Journal of Animal Ecology
, 656–665 doi: 10.1111/j.1365-2656.2009.01524.x
© 2009 The Authors. Journal compilation © 2009 British Ecological Society
Blackwell Publishing Ltd
The magnitude and selectivity of natural and multiple
anthropogenic mortality causes in hunted brown bears
*, Jon E. Swenson
, Nigel G. Yoccoz
, Atle Mysterud
and Olivier Gimenez
Department of Ecology and Natural Resource Management, Norwegian University of Life Sciences, PO Box 5003,
NO-1432 Ås, Norway;
Norwegian Institute for Nature Research, NO-7485 Trondheim, Norway;
Department of Biology,
University of Tromsø, NO-9037 Tromsø, Norway;
Centre for Ecological and Evolutionary Synthesis (CEES), Department
of Biology, University of Oslo, PO Box 1066 Blindern, NO-0316 Oslo, Norway; and
CEFE, UMR 5175, 1919 Route de
Mende, F-34293 Montpellier cedex 5, France
The population dynamic and evolutionary effects of harvesting are receiving growing attention
among biologists. Cause-speciﬁc estimates of mortality are necessary to determine and compare the
magnitude and selectivity of hunting and other types of mortalities. In addition to the logistic and
ﬁnancial constraints on longitudinal studies, they are complicated by the fact that nonhunting
mortality in managed populations usually consists of a mix of natural and human-caused factors.
We used multistate capture–recapture (MCR) models to estimate cause-speciﬁc survival of
brown bears (
) in two subpopulations in Sweden over a 23-year period. In our analysis,
we distinguished between legal hunting and other sources of mortality, such as intraspeciﬁc
predation, accidents, poaching, and damage control removals. We also tested whether a strong
increase in harvest quotas after 1997 in one of the subpopulations affected vulnerability to legal
Although only a fraction of mortalities other than legal hunting could be considered natural,
this group of causes showed a general pattern of demographic selectivity expected from natural
mortality regimes in populations of long-lived species, namely greater vulnerability of young
animals. On the other hand, demographic effects on hunting vulnerability were weak and
inconsistent. Our ﬁndings support the assumption that hunting and other mortalities were additive.
As expected, an increase in hunting pressure coincided with a correspondingly large increase in
vulnerability to hunting in the affected subpopulation. Because even unbiased harvest can lead to
selective pressures on life-history traits, such as size at primiparity, increasing harvest quotas may
not only affect population growth directly, but could also alter optimal life-history strategies in
brown bears and other carnivores.
Legal hunting is the most conveniently assessed and the most easily managed cause of mortality
in many wild populations of large mammals. Although legal hunting is the single-most important
cause of mortality for brown bears in Sweden, the combined mortality from other causes is of
considerable magnitude and additionally shows greater selectivity in terms of sex and age than legal
hunting. Therefore, its role in population dynamics and evolution should not be underestimated.
carnivore, compensatory mortality, competing risks, M-SURGE, wildlife management
In many naturally regulated populations of large mammals,
age-speciﬁc mortality has been shown to follow a similar
U-shaped pattern irrespective of the proximate causes of
mortality (Caughley, 1966; Gaillard, Festa-Bianchet &
Yoccoz, 1998; Gaillard
, 2000). This is not expected to
hold for populations that are heavily affected by human
exploitation, where prime-aged individuals that otherwise
survive well can also be targeted. Indeed, the selective
pressures in harvested marine and terrestrial populations
have recently raised concern regarding their long-term
evolutionary consequences (Coltman
, 2003; Kuparinen
& Merilä, 2007). It is thus not surprising that science dealing
*Corresponding author. E-mail: firstname.lastname@example.org
Cause-speciﬁc vulnerability in bears
© 2009 The Authors. Journal compilation © 2009 British Ecological Society,
Journal of Animal Ecology
with the management and conservation of wild populations
focuses increasingly on the effects of hunting on population
dynamics and evolution.
