The relative importance of direct and indirect effects of hunting mortality on the population dynamics of brown bears

Abstract

There is increasing evidence of indirect effects of hunting on populations. In species with sexually selected infanticide (SSI), hunting may decrease juvenile survival by increasing male turnover. We aimed to evaluate the relative importance of direct and indirect effects of hunting via SSI on the population dynamics of the Scandinavian brown bear (Ursus arctos). We performed prospective and retrospective demographic perturbation analyses for periods with low and high hunting pressures. All demographic rates, except yearling survival, were lower under high hunting pressure, which led to a decline in population growth under high hunting pressure (λ = 0.975; 95% CI = 0.914-1.011). Hunting had negative indirect effects on the population through an increase in SSI, which lowered cub survival and possibly also fecundity rates. Our study suggests that SSI could explain 13.6% of the variation in population growth. Hunting also affected the relative importance of survival and fecundity of adult females for population growth, with fecundity being more important under low hunting pressure and survival more important under high hunting pressure. Our study sheds light on the importance of direct and indirect effects of hunting on population dynamics, and supports the contention that hunting can have indirect negative effects on populations through SSI.

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Research
Cite this article: Gosselin J, Zedrosser A,
Swenson JE, Pelletier F. 2015 The relative
importance of direct and indirect effects of
hunting mortality on the population dynamics
of brown bears. Proc. R. Soc. B 282: 20141840.
http://dx.doi.org/10.1098/rspb.2014.1840
Received: 23 July 2014
Accepted: 9 October 2014
Subject Areas:
ecology, behaviour
Keywords:
population dynamics, harvesting, brown bear,
sexually selected infanticide, behaviour,
carnivore
Author for correspondence:
Jacinthe Gosselin
e-mail: jacinthe.gosselin2@usherbrooke.ca
Electronic supplementary material is available
at http://dx.doi.org/10.1098/rspb.2014.1840 or
via http://rspb.royalsocietypublishing.org.
The relative importance of direct and
indirect effects of hunting mortality on
the population dynamics of brown bears
Jacinthe Gosselin1, Andreas Zedrosser2,3, Jon E. Swenson4,5
and Fanie Pelletier1
1
De
´partement de biologie, Universite
´de Sherbrooke, 2500 boulevard de l’Universite
´, Sherbrooke,
Quebec, Canada J1K 2R1
2
Department of Environmental and Health Studies, Telemark University College, Bø 3800, Norway
3
Institute of Wildlife Biology and Game Management, University of Natural Resources and Life Sciences,
Vienna 1180, Austria
4
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, A
˚
s 1432,
Norway
5
Norwegian Institute for Nature Research, Trondheim 7485, Norway
JG, 0000-0002-0161-1343
There is increasing evidence of indirect effects of hunting on populations.
In species with sexually selected infanticide (SSI), hunting may decrease
juvenilesurvival by increasing maleturnover. We aimed to evaluate the relative
importance of direct and indirect effects of hunting via SSI on the population
dynamics of the Scandinavian brown bear (Ursus arctos). We performed pro-
spective and retrospective demographic perturbation analyses for periods
with low and high hunting pressures. All demographic rates, except yearling
survival, were lower under high hunting pressure, which led to a decline in
population growth under high hunting pressure (
l
¼0.975; 95% CI ¼0.914–
1.011). Hunting had negative indirect effects on the population through an
increase in SSI, which lowered cub survival and possibly also fecundity rates.
Our study suggests that SSI could explain 13.6% of the variation in population
growth. Hunting also affected the relative importance of survival and fecundity
of adult females for population growth, with fecundity being more important
under low hunting pressure and survival more important under high hunting
pressure. Our study sheds light on the importance of direct and indirect effects
of hunting on population dynamics, and supports the contention that hunting
can have indirect negative effects on populations through SSI.
1. Introduction
Understanding the population dynamics of exploited species is essential to
determine sustainable harvest rates for wildlife populations. Harvesting indi-
viduals obviously can have important direct effects on the growth rate of a
population by increasing mortality rates. However, there is increasing evidence
that harvesting also can have indirect effects on population growth [1]. For
instance, harvest can disrupt the sex and age structure of a population, which
can in turn affect fecundity rates [1– 3].
