ArticlePDF Available

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

Authors:

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.
rspb.royalsocietypublishing.org
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.
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
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 [2224], 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 [3234]. 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 20092011) [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
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
2
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
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%
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
3
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
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
(19902011), 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 424 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 ¼1.0521.119; 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 20062011 (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
48 y.o survival 0.904 0.017 0.306 0.087 22.383
924 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
48 y.o. fecundity 0.488 0.038 0.056 0.710 20.773
923 y.o. fecundity 0.502 0.042 0.042 0.868 13.559
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
4
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
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).
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
5
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
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 19852011, 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 19902005). 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 ¼0.9141.011) 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
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
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.
References
1. Milner JM, Nilsen EB, Andreassen HP. 2007
Demographic side effects of selective hunting in
ungulates and carnivores: review. Conserv. Biol. 21,
3647. (doi:10.1111/j.1523-1739.2006.00591.x)
2. Bunnefeld N, Baines D, Newborn D, Milner-Gulland
EJ. 2009 Factors affecting unintentional harvesting
selectivity in a monomorphic species. J Anim. Ecol.
78, 485492. (doi:10.1111/j.1365-2656.2008.
01500.x)
3. Ginsberg JR, Milner-Gulland EJ. 1994 Sex-biased
harvesting and population dynamics in ungulates:
implications for conservation and sustainable use.
Conserv. Biol. 8, 157166. (doi:10.2307/2386730)
4. Wielgus RB, Morrison DE, Cooley HS, Maletzke B. 2013
Effects of male trophy hunting on female carnivore
population growth and persistence. Biol. Conserv.
167, 69– 75. (doi:10.1016/j.biocon.2013.07.008)
5. Caro T. 1998 Behavioral ecology and conservation
biology. New York, NY: Oxford University Press.
6. Festa-Bianchet M, Apollonio M. 2003 Animal
behavior and wildlife conservation. Washington, DC:
Island Press.
7. Cleveland SM, Hebblewhite M, Thompson M,
Henderson R. 2012 Linking elk movement and
resource selection to hunting pressure in a
heterogeneous landscape. Wildl. Soc. Bull. 36,
658668. (doi:10.1002/wsb.182)
8. Ciuti S, Muhly TB, Paton DG, McDevitt AD, Musiani
M, Boyce MS. 2012 Human selection of elk
behavioural traits in a landscape of fear.
Proc. R. Soc. B 279, 44074416. (doi:10.1098/rspb.
2012.1483)
9. Ordiz A, St Støen OG, Sæbø S, Kindberg J, Delibes
M, Swenson JE. 2012 Do bears know they are being
hunted? Biol. Conserv. 152, 21– 28. (doi:10.1016/j.
biocon.2012.04.006)
10. Thurfjell H, Spong G, Ericsson G. 2013 Effects of
hunting on wild boar Sus scrofa behaviour. Wildl.
Biol. 19, 8793. (doi:10.2981/12-027)
11. Swenson JE. 1982 Effects of hunting on habitat use by
mule deer on mixed-grass prairie in Montana. Wildl.
Soc. Bull. 10, 115– 120. (doi:10.2307/3781728)
12. Swenson JE, Sandegren F, So
¨derberg A, Bja
¨rvall A,
Franze
´n R, Wabakken P. 1997 Infanticide caused by
hunting of male bears. Nature 386, 450–451.
(doi:10.1038/386450a0)
13. Loveridge AJ, Searle AW, Murindagomo F,
Macdonald DW. 2007 The impact of sport-hunting
on the population dynamics of an African lion
population in a protected area. Biol. Conserv. 134,
548558. (doi:10.1016/j.biocon.2006.09.010)
14. Hrdy SB. 1979 Infanticide among animals: a review,
classification, and examination of the implications
for the reproductive strategies of females. Ethol.
Sociobiol. 1, 1340. (doi:10.1016/0162-3095
(79)90004-9)
15. Ebensperger LA, Blumstein DT. 2008 Functions of
non-parental infanticide in rodents. In Rodent
societies: an ecological and evolutionary perspective
(eds JO Wolff, PW Sherman), pp. 267– 279.
Chicago, IL: University of Chicago Press.
16. Borries C, Launhardt K, Epplen C, Epplen JT, Winkler
P. 1999 DNA analyses support the hypothesis that
infanticide is adaptive in langur monkeys.
Proc. R. Soc. Lond. B 266, 901904. (doi:10.1098/
rspb.1999.0721)
17. Sommer V. 1994 Infanticide among the Langurs of
Jodhpur: testing the sexual selection hypothesis
with a long-term record. In Infanticide and parental
care (eds S Parmigiani, FS vom Saal), pp. 155198.
Chur, Switzerland: Hardwood Academic Publishers.
18. Ebensperger LA. 1998 Strategies and
counterstrategies to infanticide in mammals. Biol.
Rev. Camb. Philos. Soc. 73, 321–346. (doi:10.1017/
S0006323198005209)
19. Swenson JE. 2003 Implications of sexually
selected infanticide for the hunting of large
carnivores. In Animal behavior and wildlife
conservation (eds M Festa-Bianchet, M Apollonio),
pp. 171189. Washington, DC: Island Press.
