in space and time provides a more specific definition
of resource availability relative to the seasonal distribution of a
monitored animal (Compton et al., 2002; Johnson et al., 2005).
We used the interval between GPS-collar locations to define the
spatial domain of available habitat. For this calculation, we centered a circular area on the preceding collar location for each
individual study animal (Johnson et al., 2005). This circle had a
radius equal to the 95th percentile movement distance for the
respective relocation interval (e.g., 4 h, 6 h, 8 h), as calculated for
all collared caribou during that season. Five comparison locations
were randomly selected within that spatiotemporal buffer, defined
as the availability radius.
Similar to Johnson et al. (2005), we assumed caribou would not
respond to a disturbance feature at excessively large distances.
Thus, we used the paired logistic regression to statistically remove
the responses of caribou locations that exceeded a set distance
threshold to individual disturbance features. When a specific disturbance type was found outside the availability radius for a caribou location, distance values were held constant for the use and
random locations. This allowed us to model a matched sample of
caribou and random locations based on the effects of habitat, while
statistically removing the effects of an ecologically implausible
‘disturbance’ (Johnson et al., 2005).
For the purposes of this study, we fitted RSF models that
included a full range of disturbance and ecological covariates, as
determined by past research (Williamson-Ehlers, 2012). Our goal
was to generate predictive models of resource selection by each
subpopulation of caribou, not generate the most parsimonious
model as might be revealed by an information-theoretic approach.
The Burnt Pine herd had few collared animals (N = 5) and occurred
immediately adjacent to the Moberly herd. Until recently, those
two herds were considered to be the same herd so they were combined for the analysis.
We generated separate RSF models for each subpopulation
based on pooled location data across years. The matched logistic
regression controlled for interyear differences in resource availability. We used the robust Huber–White estimator of variance
to accommodate repeated sampling of individual caribou
(Williams, 2000; Nielsen et al., 2002).
We reported coefficients (b) from the fully parameterized
model and used 95% confidence intervals to identify the precision
of each covariate. Selection or avoidance of habitat or disturbance
179
features could not be determined if covariates fell close to or overlapped with zero. We used tolerance scores to determine
multicollinearity among variables (Menard, 2002). Where tolerance scores fell below the threshold value of 0.2, we used bivariate
correlation and visual inspection of standard errors to assess if
there was an effect on model inference.
We used a k-fold cross validation to assess the capability of the
top-ranked RSF model to predict resource selection by caribou
(Boyce et al., 2002). A strong Spearman rank correlation (rs)
between the frequency of occurrence of animal locations and the
predicted RSF values suggested a predictive model (Boyce et al.,
2002). Also, we used the receiver operating characteristic (ROC)
curve to further examine the classification accuracy of each RSF
model. An area under the ROC curve (AUC) of >0.7 suggests good
predictive performance. We generated independent AUC scores
by withholding approximately 20% of the animal locations from
the model-building process.
3.3. Resource and disturbance covariates
Drawing from past research on wildlife-development interactions and observations of the study area, we identified a number
of resource and human disturbance variables for modelling the
spatial responses of caribou to their environment: vegetation
cover, ecosystem type, topography, and distance to and density
of disturbance features. Vegetation cover was estimated using
the provincial Vegetation Resource Inventory (VRI; BC Ministry of
Forests and Range, 2007). We used existing knowledge of caribou
ecology to consolidate categories of vegetation cover from the
VRI into 8 classes, based on the leading species (Alpine, Black
Spruce/Tamarack, Subalpine, Pine, Spruce, Mixed Conifer/
Deciduous, Nonproductive Forest, Other). We used the
Biogeoclimatic Ecosystem Classification (BEC) to identify ecosystem type. Five BEC zones occurred across the South Peace study
region: BAFA, SBS, ESSF, BWBS, and the ICH (see methods).
Subzones classified under the BEC zones were also included during
analyses (Meidinger and Pojar, 1991).
We classified the topography across the seasonal range of each
subpopulation into four classes: valley, slope, steep slope, and
ridgeline (e.g., Dickson and Beier, 2007). These classes were generated using a digital elevation model (25 m 25 m) and the
Topographic Position Index as implemented in the ArcGIS software
CorridorDesigner (http://www.corridordesign.org/). See Jenness
(2006) for a full documentation of the modelling process. We used
visual inspection of model outputs, related to our knowledge of the
study area, and the digital elevation model, to choose the parameters that broadly represented ridgelines and valley bottoms as well
as steepness of topographic slope (1500 radius; canyon threshold = 60; ridgeline threshold = 100; slope = 6).
