Behav Ecol Sociobiol (2016) 70:1745–1754
DOI 10.1007/s00265-016-2180-5
ORIGINAL ARTICLE
Drivers of hibernation: linking food and weather to denning
behaviour of grizzly bears
Karine E. Pigeon 1,2 & Gordon Stenhouse 2 & Steeve D. Côté 1
Received: 20 April 2016 / Revised: 28 June 2016 / Accepted: 29 June 2016 / Published online: 6 July 2016
# Springer-Verlag Berlin Heidelberg 2016
Abstract
Climate-induced changes in the phenology of hibernation for
bear species could result in altered energy budgets, reduced
cub survival and fitness and increased human-bear conflicts.
Using 11 years of data, we determined the amount of variation
in den entry and den exit dates that could be attributed to sex
and reproductive status, weather and berry availability for 15
male and 58 female grizzly bears (Ursus arctos). We estimated
berry availability during autumn using a probability surface of
berry productivity within the home range of 13 individuals
over 3 years. Sex and reproductive status explained 22 and
14 % of the variation in den entry and den exit dates, respectively. Weather did not influence the timing of den entry but
berry availability in autumn explained 39 % of the variation
observed in den entry, and high berry availability was associated with late den entry. Elevation and spring temperatures,
and elevation and winter precipitation, respectively, explained
26 and 21 % of the variation observed in den exit dates.
Increasing spring average monthly maximum temperature by
4 °C resulted in bears emerging from dens 10 days earlier and
an increase of 1.25 m in snow precipitation delayed den exit
by 1 week. We demonstrate that although the phenology of
hibernation for grizzly bears depends on sex and reproductive
status, den entry appears to be driven by food availability,
while den exit is more linked to weather. Extended growing
seasons and mild meteorological conditions should result in
shorter denning periods for grizzly bears.
Significance statement
Climate change is altering the phenology of spring green-up
and the onset of winter, disrupting the seasonal behaviours of
species. Climate change can act as an additional strain on
threatened populations, especially during energetically demanding periods such as hibernation. We quantified the importance of intrinsic and extrinsic factors including food availability and weather in the hibernation behaviour of grizzly
bears. High berry availability was associated with late den
entry, while low winter precipitation and high spring temperature resulted in early den exit. We conclude that den entry is
more driven by food availability while den exit is more linked
to weather. This dichotomy in factors affecting den entry and
den exit has implications for the long-term conservation of
grizzly bear populations because extended growing seasons
and mild meteorological conditions expected under future climate conditions should result in shorter denning periods.
Communicated by K. E. Ruckstuhl
Electronic supplementary material The online version of this article
(doi:10.1007/s00265-016-2180-5) contains supplementary material,
which is available to authorized users.
* Karine E. Pigeon
[email protected]
1
Département de biologie and Centre d’études nordiques, Université
Laval, 1045 Av. de la Médecine, Québec, QC G1V 0A6, Canada
2
fRI Research Grizzly Bear Program (FRIGBP), 1176 Switzer Drive,
Hinton, AB T7V 1V3, Canada
Keywords Behavioural plasticity . Phenology . Food
availability . Den . Brown bear . Ursus arctos
Introduction
Climate change is affecting biological systems in part by altering the phenology of seasonal processes (Root et al. 2003;
Parmesan 2006). Wildlife responses to climate change extend
from shifts in temporal activity and geographic range to reductions in body condition, survival and recruitment (Walther
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et al. 2002; Post and Forchhammer 2008; Molnár et al. 2010).
Wildlife populations that are already affected by anthropogenic factors such as habitat fragmentation and over-exploitation
could incur additional costs from climate change (McCarty
2001; Mantyka-Pringle et al. 2012), and differences in behavioural plasticity, the ability to respond to environmental
changes, will dictate how well individuals can adjust or resist
to changes occurring in their environment (Williams et al.
2008).
By suppressing their metabolic activity, hibernators can
reduce their energy expenditure to less than 15 % of what
would be expended by remaining normothermic during the
same period (Geiser 2004). Food availability (Humphries
et al. 2003a), circannual rhythm and photoperiod (Williams
et al. 2014), sex and reproductive status (Haroldson et al.
2002), body condition (Michener 1978) and stored energy
resources (Vuarin et al. 2013) have been linked to the duration
of hibernation in a wide range of mammals. Recently, increases in spring ambient temperatures have been linked to
reduced length of hibernation (Inouye et al. 2000; Adamik
and Kral 2008), and changes in the timing and frequency of
snowstorms and snow depth have been linked to disrupted
hibernation and reduced fitness (Lane et al. 2012).