Harvesting may also have an indirect effect on populations by affecting behav-
iour [4]. Individual behaviour is now considered to be an important factor
influencing population dynamics [5,6]. Any individual behaviour that influences
reproductive success and survival should also influence population growth. For
example, hunting has been shown to affect individual movement rates in elk
(Cervus elaphus) [7,8], activity patterns in brown bears (Ursus arctos) [9], and habi-
tat selection in wild boar (Sus scrofa) [10] and mule deer (Odocoileus hemionus) [11].
As changes in behavioural patterns caused by hunting may affect food intake, it
has the potential to affect the survival and fecundity of individuals.
&2014 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution
License http://creativecommons.org/licenses/by/4.0/, which permits unrestricted use, provided the original
author and source are credited.

Harvesting can also affect the expression of certain beha-
viours in surviving individuals. For example, harvesting is
thought to increase the rate of social reorganization in some
species, which promotes male turnover and new encounters
between individuals, thus leading to an increase of sexually
selected infanticide (SSI) [12,13]. SSI occurs when competition
between members of one sex for the other sex may make it
advantageous for an individual (usually a male) to eliminate
offspring of another individual [14]. SSI occurs in a wide
array of species, including Rodentia (see [15] for review),
non-human primates (e.g. Hanuman langur Presbytis entellus
[16,17]; but see also [18]) and carnivores [19]. Carnivores are
often hunted, with harvest generally focused on males, particu-
larly when they are hunted for trophies [20,21]. In species with
SSI, harvesting males can have an indirect negative effect on the
population by reducing juvenile survival [4,21].
Although several studies have quantified how behaviour
can affect reproductive success and survival [22–24], only a
few have linked behaviour to population dynamics [4,25].
The influence of behaviour on population dynamics and its
interaction with harvest is difficult to quantify in long-lived
wild species, as it requires long-term data on the survival,
reproduction and behaviour of individuals as well as on
population dynamics [25]. The goal of this study was to
assess the direct and indirect effects of hunting through SSI
on the dynamics of a brown bear (U. arctos) population.
To evaluate the influence of hunting and SSI on population
dynamics, we performed prospective and retrospective pertur-
bation analyses for periods with different hunting pressures.
Our goals were to determine how demographic rates and
population growth vary under low and high hunting pressure
and to determine the relative importance of demographic rates,
including cub survival, on population growth. We predicted
(P1) that hunting would have a direct negative effect on popu-
lation growth by reducing the survival rates of age classes
available for hunting. We also expected (P2) that hunting
would have an indirect negative effect on population growth
through SSI, owing to lower cub survival. Further, we pre-
dicted (P3) that cub survival would have a lower elasticity
(i.e. relative influence on population growth) than most other
demographic rates, using the prospective analyses [26]. As
demographic rates with low elasticity, such as juvenile survi-
val, usually have high variability [26], we predicted (P4) that
cub survival would explain a substantial proportion of the
variation in population growth using the retrospective ana-
lyses. Thus, by evaluating the importance of cub survival, a
proxy of SSI, for population growth, we aimed to better under-
stand the effects of behaviour on the population dynamics of a
long-lived wild mammal species. We hoped that the results
would increase our understanding of both the direct and
indirect effects of hunting on population dynamics.
The Scandinavian brown bear offers a unique opportunity
to evaluate not only the direct effects of hunting, but also the
potential indirect effects that hunting may have on popu-
lation dynamics through behaviour. SSI is common in
Scandinavian brown bears [12,27,28], although its occurrence
in North American brown bear populations is controversial
[29,30] (but see also [31]). Nevertheless, the species has
characteristics that should promote SSI [19]. The long
period of maternal care (between 1.5 and 4.5 years) reduces
the availability of reproductive females and a female may
become receptive only 2– 4 days after losing her young
during the mating season [32–34]. Therefore, males would
benefit from killing cubs of the year (hereafter referred to as
cubs) during the mating season [28,34]. Swenson et al. [35]
found that 85% of the mortality of cubs occurs during the
mating season in Scandinavia, and all confirmed cub mortal-
ities during the mating season were cases of infanticide (14
cubs in 2009–2011) [27]. There is therefore strong evidence
of SSI in the Scandinavian brown bear population [28] and
it seems to greatly affect cub survival. Moreover, brown
bears are hunted in Scandinavia and there is evidence that
SSI might increase with hunting pressure [12,35,36]. Indeed,
cub survival is lower (from 28% to 42%) when at least one
male had been killed in the same area 0.5, and especially
1.5, years earlier [12]. This cub mortality is thought to be
caused by SSI, which is promoted by the male turnover cre-
ated when males die during the hunting season [12,35– 37]:
when a resident male is killed, he will be replaced by a
male who is probably unrelated to cubs present in the area,
thus leading to an increase in SSI [12,13].