We calculated the distance (km) from caribou locations to
human disturbance features as well as the density of disturbance
features at each animal location. Human disturbance variables
were grouped by industry type as well as their ability to influence
caribou behaviour: linear features (roads, seismic lines and pipelines); forestry cutblocks <30 years since harvest, the time when
early successional vegetation communities are selected by moose
(Alces alces) during winter (Nielsen et al., 2005); open-pit coal mining; oil and natural gas exploration and extraction facilities (P1 ha
and <30 years since development); and burns from wildfire
(650 years). When conducting the GIS analyses for RSF modelling,
we matched the caribou locations for each year with landscape disturbance as it was recorded during that year. Thus, the behavioural
responses of caribou, as recorded with GPS collars, were specific to
individual disturbance features as they developed from 2000 to
2011.
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C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
We used databases from government and industry to identify
the date and location of roads and forestry cutblocks (DataBC,
2011; Canfor Corporation; West Fraser Timber Company Ltd.).
During the period of monitoring for caribou, the Wolverine and
Trend coal mines were fully operational and spatial data were
acquired directly from their parent corporations (Western Coal
and Peace River Coal Ltd.). This mine footprint was applied to caribou locations that occurred within the vicinity of active coal mines.
Lastly, we used the Oil and Gas Commission of BC’s public database, complete through 2011, to identify the date and spatial location of seismic lines, pipelines, well sites and other developed areas
related to the exploration and development of oil and natural gas
reserves (BC Oil and Gas Commission, 2011). Seismic lines and
pipelines were rare in the database prior to 1999 suggesting
incomplete spatial data for that development type.
We combined spatial data for roads, seismic lines and pipelines
to generate a variable representing the density of linear features
(km km2; 1.56 ha moving window). As reported in WilliamsonEhlers (2012), we fit RSF models to three sizes of moving windows
(0.56, 1.56, 3.06 ha) and selected the window with the best fit for
explaining the disturbance response of caribou. Likewise, we combined spatial data for forestry (cutblocks), mines, oil and gas to create a variable representing the density of non-linear features
(ha km2; 1.56 ha moving window, Williamson-Ehlers, 2012).
The final RSF model varied by subpopulation, but generally took
the form:
wðxÞ ¼ exp ðvegetation patch ½category þ ecosystem type ½category
þtopographic position ½category
2
þdensity linear feature ½km km 2
þdensity nonlinear feature ½ha km þ wildfire½km
þclearcut ½km þ mine ½km þ road ½km
þseismic line=pipeline ½km þ petroleum feature ½kmÞ
ð1Þ
predicting the spatial distribution of habitat (i.e., maps), the RSF
coefficients produced a continuous set of values (w(x)). We used
quartile breakpoints (25th, 50th and 75th percentile) generated
from the RSF values of observed caribou locations to identify four
habitat classes (low, moderate, high, very-high quality) from the
range of mapped RSF scores for each seasonal range and year of
analysis (1990 and 2012).
We applied the RSF coefficients to the pre-disturbance (1990)
and current (2012) landscape which resulted in four maps for each
herd: 1990 winter, 1990 summer, 2012 winter, and 2012 summer.
During the late 1980s, the South Peace region had relatively few
seismic lines, pipelines and oil/gas well facilities; we considered
the year 1990 as the pre-disturbance period. We calculated landscape change for each habitat class by measuring the difference
in area of each class between the 1990 and 2012 landscape
(Johnson et al., 2005). The categorical breakpoints for the habitat
classes were consistent for each time period. Thus, relative change
in habitat area was a function of the modelled avoidance of various
disturbance features, as represented by the empirical RSF covariates (i.e., Eq. (1)), when applied to the increasing extent of disturbance across the 2012 landscape. We assumed that the RSF
covariates, premised on location data collected from 2000 to
2012, represented the behaviour and disturbance responses of
woodland caribou that would have occupied seasonal ranges during 1990.
When building the spatial surfaces of disturbance and vegetation covariates there were no inventory data that classified
roads according to their year of construction. Thus, we assumed
a 30% increase in road development over the study period, and
retained 70% of the linear distance of roads recorded in 2012 for
the projection of habitat disturbance in 1990; choice of road sections for retention was random. Given the rate of industrial development across the study area, this was likely a conservative
estimate. We expect the implications of this decision to be minor,
as the vast majority of growth in linear features can be attributed
to oil and gas exploration and infrastructure.