We further suspect that the spotlight that hunting is receiving,
particularly in large mammals, may be motivated partially by
the relative ease with which it can be assessed (hunter surveys,
tagging systems, etc.) and that it is arguably the most easily
inﬂuenced by wildlife managers (e.g. through hunting seasons,
quotas, and bag limits). Natural mortality is usually more
difﬁcult to detect and hence to estimate. Furthermore, natural
mortality schemes are often disturbed and at times replaced
by human-caused mortalities other than hunting (vehicle
accidents, wildlife damage control, poaching, etc.). This makes
the otherwise intuitive separation of ‘harvest’ and ‘natural
mortality’ (Anderson & Burnham, 1976) less useful, even if
cause-speciﬁc vulnerability estimates are desired. Yet, because
survival is determined by the combination of all causes of
death, a comprehensive look at survival requires estimates
of the magnitude and selectivity of all mortality causes,
including those due to proximate causes other than hunting.
Additionally, comparing mortality patterns for different age
and sex classes can yield insight into deviations from natural
mortality patterns and therefore contemporary selection
pressures, and may also help determine the degree of
compensation in mortality (Otis & White, 2004; Pedersen
, 2004; Schaub & Lebreton, 2004a; Lebreton, 2005).
Estimating and contrasting cause-speciﬁc mortality in
long-lived species requires longitudinal studies, which
additionally provide opportunities to evaluate how manage-
ment actions, such as a major change in harvest quotas, may
affect vulnerability patterns. The difﬁculties and costs asso-
ciated with such studies may explain why they are rare in large
mammals. The most well-known longitudinal studies have
been performed on ungulate populations, such as red deer
) on the island of Rum (Clutton-Brock, Guin-
ness & Albon, 1982) and Soay sheep (
) on the island
of St. Kilda, Scotland (Clutton-Brock & Pemberton, 2004).
To our knowledge, no study on large carnivores has yet com-
pared harvest and other mortality patterns under con-
trasting management regimes.
The Scandinavian Brown Bear Research Project has
collected an extensive data set with information on 525 marked
brown bears (
), spanning 23 years of intensive
monitoring. Many of the individuals have been followed from
the age of 1 to death, which presents a rare opportunity to
assess cause-speciﬁc vulnerabilities in a large carnivore species.
Our ﬁrst objective was to estimate age- and sex-speciﬁc
vulnerability to legal hunting in this population and deter-
mine if they are comparable to the patterns observed in other
harvested bear populations in North America, where there is
evidence for selectivity for younger, inexperienced indivi-
duals, especially males (Derocher, Stirling & Calvert, 1997;
Noyce & Garshelis, 1997; McLellan
, 1999). In Bischof
(2008a), we documented differences between males and
females in terms of the variables that explained the age of
harvested bears, but could not address vulnerability directly,
because that analysis was based solely on harvested bears.
In addition to legal hunting, brown bears in Sweden die
from a variety of other causes, such as intraspeciﬁc predation,
vehicle collision, depredation control, and poaching
, 1997; Swenson & Sandegren, 1999; Swenson,
Dahle & Sandegren, 2001; Sahlén
, 2006). Consequently,
our second objective was to compare the magnitude and
demographic selectivity of legal hunting mortality with other
mortality sources. We use multistate capture–recapture
modelling to estimate and compare the magnitude and
demographic selectivity of legal hunting with other mortality
causes and discuss our ﬁndings in the context of carnivore
population dynamics and evolution.
Finally, the potential for compensatory mortality is an
important consideration for the management of exploited
populations. The effect of changes in harvest intensity (i.e.
quotas) is dependent on the degree of compensation this causes
in other mortality sources, may they be natural or human
caused. A dramatic increase in quotas starting in the
mid-1990s in one of our two subpopulations enabled us to
look for evidence of compensation by monitoring changes in
the vulnerability to hunting and other causes of death before
and after hunting pressure increased.
