The Journal of Wildlife Management 1–14; 2019; DOI: 10.1002/jwmg.21787

Research Article

The Sustainability of Wolverine Trapping
Mortality in Southern Canada
GARTH MOWAT ,1 Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Suite 401, 333 Victoria Street, Nelson, BC
V1L 4K3, Canada
ANTHONY P. CLEVENGER, Western Transportation Institute, Montana State University, PO Box 174250, Bozeman, MT 59717, USA
ANDREA D. KORTELLO, Grylloblatta Ecological Consulting, 206 Innes Street, Nelson, BC V1L 5E3, Canada
DORIS HAUSLEITNER, Seepanee Ecological Consulting, 2880 Granite Road, Nelson, BC V1L 6Y5, Canada
MIRJAM BARRUETO, Department of Biological Sciences, University of Calgary, 2500 University Drive NW, Calgary, AB T2N 1N4, Canada
LAURA SMIT, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, Suite 401, 333 Victoria Street, Nelson, BC V1L 4K3,
Canada
CLAYTON LAMB, Center for Interdisciplinary Sciences, University of Alberta, 116 Street and 85 Avenue, Edmonton, AB T6G 2E9, Canada
BENJAMIN DORSEY, Parks Canada Agency, Box 350 Mount Revelstoke and Glacier National Parks, Revelstoke, BC V0E 2S0, Canada
PETER K. OTT, Ministry of Forests, Lands, Natural Resource Operations and Rural Development, 727 Fisgard Street, Victoria, BC V8W 1R8, Canada

ABSTRACT Range declines, habitat connectivity, and trapping have created conservation concern for
wolverines throughout their range in North America. Previous researchers used population models and
observed estimates of survival and reproduction to infer that current trapping rates limit population growth,
except perhaps in the far north where trapping rates are lower. Assessing the sustainability of trapping
requires demographic and abundance data that are expensive to acquire and are therefore usually only
achievable for small populations, which makes generalization risky. We surveyed wolverines over a large
area of southern British Columbia and Alberta, Canada, used spatial capture‐recapture models to estimate
density, and calculated trapping kill rates using provincial fur harvest data. Wolverine density averaged
2 wolverines/1,000 km2 and was positively related to spring snow cover and negatively related to road
density. Observed annual trapping mortality was >8.4%/year. This level of mortality is unlikely to be
sustainable except in rare cases where movement rates are high among sub‐populations and sizable un‐
trapped refuges exist. Our results suggest wolverine trapping is not sustainable because our study area was
fragmented by human and natural barriers and few large refuges existed. We recommend future wolverine
trapping mortality be reduced by ≥50% throughout southern British Columbia and Alberta to promote
population recovery. © 2019 The Authors. The Journal of Wildlife Management published by Wiley
Periodicals, Inc. on behalf of The Wildlife Society.
KEY WORDS DNA, genetic sampling, genetic tagging, Gulo gulo, harvest, spatial capture‐recapture, wildlife
management.

Range declines, habitat connectivity, and trapping have
created conservation concern for wolverines (Gulo gulo)
throughout their range in North America (Ruggiero et al.
2007, Canadian Wildlife Service 2014). Wolverines were
extirpated in much of their southern and eastern range post‐
European contact, and many populations along the current
southern range are still partly or entirely isolated from the
continuous population in northwest North America (Aubry
et al. 2007, Canadian Wildlife Service 2014, Idaho
Department of Fish and Game 2014). One conservation
risk to wolverine populations in some parts of their range is
Received: 10 January 2019; Accepted: 20 September 2019
This is an open access article under the terms of the Creative Commons
Attribution License, which permits use, distribution and reproduction
in any medium, provided the original work is properly cited.
1
E‐mail: garth.mowat@gov.bc.ca
Mowat et al. • Wolverine Trapping

the demographic effect of fur trapping (Banci 1994, Krebs
et al. 2004, Lofroth and Ott 2007). Trapping is only currently allowed in western and northern Canada and Alaska,
USA. Portions of southwest British Columbia, Canada, and
most of the lower 48 states have been closed to wolverine
trapping for several decades.
Wolverines are described as facultative scavengers and the
amount of food they scavenge is related to prey abundance,
the proximity of the prey to carrying capacity, and the
presence of other large carnivores that kill large prey that
wolverines could not kill themselves (van Dijk et al. 2008,
Mattisson et al. 2016). In Scandinavia, when reindeer
(Rangifer tarandus) were abundant, more reindeer calves
were killed by wolverines in spring (Mattisson et al. 2016).
Wolverine reproduction appears to be contingent on adequate female body condition (Persson 2005), and even
experienced adult females regularly fail to reproduce.
1

Wolverine survival may also be influenced by food limitation (Sæther et al. 2005). Researchers in southern Sweden
reported density dependence in wolverine survival (Broseth
et al. 2010). Wolverine growth is limited by food availability, which is moderated by variation in ungulate abundance, vulnerability, and kills by other predators (including
people) across space and time. The large potential variation
in wolverine vital rates suggests that wolverine population
growth could be quite variable across the species’ range,
which could influence the sustainability of trapping.
The distribution of wolverines and the locations of their dens
have been linked to the presence of continuous snow cover
during the spring denning period (Copeland et al. 2010,
Magoun et al. 2017). Copeland et al. (2010) and Magoun
et al. (2017) also reported that radio‐telemetry locations and
home ranges in spring were mostly in areas of continuous
spring snow. There are 3 working hypotheses to explain this
link between spring snow and wolverine distribution. The first
hypothesis proposes the need for snow to protect the safety
and thermoneutrality of the young in the den (Copeland et al.
2017). The second hypothesis is based on the observation that
wolverines cache food for winter. Inman et al. (2012a) suggested that wolverines require continuous snow during late‐fall
to spring to preserve food during winter because food is particularly scarce during this season. Third, wolverines may be
physically adapted to snow covered, temperate environments,
and these adaptations may exclude them from more moderate
environments (Lofroth et al. 2007; Schwartz et al. 2009;
Copeland et al. 2010, 2017). Spring snow cover has not been
linked to wolverine population density.
Wolverines are most at risk in the montane portion of their
range and density has been studied in 2 montane areas in
southern Canada (Lofroth and Krebs 2007). Also, M. Barrueto
(University of Calgary, unpublished data) presented an earlier
analysis of a portion of the data included here. Using live
capture data and open capture‐recapture models during 4 years
of study, Lofroth and Krebs (2007) calculated a mean abundance across years; they also used camera sightings and a closed
model to obtain a single estimate of abundance during 1 winter
of study. These estimates of abundance were likely biased low
because the substantive food baits placed at the capture sites
meant previously captured animals were more likely to be recaptured in subsequent trapping sessions than were animals
that had never been captured. In contrast, density estimates
were not corrected for the partial residency of animals living
near the study area boundary, which can underestimate the
effective trapping area and lead to positive bias in the density
estimate (i.e., closure bias). Wolverine density was also estimated using very similar methods in a sister study in the
northern limit of the montane mountains in central British
Columbia. These 2 studies yielded density estimates of about 3
individuals/1,000 km2 and other estimates of wolverine density
in comparable ecosystems were similar (Lofroth and Krebs
2007), except for 1 study in Idaho, USA (Hornocker and Hash
1981), which reported much higher density.
Natural mortality of wolverines has been observed to vary
from 4–20% per year (Krebs et al. 2004, Squires et al. 2007,
Persson et al. 2009), and reproduction is low for an animal
2