3.4. Identifying the Zone of Influence
We followed the methods of Johnson and Russell (2014) and
used an iterative model fitting procedure to identify the zone of
influence for each disturbance feature. This method works by
incrementally fitting the paired logistic regression to clusters of
caribou and random locations at cumulative distance intervals of
250 m from the nearest feature. The zone of influence is defined
as the distance at which the RSF no longer improves in model fit,
and caribou no longer demonstrate a statistical avoidance response
to a disturbance feature. This analysis is premised on a decay function where animal response to a disturbance decreases as the distance from the feature increases.
At each 250 m distance interval, we recorded the change in the
log likelihood statistic, a measure of model fit, and the coefficients
for the disturbance feature, a measure of a weakening disturbance
response by caribou. We identified the zone of (disturbance) influence as the distance at which the log likelihood reached an asymptote (Boulanger et al., 2012). Beyond the zone of influence,
additional clusters of caribou and random locations did not
improve model fit and caribou no longer demonstrated a disturbance response. If multiple asymptotes were observed, we
required a minimum of P10% of caribou use locations to occur
within the identified zone of influence.
3.5. Quantifying Habitat Loss
We applied the coefficients from the RSF model (Eq. (1)) to the
respective GIS data and produced maps representing the relative
value of habitat, by season, for each subpopulation. When
3.6. Population change
We calculated the annual multiplicative rate of change (k) for
each subpopulation. This involved choosing the most accurate population estimate for the past (Nt-i) and the present (Nt) and inferring the annual k. A high level of precision was expected for
subpopulations surveyed across treeless alpine habitats, with less
certainty for the Narraway and Bearhole–Redwillow subpopulations that wintered in forested areas. Survey techniques and
effort were consistent over time reducing bias in the estimates of
k (see Seip and Jones (2014) for methods and multi-year estimates). However, the population estimates did not include a measure of variance, thus, we considered k as an index of population
change. We used a Pearson correlation to relate k to the seasonal
percent change of the very-high quality habitat class. Because distribution and habitat change for the Moberly and Burnt Pine subpopulations were combined, we also pooled the concurrent
population estimates.
4. Results
We used 270,808 GPS-collar locations for the Moberly/Burnt Pine
(n = 44,599),
Quintette
(n = 81,526),
Bearhole–Redwillow
(n = 27,365), and Narraway subpopulations (n = 117,318) to develop
seasonal resource-selection models. Each model contained variables
representing vegetation cover, ecosystem, topography, and distance
to and density of disturbance features. Seasonal models predicted
well with AUCs ranging from 0.71 to 0.88 (Table 1). The best seasonal
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C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
models had good predictive performance with average k-fold scores
across subpopulations of 0.93 (SD = 0.070) and 0.92 (SD = 0.081) for
the summer and winter models, respectively.
4.1. Seasonal resource selection
Selection of vegetation and topographic resources was consistent with past research and understanding of these subpopulations
(Jones et al., 2007; Williamson-Ehlers, 2012). Those covariates
were included in the predictive models to statistically control for
environmental factors when quantifying the influence of industrial
features on the distribution of collared caribou (Eq. (1)). Thus, we
provide only a brief description of those results (Supplement 1).
During summer, caribou from the Moberly/Burnt Pine,
Bearhole–Redwillow, Narraway, and Quintette subpopulations
selected for high-elevation parkland and alpine ecosystems.
Those areas were characterized by the moist/cold subzones of
the ESSF and BAFA zones and ridgeline topography. Low-elevation
valley bottoms and pine dominated portions of the range were
avoided during the summer season (Supplement 1).
We observed a greater range of strategies in resource selection
during the winter with the Moberly/Burnt Pine and Quintette subpopulations generally occupying high-elevation mountainous
areas and the Bearhole–Redwillow and Narraway subpopulations
ranging across low-elevation habitats in the boreal forest. In particular, the Moberly/Burnt Pine and Quintette caribou continued
to select moderately sloped or ridgeline topography across the
BAFA and the parkland subzone of the ESSF. Throughout the winter
months, steep slopes and spruce-dominated habitats were avoided
(Supplement 1). In contrast, caribou from the Bearhole–Redwillow
and Narraway subpopulations were found across habitats dominated by black spruce, tamarack, and pine trees. Those caribou
avoided ridgelines.