20. Packer C et al. 2009 Sport hunting, predator control
and conservation of large carnivores. PLoS ONE 4,
e5941. (doi:10.1371/journal.pone.0005941)
21. Caro TM, Young CR, Cauldwell AE, Brown DDE. 2009
Animal breeding systems and big game hunting:
models and application. Biol. Conserv. 142,
909929. (doi:10.1016/j.biocon.2008.12.018)
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
7
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
22. Bloom PM, Clark RG, Howerter DW, Armstrong LM.
2013 Multi-scale habitat selection affects offspring
survival in a precocial species. Oecologia 173,
12491259. (doi:10.1007/s00442-013-2698-4)
23. Decesare NJ, Hebblewhite M, Bradley M, Hervieux
D, Neufeld L, Musiani M. 2014 Linking habitat
selection and predation risk to spatial variation in
survival. J. Anim. Ecol. 83, 343352. (doi:10.1111/
1365-2656.12144)
24. Fuiman LA, Meekan MG, McCormick MI. 2010
Maladaptive behavior reinforces a recruitment
bottleneck in newly settled fishes. Oecologia 164,
99108. (doi:10.1007/s00442-010-1712-3)
25. Pelletier F, Garant D. 2012 Population consequences
of individual variation in behaviour. In Behavioral
responses to a changing world: mechanisms and
consequences (eds U Candolin, B Wong), pp. 159–
174. New York, NY: Oxford University Press.
26. Gaillard JM, Festa-Bianchet M, Yoccoz NG, Loison A,
Toı¨go C. 2000 Temporal variation in fitness
components and population dynamics of large
herbivores. Annu. Rev. Ecol. Syst. 31, 367393.
(doi:10.1146/annurev.ecolsys.31.1.367)
27. Steyaert S. 2012 The mating system of the brown
bear in relation to the sexually selected infanticide
theory. PhD thesis, Norwegian University of Life
Science, A
˚
s, Norway.
28. Bellemain E, Swenson JE, Taberlet P. 2006 Mating
strategies in relation to sexually selected infanticide
in a non-social carnivore: the brown bear. Ethology
112, 238246. (doi:10.1111/j.1439-0310.2006.
01152.x)
29. Miller SD, Sellers RA, Keay JA. 2003 Effects of
hunting on brown bear cub survival and litter size
in Alaska. Ursus 14, 130152.
30. McLellan BN. 2005 Sexually selected infanticide in
grizzly bears: the effects of hunting on cub survival.
Ursus 16, 141156. (doi:10.2192/1537-6176
(2005)016[0141:SSIIGB]2.0.CO;2)
31. Wielgus RB, Bunnell FL. 2000 Possible negative
effects of adult male mortality on female grizzly
bear reproduction. Biol. Conserv. 93, 145– 154.
(doi:10.1016/S0006-3207(99)00152-4)
32. Hessing P, Aumiller L. 1994 Observations of
conspecific predation by brown bears, Ursus arctos,
in Alaska. Can. Field Nat. 108, 332336.
33. Swenson JE, Haroldson MA. 2008 Observations of
mixed-aged litters in brown bears. Ursus 19,
7379. (doi:10.2192/07sc017r.1)
34. Steyaert SMJG, Swenson JE, Zedrosser A. 2014 Litter
loss triggers estrus in a nonsocial seasonal breeder.
Ecol. Evol. 4, 300310. (doi:10.1002/ece3.935)
35. Swenson JE, Sandegren F, Brunberg S, Segerstro
¨mP.
2001 Factors associated with loss of brown bear cubs
in Sweden. Ursus 12, 69– 80.
36. Zedrosser A, Dahle B, Støen OG, Swenson JE. 2009
The effects of primiparity on reproductive
performance in the brown bear. Oecologia 160,
847854. (doi:10.1007/s00442-009-1343-8)
37. Maletzke BT, Wielgus R, Koehler GM, Swanson M,
Cooley H, Alldredge JR. 2014 Effects of hunting on
cougar spatial organization. Ecol. Evol. 4,
21782185. (doi:10.1002/ece3.1089)
38. Zedrosser A, Dahle B, Swenson JE. 2006 Population
density and food conditions determine adult female
body size in brown bears. J. Mammal 87, 510 518.
(doi:10.1644/05-mamm-a-218r1.1)
39. Nawaz MA, Swenson JE, Zakaria V. 2008 Pragmatic
management increases a flagship species, the
Himalayan brown bears, in Pakistan’s Deosai
national park. Biol. Conserv. 141, 2230– 2241.
(doi:10.1016/j.biocon.2008.06.012)
40. Sæther BE, Engen S, Swenson JE, Bakke Ø,
Sandegren F. 1998 Assessing the viability of
Scandinavian brown bear, Ursus arctos, populations:
the effects of uncertain parameter estimates. Oikos
83, 403416. (doi:10.2307/3546856)
41. Kindberg J, Swenson JE, Ericsson G, Bellemain E,
Miquel C, Taberlet P. 2011 Estimating population
size and trends of the Swedish brown bear Ursus
arctos population. Wildl. Biol. 17, 114– 123.