of this size. Females produce <0.5 female young per year
(Persson et al. 2006). Previous researchers used simple
population models and observed estimates of survival and
reproduction to estimate the influence of trapping mortality
on population growth. They concluded that wolverine
demographics are sensitive to adult mortality and that current harvest rates in North America may limit population
growth, except perhaps in parts of northern Canada and
Alaska where trapping mortality rates were lowest (Krebs
et al. 2004, Squires et al. 2007, Lofroth and Ott 2007).
None of these models incorporated density dependence or
accommodated differential sex‐ and age‐based trapping
vulnerabilities; therefore, they may have underestimated
sustainable harvest rates (Krebs et al. 2004, Squires et al.
2007, Lofroth and Ott 2007).
A specific analysis of harvest sustainability in British
Columbia by Lofroth and Ott (2007) suggested recent levels
of wolverine kill were sustainable at the provincial scale, but
harvest in some areas may not have been sustainable. They
reported that uncertainty in the harvest data was an important part of the conservation risk and recommended improved data collection and evaluation. Using a probabilistic
modeling approach, Dalerum et al. (2008) included various
realistic harvest scenarios and immigration levels in their
model and found the model population was sensitive to adult
female harvest and that immigration was necessary to ensure
long‐term viability and avoid local extirpations. Sæther et al.
(2005) examined conservation risk in the Scandinavian wolverine population using a population viability approach and
they also reported that harvest posed the largest conservation
risk to the population. In summary, previous studies of
wolverine demography suggest the species can support small,
male‐dominated harvests and that harvesting isolated populations can cause population decline or extirpation.
Juvenile and yearling (sub‐adults collectively) wolverine dispersal begins in January, and males disperse more commonly
than females (Magoun and Copeland 1998, Vangen et al. 2001,
Gervasi et al. 2015, Copeland et al. 2017). Sub‐adults have
larger home ranges than adults while they are searching for a
permanent range; sub‐adult males cover particularly large areas
(Inman et al. 2012b). In addition, pregnant female wolverines
begin looking for dens in January, and their young are born in
February or March (Magoun 1985, Banci 1994, Magoun and
Copeland 1998); therefore, breeding females have restricted
home ranges during late winter. The variation in home range
size among sex and age cohorts in wolverines is likely greater
during late winter than at any other time of year. Resident
females with young may have seasonal ranges <100 km2,
whereas dispersing sub‐adult males are often nomadic and may
have ranges >2,000 km2. Hence, we expect considerable bias in
all density estimators that do not specifically account for space
use and individual variation in detection probability due to these
space use patterns (Royle et al. 2011).
The main objective of this study was to evaluate the demographic risk of trapping to wolverine populations in southeast British Columbia and the adjacent Rocky Mountains of
southern Alberta. We also investigated the relationship between broad habitat factors and wolverine density so we could
The Journal of Wildlife Management

predict density across space and gain insights into the species’
habitat needs. Based on previous research, we predicted
trapping, roads, and human use would be negatively related
to density and that spring snow cover, alpine, and upper
elevation forest would be positively related to density.

STUDY AREA
Our study area included the Kootenay‐Boundary Region in
southeast British Columbia and the southern Rocky
Mountains and foothills of southwest Alberta, Canada
(Fig. 1). This area includes parts of the Monashee, Selkirk,
Purcell, and Rocky mountains and is >50,000 km2. The
area is montane and the climate, although highly stratified
by elevation, is characterized by cold winters, with long
periods of snow cover, and hot summers (MacKillop and

Ehman 2016). Many large lakes, highways, and human
settlements, which could create resistance to movement,
occurred in low elevation valleys. Extensive forest harvest
has occurred throughout the area and mining was widespread historically but is much less active currently. Both
industries built and continue to build many roads. Winter
recreation (e.g., snowmobile use, ski resorts, helicopter or
snowcat‐access skiing, ski lodges, backcountry skiing) was
common in our study area. Provincial and national parks
and protected areas occurred throughout the area.
Elevation ranged from 400 m to >3,000 m, with major
variation in summer and winter precipitation. In general,
the mountains become less rugged from north to south,
which led to lower precipitation, though precipitation also
roughly declined from west to east. Low elevation forests

Figure 1. Wolverine winter sampling areas 2011–2016 in southwest Canada showing trap locations and spatially explicit capture‐recapture mask boundaries
for each sampling area in matching colors. We created mask boundaries by assuming large lakes were hard population boundaries. Where natural boundaries
did not exist, we extended the mask approximately 40 km beyond the outer sample sites. In the legend, we provide the year at the end of the winter of
sampling after the sampling area name (BYKNP is Banff, Yoho and Kootenay National Parks and MRGNP indicates Mount Revelstoke and Glacier
National Parks).
Mowat et al. • Wolverine Trapping

3

were composed of western red cedar (Thuja plicata), western
hemlock (Tsuga heterophylla), Douglas fir (Pseudotsuga
menziesii), ponderosa pine (Pinus ponderosa), lodgepole
pine (Pinus contorta), trembling aspen (Populus tremuloides),
and western larch (Larix occidentalis). At higher elevations,
Engelmann spruce (Picea engelmannii) and subalpine fir
(Abies lasiocarpa) forests transitioned to treeless alpine
meadows, rock, and ice (MacKillop and Ehman 2016).
Potential wolverine ungulate prey included mountain
goats (Oreamnos americanus), mountain caribou, mountain
sheep (Ovis canadensis), moose (Alces alces), elk (Cervus
canadensis), mule deer (Odocoileus hemionus), and white‐tailed
deer (O. virginianus). Only mountain goats and caribou inhabited high elevations consistently during winter; goats were
irregular in their distribution and rarely abundant (Poole
2006). Caribou have declined to <200 individuals as of 2018
(A. Reid, British Columbia Ministry of Forests, Lands and
Natural Resource Operations and Rural Development
[FLNRORD], personal communication) and were confined
to 4 areas with large expanses of the study area without
caribou. Small‐mammal prey included hoary marmots (Marmota caligata), Columbian ground squirrels (Urocitellus
columbianus), snowshoe hares (Lepus americanus), American
pikas (Ochotona princeps), and porcupines (Erithizon dorsatum;
Lofroth et al. 2007).

METHODS
Wolverine DNA Sampling and Trapping Harvest Rate
We collected samples from wolverines during late winter by
remotely removing hair and occasionally collecting scat

samples. We sampled 8 areas in southeast British Columbia
and southwest Alberta between 2012 and 2016: 5 areas in
the West Kootenays from 2012 to 2016 and 3 areas in the
East Kootenays and southwest Alberta from 2014 to 2016
(Fig. 1). We partitioned each area into 10‐km by 10‐km
cells that approximated the minimum size of a female home
range. We set 1 or 2 bait sites in each cell and checked them
twice in the West Kootenays and 3 times in the East
Kootenays at roughly monthly intervals (Table 1). We
began sampling in late December in the East Kootenays and
in late January in the West Kootenays, and we removed the
sites between late March and mid‐April. Because of the
rugged nature of the terrain, we selected sites for ease of
access and used local knowledge of wildlife movements
when available. We made hair traps by attaching bait to a
tree (~2 m from the ground or snow surface) and wrapping
barbed wire around the tree below the bait to capture hair
from animals that climbed the tree to get the bait, similar to
Mulders et al. (2007; Fig. 2). We used a deer or elk head as
bait in the West Kootenays and a skinned beaver (Castor
canadensis) carcass in the East Kootenays and Alberta. Each
time we re‐visited the site, we examined the barbed wire for
hairs, collected any hair samples present, burned each barb
to clean the wire of remaining hairs, and replenished the
bait if necessary. We collected hair samples in paper envelopes and stored them in a dry environment. In the East
Kootenays we also attached a camera to a nearby tree to
photograph animals that visited the site. During each visit
to the bait site, we looked for wolverine tracks and scat. We
also included data from 5 additional sites that were sampled
contemporarily, using similar methods, for environmental

Table 1. Wolverine sampling effort and detection success in southeast British Columbia and southwest Alberta, Canada, 2011–2016. Year is the year at the
end of the sampling winter, and the area sampled is the mask area for each sampling area. We detected 126 unique individuals in our study area; we detected
some of these individuals in multiple sampling areas or years, so when we summed the individuals detected in each sampling area‐year, the total was 153
individuals. Detections are the total number of individual detections across all sampling occasions (each individual could only be detected once per occasion,
regardless of how many sites they visited during that occasion). Spatial detections also include multiple detections of an individual within an occasion, as long
as those detections occur at different sampling sites.
Sampling areaa
Rocky Mountains
BYKNP
BYKNP
BYKNP
Waterton‐Westcastle
Central Rockies
South Rockies
Purcell Mountains
South Purcells
Central Purcells
Selkirk Mountains
MRGNP
MRGNP
MRGNP
MRGNP
MRGNP
South Selkirks
Central Selkirks
Valhalla ranges
Total
a

4

Year

Area
sampled (km2)

Sites
sampled

Detection
occasions

Individuals
detected

Detections (spatial
detections)

x̄ trap
spacing (km)

2011
2012
2013
2014
2015
2016

19,617
19,617
19,617
7,347
18,785
18,714

48
10
64
20
78
75

3
3
3
3
3
3

23
8
26
1
11
11

46 (46)
14 (14)
62 (63)
1 (1)
21 (28)
22 (37)

8.5
12.9
7.6
9.3
8.7
7.1

2013
2016

10,833
7,910

66
43

2
2

8
8

9 (12)
11 (23)

7.0
7.1

2011
2012
2014
2015
2016
2012
2014
2015

7,150
7,150
7,150
7,150
7,150
5,452
7,863
4,445

6
7
12
6
6
23
63
33
560

3
3
3
3
3
2
2
2

3
9
3
10
10
4
16
2
153

4 (4)
11 (11)
3 (6)
15 (20)
13 (13)
5 (6)
22 (40)
2 (2)
261 (326)

9.7
6.1
4.4
12.4
8.9
7.8
7.0
7.5
7.75

Sampling areas included Banff, Kootenay and Yoho National Parks (BYKNP) and Mount Revelstoke and Glacier National Parks (MRGNP).