4.2. Identifying the Zone of influence
The disturbance responses modelled for caribou were complex
and variable – the measured zone of influence varied by disturbance type, season, and subpopulation. In some cases, RSF models
suggested that caribou selected habitats within the vicinity of
industrial developments. Such maladaptive behaviour may be a
result of range fidelity, forced use of suboptimal habitats, or
long-term habituation to human disturbance. We did not incorporate those selection responses when spatially representing habitat
change for the 1990 and 2012 landscapes, as attraction to anthropogenic features can act as a population sink (Table 2).
Roads influenced the distribution of caribou during each season.
Caribou in both mountainous and boreal landscapes avoided roads
during winter and summer to a distance of 1.75 km, the zone of
influence for that landscape feature (Table 2). Caribou from the
Bearhole–Redwillow subpopulation avoided seismic lines and
pipelines to a distance of 2.5 km during winter and the Moberly/
Burnt Pine subpopulations avoided these features to a distance of
2.0 km during winter (Table 2).
Caribou in both mountainous and boreal habitats avoided oil
and gas wells and facilities during summer, but not consistently
during winter. Specifically, subpopulations of caribou in high-elevation habitats demonstrated a disturbance threshold of
4.25 km and caribou in the low-elevation habitats avoided those
features to a distance of between 2.0 and 12.5 km. Caribou from
the Quintette subpopulation avoided coal mines to a distance of
3.0 km during winter. Caribou in both mountainous and boreal
landscapes demonstrated a maximum disturbance response of
5.5 km to forestry cutblocks. Caribou in the Quintette subpopulation avoided wildfire burns to a distance of 2.75 km during
summer. The zone of influence associated with burns was greatest
for the Narraway subpopulation, ranging between 5.25 and 8.0 km
during the summer and winter season, respectively (Table 2).
4.3. Habitat loss and population change
Application of RSF coefficients to the 1990 and 2012 landscapes
suggested that caribou experienced variable, but for some subpopulations extreme reductions in habitat valued as high (0.6–52.9%)
and very-high (0.2–65.9%) quality (Table 3, Supplement 2).
During summer and winter, the Moberly/Burnt Pine subpopulations lost 34.7% and 39.3% of the area of the very-high quality
habitat class. The Burnt Pine subpopulation is now considered
extirpated and the Moberly subpopulation is declining at an annual
rate of 12.7% (Table 4).
The Quintette subpopulation was most affected by the loss of
habitat across their summer range; we estimated an 11.3% reduction in very-high quality habitat between 1990 and 2012. During
winter, aggregate habitat loss was less with a 3.72% reduction in
Table 1
Sample size (Obs) and classification accuracy of seasonal resource selection functions using the area under the curve (AUC) for the receiver operating characteristic and k-fold
cross validation for 5 subpopulations of woodland caribou, South Peace region, British Columbia, Canada.
Moberly/Burnt Pine
# Obs
AUC
k-fold
Quintette
Bearhole–Redwillow
Narraway
Summer
Winter
Summer
Winter
Summer
Winter
Summer
Winter
24106
0.83
0.99
20493
0.88
0.99
46448
0.86
0.99
35078
0.83
0.99
12705
0.85
0.85
14660
0.79
0.85
87451
0.71
0.90
29867
0.79
0.85
Table 2
The zone of influence (km) resulting from the avoidance response of woodland caribou to disturbance features during summer and winter, South Peace region, British Columbia,
Canada. An asterisk (⁄) indicates a zone of influence that was confounded by more than one asymptote in the curve; a plus (+) indicates apparent selection by caribou for a
disturbance; (ns) indicates a non-significant or non-applicable disturbance.
Covariate
Roads
Seismic and pipelines
Cutblocks
Non-linear oil and gas
Mine
Fire
Moberly/Burnt Pine
Quintette
Bearhole–Redwillow
Narraway
Summer
Winter
Summer
Winter
Summer
Winter
Summer
Winter
1.75
+
+
4.25
ns
+
1.75
2.00
+
+
ns
+
1.50
+
+
+
3.00
2.75
1.25
+
0.50
+
+
+
+
2.50
3.00
12.50⁄
ns
+
1.00
0.50
+
2.00
ns
+
+
13.50⁄
+
4.00
ns
5.25
1.75
+
5.50⁄
+
ns
8.00
182
C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
Table 3
Habitat change between 1990 and 2012 for 5 subpopulations of woodland caribou across the South Peace region of British Columbia, Canada. Four classes (low, moderate, high
and very high) of habitat were defined according to predicted RSF values generated for caribou monitored with GPS collars. Change was calculated as the difference in area for
each habitat class as a percentage of the predisturbance (1990) landscape.