Impacts of climate change are more pronounced during
energetically demanding periods (Visser and Both 2005).
Parturient female bears (Ursus spp.) undergo lactation while
in dens and typically remain in dens the longest, presumably
to promote the safety and development of their cubs (Friebe
et al. 2001; Haroldson et al. 2002). Bear cubs are particularly
vulnerable at birth because they are born with virtually no hair
and are unable to walk, and their eyes do not open for more
than 1.5 month after birth (Robbins et al. 2012). Grizzly bears
(Ursus arctos horribilis), a sub-species of brown bears (Ursus
arctos), are generalists and highly adaptable, making them
resilient to perturbations occurring in their environment
(Williams et al. 2008). However, due to small population size
(Alberta Sustainable Resource Development and Alberta
Conservation Association 2010), low reproductive rates
(Garshelis et al. 2005), excessive human-caused mortality
(Nielsen et al. 2009) and habitat loss (Nielsen et al. 2006),
grizzly bears in Alberta, Canada, are threatened and additional
perturbations generated by climate change could reduce their
resiliency.
Our objective was to identify the links between environmental conditions and the hibernation behaviour of grizzly
bears as a necessary first step to identify the potential consequences of climate change on this behaviour. We hypothesized that (1) sex, reproductive status and stored energy resources, or (2) a combination of these intrinsic factors and
environmental covariates associated with food availability
and weather would explain the timing of den entry and den
exit. We predicted that (1a) gestating females would hibernate
the longest and that (1b) den entry date would explain den exit
Behav Ecol Sociobiol (2016) 70:1745–1754
date because of the limited availability of stored energy. We
also expected that (2a) low ambient temperatures and high
amounts of snowfall would be associated with early den entry
and late den exit and that bears denning at high elevation
would hibernate longer than bears denning at low elevations.
Because site-specific variations in environmental conditions
might differ considerably amongst den locations, we predicted
that in autumn (2b) low berry availability within home ranges
should trigger early den entry and that in the spring (2c) warm
ambient temperature and intense solar radiation at dens should
trigger early den exit.
Methods
Study area
The study area includes two distinct portions of protected and
unprotected mountainous terrain and rolling foothills extensively
altered by forestry, coal mining, and oil and gas exploration. The
southern portion of the study area is predominantly mountains
and extends east from Jasper National Park to the towns of
Cadomin and Hinton, and includes Whitehorse Wildland
Provincial Park (52° 48′ N/117° 7′ W). The northern portion of
the study area is predominantly foothills and extends east from
the British Columbia-Alberta border of the Kakwa Wildland
Provincial Park towards Highway 40 (54° 30′ N/119° 6′ W).
Altitude varies from 543 to 3741 m a.s.l. and natural subregions
range from alpine, subalpine and upper and lower foothills.
Dominant tree species in the mountains are subalpine fir (Abies
lasiocarpa), white spruce (Picea glauca) and Englemann spruce
(Picea englemanni). In the foothills, lodgepole pine (Pinus
contorta), black spruce (Picea mariana) and white spruce dominate, while balsam fir (Abies balsamea), balsam poplar
(Populus balsamifera) and trembling aspen (Populus
tremuloides) occur in lower abundances. In the southern area,
summer and winter temperature averages were 14 and −6 °C,
respectively, while in the northern area, averages were 10 °C for
summer and −9 °C for winter. Annual precipitation averaged
309 mm for the south and 487 mm for the north. Climate data
is available online (Jasper Warden Station 52° 55′, −118° 1′,
http://climate.weatheroffice.gc.ca/climateData/canada_e.html)
and on demand (Alberta SRD Kakwa provincial automatic
station 54° 10′, −119° 3′, http://www.srd.alberta.
ca/UpdatesFireAlerts/FireWeather/WeatherStations/Default.
aspx).