(doi:10.2981/10-100)
42. Bonenfant C et al. 2009 Empirical evidence of
density-dependence in populations of large
herbivores. In Advances in ecological research
(ed. H Caswell), pp. 313 357. San Diego, CA:
Academic Press.
43. Bischof R, Swenson JE, Yoccoz NG, Mysterud A,
Gimenez O. 2009 The magnitude and selectivity of
natural and multiple anthropogenic mortality
causes in hunted brown bears. J. Anim. Ecol.
78, 656665. (doi:10.1111/j.1365-2656.2009.
01524.x)
44. Swenson JE, Sandegren F. 1996 Sustainable brown
bear harvest in Sweden estimated from hunter-
provided information. J. Wildl. Res. 1, 229232.
45. Arnemo JM, Evans A, Fahlman A
˚
. 2011 Biomedical
protocols for free-ranging brown bears, wolves,
wolverines and lynx. Trondheim, Norway: Directorate
for Nature Management.
46. Zedrosser A, Støen O-G, Sæbø S, Swenson JE. 2007
Should I stay or should I go? Natal dispersal in the
brown bear. Anim. Behav. 74, 369– 376. (doi:10.
1016/j.anbehav.2006.09.015)
47. White GC, Garrott RA. 1990 Analysis of wildlife
radio-tracking data. London, UK: Academic Press.
48. Bischof R, Fujita R, Zedrosser A, So
¨derberg A,
Swenson JE. 2008 Hunting patterns, ban on baiting,
and harvest demographics of brown bears in
Sweden. J. Wildl. Manage. 72, 79– 88. (doi:10.
2193/2007-149)
49. Calin
´ski T, Harabasz J. 1974 A dendrite method for
cluster analysis. Commun. Stat. Theory Methods 3,
127. (doi:10.1080/03610927408827101)
50. Milligan GW, Cooper MC. 1985 An examination of
procedures for determining the number of clusters
in a data set. Psychometrika 50, 159–179. (doi:10.
1007/BF02294245)
51. Mace RD et al. 2012 Grizzly bear population vital
rates and trend in the northern continental divide
ecosystem, Montana. J. Wildl. Manage. 76,
119128. (doi:10.1002/jwmg.250)
52. Kovach SD, Collins GH, Hinkes MT, Denton JW. 2006
Reproduction and survival of brown bears in
Southwest Alaska, USA. Ursus 17, 16– 29. (doi:10.
2192/1537-6176(2006)17[16:RASOBB]2.0.CO;2)
53. Burnham KP, Anderson DR. 2002 Model selection and
multi-model inference: a practical information-
theoretic approach, 2nd edn. New York, NY: Springer.
54. Caswell H. 2001 Matrix population models:
constriction, analysis, and interpretation.
Sunderland, MA: Sinauer Associates.
55. Swenson JE, Dahle B, Sandegren F. 2001
Intraspecific predation in Scandinavian brown bears
older than cubs-of-the-year. Ursus 12, 81– 92.
56. Steyaert SMJG, Endrestøl A, Hackla
¨nder K, Swenson
JE, Zedrosser A. 2012 The mating system of the
brown bear Ursus arctos.Mammal Rev. 42, 12– 34.
(doi:10.1111/j.1365-2907.2011.00184.x)
57. Caswell H. 2000 Prospective and retrospective
perturbation analyses: their roles in conservation
biology. Ecology 81, 619– 627. (doi:10.1890/0012-
9658(2000)081[0619:PARPAT]2.0.CO;2)
58. Stubben C, Milligan B. 2007 Estimating and
analyzing demographic models using the Popbio
package in R. J. Stat. Softw. 22, 1– 23.
59. Horvitz C, Schemske DW, Caswell H. 1997 The
relative ‘importance’ of life-history stages to
population growth: prospective and retrospective
analyses. In Structured-population models in marine,
terrestrial, and freshwater systems (eds
S Tuljapurkar, H Caswell), pp. 247 271. New York,
NY: Chapman & Hall.
60. R Core Team. 2013 R: a language and environment
for statistical computing. Vienna, Austria: R
Foundation for Statistical Computing. See http://
www.R-project.org/.
61. Chapron G, Legendre S, Ferrie
`re R, Clobert J, Haight
RG. 2003 Conservation and control strategies for the
wolf (Canis Lupus) in Western Europe based on
demographic models. C.R. Biol. 326, 575–587.
(doi:10.1016/S1631-0691(03)00148-3)
62. Garshelis DL, Gibeau ML, Herrero S. 2005 Grizzly
bear demographics in and around Banff National
Park and Kananaskis Country, Alberta. J. Wildl.
Manage. 69, 277297. (doi:10.2193/0022-541X
(2005)069,0277:GBDIAA.2.0.CO;2)
63. Hostetler JA, Walter McCown J, Garrison EP, Neils
AM, Barrett MA, Sunquist ME, Simek SL, Oli MK.
2009 Demographic consequences of anthropogenic
influences: Florida black bears in north-central
Florida. Biol. Conserv. 142, 2456– 2463. (doi:10.
1016/j.biocon.2009.05.029)
64. Hamel S, Co
ˆte
´SD, Smith KG, Festa-Bianchet M.
2006 Population dynamics and harvest potential of
mountain goat herds in Alberta. J. Wildl. Manage.