The Journal of Wildlife Management

Figure 2. Photo of hair capture site used in the West Kootenay (A) and
the East Kootenay and Alberta portion (B) of our wolverine study area in
southwest Canada. The bait is nailed several meters off the ground or snow
and barbed wire is wrapped around the tree to remove hair from the target
animal as it climbs the tree to feed on the bait.

impact assessment. Animal care was regulated by the
British Columbia Ministry of FLNRORD Animal Care
Committee. We did not need an animal care permit for
this work because the sampling methods were considered
non‐invasive.
Additionally, we collected data using similar methods in
2 areas centered around national parks. The primary
objective of these studies was to examine movement across
the highways that crossed the parks (Sawaya et al. 2019).
We collected data for 5 years near Revelstoke in Mount
Revelstoke and Glacier National Parks, and for 3 years in
Banff, Kootenay, and Yoho National Parks. These studies
used 3 sampling sessions and beaver as bait, as in our East
Kootenay studies.
We sent the hair samples to Wildlife Genetics International (WGI) in Nelson, British Columbia for microsatellite
genotyping. We selected only samples that had >1 guard
hair with a root or >5 underfur hairs for analysis, and WGI
used up to 10 guard hairs or approximately 30 underfur
Mowat et al. • Wolverine Trapping

hairs in an extraction. Technicians extracted DNA using
QIAGEN DNeasy Tissue kits, following the manufacturer’s instructions (Qiagen, Toronto, ON, Canada).
Species identification was based on a sequence‐based analysis of a segment of the mitochondrial 16S rRNA gene
(Johnson and O’Brien 1997). For samples that yielded
wolverine DNA, WGI used multilocus genotyping, consisting of a ZFX‐ZFY sex marker and 7 additional microsatellite markers, for individual identification. Error
checking followed established rules (Paetkau 2003), which
have been tested using grizzly bear (Ursus arctos) hair and
deliver low error rates (Kendall et al. 2009). Samples from
the studies in the national parks were first analyzed at the
Rocky Mountain Research Station laboratory in Missoula,
Montana using nearly identical methods. We later reanalyzed 1 sample from each individual at Wildlife Genetics
International to verify that individual identities were comparable between the labs and studies so the datasets could be
combined.
We estimated wolverine trapping harvest rates using
government‐collected kill data. In British Columbia and
Alberta, trapping is regulated by a registered trapline system
where licensed trappers must own a registered trapline or
have permission to trap on private land or someone else’s
trapline. Registered traplines are exclusive areas whose
boundaries rarely change, though ownership may transfer.
Most public land that is not park is included in a registered
trapline, and traplines even occur in some recent provincial
parks. Very few areas are trapped by ≥1 person, so trapping
effort tends to be dispersed in Canada (Slough et al. 1987).
In southeast British Columbia and southwest Alberta
trappers may trap wolverines between 1 November and 31
January; each trapper may catch 1 wolverine/year in Alberta,
but there is no limit in British Columbia. Trapper kill is
recorded by mandatory reporting in southern British Columbia and Alberta and by the fur sales recording system
throughout British Columbia. Hunters must submit all
wolverines they kill to a government inspector, though few
wolverines are killed by hunters. Wolverines are also occasionally killed in highway collisions but few of these are
recorded. Lofroth and Ott (2007), Hatler and Beal (2003),
and Webb et al. (2013) provide additional details on wolverine trapping and management in British Columbia and
Alberta.
Spatial Capture‐Recapture Analysis
We used spatially explicit capture‐recapture (SECR) analysis
to estimate wolverine density (Efford 2004, 2018). This
method is fast becoming the standard method for estimating
animal density (Royle et al. 2013). Spatial capture‐recapture
methods estimate 3 parameters: detection, a spatial parameter, and density. The detection parameter can be likened to
the detection probability in non‐spatial capture‐recapture;
however, in SECR, detection probability declines with distance from the animal’s putative home range center. A link
function models the shape of the decline in detection; we
used the default logit link. The spatial parameter, termed
sigma, is an index of the range size during sampling and,
5

along with the trap and animal location data, is used to estimate range centers of individuals in and near the sampling
area. Density is the response variable and commonly the
parameter of interest.
Detection Covariates
Covariates can be fit to all estimated parameters to reduce
bias, improve model fit, or better predict density within or
beyond the study area. Covariates can also be fit to trap
sites and individuals to accommodate heterogeneity in
detection or space, or among individuals, but there is a
limit to the number and type of covariates that can be fit
simultaneously (Efford 2018). We fit detection parameters
first and then fit density covariates to minimize the number
of model runs using spatial covariates because each analysis
using spatial density covariates took 1–2 days. We accommodated variation in sampling effort by directly
coding the number of days each trap was set into the trap
data file in the usage field. Detection success for wolverines
commonly increases from mid to late winter (Broseth et al.
2010, Royle et al. 2011), so we allowed the detection parameter to vary among trapping sessions, expecting increased detection in later sessions. Female ranges are
smaller than male ranges, but we initially ran analyses
combined for each sex because we had small sample sizes,
and smaller range size is often compensated for by higher
detection success (Efford and Mowat 2014). Combined
sex models often yield nearly identical density estimates to
separate models for each sex. After selecting our best
model, we ran separate analyses for males and females to
better understand the influence of covariates on the density
of each sex. We expected considerable variation in range
size among individuals which would reduce precision and
could bias the estimate of density.
Our hair traps were far apart (~8 km on average; Table 1)
and included a substantive meat reward. Many individuals
had few traps in their home range and were more likely to
visit a site they had already visited than visit a different site,
especially given the food reward. Hence, we expected repeated detections of an individual wolverine would be
more likely at sites where they were previously detected,
and we allowed for this explicitly in our model structure.
This trap‐based behavior response has been detected in
other wolverine studies (Mulders et al. 2007, Royle et al.
2011) and many other carnivore species where baited sites
were used and is explicitly accommodated in SECR.
Snow depth varied among years and sites during our study,
so we measured snow depth on the final visit to each trap
site and used this as a trap covariate to accommodate this
possible source of capture variation. We used the final snow
depth measure because we could only include 1 measure per
trap and snow depth was most variable in the final detection
occasion. We also tested to see if the difference in baits used
in the East and West Kootenays measurably influenced
detection success. Lastly, we included a binary parameter
that allowed for different detection success based on which
lab originally analyzed the genetic data to examine whether
genotyping success affected detection success.
6

We calculated mean annual home range sizes for all
wolverine radio‐telemetry studies conducted in the montane
region of western North America. Where possible, we chose
extensive home range estimates such as 95% minimum
convex polygon estimates and did not use core estimates
(Table S1, available online in Supporting Information). We
weighted the mean from each study by the sample size of
individual animals and used the mean home range estimate
to calculate sigma values. Sigma is the parameter that scales
for density in SECR models, and it can be calculated as
sigma = r/2.45 where r is the radius of the 95% home range
(Sun et al. 2014). We compared these independently calculated sigma values to those estimated by SECR analysis of
our detection data to examine whether our estimates of
sigma aligned with other independent measures of wolverine space use.
Density Covariates
Wolverine density is affected by human‐caused mortality,
which is principally trapping in British Columbia and
Alberta (Krebs et al. 2004). We tested for this effect within
each of our study areas by coding all pixels in each trapline
polygon with the number of wolverines that were killed
during the winter we worked. We assigned trapping kills to
a trapline because exact mortality locations were rarely
known. By assigning the number of trapped wolverines to a
trapline, we had a spatial depiction of recent trapping
mortality that could be incorporated into the SECR analysis
as a spatial covariate. We corrected for the variation in
trapline size by dividing the number of wolverines trapped
in each trapline by the area of the trapline.
We predicted that wolverine density would be greater in
higher elevation ecosystems. Wolverines appear to use
higher elevations at all times of year and especially in winter
(Krebs et al. 2007, Inman et al. 2012b). We assumed that
wolverines do not choose elevation itself but rather prefer
plant associations and climate envelopes typical of higher
elevation or latitudes (Copeland et al. 2010). Additionally,
females den at higher elevations in montane areas (Krebs
et al. 2007). We used ecological mapping (MacKillop and
Ehman 2016) to divide our study area into 3 broad zonal
ecosystems: low elevation forests, which were wetter in our
western study areas than in our eastern study areas; subalpine forest of Engelmann spruce‐subalpine fir (ESSF),
typical of upper elevations in North American montane
forests; and alpine, which included all high elevation communities such as alpine tundra and grassland, parkland and
woodland forests, and rock. Because precipitation increases
with elevation, both the latter ecosystems were wetter,
snow‐affected ecosystems. We excluded permanent ice. To
test if ecosystem was related to density, we included alpine
and ESSF in our models and excluded low elevation forest
to create a contrast with the other 2 ecosystems.
We calculated spring snow cover for each year from 2000–
2016 using MODIS data following Copeland et al. (2010).
For each pixel and year, we determined if there was persistent spring snow cover from 24 April to 15 May. We then
calculated an index of presence of snow (0–17), which
The Journal of Wildlife Management