Den entry and exit dates
We investigated dates of den entry and den exit for 15 male
and 58 female grizzly bears >4 years of age between 1999 and
2011 in the boreal forest and Rocky Mountains of west-central
Alberta, Canada. Bears were captured using aerial darting, leg
Behav Ecol Sociobiol (2016) 70:1745–1754
snares and culvert traps (Cattet et al. 2008). Captured individuals were fitted with Advance Telemetry Systems (ATS,
Isanti, MN, 1999–2002) or Televilt Global Positioning
System collars (Lindesberg, Sweden, 2000–2011). To determine den entry and exit dates, we first investigated the rate of
successful GPS locations of collars placed inside dens
(n = 15). All of these dens were excavated and all of the 44
dens visited by Pigeon et al. (2014) in the same area were also
excavated. Three collars (Televilt) were programmed to acquire GPS locations every 10 min during the testing period
and were rotated between the 15 dens. Collars were placed
inside dens for a minimum of 3 h and a maximum of 144 h
between June and September of 2011. During the testing period, none of the 3 collars in any of the 15 dens were able to
acquire sufficient satellite reception to obtain a GPS location
(1135 attempts). We therefore concluded that a bear wearing a
collar had to be outside of the den or at least have its head out
of the den for the collar to transmit. Based on these findings,
we established the date of den entry as the last day of GPS
locations acquired at the programmed interval, and the date of
den exit as the first day of a regular return to GPS locations. It
was not possible to record blind data because our study involved focal animals in the field.
Intrinsic factors and environmental covariates
We determined the reproductive status of females via observations made during telemetry flights or capture events.
Females with no cub observed during at least 2 repeated observations were labelled as lone females (they may still have
been pregnant during the autumn before denning), females
observed with at least 1 cub of the year following den exit
were classified as pregnant while entering dens and females
observed with at least 1 yearling after den exit were classified
as females with cub of the year (COY) at the time of den entry.
The same procedure was used for females with older cubs (1YR and 2-YR). To simplify analyses, we generated a variable
combining sex and the reproductive status of females (RS).
We were unable to determine the reproductive status of 18
females out of 73 and used multiple imputation techniques
to correct for missing values because biases associated with
missing data can lead to erroneous conclusions (Nakagawa
and Freckleton 2011, for details, see Methods: Statistical
analyses). Although measures of body condition would have
been preferable, we used individual den entry dates as a proxy
of available stored energy at den exit to assess the potential
influence of stored resources on the timing of den exit because
individuals have finite stored energy resources (Brigham
1987; Humphries et al. 2003b).
We quantified the variation in den entry and den exit dates
that could be explained by simple environmental variables:
the average maximum daily temperature in March (Ts: spring
temperature), the average maximum daily temperature in
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September (Ta: autumn temperature), the total amount of precipitation in September (Pa: autumn precipitation), the total
amount of precipitation from December to February inclusively (logPw: winter precipitation) and elevation (m). For the
southern portion of the study area, we used data recorded at
the Jasper Warden Environment Canada weather station located 6 km northeast of the town of Jasper, Alberta (52° 55′,
−118° 1′) at an elevation of 1020 m. For the northern area,
we used temperature data recorded at the Kakwa provincial
automatic station (54° 10′, −119° 3′) at an elevation of
1344 m. Precipitation data were incomplete at this station;
we therefore obtained precipitation data from the nearest
weather station, in Grande-Prairie, at a 115-km straight-line
distance and an elevation of 669 m (Environment Canada
station 55° 10′, −118° 53′). Pearson correlation for the available daily precipitation data between stations was 0.5
(n = 767).
We obtained snow cover information from 500 m × 500 m
tiles of presence-absence of snow cover data for consecutive
8-day periods from the National Snow and Ice Data Centre
(NSIDC) and installed portable weather stations (HOBO micro station data loggers, Bourne, MA, USA) within <1 km of
13 dens (distance range 237–639 m). Portable weather stations
recorded temperature, relative humidity and solar radiation
and were installed between 2007 and 2011. Linnell et al.
(2000) found that bears were potentially sensitive to minor
disturbances within 200 m of dens. To prevent disturbances,
we therefore chose sites that were as representative as possible
but at least >200 m of dens (Online resource 1). Weather
stations were installed in December or January after the den
location could be determined. Although collars were unable to
acquire sufficient satellite reception to obtain GPS locations
while in dens, bears remained in close proximity to dens before den entry. The average number of days spent within
100 m of dens before den entry was 9.2 ± 1.1 (standard error,
SE) days (n = 66). We used the cluster of GPS locations before
den entry to determine the location of the den and validated
den locations in the spring once bears had emerged. Based on
44 dens, Pigeon et al. (2014) found that the average den location determined from collars was within 10 ± 3 (SE) m of the
actual den location.