70, 10441053. (doi:10.2193/0022-
541x(2006)70[1044:pdahpo]2.0.co;2)
65. Pfister CA. 1998 Patterns of variance in stage-
structured populations: evolutionary predictions and
ecological implications. Proc. Natl Acad. Sci. USA 95,
213218. (doi:10.1073/pnas.95.1.213)
66. Zedrosser A, Pelletier F, Bischof R, Festa-Bianchet M,
Swenson JE. 2013 Determinants of lifetime
reproduction in female brown bears: early body
mass, longevity, and hunting regulations. Ecology
94, 231240. (doi:10.1890/12-0229.1)
67. Bonenfant C, Pelletier F, Garel M, Bergeron P. 2009
Age-dependent relationship between horn growth
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
8
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
and survival in wild sheep. J. Anim. Ecol. 78, 161
171. (doi:10.1111/j.1365-2656.2008.01477.x)
68. Langvatn R, Loison A. 1999 Consequences of
harvesting on age structure, sex ratio and
population dynamics of red deer Cervus elaphus in
Central Norway. Wildl. Biol. 5, 213– 223.
69. Steyaert SMJG, Kindberg J, Swenson JE, Zedrosser A.
2013 Male reproductive strategy explains
spatiotemporal segregation in brown bears.
J. Anim. Ecol. 82, 836 845. (doi:10.1111/1365-
2656.12055)
70. Dahle B, Swenson JE. 2003 Seasonal range size in
relation to reproductive strategies in brown bears
Ursus arctos.J. Anim. Ecol. 72, 660667. (doi:10.
1046/j.1365-2656.2003.00737.x)
71. Steyaert SMJG, Reusch C, Brunberg S, Swenson JE,
Hackla
¨nder K, Zedrosser A. 2013 Infanticide as a
male reproductive strategy has a nutritive risk effect
in brown bears. Biol. Lett. 9, 20130624. (doi:10.
1098/rsbl.2013.0624)
72. Korpela K, Sundell J, Ylo
¨nen H. 2011 Does
personality in small rodents vary depending on
population density? Oecologia 165, 6777. (doi:10.
1007/s00442-010-1810-2)
73. Palombit RA. 2003 Male Infanticide in wild savanna
baboons: adaptive significance and intraspecific
variation. In Sexual selection and reproductive
competition in primates: new perspectives and
directions (ed. CB Jones), pp. 364411. Norman,
OK: American Society of Primatologists.
74. Bartmann RM, White GC, Carpenter LH. 1992
Compensatory mortality in a Colorado mule deer
population. Wildlife monographs 121. Bethesda,
MD: Wildlife Society.
75. Anderson DR, Burnham KP. 1976 Population ecology
of the mallard. VI: the effect of exploitation on
survival. Washington, DC: US Fish and Wildlife
Service.
76. Lebreton JD. 2005 Dynamical and statistical models
for exploited populations. Aust. N.Z. J. Stat. 47,
4963. (doi:10.1111/j.1467-842X.2005.00371.x)
77. Mappes T, Aspi J, Koskela E, Mills SC, Poikonen T, Tuomi
J. 2012 Advantage of rare infanticide strategies in an
invasion experiment ofbehavioural polymorphism. Nat.
Commun. 3, 611. (doi:10.1038/ncomms1613)
78. Perrigo G, Belvin L, Quindry P, Kadir T, Becker J, Van
Look C, Niewoehner J, Vom Saal FS. 1993 Genetic
mediation of infanticide and parental behavior in
male and female domestic and wild stock house
mice. Behav. Genet. 23, 525531. (doi:10.1007/
BF01068143)
rspb.royalsocietypublishing.org Proc. R. Soc. B 282: 20141840
9
on November 17, 2014http://rspb.royalsocietypublishing.org/Downloaded from
... We obtained climatic data to investigate if body growth patterns vary between climatic subperiods of different spring and summer temperatures ("cold" vs. "warm" subperiod) from the Central Institution We calculated the mean daily maximum spring (April-May) and summer (June-August) temperatures (in °C) for each year during the study period. We then tested for periods with statistically different spring and summer temperatures by dividing the overall study period into 2-5 climatic subperiods and calculating the Calinski-Harabasz (CH) index (Caliński & Harabasz, 1974) for all possible chronological combinations of climatic subperiods (Gosselin et al., 2015). The CH ...
... We compared the CH index for the most likely chronological groups and gave the most probable number of sub-periods. The maximum level of hierarchy was used to determine the correct number of partitions in the data that would maximize the inter-cluster variance and minimize the intra-cluster variance, i.e., return periods with different spring and summer temperatures (Gosselin et al., 2015). ...