2. Methods
(a) Study area and population
The study area was located in southcentral Sweden (618N, 158E),
mostly in the counties of Dalarna and Ga
¨vleborg. It is composed of
13 000 km
2
of rolling landscape (from 200 to 1000 m) with inten-
sively managed boreal forest dominated by Scots pine (Pinus
sylvestris) and Norway spruce (Picea abies) [38]. The Scandinavian
population is one of the most productive brown bear populations
in the world [39], with an early mean age at first reproduction (4.71
years [36]) and short interlitter intervals (1.6 years [35]). Density of
bears in the study area increased over our study period (1990–
2011), although not evenly nor constantly [40,41]. Demographic
consequences of this increase are unknown, but they are unlikely
to affect subadult and adult survival [42]. Indeed, hunting is the
main cause of mortality for bears aged 1 year and older, and
84.4% of deaths of marked bears in our study area were caused
by humans from 1990 to 2011. Most natural mortalities are intra-
specific predation and affect mostly yearlings and subadults [43].
Another study has also suggested that the population did not
seem to be food limited [40]. Therefore, hunting is the main
driver of the population and fluctuations in harvest rates explain
83% of the population trend [44].
(b) Data collection
(i) Captures and monitoring
Females without young and females accompanied by yearlings
were immobilized with a dart gun from a helicopter. Captures
were carried out after den emergence from mid-April to early
May. Females with cubs were not captured for animal welfare
reasons. All females were marked individually with tattoos
(inside the upper lip), and passive integrated transponder (PIT)
tags under anaesthesia. Females were fitted with radiotransmitters,
radio-implants (Telonics, model IMP/40/L HC), or both. Females
were originally fitted with VHF radiotransmitters (Telonics,
model 500). However, since 2003, most (gradually from 6% to
90%) females captured or recaptured were fitted with GPS– GMS
transmitters (GPS Plus, Vectronic Aerospace GmbH). A vestigial
premolar tooth was collected from all females not captured as a
yearling to estimate age based on the cementum annuli in the
root (Mattson’s Inc., Milltown, MT). For further information
about capture and handling of bears, see Arnemo et al. [45] and
Zedrosser et al. [46].
Females fitted withVHF radiotransmitters were located once a
week during the non-denning period using standard triangulation
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2

methods [47]. Females fitted with GPS radiotransmitters were
located at least once every 30 min during the active period. To
ascertain timing of cub loss, females with cubs were observed
from a helicopter three times per year: at den emergence (early
May), after the breeding season (mid-July) and in autumn before
den entrance (late September to early October). Most cub loss
(80.9%) occurred during the breeding season (mid-May to mid-
July). Litter size was defined as the number of cubs observed
with the mother at the first sighting following den emergence.
(ii) Hunting
Bears were hunted across the entire study area. Hunting started
in late August or early September and lasted until either 15 Octo-
ber or when the quota within the designated area had been
reached, whichever came first. Hunters could kill any solitary
bear, regardless of sex and age. The only protected segment
of the population was family groups (i.e. females and their
dependent offspring of any age) [48].
After harvesting a bear, hunters were required to report the
kill and present the carcass to an official inspector on the same
day. Hunters were required to give information about hunting
method, sex of the bear, body weight and the location of the har-
vest. In addition, hunters provided a premolar tooth for age
determination. The sex ratio of individuals harvested was 45%
female and 55% male. The Swedish bear hunt and reporting of
hunter-killed bears are further described by Bischof et al. [48].