Berry availability
We established and monitored 510 quadrats (1 m2) of 5 berryproducing shrub species at 56 sites in the boreal forest between 2008 and 2010. We counted the number of berries produced per year in each of these quadrats for Vaccinium
membranaceum (n quadrats = 118), Vaccinium myrtilloides
(n quadrats = 101), Vmyrtilloides vitis-idaea (n quadrats = 157), Vmyrtilloides caespitosum (n quadrats = 93) and
Shepherdia canadensis (n quadrats = 41). These species are
known to be favoured by grizzly bears in the study area
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(Nielsen et al. 2004; Munro et al. 2006). We established quadrats within conifer stands dominated by P. contorta and Picea
spp., and within regenerating stands 10 to 20 years of age
because these landcovers are dominant in the study area and
are favourable to berry-producing shrub species (Noyce and
Coy 1990; Nielsen et al. 2004). We chose quadrat locations
based on the presence of at least 5 % cover of berry-producing
shrubs. To obtain a measure of the abundance of each berryproducing shrub species on the landscape, we also conducted
presence-absence surveys at 1350 random locations at 27 sites
(1 m2 quadrats). Fifty locations were visited within a 5-ha plot
at each site. Twenty of these 5-ha plots were located in conifer
stands (10 pine-dominant stands, and 10 spruce-dominant
stands) and 7 were located in 10- to 20 year-old regenerating
stands. We used a random number generator in Hawth’s tools
in ArcGIS 9.3 to select the location of the 5-ha plots and the 50
locations within each 5-ha plot (Beyer 2004; ESRI 2008).
Statistical analyses—regional scale
Missing data on the reproductive status of species such as
grizzly bears is common because young cubs are easily
missed during periodic assessments. Missing values such as
these can introduce bias because females with older cubs are
more likely to be assigned a reproductive status than females
with cubs of the year or females without cubs. To prevent
biases, we used multiple imputation (MI) procedures to generate 40 datasets based on the fraction of missing information
in our data (Graham et al. 2007). We used PROC MI in SAS
9.3 and chose a method based on Markov Chain Monte Carlo
(MCMC) that is robust even with non-normal distributions of
variables (Nakagawa and Freckleton 2011; SAS Institute Inc.
2011). We built candidate models based on previous knowledge of grizzly bear hibernation to investigate the relative
influence of intrinsic factors, elevation, and regional temperature and precipitation on the timing of den entry and den exit
dates (Craighead and Craighead 1972; Servheen and Klaver
1983). We specified age and individuals as random factors and
used the Kenward-Roger correction for mixed models to calculate appropriate degrees of freedom (Schaalje et al. 2002;
Zuur et al. 2009; PROC MIXED SAS 9.3; SAS Institute Inc.
2011). We used an information-theoretic model selection approach with multiple working hypotheses based on the Akaike
information criterion for small sample sizes (AICC, Burnham
and Anderson 2002). We ranked and compared model performance using delta AICC (ΔAICC). Without MI, fitting models
including the random effects for age and individual bears
(k = 2), reproductive status (k = 4) and weather-related covariates lead to over-parameterized models. In addition, because
a priori data exploration suggested that interactions amongst
covariates were not relevant, we only included simple covariates in candidate models. We a priori assessed hypothesized
non-linear forms of covariates by fitting generalized additive
Behav Ecol Sociobiol (2016) 70:1745–1754
models (GAM, Hosmer and Lemeshow 2000). Variance inflation factors (VIF) amongst all covariates were <3 (Zuur et al.
2010). We combined results from the 40 datasets using PROC
MI ANALYZE in SAS 9.3 to obtain parameter estimates taking into account the variability included in the 40 datasets
generated via multiple imputation and the uncertainty associated with model selection (SAS Institute Inc. 2011; Nakagawa
and Freckleton 2011). The residuals of the most complete
models met the criteria of homogeneity and normality (Zuur
et al. 2010). To obtain information on the overall variance
explained by each model across the 40 datasets, we calculated
the average conditional R2 (R2c); to compare amongst models
including different fixed effects, we calculated the average
marginal R2 (R2m, Nakagawa and Schielzeth 2013). We also
calculated conditional standard errors based on the 40 datasets
(Burnham and Anderson 2002). We assessed the relevance of
the multiple imputation procedure by comparing model selection from the 40 datasets to univariate and bivariate models
using the complete case datasets (deleted missing information,
Online resource 2). All reported error terms refer to standard
errors (est ± SE).
Statistical analyses—local scale
We used zero-inflated negative binomial mixed models to
estimate the production of berries as a function of shrub size
(% cover) and GIS-derived landscape and topographic covariates for the 5 berry-producing shrub species mentioned
above. Methods and model covariates are described in
Online resource 3. From the 1350 random locations, we defined presence-absence models for the berry-producing shrub
species using generalized linear mixed models including a
random intercept for each 5-ha site (methods are described
in Online resource 3). We multiplied the probability surface
of presence-absence of berry-producing shrub species with
their respective estimated berry productivity surface to obtain
a probability surface of the number of berries produced within
13 autumn home ranges of bears using Hawth’s tools and
ArcGIS 9.3 (Beyer 2004; ESRI 2008). We generated 95 %
kernel autumn home ranges (16 August to onset of denning,
Pigeon et al. 2014) using the program ABODE and determined smoothing factors with least-squares cross-validation
(Laver 2005). The number of berries available within each
home range was used as a covariate in linear regression
models built to quantify the variation in den entry dates explained by berry availability in autumn.
