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



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.
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.
Received: 23 July 2014
Accepted: 9 October 2014
Subject Areas:
ecology, behaviour
population dynamics, harvesting, brown bear,
sexually selected infanticide, behaviour,
Author for correspondence:
Jacinthe Gosselin
Electronic supplementary material is available
at or
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
´partement de biologie, Universite
´de Sherbrooke, 2500 boulevard de l’Universite
´, Sherbrooke,
Quebec, Canada J1K 2R1
Department of Environmental and Health Studies, Telemark University College, 3800, Norway
Institute of Wildlife Biology and Game Management, University of Natural Resources and Life Sciences,
Vienna 1180, Austria
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, A
s 1432,
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 (
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, 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 [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
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 Proc. R. Soc. B 282: 20141840
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
). 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 (
, 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
from a proportional change in a demographic rate (r
), Dlog
Dlog r
[54]. Prospective analyses were performed with the
‘popbio’ package in R [58]; the confidence intervals of
calculated with the ‘boot.transitions’ function.
(iv) Retrospective analysis
Retrospective analyses compare the contributions of past changes
in demographic rates with the variation in
and are not indicative
of future changes [57]. We estimated the association between
variation in a demographic rate r
and variation in
by: s2
where s
is the sensitivity of the population growth rate to a demo-
graphic rate r
, and
is the variance of r
[59]. These associations
are presented as contributions to variation in
, 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% Proc. R. Soc. B 282: 20141840
females, 52% males in 1990– 2005 versus 43% females, 57%
males in 2006– 2011; Yates
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 (
) 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 (
¼1.082; 95% CI ¼1.0521.119; see
the electronic supplementary material, figure S2) and was
lower during the high hunting pressure subperiod (
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
hunting pressure
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
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 Proc. R. Soc. B 282: 20141840
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
(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
. 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
, in accordance
with P1 and P2. In addition, the relative contribution of
survival and fecundity to the variance of
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
in the retro-
spective analyses (ranging from 14.6 to 18.8%) in accordance
with P4.
S0 S1 S2 S3 S4–8 S9–24 F3 F4–8 F9–23
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).
S0 S1 S2 S3 S4–8 S9–24 F3 F4–8 F9–23
proportion of the variation explained in l (%)
hic rates
Figure 3. Proportion of the variation in
(%) 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). Proc. R. Soc. B 282: 20141840
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
. It has been suggested that there
may be a trade-off between the intrinsic dependence of
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 (
¼1.082, 95% CI ¼1.052 1.119),
but this population trend changed to a decline (
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
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
than their survival. This pattern was
reversed under high hunting pressure, where the survival
of adult females explained more of the variation in
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
. 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 Proc. R. Soc. B 282: 20141840
to our matrix model, increasing cub survival by 80.9% would
by 8.17% or 0.080, making
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
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.
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.
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.
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.
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,
´n R, Wabakken P. 1997 Infanticide caused by
hunting of male bears. Nature 386, 450–451.
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
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/
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/
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) Proc. R. Soc. B 282: 20141840
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/
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.
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.
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
31. Wielgus RB, Bunnell FL. 2000 Possible negative
effects of adult male mortality on female grizzly
bear reproduction. Biol. Conserv. 93, 145– 154.
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
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.
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.
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.
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.
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.
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.
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.
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.
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.
57. Caswell H. 2000 Prospective and retrospective
perturbation analyses: their roles in conservation
biology. Ecology 81, 619– 627. (doi:10.1890/0012-
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://
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.
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
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.
64. Hamel S, Co
´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-
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 Proc. R. Soc. B 282: 20141840
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-
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.
71. Steyaert SMJG, Reusch C, Brunberg S, Swenson JE,
¨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.
72. Korpela K, Sundell J, Ylo
¨nen H. 2011 Does
personality in small rodents vary depending on
population density? Oecologia 165, 6777. (doi:10.