(c) Statistical analyses
(i) Subperiods of consistent hunting pressure
As demographic models are better performed on relatively long
periods of time, we tested the effect of hunting on the population
by comparingthe population dynamics in periods of different hunt-
ing pressure. We calculatedthe yearly hunting pressure in our study
area as the number of marked bears that had been killed legally
divided by the number of marked bears available for hunting (i.e.
the number of marked bears known to be alive at the start of the
hunting season, excluding family groups). We tested whether
there were periods with statistically different hunting pressure
over the study period by dividing the study period into 2– 5 subper-
iods and calculating the Calinski–Harabasz (CH) index for all
possible chronological combinations of subperiods. The CH index
is computed as [trace B/(k21)]/[trace W/(n2k)], where nand k
are the total number of items and the number of clusters in the
solution, respectively. The Band Wterms are the between- and
within-cluster sum of squares and cross product matrices, and the
trace is the sum of the main diagonal of the matrices [49,50].
Higher values of the CH index represent higher between-cluster
variance relative to within-cluster variance. We compared the CH
index for the most probable chronological groups and determined
the most likely number of subperiods. The maximum hierarchy
level was used to indicate the correct number of partitions in the
data, which maximized between-cluster variance and minimized
within-cluster variance.
(ii) Demographic parameters
We modelled only the female component of the population,
because in brown bears, as in most large mammals, it is the
number of reproductive females that limits reproduction [51,52].
To ascertain which age classes best represented the life stages in
the population, we tested different age-class models and selected
the one that best described the survival pattern. Model selection
was based on Akaike’s information criterion corrected for small
sample sizes (AICc) [53].
The recapture probabilityof females alive in the study area was
estimated to be 100% [43]. Therefore, survival and reproductive
output of the females were assessed from repeated observations
of the individuals. Based on these data, we calculated the mean sur-
vival and fecundity foreach age class over the studyperiod and for
each subperiod. Fecundity rates represent the probability that a
female produces a cub the following year (fecundity
t!tþ1
¼
survival
t!tþ1
reproduction
t!tþ1
). The demographic rates were
calculated from all of the survival and reproduction information
available from the females followed during 1990– 2011. We lost
contact with some females (about 14%) without known mortality.
A sensitivity analysis revealed that whether or not we included
individuals with truncated life histories did not affect demographic
rates (see the electronic supplementary material, table S1). There-
fore, we included individuals with unknown mortality for the
period they were followed. Demographic rates were used to con-
struct pre-breeding quasi-Leslie matrices describing the transition
probabilities between or within age classes from one year to the
next [54]. One matrix was built for the entire study period, and
other matrices were built on subsets of the data corresponding to
each hunting pressure subperiod (1990– 2005 and 2006–2011; see
Results). Because cubs were not captured, their sex was therefore
unknown. All cubs were used for cub survival and fecundity esti-
mations. We assumed that there was no difference in survival
between male and female cubs, which has been suggested in our
population [55]. Fecundity rates were adjusted using a secondary
sex ratio of 50 : 50 [56].
(iii) Prospective analysis
Prospective analyses predict the change in the asymptotic growth
rate that would result from a change in a demographic rate and
are independent of past variation in demographic rates [57]. We
calculated the asymptotic growth rate of the population (
l
, the
exponential growth rate at the stable age distribution) for
the entire study period and for each hunting pressure subperiod.
We calculated elasticities of the population growth rate indepen-
dently from each matrix for each demographic rate. Elasticities of
the population growth are the proportional change in
l
resulting
from a proportional change in a demographic rate (r
i
), Dlog
l
/
Dlog r
i
[54]. Prospective analyses were performed with the
‘popbio’ package in R [58]; the confidence intervals of
l
were
calculated with the ‘boot.transitions’ function.
(iv) Retrospective analysis
Retrospective analyses compare the contributions of past changes
in demographic rates with the variation in
l
and are not indicative
of future changes [57]. We estimated the association between
variation in a demographic rate r
i
and variation in
l
by: s2
i
y
i,
where s
i
is the sensitivity of the population growth rate to a demo-
graphic rate r
i
, and
y
i
is the variance of r
i
[59]. These associations
are presented as contributions to variation in
l
, when rescaled as
percentages. We did not include covariations of demographic
rates in the analysis, owing to lowannual sample size. Calculations
and statistics were performed using R v. 3.0.0 [60].