Based on the effect size of the best-selected model for den
entry at the regional scale, we built a set of 3 local-scale univariate models including a modified sex and reproductive status variable (see below RS1), home range size (HR) and berry
availability as a surrogate for autumn food availability
(Berry). We used home range size as a covariate to separate
the effect of home range size on den entry from the effect of
Behav Ecol Sociobiol (2016) 70:1745–1754
berry availability on den entry. We modified the sex and reproductive status variable by collapsing the 1-YR and 2-YR
females into a single category of females with at least one cub
≥1 year old (CUBS) because of low sample size (n = 13) but
kept females with at least one cub of the year as a separate
category (COY).
Using data obtained from the 13 portable weather stations
installed near dens, we generated monthly average and local
maximum temperatures, solar radiation, relative humidity and
dew points for the months of February and March. We summarized weather variables in a principal component analysis
(PCA) to further explain variations in den exit date using an
index of overall local weather conditions (Online resource 1).
February and March weather was well summarized by temperature, solar radiation, relative humidity and dew points,
and most of the variability (82 %) was captured by the first
2 principal components (Online resource 1). The first 2 principal components of the PCA were included as covariates in
linear regression models to further quantify variations in den
exit dates (Zuur et al. 2009; Nakagawa and Schielzeth 2013).
We were unable to include the presence-absence of snow cover data obtained from NSIDC in the den exit models because
12 out of the 13 dens were still covered in snow at the end of
March.
Based on the effect size of the best-selected models for den
exit date at the regional scale, we used an informationtheoretic approach to build a set of 5 local-scale models including the modified sex and reproductive status variable used
in the local-scale den entry models described above (RS1),
principal components 1 and 2 (PC1 and PC2), the local average maximum temperature in March (TmaxL) and the local
average maximum solar radiation in March (SmaxL). To avoid
collinearity and overparametrization, we restricted models to
univariate models.
Results
We found no difference in the average date of den entry and
den exit across all datasets amongst lone females, females
with older cubs (1-YR and 2-YR) and males (Online
resource 4). Gestating females entered dens on average
2 weeks earlier than males, females with yearlings and lone
females (est ± SE, males: β = 18 ± 5, 1-YR: β = 17 ± 5, lone:
β = 15 ± 5), and 1.5 weeks earlier than females with 2-year
olds (β = 11 ± 5 SE). Females with cubs of the year also
emerged from dens 2 weeks later than other individuals
(β = −13 ± 6 SE). Den entry and exit dates are described in
Graham and Stenhouse (2014).
Sample sizes varied between datasets for den entry (n = 69),
den exit dates (n = 55) and reproductive status (n = 61). The
fraction of missing information was 0.16 for reproductive status, 0.05 for den entry and 0.25 for den exit, resulting in 0.45
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of combined missing information (den entry: n = 57, den exit:
n = 40). Battery life and collar failure were responsible for the
reduced sample size at den exit. MI allowed for the investigation of the combined influence of intrinsic factors and environmental variables because model selection using only the
complete case data failed to recognize the role of reproductive
status and environmental variables in den exit dates (Online
resource 2).