Our two study areas were located in northern and south-central
Sweden. The northern study area (‘north’, 67
N, 1 8
12 000 km
, the other site (‘south’, 61
E) is 11 500 km
These areas are based on genetically distinct subpopulations that
match geographical clusters of bears with no or very little interchange
of females (Manel
, 2004). Both study areas occur within the
southern, intermediate, and northern boreal vegetation zones
(Nordiska inisterrådet, 1984; Bernes, 1994). The study areas are
described in detail in Zedrosser, Dahle & Swenson (2006).
Protective measures, implemented in Sweden as early as the end of
the 19th century, brought the brown bear population back from the
brink of extinction (Swenson
, 1995). In 2005, the population
size of brown bears in Sweden was estimated to be between 2350 and
2900 (Kindberg & Swenson, 2006). Hunting brown bears is legal in
Sweden, where a fall season results (in recent years) in the harvest
of approximately 5% of the estimated population (Bischof
Most bears were captured from a helicopter with immobilizing darts
during the spring (20 March–10 June) from 1984–2006. Captured
bears were measured and weighed, and blood, tissue, and hair
samples were collected. Unless they were followed from birth, the
ﬁrst premolar was extracted and sent to Matson’s, Inc., Milltown,
MT, USA for age estimation using counts of cementum annuli layers
, 1993). Bears designated for radiotelemetry (
were equipped with collar-mounted radiotransmitters, radio-
implants, or both. All bears, including non-instrumented ones
= 137), were marked individually with tattoos (inside of the
upper lip), ear tags, and passive integrated transponder (PIT) tags
placed subcutaneously between the shoulder blades. Radio-marked
bears were recaptured every 2–3 years to collect new measurements
© 2009 The Authors. Journal compilation © 2009 British Ecological Society,
Journal of Animal Ecology
and to exchange used radiotransmitters for ones with new batteries.
Great effort was made to capture all yearlings accompanying
radio-marked females. Non-instrumented animals were (re)captured
opportunistically based on priorities and available funding. Radio-
marked bears were located once every 1–2 weeks during the active
period (March–November) and sporadically throughout the
denning period with standard triangulation from the ground or from
a ﬁxed wing aircraft or helicopter. The radiotelemetry portion of the
study has generally focused more on females than males. Arnemo
(2007), and Dahle & Swenson (2003b)
provide additional information about the capture of bears, monitoring,
and data collection procedures. Capture, manipulation, marking
and monitoring of bears complied with current laws regulating the
treatment of animals in Sweden and Norway, where a few bears were
captured, and were approved by the appropriate ethical committees
in both countries.
The main sources for recoveries of bears (outside of regular monitoring
activities of radio-tagged bears) were mandatory hunter reporting,
dead bears discovered and reported by members of the public, and
bears killed as part of damage control activities. By regulation,
successful brown bear hunters in Sweden were required to notify the
police on the day of the kill, present their bear carcass to an ofﬁcially
appointed inspector and provide information about harvest
methods, the sex of the harvested bear, body mass, and kill location.
The Swedish brown bear hunt and reporting of hunter-killed bears
are described in Bischof
(2008a). Between 1984 and 2006, 124
marked bears were shot during legal hunting, accounting for 59·6%
of all marked bears recovered dead (
= 208). Conﬁrmed mortali-
ties of marked bears due to causes other than legal hunting included
the following (in order of prevalence and with the proportion of
deaths in parentheses):
= 28, 13·5 %, mainly intraspeciﬁc kills)
Damage control removal and self-defense (
= 23, 11·1%)
Cause unknown (
= 15, 7·2 %)
Death as a result of capture (
= 7, 3·4%)
Conﬁrmed illegal hunting (
= 7, 3·4%)
Accident (including trafﬁc) (
= 4, 1.9%)
Although a breakdown into these causes would increase resolution
in terms of cause-speciﬁc mortalities, in our case data limitations
and resulting parameter estimation problems for the various
transitions (see below), made such distinction unfeasible. It is worth
noting that natural mortality (in the sense of nonhuman-caused
mortality) constituted only a small portion (13·5%) of conﬁrmed
deaths of marked animals and 1/3 of bears dying due to causes other
than legal hunting.