Article
Full-text available
Uptake and use of energy are of key importance for animals living in temperate environments that undergo strong seasonal changes in forage quality and quantity. In ungulates, energy intake strongly affects body mass gain, an important component of individual fitness. Energy allocation among life-history traits can be affected by internal and external factors. Here, we investigate large-scale variation in body growth patterns of Alpine chamois Rupicapra rupicapra rupicapra, in relation to sex, age, temperature, and habitat variations across 31 (sub)populations in the Central European Alps. Taking advantage of an exceptionally large dataset (n = 178,175) of chamois hunted over 27 consecutive years between 1993 and 2019 in mountain ranges with different proportions of forest cover, we found that (i) patterns of body mass growth differ between mountain ranges, with lower body mass but faster mass growth with increasing proportion of forest cover and that (ii) the effect of spring and summer temperatures on changes in body growth patterns are larger in mountain ranges with lower forest cover compared to mountain ranges with higher forest cover. Our results show that patterns of body mass growth within a species are more plastic than expected and depend on environmental and climatic conditions. The recent decline in body mass observed in Alpine chamois populations may have greater impacts on populations living above the treeline than in forests, which may buffer against the effects of increasing temperatures on life-history traits.
... This suggests that important heterogeneity cues for settlement decisions occurs within the social landscape. Changes in the social makeup of this population are largely driven by hunting (Gosselin et al. 2015;Bischof et al. 2018). As adult females are removed from the population via harvest, surviving females will shift their home ranges to "fill in" vacancies left by the deceased female (Frank et al. 2017). ...
Article
Full-text available
How and where a female selects an area to settle and breed is of central importance in dispersal and population ecology as it governs range expansion and gene flow. Social structure and organization have been shown to influence settlement decisions, but its importance in the settlement of large, solitary mammals is largely unknown. We investigate how the identity of overlapping conspecifics on the landscape, acquired during the maternal care period, influences the selection of settlement home ranges in a non-territorial, solitary mammal using location data of 56 female brown bears (Ursus arctos). We used a resource selection function to determine whether females’ settlement behavior was influenced by the presence of their mother, related females, familiar females, and female population density. Hunting may remove mothers and result in socio-spatial changes before settlement. We compared overlap between settling females and their mother’s concurrent or most recent home ranges to examine the settling female’s response to the absence or presence of her mother on the landscape. We found that females selected settlement home ranges that overlapped their mother’s home range, familiar females, that is, those they had previously overlapped with, and areas with higher density than their natal ranges. However, they did not select areas overlapping related females. We also found that when mothers were removed from the landscape, female offspring selected settlement home ranges with greater overlap of their mother’s range, compared with mothers who were alive. Our results suggest that females are acquiring and using information about their social environment when making settlement decisions.
... considering that this is a correct approach in relatively undisturbed populations, we believe this practice might be overestimating-for both subpopulations-the numbers of reproductive females because it does not account for the chance of cub mortality. In brown bear populations highly disturbed by hunting or poaching, there is a risk of cub mortality or lowered recruitment [23,28,29] and, if a female loses her cubs of the year, she would be likely to reproduce again in the following year and would be counted twice. We consider that there is room for reasonable doubt in the context of Cantabrian brown bear subpopulations, for there is evidence of long-term and ongoing disturbance and direct persecution in these two brown bear subpopulations. ...
Article
Full-text available
In a recent paper, we presented new evidence and provided new insights on the status of Cantabrian brown bear subpopulations, relevant for this species conservation. Namely, we revealed the likely phylogeographic relation between eastern Cantabrian subpopulation and the historical Pyrenean population. We have also detected an asymmetric flow of alleles and individuals from the eastern to the western subpopulation, including seven first-generation male migrants. Based on our results and on those of previous studies, we called the attention to the fact that Eastern Cantabrian brown bears might be taking advantage of increased connectivity to avoid higher human pressure and direct persecution in the areas occupied by the eastern Cantabrian subpopulation. In reply, Blanco et al (2020) [11] have criticized our ecological interpretation of the data presented in our paper. Namely, Blanco and co-authors criticize: (1) the use of the exodus concept in the title and discussion of the paper; (2) the apparent contradiction with source-sink theory; (3) the apparent overlooking of historical demographic data on Cantabrian brown bear and the use of the expression of population decline when referring to eastern subpopulation. Rather than contradicting the long and growing body of knowledge on the two brown bear subpopulations, the results presented in our paper allow a new perspective on the causes of the distinct pace of population growth of the two brown bear subpopulations in the last decades. Here, we reply to the criticisms by: clarifying our ecological interpretation of the results; refocusing the discussion on how the new genetic data suggest that currently, the flow of individuals and alleles is stronger westward, and how it may be linked to direct persecution and killing of brown bears. We provide detailed data on brown bear mortality in the Cantabrian Mountains and show that neither migration, gene flow, population increase nor mortality are balanced among the two subpopulations.
... Accordingly, hunting may differentially affect bird populations, depending on their migratory flyways and their winter destination. As a consequence, identifying the origins and the migratory flyways and overwintering areas are crucial issues to assess the underlying migration connectivity (Webster and Marra 2005;Guzmán et al. 2010;Procházka et al. 2017;Guillemain et al. 2019) and to mitigate the impact of hunting on population dynamics (Gosselin et al. 2015;Clausen et al. 2017;Leach et al. 2017). ...