3. Results
(a) Subperiods of consistent hunting pressure
Based on the highest CH index, the most likely number of sub-
periods with different levels of hunting pressure was two (see
the electronic supplementary material, table S2). The two sub-
periods that minimized intragroup variation and maximized
intergroup variation were 1990–2005, with low hunting
pressure (0.073 +0.014, mean +s.e.; figure 1), and 2006–
2011, with high hunting pressure (0.199+0.018; figure 1).
Consequently, we retained these two periods in our sub-
sequent analyses. The sex ratio of bears harvested changed
slightly between the two hunting pressure subperiods (48%
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3

females, 52% males in 1990– 2005 versus 43% females, 57%
males in 2006– 2011; Yates
x
2
¼3.97, p-value ¼0.046).
(b) Demographic rates
The model that best represented the life stages in the population
identified six distinct age groups: 0-, 1-, 2- and 3-year-
olds, young adults (4–8 years old) and older adults (9 years
and older; electronic supplementary material, figure S1).
Matrix dimensions were therefore 6 6. Details on model selec-
tion can be found in the electronic supplementary material,
tables S3–S5.
We estimated cub survival from 466 cubs born in 203 lit-
ters to 69 marked females between 1990 and 2011. Survival of
females aged 1 year and older was estimated from 180
marked females of known age (n¼901 individual-years;
for further information on sample size, see the electronic sup-
plementary material, table S6). During the entire study period
(1990–2011), mean cub survival was estimated at 58.8% and
survival of females was highest at 3 years of age (table 1). In
general, survival rates in the high harvest subperiod were
lower than in the low harvest subperiod, with the exception
of yearling survival, which was higher in the high harvest
subperiod (figure 2).
We calculated fecundity from the reproduction of marked
females 4–24 years old (n¼178 individuals; n¼493 individ-
ual-years, data from 1990 to 2011; for further information on
sample size, see the electronic supplementary material, table
S7). In the entire study period, fecundity was highest for
females aged 9 years and older (table 1). Fecundity rates
were lower in the high hunting pressure subperiod than in
the low hunting pressure subperiod (figure 2).
(c) Prospective analysis
For the entire period, the asymptotic growth rate (
l
) of the
population was 1.041 (95% CI ¼1.012–1.069; see the elec-
tronic supplementary material, figure S2). The asymptotic
population growth rate was higher in the low hunting
pressure subperiod (
l
¼1.082; 95% CI ¼1.052–1.119; see
the electronic supplementary material, figure S2) and was
lower during the high hunting pressure subperiod (
l
¼
0.975; 95% CI ¼0.914– 1.011; see the electronic supplemen-
tary material, figure S2). Survival of adult females had the
greatest elasticities (0.306 for young and 0.178 for old adults
for the entire period; table 1), followed by the survival
of juveniles, including cub survival (approx. 0.1; table 1).
Elasticities of survival rates were greater than for the
0
0.05
0.10
0.15
0.20
0.25
0.30
1990
1991
1992
1993
1994
1995
1996
1997
1998
1999
2000
2001
2002
2003
2004
2005
2006
2007
2008
2009
2010
2011
hunting pressure
y
ear
Figure 1. Hunting pressure (the number of marked bears that were legally killed divided by the number of marked bears available for hunting; see Methods) on
brown bears in southcentral Sweden from 1990 to 2011. There were two subperiods with different hunting pressures: 1990– 2005 (low) and 2006–2011 (high) (see
the electronic supplementary material, table S2). The dashed line separates the two hunting pressure subperiods.