represented the number of years a location had spring snow
cover, to test the relationship between spring snow cover
and local wolverine density.
We calculated road density using current road data acquired
from provincial governments (Supplement S4, available online in Supporting Information). We used open source data
for roads in the United States. We also acquired Human
Influence Index mapping as an alternative and more generalized measure of disturbance (http://sedac.ciesin.columbia.
edu/data/set/wildareas‐v2‐human‐influence‐index‐geographic,
accessed 17 Mar 2019).
We re‐scaled the spatial data for all variables using a
moving window analysis with a radius of 10 km, which is
roughly the radius of a female home range in late winter.
Spatially explicit capture‐recapture uses the variable value at
the putative home range center to calculate the relationship
between density and habitat covariates. We felt wolverine
density would be more strongly related to the mean value of
each spatial variable in the entire home range, not at a
specific site.
We used Akaike’s Information Criterion corrected for
small sample size (AICc) to compare model fit among
candidate models. We first compared detection covariates to
select which of these variables to include in future spatial
modeling. Models that include density covariates take a
long time to run and we reduced the number of candidate
spatial models by fitting detection variables first because
detection models ran more quickly. We chose detection
variables based on fit, our knowledge of the detection
methods, and wolverine biology. We chose our best density
model based on fit and realism and we did not attempt to
accommodate uncertainty in model selection because our
final model was much better supported than other models.
Population Model
We built an annual discrete‐time population model to better
understand sustainable harvest rates of wolverines. A
number of previous efforts have used population modeling
to examine the sustainability of harvest (Krebs et al. 2004,
Dalerum et al. 2008, Lofroth and Ott 2007, Squires et al.
2007) or population viability (Sæther et al. 2005). In particular, we wanted to better understand how sex and age‐
biased harvest affected harvest sustainability, and how environmental stochasticity and density dependence might
further influence harvest rates. We used field data from
radio‐telemetry studies to parameterize reproduction and
survival and we used carcass studies to estimate potential
reproduction based on in utero measures of pregnancy rate
and the proportions of each age and sex cohort in the
trapped sample. We added environmental stochasticity to
the reproduction component of the model because successful reproduction appears to be closely linked to late
winter food abundance, which can be as random an event as
the discovery of a single ungulate carcass by an individual
female (Mattisson et al. 2016).
We structured our model into 5 cohorts: juveniles, yearling
females and males, and adult females and males. We split
annual survival into an initial pre‐harvest rate and a second
Mowat et al. • Wolverine Trapping

post‐harvest rate that modestly reduced the initial rate as a
function of harvest rate. We incorporated density dependence
into reproduction using a theta power function. Only 1 study
has estimated theta for wolverines (Sæther et al. 2005), and
they reported evidence for strong density dependence near
carrying capacity (K; theta = 12.5). We set K at roughly 50%
higher than our observed population estimate because much
of the study area appeared to be unoccupied. Density dependence was trivial when K was 50% higher than the
starting population size. We used survival rates from Krebs
et al. (2004) for yearlings (0.85) and adults (0.88), and a
juvenile survival rate (0.68) as measured by Persson et al.
(2006). We used the mean reproduction rate observed by 3
field studies (Magoun 1985, Copeland 1996, Persson et al.
2006). This value (0.77 young/year/female) was for adult
females only because yearling females have not been observed
to reproduce. Stochasticity could be incorporated in all vital
rates; however, we allowed it only in the reproductive rate.
For each reproductive parameter, we used a beta distribution
to generate random realizations that fell within a set expected
value (tol) of the parameter approximately 95% of the time.
We assumed the sex ratio of litters at birth was equal. Age
ratios of trapper killed carcasses were 36% juveniles, 20.2%
yearling males, 12.6% yearling females, 20.4% adult males,
and 10.8% adult females and we derived these age ratios by
taking the mean ratio from 5 mid‐ to long‐term carcass
collection studies from northern Canada (Banci and Harestad
1988, Mulders 2000, Awan and Szor 2012, Lee 2016, Kukka
et al. 2017). We first ran a model to calculate harvest vulnerabilities for each age and sex based on the age and sex
ratio of trapped wolverine samples above. We then used these
vulnerability estimates in the subsequent population modeling process. We ran the model for 60 years and focused
attention on its long‐term steady state behavior. Initial cohort
sizes were based on observed age and sex ratios that totaled to
our estimate of the population size for the study area. It took
about 10 years for the age structure to stabilize for each new
model run. We built all models in R (R Core Team 2016;
code available online in Supporting Information S5–S6 and
at https://github.com/ctlamb/WolverineSCR).

RESULTS
Density and Harvest Rate
We sampled wolverines during 6 winters between
December 2010 and April 2016 throughout southeast
British Columbia and southwest Alberta (Fig. 1). We
identified 126 individual wolverines that were detected 326
different times across years, study areas, trapping occasions,
and sites (Table 1). Only the 2 study areas in the national
parks were sampled in >1 year, and these study areas generated much of the recapture data in the dataset (Table 1;
Fig. S1, available online in Supporting Information).
Wolverines were more commonly detected at traps where
they had previously been detected, which is expected when
food rewards are provided at trap sites. We also expected
detection success to increase as the winter progressed, but
variation among capture occasions was not supported
7

Table 2. Model selection table to evaluate possible variation in detection
success for wolverines sampled in southeast British Columbia and southwest Alberta, Canada, 2011–2016. We estimated 3 parameters: density
(D), detection probability (g0), and a movement parameter (sigma). No
density or sigma covariates were included in this analysis. We present the
number of model parameters (K), model log likelihood (logLik), Akaike’s
Information Criterion corrected for small sample size (AICc), the difference in AICc values (ΔAICc), and relative model weight (wi).
Modela

K

logLik

AICc

ΔAICc

wi

g0~bk
g0~DNA + bk
g0~bk + snow depth
g0~bait + bk
g0~t + bk
g0~t
g0~1
g0~snow depth

4
5
5
5
6
5
3
4

−1,078.20
−1,077.14
−1,077.19
−1,077.42
−1,077.14
−1,101.78
−1,107.33
−1,107.08

2,164.68
2,164.70
2,164.80
2,165.24
2,166.86
2,213.97
2,220.83
2,222.43

0.00
0.02
0.12
0.56
2.19
49.29
56.15
57.75

0.25
0.25
0.23
0.19
0.08
0.00
0.00
0.00

a

Detection covariates included trap‐specific behavior (bk), a categorical
variable for each genetic lab (DNA), snow depth at trap site at last
check (snow depth), ungulate versus beaver bait (bait), and trapping
occasion (t).

(Table 2). The best fitting detection model included separate detection parameters for each sampling area and year,
but this model had 19 parameters and was unstable. This
model was unrealistic given the small sample sizes in some
studies (Table 1) and we did not consider it further. The
binary variable representing the 2 genetic labs, snow depth
at the trap site, and bait type generated minor improvements in fit so we did not include these variables in further
analyses. Only trap‐effort and trap‐specific behavior were
included in future model fitting to account for the variation
in detection probability among traps and individuals.
Detection probability when the trap is at the center of the
home range (g0) was 0.006 ± 0.001 (SE) for wolverines that
were detected at a trap for the first time and 0.023 ± 0.003
for individuals that had already been detected at the same
trap. Sigma, the spatial parameter, was 9.8 ± 0.54 km for
both sexes combined and 11.2 ± 0.95 km for males and
8.4 ± 0.61 km for females. Sigma values, as calculated from
home range data for wolverines living in montane areas,
varied from 4.4 km for adult females to 11.4 km for sub‐
adult males (Table 3). Sub‐adults may make such large
movements while exploring for a permanent home range
that they may effectively emigrate from many study areas
(Inman et al. 2012b). The sigma value from our SECR
analysis, which was pooled across age‐classes, was closer to
the size expected for males than females, and the sex‐based
Table 3. Mean annual home range size for wolverines in the montane
mountains of western North America (1992–2013). Sigma is the movement parameter in spatially explicit capture‐recapture and was calculated
as sigma = r/2.45 where r is the radius of the 95% home range (Sun
et al. 2014).
Sex
Female
Female
Male
Male

8

Age

Mean home
range size (km2)