Article
Full-text available
Knowledge of the origin and spatial distribution of migratory bird contingents is essential information for the study and conservation of their populations. In short-distance migratory birds, their propensity to migrate has reduced over the past decades: more individuals remain year-round on the breeding grounds, and those that migrate winter at closer distance. To inform the management of these migratory populations and species subject to intensive hunting during the non-breeding season, we must document and understand how their migratory behaviours have changed over the past decades. Using ringing-recovery data spanning over the past 70 years (1950–2018), we updated knowledge on origins and within-France spatial distribution of five hunted European turdids migrating to/through France (common blackbird, fieldfare, song thrush, redwing and mistle thrush), and documented how these aspects have changed over time. Our results confirm that France hosts non-breeding birds from all Continental Europe. Partial and short-distance migratory populations from blackbird mainly come from western and central Europe and are strongly segregated in autumn and winter in France according to their origin. Surprisingly, our results relativize the presence of populations originating from Fennoscandia for the northernmost species (redwing and fieldfare), which present a slightly marked segregation. Finally, this study highlighted temporal changes in the migratory propensity for song thrush and mistle thrush, and modifications of the within-France spatial distribution for fieldfare, song thrush, and redwing. Through this new knowledge, we hope to provide a replicable method to characterize migratory populations’ distribution and bring elements to promote the differentiated management of hunted thrushes in Europe
... Given that ovulation in female bears can be potentially induced by the loss of cubs (Boone et al. 2004, Curry et al. 2014Stewart 2016, it is conceivably beneficial for a male to kill cubs assuming they are not his from a previous mating, and assuming that the female does indeed ovulate and then become receptive to his advances (e.g., Bunnell & Tait 1981, Larivière & Ferguson 2003, Himelright et al. 2014). There is increasing evidence for the existence of this somewhat speculate sequence, consistent with well-documented increases in infanticide under conditions were there is an influx of non-resident male bears (Bellemain et al. 2006;Gosselin et al. 2015Gosselin et al. , 2017Leclerc et al. 2017). ...
Technical Report
Full-text available
Bear managers are increasingly using non-lethal methods to resolve human-bear conflicts—largely because the public is demanding that wildlife be treated more humanely and with greater regard for their intrinsic value. Hazing or a fixed infrastructure designed to inflict pain and discomfort are the most common non-lethal means employed by managers to drive bears away from people and human facilities or, even more ambitiously, teach them to indefinitely avoid roads, residences, and campgrounds. The 2021 technical report entitled “Teaching Bears: Complexities and Contingencies of Deterrence and Aversive Conditioning” focuses not only on the uses of deterrents to haze bears away from conflict situations, but also, more importantly, on the complexities that bedevil efforts to educate wild bears under field conditions. Aversive conditioning—a general term for pain-based fear-instilling learning processes—is probably the most complex endeavor that a manager can undertake with a bear. “Teaching Bears” delves into the many facets of aversive conditioning, including terminology and concepts relevant to understanding the basics of how animals learn about their world. However, most of this report is devoted to describing what it is that individual animals bring to a learning process, and how these internal complexities along with the particulars of a given context largely dictate whether efforts by managers to deter and aversively-condition bears are likely to be successful or not. The report concludes that aversive conditioning will almost invariably have a limited role in non-lethal management of human-bear conflicts, especially in contrast to efforts focused on people. At its most useful, hazing can be used to temporarily drive bears away from a conflict situation, providing a respite during which managers can then address human-related elements such as the availability of attractants or problematic behaviors of people.
... Such studies have shown that reduction in population size due to hunting (even in groups as disparate as teleosts, pinnipeds, and cetaceans) results in a decrease in average age of maturation Lockyer, 1972;Rijnsdorp, 1993;Trippel, 1995;Rochet, 1998;Festa-Bianchet, 2003, Gabriele et al., 2007. Likewise, increased fecundity has been documented in overhunted populations of teleost fish (Bagenal, 1966;Healey, 1978;Horwood and Howlett, 1986;Kelly and Stevenson, 1985), while birthing interval decreases in various iteroparous tetrapod species (Anderson and Burnham, 1976;Swihart et al., 1998;Gosselin et al., 2015). Similar studies have not been conducted for modern proboscideans, but the link of hunting pressures to earlier reproductive schedules and increased birth rates across many distantly related vertebrate groups ...
Thesis
Full-text available
The end of the Pleistocene saw the extinction of many large vertebrate species, including mammoths (genus Mammuthus). Despite many decades of work by various researchers, the cause(s) of mammoth extinction are still heavily debated, with climate change and human hunting being the two primary hypothesized agents of extinction. One major problem with identifying the cause of this extinction is the fact that changing climates and movement of human hunters into ecosystems containing mammoths are both broadly associated with the time of extinction, making it difficult to decouple one potential cause from the other using only temporal data. This study bypasses the strictly chronological approach of many previous studies and instead investigates the cause of the end-Pleistocene extinction using information about reproductive life history. The age of first conception and the average time between conceptions are both expected to change predictably and divergently under the hypotheses of climate-driven extinction and hunting-driven extinction, so assessment of changes in these aspects of life history approaching the time of extinction could provide a test for cause of extinction. I use the record of growth within tusk dentin to identify patterns associated with reproductive life history in mammoths. Thin sections and serial isotope analyses document the periodicity of X-ray density features observed in microCT sections of tusks. These attenuation features form annually in both Columbian and woolly mammoths (Mammuthus columbi and Mammuthus primigenius, respectively), but form semiannually in a gomphothere from South America. MicroCT scans of entire tusks are employed to provide a record of multiple decades of growth for ten Siberian woolly mammoths. In eight of these specimens, all of them adult females, we observe a repeated 3- to 6-year-long cyclical pattern of regularly varying growth rate. This pattern was absent in both adult males and juveniles. We interpret this pattern as a record of calving in females, and its onset is observed in several individuals to occur at an age approximating that of sexual maturation in extant elephants. Our dataset shows a minor decrease in age of maturation and average calving interval near the end of the Pleistocene. This is predicted by a hunting-driven model of extinction but is not expected for extinction driven by climate change. This work contributes to our knowledge of the reproductive life history of mammoths, which we argue is key to understanding the cause of their extinction.