Caribou subpopulation
Habitat class
Winter
Summer
1990 (km2)
2012 (km2)
% Change
1990 (km2)
2012 (km2)
% Change
Moberly/BP
Very high
High
Moderate
Low
106.67
159.92
513.39
4361.18
64.72
165.73
434.42
4476.29
39.33
+3.63
15.38
+2.64
104.13
499.59
726.88
3752.45
68.02
460.26
687.28
3867.49
34.68
7.87
5.45
+3.07
Quintette
Very high
High
Moderate
Low
138.47
191.53
456.69
2924.31
133.32
194.27
472.40
2911.00
3.72
+1.43
+3.44
0.45
118.59
180.81
283.69
2918.79
105.19
192.20
278.99
2925.49
11.30
+6.30
1.66
+0.23
BearholeRW
Very high
High
Moderate
Low
218.50
535.40
744.08
1842.69
74.51
342.92
374.13
2549.12
65.90
35.95
49.72
+38.34
516.43
851.64
2032.57
1682.47
272.91
613.97
2247.46
1948.77
47.15
27.91
+10.57
+15.83
Narraway
Very high
High
Moderate
Low
25.35
44.50
79.01
449.23
16.26
20.95
41.25
519.62
35.84
52.91
47.80
+15.67
626.88
1116.30
1152.14
2834.54
596.30
944.11
929.63
3259.83
4.88
15.43
19.31
+15.00
Table 4
Seasonal habitat loss (%) for the very-high habitat class between 1990 and 2012,
population estimates (Seip and Jones, 2014), and the annual multiplicative growth
rate (k) for subpopulations of caribou across the South Peace region of British
Columbia, Canada.
Caribou
subpopulation
% Loss of very high
Summer
Winter
Moberly + Burnt
Pinea
Moberlya
34.68
39.33
34.68
39.33
Burnt Pine
34.68
39.33
Quintette
11.30
3.72
Bearhole–
Redwillow
Narraway
47.15
65.90
4.88
35.84
a
Population
estimate (year
of survey)
Annual
multiplicative
growth rate (k)
26/211 (2012/
1997)
25/191 (2012/
1997)
1/17 (2012/
2006)
98/166 (2013/
2008)
14/49 (2014/
2008)
59/102 (2014/
2008)
0.870
0.873
0.624
(extirpated)b
0.900
0.812
0.913
a
Habitat change was calculated for combined Moberly and Burnt Pine
subpopulations.
b
Population surveys in 2013 and 2014 revealed 0 and 1 caribou, respectively,
across the range of the Burnt Pine subpopulation.
area of that class. This subpopulation had a relatively low rate of
decline with an estimated multiplicative growth rate of 0.90
(Table 4).
Of the subpopulations we studied, the Bearhole–Redwillow
subpopulation experienced the greatest level of habitat loss for
both the summer and winter ranges. In 22 years, these caribou in
the low-elevation boreal forest lost 65.9% of winter habitat classified as very-high quality (Table 3). The annual growth rate was
estimated at 0.81 (Table 4).
The Narraway subpopulation also experienced a rapid loss of
winter range with a 35.8% reduction in the area of very-high quality
habitat. The loss of summer habitat, typically high-elevation range,
was less extreme. Summer habitat classified as very-high quality
decreased by 4.9%. The number of caribou in the Narraway subpopulation decreased although at an estimated rate (k = 0.91) that was
less than observed for the other 4 subpopulations (Table 4).
All subpopulations demonstrated dramatic decreases in estimated abundance over a short time period. This rapid decline
was highlighted by the extirpation of the Burnt Pine subpopulation
(Table 4). Although the sample was small, there was a strong
positive correlation between the percentage reduction in area of
very-high quality habitat and the multiplicative growth rate. The
correlation was greatest when considering the loss of summer
habitat (r = 0.96) relative to winter (r = 0.79; Fig. 2).
5. Discussion
Our analyses showed that the cumulative effects of industrial
development had strong influences on the patterns of resource
selection by caribou representing 50% of the subpopulations within
the Central Mountain DU. When the resulting coefficients were
related to the landscapes occupied by those caribou, we found that
the total availability and quality of habitat had decreased substantially over the past 22 years. Furthermore, indices of the change in
abundance suggested a strong correlation between decline in the
number of woodland caribou and the loss of high-quality habitat.