Home range
radius

Sigma

n

Adult
Sub‐adult
Adult
Sub‐adult

339
787
1,097
2,333

10.4
15.8
18.7
27.3

4.2
6.5
7.6
11.1

28
22
25
16

Table 4. A comparison of the fit of a selected group of models to estimate
density of wolverines in southeast British Columbia and southwest Alberta,
Canada, 2011–2016. All models include, sampling effort for each trap, a
trap‐specific behavior effect on detection, and no covariation for the spatial
parameter. Models vary only based on the density covariates included in
each model. We present the number of model parameters (K), model log
likelihood (logLik), Akaike’s Information Criterion corrected for small
sample size (AICc), the difference in AICc values (ΔAICc), and relative
model weight (wi).
Modela

K

logLik

AICc

ΔAICc

wi

Road density + snow
Road density + snow + trap
harvest
Snow
HII + snow
Road density + alpine
Road density + ESSF +
alpine
Road density + ESSF +
alpine + trap harvest
Road density
Alpine
HII
ESSF
Null
Trap harvest

6
7

−1,037.6
−1,037.4

2,087.7
2,089.6

0.0
1.9

0.52
0.20

5
6
6
7

−1,039.9
−1,038.9
−1,042.5
−1,042.3

2,090.2
2,090.4
2,097.5
2,099.4

2.5
2.7
9.8
11.7

0.15
0.13
0.00
0.00

8

−1,041.9

2,100.8

13.1

0.00

5
5
5
5
4
5

−1,045.7
−1,048.2
−1,063.9
−1,076.4
−1,078.2
−1,077.8

2,101.7
2,106.8
2,138.3
2,163.2
2,164.7
2,166.0

14.0
19.1
50.5
75.5
77.0
78.2

0.00
0.00
0.00
0.00
0.00
0.00

a

Snow = the number of years with spring snow cover between 2000–
2016, trap harvest = area weighted measure of the number wolverines
killed the winter of our sampling, alpine = the proportion of alpine
ecosystem, ESSF = the proportion of upper elevation forest, HII =
human impact index, which is a cumulative measure based on road
density, human habitation, and other human footprints. All variables
were smoothed using an 8‐km‐radius moving window analysis.

values were closer to those expected for sub‐adults than
adults (Table 3).
After testing the influence of covariates on detection
probability, we tested variables we hypothesized would be
related to density using the best fit detection model. We did
not include spring snow with alpine or ESSF (high elevation forest) in the same model because these variables
were strongly correlated (Fig. S2, available online in Supporting Information). Spring snow and road density were
most related to estimated density (Table 4); all other variables generated only minor improvements in fit. The trapping mortality variable was not related to density (Table 4).
Density varied from 0.9–4.4 wolverines/1,000 km2 among
our sampling areas (Table S2, available online in Supporting
Information) and averaged 2.0/1,000 km2 (CI = 1.70–2.5/
1,000 km2) across the study area. When we ran the top
model separately for each sex, the summed male and female
densities were nearly identical to the mean density as estimated by the model that did not accommodate sex. Females
were 62% of the estimated population (Table S2). Density
was positively related to the annual consistency of spring
snow cover and negatively related to road density (Fig. 3).
We used this model to extrapolate wolverine density to our
entire study area; estimated density generally declined from
north to south (Fig. 4). We derived population estimates for
the Kootenay Region of British Columbia, the Alberta portion of our greater study area, and for the 2 areas combined.
The wolverine harvest during the 6 years of our field sampling and the 3 years previous averaged 19 animals/year for
The Journal of Wildlife Management

Figure 3. The relationship between spring snow cover, road density, and wolverine density based on our best fit model from southeast British Columbia and
southwest Alberta. We built our spring snow map using snow cover data from 2000–2016. The figures on the left are for the combined sex model and the
figures on the right are for separate sex models (F = female and M = male); shaded areas are 95% confidence intervals.

the greater study area, 16.6 animals/year in the British
Columbia portion and 2.3 animals/year in the Alberta portion of our study area. Our estimate of the annual kill rate for
the entire study area was 8.4%. The kill rate in British
Columbia was higher than the kill rate in Alberta (Fig. 5)
because much of the wolverine distribution in southern
Alberta was in national parks (Fig. 4). We also set road
density to zero and predicted wolverine abundance without
the depressing effect of the road covariate; abundance
increased 44% from 226 ± 21.5 to 326 ± 66.2.
Population Modeling
Our population model suggested the maximum sustainable
mortality rate was 6.2%/year when harvest was drawn at
random among sex and age classes. We used the mean age
structure from published carcass studies to calculate harvest
vulnerabilities by age and sex cohorts and found that young
age classes were 6–10 times more vulnerable to harvest than
adult females. When we incorporated these vulnerabilities
in our model, the average maximum sustainable harvest
increased to 8.3%. Stochasticity in reproduction caused
rapid declines in the sustainable harvest rate from 8.1% at
tol = 0.05 to 7.2% at tol = 0.1 to zero when tol was 0.4.
The above models used reproduction and survival values
measured in the field and the maximum sustainable harvest
was sensitive to variation in reproduction. When we varied
adult reproduction from 0.6 to 1 young/female/year (yearling reproduction was zero), the maximum sustainable
harvest rate varied from 5% to 12%.
Mowat et al. • Wolverine Trapping

Potential reproduction in wolverines is much higher than
what has been observed in the field postpartum because
many more females, including some yearlings, are pregnant
than give birth (Banci 1994, Copeland et al. 2017). When
we used in utero pregnancy rates in our model, the maximum harvest rate increased to 18–23%; hence, the potential
bias caused by using in utero reproduction rates is large. We
conclude the maximum sustainable harvest rate for wolverines is about 8%, which incorporates the influences of
higher trap vulnerability of juveniles and males and realistic
stochasticity in juvenile recruitment rates as measured in live
wolverine populations.

DISCUSSION
Currently, wolverine trapping is likely not sustainable in
southeast British Columbia and southwest Alberta, and our
estimated level of mortality presents considerable risk to this
population. Although the observed harvest rate (8.4%/year)
roughly equaled the theoretical maximum harvest rate (8%)
we calculated in our population modeling exercise, the study
area includes many parks and protected areas so the harvest
rate in the portion of the study where trapping was allowed
exceeded sustainable levels. The uncertainty in the recording
of the wolverine harvest largely led to under‐reporting and
this uncertainty is also a risk to this population (Fig. 5).
Several other North American researchers have concluded
that wolverine harvest in their study populations was not
sustainable or was being sustained by immigration (Krebs
et al. 2004, Squires et al. 2007, Dalerum et al. 2008).
9

Figure 4. Wolverine density (wolverines/1,000 km2) in southeast British Columbia and southwest Alberta, Canada estimated from spatial capture‐recapture
analysis of genetically identified wolverines sampled during winter 2011–2016. There was no trapping in national parks, but wolverine trapping was
permitted in some provincial parks.

What is the sustainable harvest rate for wolverines in this
area? Harvest rates can be considered the policy portion of
the harvest regime because the selection of an allowable
harvest rate involves social and biological considerations
(Mowat et al. 2013). The scientific part of the regime is
often described by data. The social component involves the
trade‐off between the value to society and the perceived risk
to the population. Wolverines generate relatively little value
to the trapping industry compared to other furbearers like
marten (Martes americana) or lynx (Lynx canadensis), but
they do generate moderate value to the few trappers that
catch them. About 15 trappers catch wolverines in our study
area each year. Conservation risk from harvest is high because wolverines occur in a discontinuous fashion at low
densities and have few young, and reproduction is affected
by environmental stochasticity (Persson 2005). Because of
10

their low monetary value and the high conservation risk
when this species is trapped, we suggest the target harvest
rate should be conservative and less than half the theoretical
maximum; we suggest a target harvest rate of ≤4% of the
population per year.
Pregnancy rates and litter sizes observed before birth
suggest most females breed each year, which results in a
potential birth rate that is double that observed in the field;
however, all field studies of wolverine reproduction suggest
females do not reproduce every year (Rauset et al. 2015).
The only observation of wild wolverines breeding every year
was during an experimental study for a select few females
that were fed all winter (Persson 2005). Similarly, no field
studies have recorded litter sizes above 3, which is commonly observed in utero (Banci 1994). Many recent field
studies were on populations that were harvested, some
The Journal of Wildlife Management

Figure 5. The harvest rate of wolverines based on population estimates
extrapolated from spatial capture‐recapture analysis of genetically identified
wolverines sampled during winter 2011–2016 in southeast British
Columbia (BC) and southwest Alberta, Canada. Horizontal error bars
are 95% confidence intervals of population estimates, and vertical error bars
are our subjective assessment of the likely error in the recording of
wolverine trapping kill. Pink shading denotes harvest rates above our
recommended level, and red shading denotes harvest rates that are likely
not sustainable.