Model and parameter description
Modelling of movement was the main motivation for the initial
development of multistate capture–recapture (MCR) models
(Hestbeck, Nichols & Malecki, 1991; Brownie
, 1993). Their
usefulness for modelling transitions between other types of states,
e.g. behavioural and reproductive states (Barbraud & Weimerskirch,
, 2008), has since become apparent, and Lebreton,
Almeras & Pradel (1999) showed how multistate models can be used
to combine live recaptures and dead recoveries by designating
separate states for alive and newly dead, each state with its respective
detection probability. Following Schaub & Pradel (2004b), we
extended Burnham’s (Burnham, 1993) model (presented as a three-
stratum model in Lebreton
, 1999) for combined analysis of
tag recovery and recapture data. Our model (Fig. 1) included an
additional cause of mortality and the possibility of return for animals
that had left the study area, resulting in four possible states: (1) alive
inside the study area, (2) alive outside the study area, (3) newly dead
due to legal hunting, and (4) newly dead due to other causes.
State transitions probabilities are deﬁned in the following matrix
(row, states of departure; column, states of arrival):
being the probability of dying due to legal hunting during the
the probability of dying due to causes other than
legal hunting during the same time period, and 1
, a ﬁdelity term, represents the probability of remaining
within the study area, and
is the probability of returning to the
study area for animals that are outside. The mortality parameters
associated with the transition to states 3 and 4 are true mortalities,
whereas the parameters in the other two states are only local survival.
Detection probabilities differ depending on the cause of mortality
among animals newly dead, but the model assumes that dead animals
are detected with equal probability inside and outside the study area,
as does Burnham’s model (Burnham, 1993). Equal detection probability
inside and outside our study areas is a reasonable assumption given
that animals killed by legal hunting were all detected by deﬁnition,
and bears that died due to other causes were either detected because
they were followed by radiotelemetry, or incidentally encountered. The
weakest part of the assumption is equal detection of instrumented
bears dead due to causes other than legal hunting, regardless of
Fig. 1. Fate diagram illustrating MCR model state transitions o
marked brown bears in Sweden. Bears can die due to two competing
risks (legal hunting and all other mortality causes) or stay alive. Bears
alive inside or outside the study area may remain in their current
location or move out of or into the study area, respectively.
hwF hw F hw
hwR hw R hw
( ) ( )( )
( ) ( )( )
−− −− −
−− −− −
Cause-speciﬁc vulnerability in bears
© 2009 The Authors. Journal compilation © 2009 British Ecological Society,
Journal of Animal Ecology
location in- or outside the study area. This potential lack of realism
is necessitated by the need for parameter identiﬁability (Gimenez,
Choquet & Lebreton, 2003; Hunter & Caswell, 2009).
Schaub & Pradel (2004) demonstrated the use of multistate
models to assess the relative importance of different sources of
mortality. Our approach is similar to theirs, however, whereas they
estimated the probability of death being caused by a certain source of
mortality conditional on having died during the interval, we esti-
mated the cause-speciﬁc probability of dying conditional on being
alive at the beginning of the interval.