Regional-scale den entry and den exit
For den entry, more than 80 % of the average weight of evidence was distributed between 2 best models: reproductive
status (RS), elevation (E) and total amount of precipitation
in September (Pa) ωix̄ = 0.6 and reproductive status (RS), elevation (E) and average maximum temperature in September
(Ta) ωix̄ = 0.22 (ΔAICcx̄ = 1.89). Although the best model (RS
E Pa) explained 38 % (R2c) of the variation in den entry date
and the 2nd best model (RS E Ta) explained 31 % (R2c) of the
observed variation, weather-related variables did not influence
den entry. The only variable with conditional 95 % confidence
interval that did not include zero was the class variable of
females emerging with at least one cub of the year. These
females entered dens 17 days earlier than other individuals
(est ± SE, RS E Pa: β = 17.7 ± 4.6, average 95 % lower and
upper confidence interval (xLCI
and xUCI)
= −26.8 and −8.6,
̄
̄
and
UCI
=
−26.7 and −8.2,
RS E Ta: β = −17.5 ± 4.7, xLCI
x
̄
̄
Online resource 4). For den exit, 80 % of the average weight
of evidence was distributed between the models reproductive
status (RS), elevation (E) and average maximum temperature
in March (Ts: ωix̄ = 0.6) and reproductive status (RS), elevation
(E) and log of the total winter precipitation (logPw: ωix̄ = 0.2,
AICc x̄ = 2.2) with respective R2c values of 0.41 ± 0.01 and
0.39 ± 0.02. These 2 best models had R2m of 0.35 ± 0.01 (RS
E Ts) and 0.33 ± 0.01 (RS E logPw). While RS explained 14 %
(R2m = 0.14 ± 0.007) of the variation in den exit date, E and Ts
explained 26 % (R2m = 0.26 ± 0.009), and E and logPw explained 21 % (R2m = 0.21 ± 0.01) of the variation. As observed
for den entry, the only sex and reproductive status category
with a 95 % confidence interval that did not include zero was
the class variable of females emerging with at least 1 COY.
These females emerged more than 13 ± 6 ( x̄ LCI and
= 0.9 and 24.4, RS E Ts model) or 15 ± 6 (xLCI
and
xUCI
̄
̄
=
2.3
and
26.0,
RS
E
logP
model)
days
after
males
xUCI
w
̄
(Online resource 4). Warmer maximum spring temperatures
and drier winter conditions resulted in earlier den exit (est
± SE, Ts: β = −2.1 ± 0.6, logPw: β = 14.8 ± 5.7, Fig. 1, Online
resource 4). Increasing spring average monthly maximum
temperatures by 4 °C resulted in bears emerging from dens
10 days earlier, and an increase of 1.25 m in snow precipitation delayed den emergence by 7 days. Bears denning at
higher elevation emerged from dens later than bears denning
at lower elevation, and increasing elevation by 100 m delayed
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Behav Ecol Sociobiol (2016) 70:1745–1754
Fig. 1 Den exit day (ordinal
date) for male and female grizzly
bears in the boreal forest and
Rocky Mountains of Alberta,
Canada, between 1999 and 2011
as a function of a the average
maximum daily temperature in
March (°C), b elevation (m) and c
log-winter precipitation (mm)
from the most supported
(reproductive status (RS),
elevation (E) and average
maximum temperature in
September (Ts)) and second most
supported (RS, E and log of the
total winter precipitation (logPw):
ΔAICcx̄ = 2.2, ωix̄ = 0.2) models
(Online resource 4). Each
predictor variable is plotted
within its observed range while
other variables are held constant
at their respective mean. Open
diamonds are observed data
den exit by 1 day (est ± SE, RS E Ts: β = 0.01 ± 0.006, RS E
logPw: β = 0.02 ± 0.006, Fig. 1, Online resource 4).
Local-scale den entry and exit
The berry model was the best-selected model with a weight of
evidence of 0.8, and no other model had a ΔAICc ≤2 (HR:
ΔAICc = 2.3, RS1: ΔAICc = 8.4, Online resource 5). The
availability of berries within home ranges explained 39 %
(R2) of the variation observed in den entry, and low berry
availability was associated with early den entry regardless of
reproductive status. Home range size was expectedly correlated with the estimated number of berries available (correlation
coefficient, r = 0.6) because males have larger home ranges
than females and because females with cubs of the year have
small home ranges (Pasitschniak-Arts 1993; Graham and
Stenhouse 2014). Still, the home range size model had a ωi
of 0.2 (Online resource 5). A 10-fold increase in the productivity of the 4 Vaccinium species preferred by grizzly bears
delayed den entry by about 2 weeks (β = 8.2−6 ± 3.1−6 SE,
Fig. 2), bears with larger home ranges entered dens later than
bears with smaller home ranges (β = 0.01 ± 0.007 SE) and
females with cubs (β = 13.5 ± 6.3 SE) and males (β = 18.5 ± 7.7
SE) entered dens later than females with at least one cub of the
year (Online resource 5). For den exit, univariate models at the
local scale performed poorly: the best-selected model was the
solar radiation (SmaxL) model but the standard error of the
parameter estimate included zero (β = 0.06 ± 0.03, t value = 1.7, p = 0.1, Online resource 5).