heavily, and none of these studies suggested a density‐
dependent response in reproduction that approached levels
seen in utero (Banci 1994, Copeland 1996, Krebs et al.
2004). We conclude that potential reproduction in wolverines is much higher than observed in the wild and any
analysis of wolverine population dynamics should use reproductive rates measured in wild populations of wolverines,
not those measured in utero.
In our population modeling exercise, we found that
sub‐adult males and sub‐adult females were much more
vulnerable to trapping (10× and 6×, respectively) than were
adult females. Adult males were 3× more vulnerable to
trapping than were adult females. In a meta‐analysis of
survival of North American wolverines, Krebs et al. (2004)
also reported sub‐adult males were most vulnerable to
trapping. Our population model suggested the maximum
sustainable harvest rate increased by 2% when accounting
for trap vulnerability by sex age class because sub‐adults
were more vulnerable to harvest. Because adult females had
lower mortality per capita, reproduction did not decline with
an increasing harvest rate as rapidly as it would have if all
age cohorts were equally vulnerable to harvest. Greater
vulnerability of young animals to trapping moderates the
conservation risk of trapping.
Why was the spatial trapping mortality variable not negatively related to density if trapping was limiting wolverine
abundance in our study area? This may be because wolverine
mortality can only happen in areas that support wolverines,
and large portions of our study area appear to support few
or no wolverines. This creates a positive relationship between density and trapping kill, at least at low to medium
abundance. At higher abundance this relationship may
switch, resulting in a non‐linear relationship. Also, resident
wolverines that are killed may be quickly replaced by
Mowat et al. • Wolverine Trapping

sub‐adults, given the large effort sub‐adults put into
searching for a territory after dispersing from their natal
range (Inman et al. 2012b). In the Rocky Mountain national
parks, density increased with distance into the park from
the park boundary, suggesting trapping outside the parks
reduced density measurably (M. Barrueto, University of
Calgary, unpublished data).
Population density averaged 2 wolverines/1,000 km2
across the study area. Previous density estimates in the
montane mountains of western North America were mostly
higher than our estimate (range = 1.8–15.4 wolverines/
1,000 km2; Table S3, available online in Supporting Information). This difference could be explained by the
mortality rate observed in our study area, or the large size of
our study area, which included areas of high and low habitat
quality. With smaller study areas, researchers may unconsciously select areas of higher than average density to meet
sampling objectives (Smallwood and Schonewald 1998).
None of the earlier studies, however, corrected for closure
bias, which could lead to large over‐estimates for animals
such as wolverines that have large home ranges and who
disperse during the period of study. Wolverine densities
appear similar in montane and boreal forests, although only
one of the boreal estimates were corrected for closure
(Table S3), so it is possible wolverine density in boreal
environments is actually lower than in montane environments. Higher densities have been recorded in coastal
Alaska and in several places in the arctic; however, again,
the highest observed densities were not corrected for closure
(Table S3). In a comparative study in arctic Canada, uncorrected density was 2.5‐fold greater than the closure‐
corrected density estimate. Efford and Boulanger (2018)
estimated wolverine density using the same data as Mulders
et al. (2007), but they corrected for closure bias explicitly
using spatial capture‐recapture methods. Their estimate
was 6.7 wolverines/1,000 km2 (CI = 5.4–8.3) compared to
17.2 wolverines/1,000 km2 (CI = 16.4–24.3) from the earlier work. It would appear imperative to correct for closure
bias in all wolverine inventories given the possibility for
large biases, especially if the inventory area is relatively
small. In summary, the highest reliable wolverine densities
(~10 wolverines/1,000 km2) were observed in coastal Alaska
and the Yukon north slope, with moderate densities observed in the central arctic when caribou were abundant
(Table S3). Our work suggests densities in montane environments are low compared to densities in environments
farther north.
We suggest that some previous studies that did not use
SECR methods to analyze their sampling data have substantively over‐estimated wolverine abundance. All studies
that presented densities >10 wolverines/1,000 km2 had
study areas <3,000 km2 (Table S3). For example, Lofroth
and Ott (2007) used density estimates from 2 study areas in
British Columbia and extrapolated a population estimate for
all of British Columbia. They predicted the Kootenay
Region to have 324 wolverines, whereas our model predicted a population of 166. If this difference is a measure of
bias in their population estimates, then closure bias could
11

lead to considerable underestimates of the effect of trapping
mortality throughout the wolverine range.
Our data present a positive relationship between wolverine
abundance and spring snow cover. This observation supports the hypothesis of Copeland et al. (2010) that the
distribution of wolverines is related to the probability that
an area has complete snow cover during the late denning
period. The circumpolar distribution of known wolverine
den sites was related to spring snow in a multi‐continent
scale analysis (Copeland et al. 2010). Other studies have
examined the relationship between spring snow and habitat
selection and most find some positive relationship
(Copeland et al. 2010, Heim et al. 2017, Kortello et al.
2019), and these relationships appear to be stronger in more
topographically complex environments (Webb et al. 2016).
Our results demonstrate that spring snow is related to
density in montane environments, which suggests a functional relationship with wolverine ecology. Several reasons
have been posited for this relationship including the need or
preference for snow to cover dens, the need for snow to
preserve cached meat, or a preference for snowier areas. We
cannot unequivocally test among these hypotheses with our
data, but females selected for snow more strongly than
males (Fig. 3B), which supports the denning hypothesis
more than the 2 alternatives. This is a weak test among
these hypotheses because the stronger selection for snow by
females could simply be due to the smaller ranges of female
wolverines, which allows them to locate their ranges in
relatively better habitat conditions. Clarifying the functional
relationship between wolverine ecology and spring snow will
require detailed study of their autecology.
Wolverine density was negatively related to roads (Fig. 3C)
and the functional nature of this relationship is perhaps even
less well understood than spring snow. Other workers have
reported similar relationships for wolverines (Krebs et al. 2007,
Fisher et al. 2013), including an occupancy‐based analysis of
our West Kootenay data (Kortello et al. 2019). The simplest
explanation for this result is the tendency for wolverines to
select high elevation areas, which are mostly found above the
road network (Inman et al. 2012b, Kortello et al. 2019);
however, trappers use roads to access their trapping areas, so
this relationship may be partly explained by the recent or
historical effects of trapping. Then again, only about 70% of
the traplines in the Kootenay are trapped in any year (A. Reid,
personal communication), and few trappers try to catch wolverines, though some wolverines are caught as by‐catch in
traps set for other species. Further, only a small fraction of
roads are travelled by trappers during winter, so it seems likely
that there are other negative effects of roads on wolverine
density. Given other cases of human‐caused mortality are rare,
either food is less abundant near roads, or wolverines are
avoiding roads to the point it influences density.
Helicopter and backcountry skiing were negatively related
to winter habitat selection in the north part of our study area
(Krebs et al. 2007). Female wolverines have abandoned dens
following human disturbance (Pulliainen 1968, Magoun
and Copeland 1998), and they do not place den sites near
human infrastructure (May et al. 2012). These observations
12

suggest disturbance can influence habitat use and perhaps
density. Forestry roads are also travelled by recreationists on
snowmobiles and used during winter logging operations, so
perhaps wolverines avoid these collective uses. The human
influence variable measures human habitation, which is
most correlated with front‐country all surface roads. The
low explanatory power of this variable compared to forestry
roads suggests it is the back‐country forestry roads that
wolverines are most strongly avoiding. We conclude that the
functional significance of the relationship between roads
and wolverine density is unclear and requires further study.
Sub‐adult wolverines begin to disperse in late winter
(Vangen et al. 2001, Gervasi et al. 2015), which presents a
potential positive bias to density estimates because recapture
rates would be negatively biased if juveniles move out of a
study area entirely. This is possible given the large movements that have been documented (Inman et al. 2012b). It is
also possible that spatial models may largely correct for this
bias, and the estimate of the spatial parameter in this study
was higher than expected based on the estimates of home
range size. In addition, our trap spacing (x̄ = 7.75 km) was
lower than 2 × sigma (19.6 km), which is the minimum
spacing suggested in recent study design work by Sun et al.
(2014). Further, clumping of traps, which occurred in several of our sampling areas (Table 1), has been suggested as a
way to better estimate the spacing parameter.