... Large mammal populations are limited by the number of reproductive females (Gaillard et al., 2000;Gosselin et al., 2014). Ensuring and improving the lifetime reproductive output of rhino cows should thus be the highest priority for rhino management as it will result in high population growth rates. ...
Article
Large herbivores, particularly in water limited systems, are vulnerable to the impacts of poaching (illegal hunting) and human‐induced climate changes. However, we have little understanding of how these processes can reshape their populations. With some rapidly declining populations there is a need to understand the effects of these stressors on populations of vulnerable large herbivores like the white rhino (Ceratotherium simum simum). We developed age‐structured models for the rhino population in Kruger National Park, home to 49% of South Africa’s rhinos. We wanted to determine the relative influence of poaching and climate on the current and future population size and demographics, examine the potential of a dependency effect (the loss of calves from poached females) and quantify the compound effect (loss of future young). Our results indicated that population declines were largely driven by poaching and included a dependency effect. Rainfall had a measurable but smaller influence on rhino populations and had an additive effect; reduced rainfall exacerbated poaching losses. Current poaching levels have resulted in a reduction to the lifetime reproductive output per cow from approximately 6 to 0.7 calves: a compound effect of 5.3 future offspring. Under current levels of poaching, we project a 35% decline in the Kruger rhino population in the next 10 years. However, if poaching intensity is cut in half, we project a doubling of the current population over the same time frame. Overall, our models showed little sensitivity to demographic and environmental parameters, except for adult survival. Our results suggest that maintaining and improving the lifetime reproductive output of rhino cows should thus be the highest management priority and that new management targets should consider both the dependency and compound effects associated with poaching on rhino cows. With some rapidly declining populations there is a need to understand the effects of these stressors on populations of vulnerable large herbivores like the white rhino. We developed age‐structured models for the rhino population in Kruger National Park, home to 49% of South Africa’s rhinos. We found that white rhino population declines were largely driven by poaching and included a dependency effect through the loss of dependent calves. Current poaching levels have resulted in a compound effect of 5.3 future offspring being lost per cow. Rainfall exacerbates poaching losses by adding an extra 10% losses.
... The total mortality Z is often subdivided such that Z = M + F, where M represents all natural causes of death and F represents mortality caused by humans e.g. through hunting or fishing. The rate of natural mortality M is of key importance in population dynamics and the sustainable management of natural resources [5][6][7][8] . Yet, the natural mortality or natural survival rate is exceedingly difficult to estimate in wild populations, often requiring data-intensive and costly approaches 5,9 . ...
Article
Full-text available
Information about the survival of species is important in many ecological applications. Yet, the estimation of a species’ natural mortality rate M remains a major problem in the management and conservation of wild populations, often circumvented by applying empirical equations that relate mortality to other traits that are more easily observed. We show that mean adult M can be approximated from the general law of decay if the average maximum age reached by individuals in a cohort is known. This is possible because the proportion P of individuals surviving to the average maximum age in a cohort is surprisingly similar across a wide range of examined species at 1.5%. The likely reason for the narrow range of P is a universal increase in the rate of mortality near the end of life, providing strong evidence that the evolutionary theories of ageing are the norm in natural populations. Dureuil and Froese present a universal equation for estimating the mean adult mortality rate in natural populations. This equation is based on remarkable similarities in survival to average maximum age across a wide range of species.
Chapter
This chapter briefly introduces forestry and describes the differences between natural forests and various forms of human-managed forest. This chapter also introduces tree species most commonly found in the European and North American forestry, describes the basic steps of forestry technologies, and explains how various ways of forest management affect various components of ecosystem including biodiversity, nutrient cycling energy flows soils, water, etc. Special attention is paid to interaction of forestry and ongoing global change. In particular, this chapter deals with the following question: What are the potentials and risks of using forests plantation to mitigate global change? It also deals with the basic difference between human hunters and natural predators and introduces the major principles of hunting regulations. Finally, it explains the effect of hunting on game population and other component of ecosystem.