Table 1. Means, standard errors, elasticities, variances and the retrospective analysis results of the demographic rates for different age classes of femalebrown
bears in southcentral Sweden from 1990 to 2011. The results of the retrospective analysis give the proportion of the variation in
l
that is explained by the
variation in each demographic rate (y.o., years old).
demographic rate mean standard error elasticity variance retrospective analysis (%)
cub survival 0.588 0.023 0.104 0.243 16.838
yearling survival 0.791 0.035 0.104 0.167 6.381
2 y.o. survival 0.840 0.037 0.104 0.136 4.613
3 y.o. survival 0.938 0.027 0.098 0.059 1.426
4–8 y.o survival 0.904 0.017 0.306 0.087 22.383
9–24 y.o survival 0.842 0.022 0.178 0.134 13.281
3 y.o. fecundity 0.166 0.044 0.006 0.281 0.745
4–8 y.o. fecundity 0.488 0.038 0.056 0.710 20.773
9–23 y.o. fecundity 0.502 0.042 0.042 0.868 13.559
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corresponding fecundity rates (table 1). Summed elasticities
for female survival (0.894 for the entire study period) far
exceeded elasticities for reproduction (0.104 for the entire
study period). Elasticities of the demographic rates were
qualitatively equivalent in the two different hunting pressure
subperiods and were similar to those obtained in the global
period (see the electronic supplementary material, table S8).
(d) Retrospective analysis
In all periods, the survival and fecundity of adult females
explained the most variation in
l
(table 1 and figure 3). In the
global model and in the high hunting pressure subperiod,
the survival of adult females explained the most variation
in the growth rate (35.7% and 42.5%, respectively; table 1 and
figure 3), followed by the fecundity of adult females (35.1%
and 33.1%, respectively; table 1 and figure 3). In the low hunt-
ing pressure subperiod, however, the fecundity of adult
females explained the most variation (36.1%), followed by
their survival (30.5%; figure 3). Cub survival explained
between 14.6% and 18.8% of the variation in population
growth in the different models (table 1 and figure 3).
4. Discussion
The goal of this study was to quantify the direct and indirect
effects of hunting on the population dynamics of a large
long-lived mammal, the brown bear. Our analyses produced
three main results. First, we found that adult females were
the most important groups affecting population dyna-
mics, having the highest elasticities and explaining the most
variation in
l
. Second, we found pronounced differences
between the two subperiods with different hunting pressures:
the demographic rates, including survival rates of age classes
available for hunting and cub survival, were lower under
high hunting pressure, leading to a decrease in
l
, in accordance
with P1 and P2. In addition, the relative contribution of
survival and fecundity to the variance of
l
changed with
hunting pressure, with fecundity being more important
under low hunting pressure and survival being more impor-
tant under high hunting pressure. Third, we found that cub
survival showed a relatively high importance for population
growth (third highest elasticity, contrary to P3) and explained
a substantial proportion of the variation in
l
in the retro-
spective analyses (ranging from 14.6 to 18.8%) in accordance
with P4.
0
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
0.90
1.00
S0 S1 S2 S3 S4–8 S9–24 F3 F4–8 F9–23
mean
demo
g
raphic rates
Figure 2. Means and standard errors of the survival (S) and fecundity (F) rates for different age classes (see text) of female brown bears in southcentral Sweden
from 1990 to 2005 (grey bars) and 2006 to 2011 (white bars).
0
5
10
15
20
25
30
S0 S1 S2 S3 S4–8 S9–24 F3 F4–8 F9–23
proportion of the variation explained in l (%)
demo
g
ra
p
hic rates
Figure 3. Proportion of the variation in
l
(%) that is explained by the variation in survival (S) and fecundity (F) rates for different age classes (see text) of female
brown bears in southcentral Sweden from 1990 to 2005 (grey bars) and 2006 to 2011 (white bars).
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Previous studies have revealed that survival of prime-aged
females is the vital rate, with the highest elasticity in most
large mammal populations (e.g. [26,52,61– 64]). This pattern
is expected in long-lived species, because higher adult survival
leads to more reproductive opportunities. Our study also
was consistent with this pattern, with survival rates of adult
females having the highest elasticities and the variation in sur-
vival and fecundity rates of adult females explaining the largest
proportion of the variation in
l
. It has been suggested that there
may be a trade-off between the intrinsic dependence of
l
on a
demographic rate and the degree of observed temporal variation
in that demographic rate [26,65]. In fact, traits with the greatest
potential impact on population growth tend to be under high
selection and to have lower temporal variability [26,65]. Our
results, however, suggest that when human-induced mortality
is high (in Sweden, nearly all of adult female mortality is
human-caused [66]), both elasticity and variability can be high.