We constrained the capture probability (
) for state 3 to equal 1,
recognizing that all legally shot bears had to be reported to the
management authorities in Sweden. Consequently, we only estimated
capture probabilities in states 1, 2 and 4. Being able to constrain
capture probability in state 3 supplied a signiﬁcant beneﬁt, by allowing
for the separate estimation of the capture probability in state 4 and
transition probabilities from the live states to state 4. As Lebreton
(1999) pointed out, in cases where recoveries are obtained from
speciﬁc causes of death (with associated cause-speciﬁc mortality
, survival), the detection probability
cannot be identiﬁed separately from a speciﬁc type of mortality. For
this reason, the pair of parameters (
) is often replaced by [
In our case, constraining the capture probability in state 3 to 1 made
To construct capture histories, we pooled captures and live
resightings for each individual during the spring capture season
(20 March–10 June), using a capture interval of 1 year. We used an
extended period (3·5 months) as a single occasion, because the biases
associated with parameters derived from pooled estimates are mini-
mal if mortality during the capture interval does not exceed about
50% (Hargrove & Borland, 1994). Animals encountered alive and
inside the study area during the capture season were assigned to state
1, live animals outside the study area were assigned to state 2.
Animals killed by legal hunting during the hunting season preceding
+ 1 (regardless of whether or not they were shot
inside or outside the study area) were assigned to state 3 at occasion
+ 1, and animals discovered as having died for reasons other than
legal hunting between the end of capture occasion
and the end of
+ 1 were assigned to state 4 at occasion
+ 1. We
assigned animals encountered in the ‘newly dead’ states (3 and 4)
between capture occasions
+ 1 to occasion
+ 1, instead of
the previous occasion (as is carried out in combined tag recovery and
live recapture data; Barker, White & McDougall, 2005), because we
were estimating survival indirectly as a transition probability from
+ 1. Whereas direct survival estimates at
are interpreted as having survived from occasion
+ 1, transition probabilities at occasion i are interpreted as
having made a transition during the interval between
Animals not encountered alive at occasion
+ 1 and not discovered
dead between the end of occasion i and the end of occasion
received a 0 in the capture history at occasion
+ 1. Capture histories
were constructed for 464 individuals.
Model selection and parameter estimation
We used the program
, 2004; Choquet
2006) for model ﬁtting and parameter estimation. We assessed the
effects of the following variables in the multistate modelling
Sex (male, female; symbol: s) – for transition and capture
Age class (yearlings = 1y, subadults = 2–4y, adults = 5y +;
symbol: a) – for transition and capture
Subpopulation (north, south; symbol: p) – for transition and capture
Radiocollar (yes, no; symbol: r) - for capture
Harvest intensity (low, high; symbol: i) – for transition
The symbols for explanatory variables deﬁned above were used in
notation presented later and are not italicized to avoid
confusion with variables used earlier in the text. We implemented
and compared several candidate MCR models, with the most complex
model including all of the above variables and biologically meaningful/
interpretable interactions between them (full model, Table 1). No
tests are currently available to test the goodness-of-ﬁt (GOF) of
multistate models to data consisting of a combination of recaptures
and recoveries. Nevertheless, because most of the information about
cause-speciﬁc mortality came from dead recoveries, we carried out a
GOF test using only the recovery data (Brownie
, 1985), and the
ﬁt was found to be satisfactory Because data
demands are high for multistate models and the number of parameters
increases quickly with increasing number of states and groups
(Lebreton & Pradel, 2002), we did not consider the fully time-dependent
model, but instead used time periods we believed to be relevant for
survival , i.e. two time periods representing a change in harvest intensity
due to a 3·4-fold increase in average annual quotas in the south,
beginning with the 1998 hunting season (from 11·4 bears in 1984–97
to 38·6 bears after 1997). Similarly, age was deﬁned as a categorical
variable with cuts roughly identiﬁed based on splines in a preliminary
Cox proportional hazards regression model (Lunn & McNeil, 1995).
We estimated capture probabilities separately for instrumented
and non-instrumented bears, as bears equipped with radio transmit-
ters can be expected to have much greater recapture probabilities
than bears without (e.g. Amstrup, McDonald & Stirling, 2001).