Past research, notably studies of boreal woodland caribou, also
reported a decline in population numbers relative to humancaused landscape alteration (Sorensen et al., 2008; Environment
Canada, 2011). In those cases, however, habitat change represented
the contemporary footprint, including a 250 or 500 m zone of influence, of industrial activity and fire; avoidance, and thus the zone of
influence, was inferred from other studies (but see Rudolph et al.,
2012). In contrast, we modelled the spatial responses of caribou
to industrial features and natural disturbance. When statistically
controlling for other resource covariates, those empirical avoidance responses, and associated zones of influence, provided a measure of the incremental loss of functional habitat for caribou over
time (1990–2012).
5.1. Seasonal resource selection
This study was not designed to fully explore the ecological
strategies and resource selection patterns of woodland caribou;
such results for these caribou were reported elsewhere (Jones
et al., 2007; Williamson-Ehlers, 2012). However, when modelling
the disturbance responses of wide-ranging animals it is necessary
to statistically control for habitat effects. Populations of caribou
across the central Rocky Mountains demonstrate a variety of wintering strategies. Consistent with past research, these analyses
revealed that caribou in the Moberly/Burnt Pine and Quintette subpopulations wintered across high-elevation alpine and subalpine
habitats (Jones et al., 2007; Williamson-Ehlers, 2012). As a second
183
C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
0.95
Winter Habitat
Mulplicave Growth Rate (λ)
0.925
Summer Habitat
Narraway
Narraway
0.9
QuinteeQuintee
0.875
Moberly/Burnt Pine
Moberly/Burnt Pine
0.85
0.825
Bearhhole-Redwillow
Bearhole-Redwillow
0.8
0
10
20
30
40
50
60
70
Reducon in Very High Quality Habitat (%)
Fig. 2. Correlative relationship between the decrease in the very-high quality habitat class (1990–2012) for both the winter and summer seasons and the multiplicative
growth rate of 5 subpopulations of woodland caribou found across the, South Peace region, British Columbia, Canada. Each datum represents the estimated growth rate of 1
subpopulation (Moberly/Burntpine, Quintette, Bearhole–Redwillow, Narraway) not multiple estimates for a single subpopulation.
ecological strategy, caribou of the Bearhole–Redwillow and
Narraway subpopulations wintered in the low-elevation boreal
forest.
The majority of caribou demonstrated altitudinal migrations
during the summer, selecting habitats along mountainous ridgelines. Areas classified as a valley bottom or those with a steep slope
were consistently avoided by caribou in both mountainous and
boreal habitats. Such avoidance may be a response to a relatively
high abundance of other ungulates and associated predators typically found within these forest types (Courbin et al., 2009;
Latham et al., 2011a,b).
5.2. Quantifying the Zone of Influence
We provided empirical evidence demonstrating that subpopulations of caribou within the Central Mountain DU avoided some
linear and non-linear features associated with industrial developments. In general, however, caribou demonstrated the strongest
response and largest zone of influence for non-linear patch-type
disturbances. We suggest a cautious interpretation of the reported
zone of influence (Table 2). These distances may be unique to the
South Peace study area, study animals, and our chosen methods
of analysis.
A variety of analytical tools and statistical methods have been
used to quantify disturbance responses and associated thresholds
(e.g., Johnson et al., 2005; Leblond et al., 2011; Nagy, 2011;
Polfus et al., 2011), but there is no clear guidance on the most ecologically precise or accurate method (Ficetola and Denoël, 2009).
Furthermore, there is rarely an empirical verification of a fitness
effect resulting from a modelled disturbance response. The method
we applied has some advantages, including: multiple metrics for
assessing the strength of avoidance; accounts for other covariates
that may influence a species’ distribution; the method is not
dependent on a precise P-value that encumbers measures of avoidance of concentric areas around a disturbance (e.g., Nagy, 2011;
Polfus et al., 2011); and the technique does not assume a specific
statistical response associated with a nonlinear function (e.g.,
Johnson et al., 2005). As reported by others (Boulanger et al.,
2012; Johnson and Russell, 2014), however, the log likelihood
decay function can be difficult to interpret.
These data and the associated method suggested that some
caribou across the South Peace study area selected for industrial
features. Such results are counter-intuitive for a species that is
widely accepted as being sensitive to human disturbance with
numerous empirical studies demonstrating displacement from
habitats (Dyer et al., 2001; Seip et al., 2007; Vors et al., 2007;
Leblond et al., 2011; Boulanger et al., 2012; Johnson and Russell,
2014). Logic and past observation suggests that caribou should
avoid industrial features that result in habitat change, a negative
sensory experience, or a greater real or perceived risk of predation.