Therefore, the negative correlation between the elasticity and
variance of a demographic rate may not hold in harvested popu-
lations, because artificial mortality patterns differ from natural
selection [67,68]. Also, although prime-age female survival
might be lower in harvested populations [3], it should be of
high importance for population growth.
We found that fecundity rates were lower during the
subperiod with high hunting pressure. This could be an unex-
pected indirect negative effect of hunting on the population.
Female brown bears, when with cubs, have been shown to
avoid males during the mating season as a counterstrategy to
SSI [69,70]. They do so by avoiding good habitats and selecting
for habitat in proximity of humans [69], which has a negative
effect on their diet quality [71] and could ultimately reduce
their subsequent reproductive output [31]. Therefore, as an
increase in hunting pressure seems to lead to higher risk of
SSI [12,35,36], it could also lead to increased avoidance of
males by females with cubs, and lower fecundity. On the
other hand, population density generally increased in our
study area from 1990 to 2011 [40,41], and density dependence
effects may also have resulted in lower fecundity rates in the
later period (2006– 2011). There is evidence for a decrease in
the mean litter size (with more females now having singletons)
and an increase in the interlitter interval (with more females
weaning their young at 2.5 years old rather than at 1.5 years
old) in the latter years of the study (Scandinavian Brown Bear
Research Project 1985– 2011, unpublished data). Moreover, as
we used a pre-breeding census, fecundity rates included the
survival of the female to the next census (see Methods). There-
fore, a part (between 11% and 18%) of the decrease in fecundity
rates observed in this study can be explained by the decrease in
survival rates.
Not surprisingly, survival rates of most age classes were
lower under high hunting pressure, with the exception of
yearlings. Yearling survival might have been higher in the
high hunting pressure subperiod because females tended to
wean their offspring later in recent years (Scandinavian
Brown Bear Research Project 1985–2011, unpublished data).
Yearlings staying with their mother until they are 2-year-
olds have higher survival than independent yearlings,
partly because they are protected from hunting [66]. Cubs
are also protected from hunting, but the lower cub survival
under high hunting pressure might have reflected increased
SSI, caused by an increase in male turnover with the increase
in hunting pressure [12,35]. The increase in SSI in the high
hunting pressure subperiod might also be influenced by the
increase in the proportion of males harvested during this
period (57% males in the harvest in 2006– 2011 compared
with 52% in 1990–2005). In addition, increased density could
have negatively affected cub survival by increasing food com-
petition [36]. Density might lower cub survival particularly as
it has been found to positively affect the frequency of infanti-
cide [72,73]. Furthermore, although we have no evidence of
possible density effects on the survival rates of subadults and
adults in our population, and density effects on adult survival
are unlikely in large mammals [42], we are unable to exclude
the possibility that changes in density may affect the survival
rates of all age classes in the population.
Hunting pressure had substantial effects on bear popu-
lation dynamics; at low hunting pressure, the population
appeared to be growing (
l
¼1.082, 95% CI ¼1.052 – 1.119),
but this population trend changed to a decline (
l
¼0.975,
95% CI ¼0.914–1.011) during the period of high hunting
pressure. Therefore, if hunting pressure remains the same,
the population should, on a long-term scale, decline by
about 2% annually. However, the Swedish brown bear popu-
lation is large, with an estimated 3298 individuals in 2008
[41]. The current management goal in Sweden is to maintain
the number of bears on a national level, but allow it to
increase or decrease on local scales [41]. As such, the popu-
lation should be closely monitored to ensure that hunting
in the study area does not cause an important decline in
the area or a larger-scale decline in the population.
Elasticities were similar in both subperiods as well as in
the global study period. This result was expected, as elasti-
cites represent the intrinsic dependence of
l
to each
demographic rate [54]. However, the results of the retrospec-
tive analysis differed among periods. At low hunting
pressure, the fecundity of adult females explained more of
the variation in
l
than their survival. This pattern was
reversed under high hunting pressure, where the survival
of adult females explained more of the variation in
l
than
their fecundity. This effect was caused by an increase in the
variance of the survival rates, which is expected with an
increase in hunting pressure and mortality, but also owing
to the decrease in the variance of the fecundity rates. Our
results show that harvesting has the potential to severely
affect the way a population is regulated. Moreover, this
suggests that population growth is mostly driven by recruit-
ment when hunting-induced mortality is low. This prediction
is supported by the observation that cub survival explained
more variation in population growth under low hunting
pressure than under high hunting pressure.