Discussion
Using 11 years of data, we demonstrated that although the
phenology of hibernation for grizzly bears depends on sex
and reproductive status, den entry appears to be also driven
by variables associated with food availability in autumn, while
den exit is more linked to weather-related environmental
Fig. 2 Ordinal date of den entry for 2 male and 11 female grizzly bears in
the boreal forest (foothills) of the Rocky Mountains, Alberta, Canada, as a
function of berry availability within their respective home range between
2008 and 2010 from the most supported (Berry) model (Online resources
3 and 5). Open circles are observed data
Behav Ecol Sociobiol (2016) 70:1745–1754
conditions. Parturient females entered dens earlier and
emerged with cubs of the year later than other individuals,
bears that had access to more berries in autumn denned later
than bears that had access to less berries, bears that denned at
high elevation emerged late, and although cold spring temperature and increased winter precipitation delayed den exit,
weather variables did not influence den entry date. Overall,
our results demonstrate that hibernation behaviour of grizzly
bears is governed by intrinsic and extrinsic factors related to
sex and reproductive status, food availability and weather.
Our study is unique in that we used a long-term dataset
with den entry and exit dates based on GPS locations of individual bears, and quantitative food availability data to understand the relative importance of intrinsic and extrinsic factors
in the hibernation behaviour of grizzly bears. Except for a
recent study by Evans et al. (2016), who investigated the
chronology of hibernation using activity sensors from GPS
collars, previous research on hibernation phenology of bears
had precision errors ranging from ≥2 weeks to 1 week at best
(e.g. Haroldson et al. 2002; Manchi and Swenson 2005).
Because the links between environmental variables and hibernation are quantifiable within a short temporal scale (i.e. a few
days), these previous investigations did not allow for an accurate quantification of the relative importance of the different
triggers of hibernation. Even though our approach to estimate
autumn food availability did not include data on the availability of ungulate or Hedysarum spp., and even though we could
not validate our food availability model with independent data, our results provide the first quantitative evidence of a relationship between autumn food availability and hibernation
using berries, the main food source for grizzly bears in autumn
(Munro et al. 2006). Welch et al. (1997) emphasized that bears
are constrained by fruit density and foraging efficiency at the
patch scale, and while our food availability model provides a
novel and realistic approach to investigate the importance of
food availability as a driver of hibernation, the scale of our
model likely underestimates the importance of local berry
patches, especially for large bears. Future research would
therefore benefit from investigating forage availability at more
localized scales. Still, previous studies investigating food
availability as a potential driver of den entry used qualitative
comparisons between years of scarce vs. abundant food production (e.g. Schooley et al. 1994; Haroldson et al. 2002).
In accordance with Schooley et al. (1994) and Johnson and
Pelton (1980) who found that black bears (Ursus americanus)
denned early when beechnut (Fagus grandifolia) or oak
(Quercus prinus) production was scarce, our results supported
the prediction that low autumn food availability within home
ranges should trigger early den entry. Unlike Servheen and
Klaver (1983) who reported that bears in excellent body condition entered dens earlier than other bears, in our study area,
bears that had access to more berries entered dens later than
bears that had access to less berries. Also, unlike previously
1751
suggested by Craighead and Craighead (1972) and recently
observed by Friebe et al. (2014) and Evans et al. (2016), we
found no relationship between autumn temperature and den
entry. Our results support the hypothesis that food availability,
rather than weather, triggers den entry. Friebe et al. (2014)
defined den entry as the date when activity from GPS-GSM
collars dropped below 1 h per day, and Evans et al. (2016)
investigated the chronology of ecological and physiological
events related to hibernation along with the timing of den
entry and den exit derived from behavioural changes. It is
therefore possible that low autumn temperatures are
associated with a marked reduction in movement rates, body
temperature, heart rate and behavioural changes associated
with hibernation, but not necessarily with the final date of
den entry. Evans et al. (2016) observed that some brown bears
entered and exited dens several times before final entry or exit,
and we established the date of den entry as the last day of GPS
locations acquired at the programmed interval, and the date of
den exit as the first day of a regular return to GPS locations.
Our definition of den entry and den exit is therefore less likely
to be related to the physiology of hibernation per se and more
associated with denning behaviour. Craighead and Craighead
(1972) observed that grizzly bears moved towards dens during
snowstorms, and Evans et al. (2016) also observed a correlation between snow depth and den entry dates. Although we
could not measure snow depth accurately for our study area,
snow depth is likely associated with food availability for freeranging bears because persistent snow likely covers food that
would otherwise be available.