Selection for an industrial feature would be maladaptive, with
these disturbance types representing ecological sinks (Schlaepfer
et al., 2002). Although such a result may be a statistical artefact,
it might also represent a real ecological effect. Recent work from
our study area and other jurisdictions suggests that caribou may
occur or even select for habitat types or industrial features that will
result in a higher likelihood of encountering predators (Johnson
et al., 2005; Faille et al., 2010; Williamson-Ehlers, 2012;
Beauchesne et al., 2013; Losier et al., 2015). Such behaviours could
be the result of range fidelity, maternally learned habits, an inability to assess the fitness costs of novel human-altered environments
or even habituation (Dussault et al., 2012; Johnson and Russell,
2014). When modelling the zones of influence and the resulting
habitat change from 1990 to 2012, we chose to ignore such maladaptive behaviours. Instead, we focused exclusively on the reduction in habitat value associated with the observed avoidance of
disturbance features. Our results suggested that the behaviour of
caribou and potential effects of industrial development were complex and warrant greater mechanistic study.
Caribou across the South Peace study area were more likely to
select summer and winter habitats at distances >1.0 km from roads
and >0.5 km from seismic lines and/or pipelines. Woodland caribou demonstrated similar avoidance responses to roads (1.0–
2.0 km) in Alberta (Dyer et al., 2001) and northwestern BC
(Polfus et al., 2011). Linear features can serve as a travel corridor
or habitat for predators and other ungulate species (Latham
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C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
et al., 2011c; Williamson-Ehlers, 2012). Furthermore, some linear
features were the most likely places for human activity, possibly
displacing caribou from adjacent habitats which could lead to
fewer forage opportunities or increased vigilance (James and
Stuart-Smith, 2000; Seip et al., 2007). Caribou ranging across the
boreal forest demonstrated a greater avoidance of seismic lines
and pipelines, 0.5–13.5 km, when compared to those subpopulations residing in mountainous regions of the study area. The
zone of influence for pipelines was unusually large. More than
10% of use locations for caribou in the Narraway subpopulation
occurred at a distance >13.5 km from the nearest pipeline, suggesting that this feature may correlate with a larger scale avoidance
response to a portion of the range where the pipeline is located.
Caribou from the boreal subpopulations selected summer range
that was >3.0 km from a cutblock. Such avoidance behaviours were
documented for other populations of woodland caribou (Chubbs
et al., 1993; Smith et al., 2000; Vors et al., 2007). For example,
Schaefer and Mahoney (2007) reported that female woodland caribou avoided recent cutblocks to a distance of 9.2 km. Only the
Narraway subpopulation demonstrated an avoidance of wildfire
burns during both seasons. Encounters between caribou in the boreal forest and their predators are more likely to occur in areas with
early successional vegetation; habitats that support moose, the primary prey species of wolves (Ehlers et al., 2014). Such vegetation
communities are associated with recent cutblocks and fires
(Nielsen et al., 2005; Losier et al., 2015).
During the summer, caribou were likely to occur in habitats
located >3.5 km from well sites and other features associated with
oil and gas development. This distance is >3 times that observed by
Dyer et al. (2001) in Alberta, Canada. The greater distance observed
for the South Peace study area might be related to a difference in
the density of those features or levels of human activity.
Caribou of the Quintette subpopulation avoided coal mines during the summer season only, with a higher relative probability of
occurrence beyond a 3 km zone of influence. Caribou calve during
early summer and may have less tolerance for high levels of human
activity and vehicular traffic associated with mine sites (Weir et al.,
2007). Also, some of the highest quality winter habitat was immediately adjacent to mines; selection for that habitat may have overwhelmed a tendency to avoid the mine. Despite these interseason
differences, similar response distances were observed for woodland caribou in northwestern BC (2 km – small placer mines;
Polfus et al., 2011) as well as in Newfoundland, Canada (4 km –
large open-pit gold mine; Weir et al., 2007). However, the footprint
and magnitude of mining activities, including noise and human
presence, can vary greatly making site-specific comparisons
difficult.