One of our goals was to evaluate the importance of cub
survival for population growth to test whether SSI can affect
population dynamics. We found that cub survival was rela-
tively important for population growth, with the third
highest elasticity, and survival of cubs explained almost as
much variation in population growth as the survival of
young adult females. When calculated for the entire study
period, cub survival explained 16.8% of the variation in
l
. Con-
sidering that 80.9% of the cub mortality occurs during the
mating season, and that most, if not all, of this mortality is
due to SSI [27], then our results suggest that SSI may explain
up to 13.6% of the variation in the population growth rate
during our study period (1990–2011). If SSI had not been pre-
sent (i.e. no cub mortality during the mating season), and
everything else being equal, cub survival would have been
80.9% higher (i.e. around 0.968) during 2006– 2011. According
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
6

to our matrix model, increasing cub survival by 80.9% would
increase
l
by 8.17% or 0.080, making
l
¼1.055 in 2006– 2011.
This suggests that, even under high hunting pressure, the
population would have increased in the absence of SSI. There-
fore, male behaviour seems to have an important effect on
population dynamics of Scandinavian brown bears.
It has been suggested that human-induced mortality may
not be additive to natural mortality, as some compensatory
effects might take place [74,75]. As human-induced mortality
typically decreases population size, there might be a density-
dependent response, increasing natural survival or reproduc-
tive rates owing to lower food competition [74,75]. Given that
both survival and reproductive rates were lower during the
high hunting pressure period, our results indicated that
there was no compensatory response to hunting through
reproduction in our study population. Bischof et al. [43]
also found that there was no evidence of compensatory
effects of hunting on other sources of mortality in our
population. Strong compensation can rarely be expected in
long-lived mammals [76]. However, our study supports the
contention that hunting can have additional indirect negative
effects on populations of large carnivores through SSI [4,21].
As there is evidence that the behaviour of infanticide can be
heritable [77,78], this could lead to eco-evolutionary feed-
backs on population dynamics. In fact, a reduction in the
density of individuals in the population could be a selective
pressure to increase SSI as mates become harder to find.
Also, an increase in the prevalence of SSI in the population
could amplify the decline of the population.
Our study shows that behaviour of individuals and the
social biology of a species have important effects on popu-
lation growth and can interact with hunting mortality to
create additional negative effects on the population. There-
fore, these factors should be considered when establishing
harvest quotas and management policies.
Ethics statement. All capture and handling of animals was approved by
the appropriate authority and ethical committee (Djuretiska na
¨mden
i Uppsala, Sweden).
Data accessibility. The datasets supporting this article have been
uploaded as part of the electronic supplementary material.
Acknowledgements. We thank S. Brunberg and the field personnel of the
Scandinavian Brown Bear Research Project (SBBRP). We are grateful
to S. Rioux Paquette for providing codes. We thank T. Ezard,
D. Garant, M. Festa-Bianchet, C. Darimont and an anonymous
reviewer for providing helpful comments on earlier versions of this
manuscript. J.G., A.Z., J.E.S. and F.P. participated in the design of
the study; J.G. carried out data analysis; J.G., A.Z,. J.E.S. and F.P.
wrote the manuscript. A.Z. participated in the coordination of the
SBBRP; J.E.S. coordinated the SBBRP. All authors gave final approval
for publication.
Funding statement. J.G. and F.P. were funded by NSERC Discovery
Grants, and by the Canada Research Chair in Evolutionary Demogra-
phy and Conservation. The SBBRP was supported by the Swedish
Environmental Protection Agency, the Norwegian Directorate for
Nature Management, the Swedish Association for Hunting and Wild-
life Management, the Research Council of Norway and the Austrian
Science Fund (project P20182). This is scientific paper no. 176 from
the SBBRP.
Competing interests. We have no competing interests.
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