MANAGEMENT IMPLICATIONS
Based on our work, we suggest wolverine trapping mortality
should be reduced to ≤4% in our greater study area, and
perhaps by more than that for an interim period of recovery.
Negative human effects on wolverine density could be mitigated by reducing road density, but the uncertainty of the
mechanism behind this relationship makes it difficult to
identify the best areas to implement closures or traffic restrictions. Many forestry roads in British Columbia have
little traffic in winter, especially at higher elevation. Most
winter traffic is by snowmobiles for recreation and, to a
lesser extent, industry. Given the strong relationship we
observed between wolverine density and spring snow, it may
be best to select areas with both consistent spring snow
cover and roads with substantive winter use when planning
access mitigation for wolverine conservation. Denning females are most vulnerable to disturbance and of greatest
population importance, so further research to identify denning habitat would offer more area‐specific access recommendations and provide the greatest benefit to wolverines.

ACKNOWLEDGMENTS
Our thanks to field technicians B. Phillips, T. Malish,
B. Bertch, R. Bunyan, N. Heim, A. Banting, J. Zettel,
T. Abraham, J. Robbins, C. Lehman, S. Forrest, D. Fear,
S. Himmer, A. Page, A. Bourelle, D. Lynch, C. Hiebert,
D. Backman, M. Beauchemin, S. Doyle, J. Flaa, K. Furke,
T. Hewitt, L. Larson, C. Seiber, R. Schmidt, C. Bird,
K. MacDonald, A. Jones, J. McCullough, and many volunteers. We thank the regional trapping community for
wolverine carcasses, bait, site location advice, and assisting
The Journal of Wildlife Management

in field operations, and K. McGuinness for geographic information system work; MFLNRORD, British Columbia
Provincial Parks, Idaho Fish and Game and Idaho Panhandle National Forest, Teck Coal, MD of Ranchlands,
Alberta Environment and Parks—Fish and Wildlife, and
Wildsight also provided logistical support and assistance.
J. Jorgenson, G. Hale, and M. Didkowski helped with logistics in Alberta, and D. Gorrie, B. Hunt, G. Kubian,
T. Kinley, A. Dibb, and B. Fyten helped in the national
parks. We thank J. Parker and D. Hawrys for helicopter
transportation and field assistance. B. Hanlon helped with
British Columbia funding. Thanks also to staff at WGI and
RMRS for genetic analysis. Our thanks to CanAus Coal
Ltd., Jameson Resources Ltd., and Parks Canada for
sharing their wolverine sampling data. We thank J. Krebs,
E. Lofroth, R. A. Long, and 2 anonymous reviewers for
reviewing the manuscript. Funding was provided by the
Columbia Basin Trust, the Fish and Wildlife Compensation Program, the British Columbia MFLNRORD, Parks
Canada, Vanier Canada Graduate Scholarship, the Western
Transportation Institute–Montana State University,
Woodcock Foundation, Wilburforce Foundation, Liz
Claiborne Art Ortenberg Foundation, Volgenau Foundation, Cross Foundation, United States Fish and Wildlife
Service Great Northern Landscape Conservation Cooperative, Alberta Conservation Association, Mountain Equipment Co‐op, McLean Foundation, Patagonia, Cameron
Plewes, Alberta Sport Parks Recreation and Wildlife
Foundation, National Geographic Society, Disney Wildlife
Conservation Fund, Norcross Foundation, Bow Valley
Naturalists, Yellowstone to Yukon Conservation Initiative,
Lake O’Hara Lodge, Alpine Club of Canada, and The
Wolverine Foundation.

LITERATURE CITED
Aubry, K. B., K. S. McKelvey, and J. P. Copeland. 2007. Distribution and
broadscale habitat relations of the wolverine in the contiguous United
States. Journal of Wildlife Management 71:2147–2158.
Awan, M., and G. Szor. 2012. Wolverine (Gulo gulo) carcass collection and
harvest monitoring in Nunavut. Summary report, Department of Environment, Government of Nunavut, Iqaluit, Canada.
Banci, V. 1994. Wolverine. Pages 99–127 in L. F. Ruggiero, K. B. Aubry,
S. W. Buskirk, L. J. Lyon, and W. J. Zielinski, editors. The scientific basis
for conserving forest carnivores: American marten, fisher, lynx, and wolverine in the western United States. General Technical Report RM‐254.
U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest
and Range Experiment Station, Fort Collins, Colorado, USA.
Banci, V., and A. Harestad. 1988. Reproduction and natality of wolverine
(Gulo gulo) in Yukon. Annales Zoologici Fennici 25:265–270.
Broseth, H., O. Flagstad, C. Wardig, M. Johansson, and H. Ellegren.
2010. Large‐scale noninvasive genetic monitoring of wolverines using
scats reveals density dependent adult survival. Biological Conservation
143:113–120.
Canadian Wildlife Service. 2014. COSEWIC assessment and status report
on the Wolverine (Gulo gulo) in Canada. <http://publications.gc.ca/
collections/collection_2014/ec/CW69‐14‐329‐2014‐eng.pdf>. Accessed
29 Aug 2018.
Copeland, J. P. 1996. Biology of the wolverine in central Idaho. Thesis,
University of Idaho, Moscow, USA.
Copeland, J. P., A. Landa, K. Heinemeyer, K. B. Aubry, J. van Dijk,
R. May, J. Persson, J. R. Squires, and R. E. Yates. 2017. Social ethology
of the wolverine. Pages 389–398 in D. W. Macdonald, C. Newman, and
L. A. Harrington, editors. Biology and conservation of musteloids.
Oxford University Press, Oxford, United Kingdom.
Mowat et al. • Wolverine Trapping

Copeland, J. P., K. S. McKelvey, K. B. Aubry, A. Landa, J. Persson, R. M.
Inman, J. Krebs, E. Lofroth, H. Golden, J. R. Squires, et al. 2010. The
bioclimatic envelope of the wolverine (Gulo gulo): do climatic constraints
limit its geographic distribution? Canadian Journal of Zoology
88:233–246.
Dalerum, F., B. Shults, and K. Kunkel. 2008. Estimating sustainable
harvest in wolverine populations using logistic regression. Journal of
Wildlife Management 72:1125–1132.
van Dijk, J., L. Gustavsen, A. Mysterud, R. May, Ø. Flagstad, H. Brøseth,
R. Andersen, R. Andersen, H. Steen, and A. Landa. 2008. Diet shift of a
facultative scavenger, the wolverine, following recolonization of wolves.
Journal of Animal Ecology 77:1183–1190.
Efford, M. 2004. Density estimation in live‐trapping studies. Oikos
106:598–610.
Efford, M. 2018. secr 3.1‐spatially explicit capture–recapture in R.
R‐CRAN. <ftp://ess.r‐project.org/R‐CRAN/web/packages/secr/vignettes/
secr‐overview.pdf>. Accessed 29 Aug 2018.
Efford, M., and J. B. Boulanger. 2018. Analyses of wolverine DNA mark‐
recapture sampling in the Northwest Territories 2004–2015. Unpublished report for Government of NWT. Integrated Ecological Research, Nelson, Canada.
Efford, M. G., and G. Mowat. 2014. Compensatory heterogeneity in
spatially explicit capture‐recapture data. Ecology 95:1341–1348.
Fisher, J. T., S. Bradbury, B. Anholt, L. Nolan, L. Roy, J. P. Volpe, and
M. Wheatley. 2013. Wolverines (Gulo gulo luscus) on the Rocky
Mountain slopes: natural heterogeneity and landscape alteration as predictors of distribution. Canadian Journal of Zoology 91:706–716.
Gervasi, V., H. Brøseth, E. B. Nilsen, H. Ellegren, Ø. Flagstad, and
J. D. C. Linnell. 2015. Compensatory immigration counteracts contrasting conservation strategies of wolverines (Gulo gulo) within Scandinavia. Biological Conservation 191:632–639.
Hatler, D. F., and A. M. M. Beal. 2003. Wolverine. Furbearer management guidelines, Government of British Columbia. <www.env.gov.bc.
ca/fw/wildlife/trapping/docs/wolverine.pdf>. Accessed 29 Aug 2018.
Heim, N., J. T. Fisher, A. Clevenger, J. Paczkowski, and J. Volpe. 2017.
Cumulative effects of climate and landscape change drive spatial distribution of Rocky Mountain wolverine (Gulo gulo L.). Ecology and
Evolution 7:8903–8914.
Hornocker, M. G., and H. S. Hash. 1981. Ecology of the wolverine in
northwestern Montana. Canadian Journal of Zoology 59:1286–1301.
Idaho Department of Fish and Game. 2014. Management Plan for the
Conservation of Wolverines in Idaho 2014–2019. Idaho Department of
Fish and Game, Boise, USA.
Inman, R. M., A. J. Magoun, J. Persson, and J. Mattisson. 2012a. The
wolverine’s niche: linking reproductive chronology, caching, competition,
and climate. Journal of Mammalogy 93:634–644.
Inman, R. M., M. L. Packila, K. H. Inman, A. J. McCue, G. C. White,
J. Persson, B. C. Aber, M. L. Orme, K. L. Alt, S. L. Cain, et al. 2012b.
Spatial ecology of wolverines at the southern periphery of distribution.
Journal of Wildlife Management 76:778–792.
Johnson, W. E., and S. J. O’Brien. 1997. Phylogenetic reconstruction of
the Felidae using 16S rRNA and NADH‐5 mitochondrial genes. Journal
of Molecular Evolution 44:S98–S116.
Kendall, K. C., J. B. Stetz, J. Boulanger, A. C. Macleod, D. Paetkau, and
G. C. White. 2009. Demography and genetic structure of a recovering
grizzly bear population. Journal of Wildlife Management 73:3–16.
Kortello, A., D. Hausleitner, and G. Mowat. 2019. Mechanisms influencing the winter distribution of wolverine Gulo gulo luscus in the
southern Columbia Mountains, Canada. Wildlife Biology 2019:
1–13.
Krebs, J., E. Lofroth, J. Copeland, V. Banci, D. Cooley, H. Golden, A.
Magoun, R. Mulders, and B. Shults. 2004. Synthesis of survival rates and
causes of mortality in North American wolverines. Journal of Wildlife
Management 68:493–502.
Krebs, J., E. C. Lofroth, and I. Parfitt. 2007. Multiscale habitat use by
wolverines in British Columbia, Canada. Journal of Wildlife Management 71:2180–2192.
Kukka, P. M., T. S. Jung, J.‐F. Robitaille, and F. K. A. Schmiegelow.
2017. Temporal variation in the population characteristics of harvested
wolverine (Gulo gulo) in northwestern Canada. Wildlife Research
44:497–503.
Lee, J. 2016. Database description, data summary and analysis of the
wolverine harvest from Kugluktuk, Umingmaktok and Bathurst Inlet,
13