Article
Moonlight plays a significant role in prey–predator relationships. At full moon, predators' hunting success and activity rates generally increase. Even though the analysis of facultative carnivore movement patterns can improve our knowledge of how moonlight can change the behaviour of such a group of species with diverse ecological needs, few studies have been conducted with facultative carnivores and none with telemetric data. Here, we studied whether moonlight influences brown bear, Ursus arctos, movement behaviours. By analysing data collected from 2002 to 2014 for 71 collared individuals inhabiting Finland and Russian Karelia, we found that some internal and external factors are influencing brown bear movement patterns. In particular, this facultative carnivore moves more slowly and over shorter distances during hyperphagia periods than during the mating season. However, moonlight does not affect brown bear movements. Although brown bears are large carnivores, they are opportunistic omnivores with a high fruit diet and, therefore, the prey–predator relationships that are behind the dependence of carnivores on moonlight seem to be weaker than in obligate carnivores. Moonlight plays a significant role in prey‐predator relationships. At full moon, predators’ hunting success and activity rates generally increase. Here, we studied whether moonlight influences brown bear, Ursus arctos, movement behaviours. By analysing data collected from 2002 to 2014 for 71 collared individuals inhabiting Finland and Russian Karelia, we found that some internal and external factors are influencing brown bear movement patterns, however, moonlight does not affect brown bear movements. Although brown bears are large carnivores, they are opportunistic omnivores with a high fruit diet and, therefore, the prey‐predator relationships that are behind the dependence of carnivores on moonlight seem to be weaker than in obligate carnivores.
Chapter
Full-text available
Determining the importance of life-history events for population growth is a significant, if ill-defined, goal of population-dynamics research. Perturbation analyses, which explore the effects on population growth of changes in the vital rates, provide an approach to this problem. They have become a standard part of demographic practice. It is now rare to find a published report of population growth rate that does not investigate how that rate changes as the vital rates are perturbed, either actually (comparing differ ent treatments, sites, species, etc.) or hypothetically (exploring the consequences of potential management strategies or of evolutionary changes). Applications include life-history theory (where it is important to know how the different vital rates influence fitness; see, e.g., Caswell & Werner 1978; Caswell 1985; Calvo & Horvitz 1990; Kalisz & McPeek 1992; Calvo 1993), conservation biology (where it is important to know how protecting different stages in the life cycle would affect population growth; see, e.g., Crouse et al. 1987; Menges 1990; Doak et al. 1994; Heppell et al. 1994; Schemske et al. 1994), ecotoxicology (where it is important to know how pollutants affect population growth; see, e.g., Caswell 1996a; Sibly 1996; Levin et al., in press), and assessing the accuracy of estimates of population growth rate (Lande 1988).
Article
Full-text available
We present data from 4 studies of radiomarked brown bears (Ursus arctos) in Alaska to evaluate the effects of hunting and differential removal of males on cub survival and litter size. In the Susitna area in southcentral Alaska, the proportion of males declined during a period of increasing hunting pressure (1980-96). Cub survivorship was higher in the heavily hunted Susitna population (0.67, n = 167 cubs) than in a nearby unhunted population in Denali National Park (0.34, n = 88 cubs). On the Alaska Peninsula, in coastal areas rich in salmon (Oncorhyncliits spp.) and with higher brown bear densities, cub survivorship was significantly higher in the hunted Black Lake population (0.57, n - 107 cubs) than in an unhunted population in Katmai National Park (0.34, n = 99 cubs). The Black Lake population had alternate-year hunting, and cub survivorship was similar during years with and without hunting during the preceding fall and spring. In both coastal and interior comparisons, litter sizes were either larger or not significantly different in hunted areas than in nearby unhunted national parks. We found no evidence that removal of adult male bears by hunters reduced cub survival or litter size. For populations below carrying capacity, convincing evidence is lacking for density dependent effects on cub survivorship or litter size. In our studies, variations in cub survivorship and litter size were best explained by proximity to carrying capacity; local environmental factors and stochastic events probably also influence these parameters. We believe that cub survivorship in our national park study areas was lower than in nearby hunted areas because of density-dependent responses to proximity to carrying capacity.
Technical Report
Full-text available
PREFACE Compilation of this document was initiated by the Norwegian Directorate for Nature Management in order to establish recommended protocols for capture, chemical immobilization, anesthesia and radiotagging of free-ranging brown bears (Ursus arctos), gray wolves (Canis lupus), wolverines (Gulo gulo) and Eurasian lynx (Lynx lynx). In addition, procedures to ensure proper sampling of biological materials for management, research and banking purposes have been included. The current protocols are based on nearly 3,000 captures of free-ranging brown bears, wolves, wolverines and lynx carried out from 1984 through 2012 in Scandinavia. Some of the results have been published as peer reviewed papers, conference presentations, theses, and reports. However, a large amount of data are still on file and will be published in the future. In addition, comprehensive reviews of the global literature on brown bears, wolves, wolverines and lynx have been carried out in order to include pertinent information from other sources. These protocols have been approved by all ongoing research projects on brown bears, wolves, wolverines and lynx in Scandinavia.
Article
This chapter discusses the potential interplay between behavioural phenotypes and population processes by illustrating how human-induced environmental changes can promote these feedbacks. It presents a rationale for why one would expect a feedback between behaviour and population dynamics, and suggests hypotheses as to what behaviours are more likely to affect population growth. It briefly reviews research that explores the classic links between population-level processes and behaviour, and how population dynamics can affect individual behaviour. It highlights empirical studies that support the recent suggestion of a reverse link between population and behaviour, and provides examples of study systems where researchers have documented the complete feedback loop. It also emphasizes instances where humans have affected the interaction between these processes.