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
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) Proc. R. Soc. B 282: 20141840
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Technical Report
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The Scandinavian brown bear population (Ursus arctos) in Norway and Sweden has been monitored extensively and continuously for almost 25 years. Due to the extensive and regular application of DNA-based methods, we likely have one of the most comprehensive, detailed data sets as well as knowledge on a brown bear population available today. The Norwegian Large Predator Monitoring Program analyzed 1,389 biological samples in 2022, assumingly originating from brown bears. From those, 770 samples (55%) provided a DNA-profile suitable for successful identification of sex and individual. Based on the DNA-profiles 175 different individuals were identified, of which 79 (45%) were female and 96 (55%) male brown bears. Compared to the results obtained in 2021, this is an increase of 15 individuals (9%) and is the highest number of individuals identified since countrywide DNA-based monitoring of brown bears was initiated in 2009 within Norway. Brown bears occurred mainly in previously described areas in Norway; in eastern Hedmark, northeast in Trøndelag, inner Troms, Anárjohka-Karasjok and Sør-Varanger. The trajectory of the number of identified individuals in different predator management regions followed the general trend recorded over the last years, showing a distinct increase in region 5 (Hedmark), a slight decrease in region 6 (Trøndelag) and a slight increase in region 8 (Troms and Finnmark). Compared to 2021, the number of identified female brown bears increased by 12 (18%) to a total of 79 different individuals. This is the highest number of females detected since the countrywide, DNA-based monitoring of brown bears started. This increase in numbers of females reflects the general trajectory on the number of detected individuals in the different predator management regions in Norway, with a noticeable increase in region 5 (Hedmark). The samples, which were successfully identified as female brown bears in the season of 2022, were used to estimate the annual number of reproductions in Norway: 9.5 (95% CI: 4.6-15.1). The number increased by 1.4 compared to 2021, and is the highest, estimated number of annual reproductions in Norway since countrywide DNA-based monitoring was initiated in Norway. In the different predator management regions, the trajectory of identified individuals and thus estimated number of annual reproductions was significantly different. Females were detected in three of the regions in 2022. The number of annual reproductions increased in region 5 (Hedmark) from 3.6 (2021) to 5.1 (2022), which also led to an increase of the overall estimate for Norway from 8.1 (2021) to 9.5 (2022). In region 6 (Trøndelag) the number of reproductions decreased slightly from 1.6 (2021) to 1.4 (2022) and increased slightly in region 8 (Troms and Finnmark) from 2.9 (2021) to 3.0 (2022). In 2022, 14 dead brown bears were registered in Norway, which is at the same level as last year. The dead individuals are included among the identified 175 brown bears. Seven brown bears were removed due to lethal management control, four were shot during claimed defense of life and property and a further three were shot under licensed hunt. As in previous years in Norway, most of the dead recoveries were male brown bears. For all dead bears, their DNA-profile allowed for individual identification, and all dead individuals were also detected among the non-invasive genetic samples collected. Two of the three dead female brown bears were from pred-ator management region 6 (Trøndelag) which will likely lead to a lower estimated number of reproductions in this region next year.
... Actually, when human pressure is high, factors such as fear drive animals to adjust their behaviour to reduce risk. That is, bears can allocate part of their time to vigilance at the expense of other vital needs, such as searching for food, to avoid threatening encounters with humans (Gosselin et al., 2015;Loveridge et al., 2007;Ordiz et al., 2011Ordiz et al., , 2012Ordiz et al., , 2013. Thus, hunting pressure might also help explaining why brown bears moved slowly, over short distances and follow more tortuous paths when the hunting season was opened. ...
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.
... 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). ...
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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. ...
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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.
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Estimates of wildlife population size are critical for conservation and management, but accurate estimates are difficult to obtain for many species. Several methods have recently been developed that estimate abundance using kinship relationships observed in genetic samples, particularly parent-offspring pairs. While these methods are similar to traditional Capture-Mark-Recapture, they do not need physical recapture, as individuals are considered recaptured if a sample contains one or more close relatives. This makes methods based on genetically-identified parent-offspring pairs particularly interesting for species for which releasing marked animals back into the population is not desirable or not possible ( e.g. , harvested fish or game species). However, while these methods have successfully been applied in commercially important fish species, in the absence of life-history data, they are making several assumptions unlikely to be met for harvested terrestrial species. They assume that a sample contains only one generation of parents and one generation of juveniles of the year, while more than two generations can coexist in the hunting bags of long-lived species, or that the sampling probability is the same for each individual, an assumption that is violated when fecundity and/or survival depend on sex or other individual traits. In order to assess the usefulness of kin-based methods to estimate population sizes of terrestrial game species, we simulated population pedigrees of two different species with contrasting demographic strategies (wild boar and red deer), applied four different methods and compared the accuracy and precision of their estimates. We also performed a sensitivity analysis, simulating population pedigrees with varying fecundity characteristics and various levels of harvesting to identify optimal conditions of applicability of each method. We showed that all these methods reached the required levels of accuracy and precision to be effective in wildlife management under simulated circumstances ( i.e. , for species within a given range of fecundity and for a given range of sampling intensity), while being robust to fecundity variation. Despite the potential usefulness of the methods for terrestrial game species, care is needed as several biases linked to hunting practices still need to be investigated ( e.g. , when hunting bags are biased toward a particular group of individuals).
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.
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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).
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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.
I analyse and summarize the empirical evidence in mammals supporting alternative benefits that individuals may accrue when committing nonparental infanticide. Nonparental infanticide may provide the perpetrator with nutritional benefits, increased access to limited resources, increased reproductive opportunities, or it may prevent misdirecting parental care to unrelated offspring. The possibility that infanticide is either a neutral or maladaptive behaviour also is considered. I devote the second half of this article to reviewing potential mechanisms that individuals may use to prevent infanticide. These counterstrategies include the early termination of pregnancy, direct aggression by the mother against intruders, the formation of coalitions for group defence, the avoidance of infanticidal conspecifics, female promiscuity, and territoriality. I evaluate the support for each benefit and counterstrategy across different groups of mammals and make suggestions for future research.