As expected, our results suggest that warm spring and reduced snow cover that are consistent with global climate model (GCM) predictions (Zhang et al. 2000; Price et al. 2013)
reduce the length of the hibernation period by triggering early
den exit. Our findings show that increasing average monthly
maximum temperatures by only 2 °C and reducing winter
precipitation by 20 % resulted in bears emerging from dens
5 days earlier. Based on the global climate models of the Fifth
Assessment Report of IPCC, the global average temperature
will have warmed by at least 1.5 °C between the twentieth and
twenty-first century with greater, more frequent warm temperatures during spring and winter (Bonsal et al. 2001; IPCC
2013). The extent of spring snow cover in the northern hemisphere will also continue to decrease in mid-latitude dry regions (Mekis and Vincent 2011; IPCC 2013). In accordance
with evidence linking warm spring temperatures with early
den emergence in yellow-bellied marmots (Marmota
flaviventris; Inouye et al. 2000) and delayed den emergence
associated with late snowmelt for Columbian ground squirrels
(Urocitellus columbianus; Lane et al. 2012), we can expect
bears to emerge from dens earlier as the climate continues to
warm. Evans et al. (2016) found that ambient temperatures did
not appear to drive body temperature before den exit but suggested that temperature within the den may be a more relevant
1752
cue to den exit and that den exit could be associated with bears
becoming too warm and seeking more optimal temperatures
outside of dens. Although we were unable to investigate interactions amongst environmental variables and females of
different reproductive status, data exploration showed no interaction, and our results showed that all females, regardless of
reproductive status, were affected by weather conditions at the
time of den exit. Early den exit by females with cubs of the
year may therefore have repercussions on the condition of
cubs at den exit because early den exit may lead to vulnerable
cubs being out of dens sooner, thereby increasing infanticide
opportunities (Bellemain et al. 2006) or human-caused mortalities. Still, early den exit is likely to positively affect body
condition and cub development because of a probable increase
in foraging opportunities associated with long growing seasons and active periods.
Our results, in combination with previous results from
studies investigating triggers of hibernation in bears, have
implications for the long-term conservation of bear populations because we found that changes in hibernation behaviour
are expected with warm temperatures and reduced snow precipitation predicted as future climate conditions (Zhang et al.
2000; Mekis and Vincent 2011). Phenotypic plasticity will
likely provide some resilience to environmental changes but
because climate change may act as an additional strain on
already threatened populations, we could observe associated
fitness costs (Boutin and Lane 2014). Humans are the primary
cause of grizzly bear mortality worldwide (McLellan et al.
2008; Can et al. 2014), and climate-induced changes in the
hibernation behaviour of bears may further increase humanbear interactions because bears and humans could be simultaneously active on the landscape for longer periods associated
with unseasonably warm springs or autumns expected with
future climate conditions (Bonsal et al. 2001; IPCC 2013).
While our approach remains applicable to a wide range of
hibernators, our results provide the necessary first steps to
understand the potential impacts of climate change on hibernation behaviour of grizzly bears. Our models provide the
necessary tools to make empirically based decisions and policies aimed at mitigating future impacts of climate change on
grizzly bear populations in Alberta, Canada, and our method
can easily be adapted to populations elsewhere. By using simple regional weather data, we were able to clarify links between environmental conditions and the hibernating behaviour of grizzly bears, and the same approach could be used
for other populations and hibernators.
Acknowledgments We thank the Alberta Ecotrust, Y2Y Sarah Baker
Memorial Fund, Alberta Conservation Association, Natural Sciences and
Engineering Research Council of Canada (NSERC) and fRI Research
partners for providing research funds. Université Laval, Centre
d’Études Nordiques and NSERC provided funding for conferences to
KEP. We thank J. Duval and D. Weins for GIS support; D. Talbot and
A.-S. Julien for their help with statistical analyses; R. Théorêt-Gosselin,
Behav Ecol Sociobiol (2016) 70:1745–1754
R. Strong, A. Auger, E. Rogers, C. Curle, T. Larsen, E. Cardinal, P.
Stenhouse and A. Stenhouse for collecting field data; and J. Saunders
and S. Wotton at Peregrine Helicopters. E. Cardinal, T. Larsen, Ö. E.
Can and two anonymous reviewers provided helpful comments and suggestions that improved the manuscript.
Compliance with ethical standards Capture and handling of grizzly
bears were approved by the University of Saskatchewan Animal Care
Committee and were in accordance with the American Society of
Mammalogists guidelines (Sikes et al. 2011), and all applicable national
and institutional guidelines for the care of animals were followed.
Conflict of interest KEP received a research grant from Alberta
Conservation Association Grant in Biodiversity (2009). All authors declare that they have no conflict of interest.
Informed consent Not applicable.
Funding This study was funded by Alberta Ecotrust (2011) and fRI
Research Partners.
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