Northwest Territories. Environment and Natural Resources, Government of the Northwest Territories, Yellowknife, Canada.
Lofroth, E. C., and J. Krebs. 2007. The abundance and distribution of
wolverines in British Columbia, Canada. Journal of Wildlife Management 71:2159–2169.
Lofroth, E. C., J. A. Krebs, W. L. Harrower, and D. Lewis. 2007. Food
habits of wolverine Gulo gulo in montane ecosystems of British
Columbia, Canada. Wildlife Biology 13(suppl):31–37.
Lofroth, E. C., and P. K. Ott. 2007. Assessment of the sustainability of
wolverine harvest in British Columbia, Canada. Journal of Wildlife
Management 71:2193–2200.
MacKillop, D. J., and A. J. Ehman. 2016. A field guide to site classification
and identification for southeast British Columbia: the south‐central
Columbia Mountains. Province of British Columbia, Victoria, Canada.
Magoun, A. 1985. Population characteristics, ecology, and management of
wolverines in northwestern Alaska. Dissertation, University of Alaska,
Fairbanks, USA.
Magoun, A. J., and J. P. Copeland. 1998. Characteristics of wolverine
reproductive den sites. Journal of Wildlife Management 62:1313–1320.
Magoun, A. J., M. D. Robards, M. L. Packila, and T. W. Glass. 2017.
Detecting snow at the den‐site scale in wolverine denning habitat.
Wildlife Society Bulletin 41:381–387.
Mattisson, J., G. R. Rauset, J. Odden, H. Andren, J. D. C. Linnell, and J.
Persson. 2016. Predation or scavenging? Prey body condition influences
decision‐making in a facultative predator, the wolverine. Ecosphere 7:
e01407.
May, R., L. Gorini, J. van Dijk, H. Brøseth, J. D. C. Linnell, and A.
Landa. 2012. Habitat characteristics associated with wolverine den sites
in Norwegian multiple‐use landscapes. Journal of Zoology 287:195–204.
Mowat, G., D. C. Heard, and C. J. Schwarz. 2013. Predicting grizzly bear
density in western North America. PLoS ONE 8:e82757.
Mulders, R. L. 2000. Wolverine ecology, distribution, and productivity in
the Slave Geological Province. Final Report. West Kitikmeot/Slave
Study Society, Yellowknife, Canada.
Mulders, R., J. Boulanger, and D. Paetkau. 2007. Estimation of population size for wolverines Gulo gulo at Daring Lake, Northwest Territories, using DNA based mark‐recapture methods. Wildlife Biology
13:38–51.
Paetkau, D. 2003. An empirical exploration of data quality in DNA‐based
population inventories. Molecular Ecology 12:1375–1387.
Persson, J. 2005. Female wolverine (Gulo gulo) reproduction: reproductive
costs and winter food availability. Canadian Journal of Zoology
83:1453–1459.
Persson, J., G. Ericsson, and P. Segerstrom. 2009. Human caused mortality in the endangered Scandinavian wolverine population. Biological
Conservation 142:325–331.
Persson, J., A. Landa, R. Andersen, and P. Segerstrom. 2006. Reproductive characteristics of female wolverines (Gulo gulo) in Scandinavia. Journal of Mammalogy 87:75–79.
Poole, K. G. 2006. A population review of mountain goats in the Kootenay
Region. British Columbia Ministry of Environment, Nelson, Canada.
Pulliainen, E. 1968. Breeding biology of the wolverine (Gulo gulo L.) in
Finland. Annales Zoologici Fennici 5:338–344.
R Core Team. 2016. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

14

Rauset, G. R., M. Low, and J. Persson. 2015. Reproductive patterns result
from age‐related sensitivity to resources and reproductive costs in a
mammalian carnivore. Ecology 96:3153–3164.
Royle, J. A., R. B. Chandler, R. Sollmann, and B. Gardner. 2013. Spatial
capture‐recapture. Academic Press, Waltham, Massachusetts, USA.
Royle, J. A., A. J. Magoun, B. Gardner, P. Valkenburg, and R. E. Lowell.
2011. Density estimation in a wolverine population using spatial capture‐
recapture models. Journal of Wildlife Management 75:604–611.
Ruggiero, L. F., K. S. McKelvey, K. B. Aubry, J. P. Copeland, D. H.
Pletscher, and I. G. Hornocker. 2007. Wolverine conservation and
management. Journal of Wildlife Management 71:2145–2146.
Sæther, B.‐E., S. Engen, J. Persson, H. Broseth, A. Landa, and T.
Willebrand. 2005. Management strategies for the wolverine in Scandinavia. Journal of Wildlife Management 69:1001–1014.
Sawaya, M., A. P. Clevenger, and M. K. Schwartz. 2019. Demographic
fragmentation of a protected wolverine population bisected by a major
transportation corridor. Biological Conservation 236:616–625.
Schwartz, M. K., J. P. Copeland, N. J. Anderson, J. R. Squires, R. M.
Inman, K. S. McKelvey, K. L. Pilgrim, L. P. Waits, and S. A. Cushman.
2009. Wolverine gene flow across a narrow climatic niche. Ecology
90:3222–3232.
Slough, B. G., R. H. Jessup, D. I. McKay, and A. B. Stephenson. 1987.
Wild furbearer management in western and northern Canada. Pages
1062–1976 in M. Novak, J. A. Baker, M. E. Obbard, and B. Malloch,
editors. Wild furbearer management and conservation in North America.
Ontario Trappers Association, North Bay, Canada.
Smallwood, K. S., and C. Schonewald. 1998. Study design and interpretation of mammalian carnivore density estimates. Oecologia
113:474–491.
Squires, J. R., J. Copeland, T. J. Ulizio, I. K. Schnvartz, and L. F.
Ruggiero. 2007. Sources and patterns of wolverine mortality in western
Montana. Journal of Wildlife Management 71:2213–2220.
Sun, C. C., A. K. Fuller, and J. A. Royle. 2014. Trap configuration and
spacing influences parameter estimates in spatial capture‐recapture
models. PloS One 9:e88025.
Vangen, K. M., J. Persson, A. Landa, R. Andersen, and P. Segerström.
2001. Characteristics of dispersal in wolverines. Canadian Journal of
Zoology 79:1641–1649.
Webb, S. M., R. B. Anderson, D. L. Manzer, B. Abercrombie, B. Bildson,
M. A. Scrafford, and M. S. Boyce. 2016. Distribution of female wolverines relative to snow cover, Alberta, Canada. Journal of Wildlife
Management 80:1461–1470.
Webb, S. M., D. Manzer, R. Anderson, M. Jokinen, and S. Park. 2013.
Wolverine harvest summary from registered traplines in Alberta, 1985–
2011. Technical Report. Alberta Conservation Association, Sherwood
Park, Canada.
Associate Editor: Ryan Long.

SUPPORTING INFORMATION
Additional supporting information may be found online in
the Supporting Information section at the end of the article.

The Journal of Wildlife Management