5.3. Quantifying Habitat Change
These results strongly suggest that activities related to the
exploration and extraction of energy, forest, and mineral reserves
are dramatically threatening the quantity and quality of habitat
for a large proportion of woodland caribou from one evolutionarily
significant unit. Although habitat loss is dramatic, we speculate
that the mechanism of population decline is complex. Across much
of the range of woodland caribou, predation, facilitated by environmental change, is thought to be the proximate cause of low survival or recruitment (Wittmer et al., 2005; Festa-Bianchet et al.,
2011; Latham et al., 2011a,b; Apps et al., 2013). Referred to as
apparent competition, more abundant and widely distributed
ungulate populations do not directly compete with caribou for
nutritional resources, but support greater numbers of predators
(Wittmer et al., 2007; DeCesare et al., 2010; Serrouya et al.,
2011). In the case of the South Peace region, more early successional forage, as a result of forest harvesting and other types of land
clearing, has allowed for a broader distribution of moose, wolves,
and bears resulting in a higher predation for caribou. With these
subpopulations of caribou being at extremely low densities, it is
unlikely that competition for forage is influencing reproduction
or survival (COSEWIC, 2014).
During winter, caribou subpopulations in the low-elevation
boreal forests experienced the greatest loss (up to 65.9%) of habitat
as a result of development activities. Habitat loss was less severe
during the summer, but those reductions in high and very-high
quality habitat may have severe implications for the survival and
recruitment of caribou. Other studies have revealed that predation-related mortality for adult and newborn caribou mostly
occurs during summer (Seip, 1992; Wittmer et al., 2005; Gustine
et al., 2006). Consistent with those observations, the strongest
relationship between population decline and habitat loss occurred
for the summer season (Fig. 2). Through habitat change, industrial
development may result in a greater adjacency of predators and
caribou during summer or facilitate the movements of predators
along snow-free linear corridors that allow for more efficient
searching and hunting behaviour (James et al., 2004; Apps et al.,
2013). This is despite data that revealed that caribou were located
across relatively high-elevation habitats during that season.
Regardless of the mechanism, the rapid decline of caribou
across the South Peace region suggests that there is an immediate
need for habitat protection and restoration (Fig. 2). Indeed, more
drastic and potentially controversial measures may be required
to arrest the steep decline of the subpopulations highlighted in this
study. The rate of development and resulting loss of contiguous
habitat is pushing already small populations of caribou to low
numbers that are susceptible to stochastic events. The Burnt Pine
subpopulation was declared extirpated during the course of this
study. And recently, a maternal penning project was implemented
to increase calf recruitment in a bid to prevent extirpation of the
Moberly subpopulation. These trends are especially troubling
when considering that the subpopulations documented in this
study represent a large proportion of the caribou found within
the Central Mountain DU (COSEWIC, 2011). Thus, we have documented significant landscape change and risk to not just one population, but a collection of subpopulations that represent a unique
and irreplaceable component of Canada’s biodiversity. Similar
declines are reported for the other 5 subpopulations, including
extirpation of the Banff subpopulation (COSEWIC, 2014).
Legislated recovery of caribou across the South Peace region is
made difficult by the occurrence of resource industries with considerable significance to the BC economy. These industries, and
their footprint on the landscape, will expand in the future with
impacts for caribou habitat and the persistence of those subpopulations. We have documented a history of development that has
resulted in impacts over a large area that will be very difficult to
address over the short term even with aggressive habitat restoration or other recovery efforts (e.g., maternal penning). Although
these caribou receive protections under the federal Species at
Risk Act, there may be few opportunities to maintain some subpopulations while allowing for the existing or an increasing industrial footprint (Environment Canada, 2014). The outlook for these
subpopulations and likely the broader Central Mountain DU is
bleak if the observed rates of habitat change and concurrent population declines continue. Thus, extinction is a possibility that
must be addressed by conservation professionals today not just a
hypothetical outcome to be considered in the distant future.
Acknowledgements
The Habitat Conservation Trust Fund and the Natural Sciences
and Engineering Research Council supported this study. Funding
for the caribou research program was provided by the
C.J. Johnson et al. / Biological Conservation 186 (2015) 176–186
Government of BC and numerous industrial contributors to the
Peace Caribou Research Program. We thank K. Verbruggen and T.
Raabis for providing inventory data; Elena Jones, Brad Culling, and
Diane Culling conducted most of the fieldwork and data collection.
Telemetry data for the Narraway herd were provided by the Alberta
Ministry of Sustainable Resource Development. We thank the handling editor, Dr. Robin Pakeman, two anonymous reviewers, and F.
Lesmerises for providing valuable comments that improved the
manuscript.
Appendix A. Supplementary material
Supplementary data associated with this article can be found, in
the online version, at http://dx.doi.org/10.1016/j.biocon.2015.03.
012.
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