Because convergence on local minima is a concern in multistate
models (Choquet et al. 2006), we either re-ran models at least three
times with random starting values for unconstrained parameters, or
(when available) re-ran models with starting values from a well-
deﬁned simpler model (Choquet et al. 2006). As mentioned above,
identiﬁability is a crucial issue in multistate models combining dead
recoveries and live recaptures (Gimenez et al., 2003), both in terms
of model selection and interpretation of parameter estimates. We
relied on m-surge which implements up-to-date algorithms to check
for parameter identiﬁability (Choquet et al., 2004). Model selection
was based on Akaike’s information criterion values corrected for
small sample sizes (AICc; Burnham & Anderson, 2002).
Sex, age, subpopulation, and harvest intensity were retained
as variables predicting survival in the best MCR models
(Table 1). Demographic effects were relatively mild, with a
trend towards greater vulnerability of male bears to legal
hunting, at least in the north. The best-performing models
indicated no differences in vulnerability between age categories,
except that cause-speciﬁc risk to hunting was estimated to be
0 for yearlings in the north. However, due to a small sample
size and a lack of mortalities in that age category in our
sample, standard error could not be estimated for the parameter.
During the period with increased harvest quotas (1998–2006)
in the south, the average cause-speciﬁc risk of dying due to
legal hunting was 2·8 times higher than during the preceding
low-pressure period (Fig. 2, Table 2). Harvest intensity had
no signiﬁcant effect on vulnerability in the north, where there
was no corresponding increase in harvest quotas.
( ., .).XP
265 23 0 12==
660 R. Bischof et al.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Journal of Animal Ecology, 78, 656–665
Table 1. Model ranking and ﬁt parametersa with respect to the focal transitions (mortality parameters h and w) for Swedish brown bears.
Parameters were estimated using multistate capture–recapture (MCR) modelling in m-surge. Shown are the most complex model considered
and representative candidate models, including three top models that differ only slightly in AICc value (wi = AICc weights). Regression terms
are shown for transition probabilities of the MCR model. Following m-surge notation, interactions are signiﬁed by a period between the
interacting factors. The last two columns indicate the model for immediate comparison (‘comp.’) and the term(s) targeted (‘effect’). Model terms
for capture probabilities and conditional movement in and out of the study areas are shown separately in Table 3.
Mortality (h and w in Transition, Ψ) Model performance
Legal hunting Other NP Deviance ΔAICcwiComp. Effect
a + s + p + i + a.s + p.s + p.a + p.i a + s + p + i + a.s + p.s + p.a + p.i 52 3164.7 15.5 0.0002
Other candidate models:
1 a + s + p + i + p.a + p.i + p.s a + s + a.s 41 3176.4 0 0.3804
2 a + s + p + i + p.a + p.i a + s + a.s 40 3180.4 1.6 0.1709 1 p.s. on hunting
3 a + s + p + i + p.a + p.i a + s + p + a.s 41 3178.4 2 0.1400 2 p on other
4 a + s + p + i + p.a + p.i a + s + p + a.s + p.s 42 3176.9 2.9 0.0892 2 p.s. on other
5 a + s + p + i + p.a + p.i + a.s a + s + a.s 42 3177 3 0.0849 2 a.s. on hunting
6 a + s + p + i + p.a + p.i a + s + p + a.s + p.a 43 3175.6 4 0.0515 2 p.a. on other
7 a + s + p + i + p.a + p.i a + s + p + i + a.s + p.i 43 3176.6 5 0.0312 3 i + p.i. on other
8 s + p + i + p.i + p.s a + s + a.s 37 3191.3 5.3 0.0269 1 a + p.a. on hunting
9 a + s + p + i + p.i + p.s a + s + a.s 39 3187.5 6.3 0.0163 1 p.a. on hunting
10 a + s + p + i + p.a a + s + a.s 39 3189.1 7.9 0.0073 2 p.i. on hunting
11 a + s + p + p.a a + s + a.s 38 3196.8 13.2 0.0005 10 i on hunting
12 a + s + p + i + p.a + p.i a + s + a.s 39 3194.5 13.3 0.0005 2 s on hunting
13 a + s + p + i + p.a + p.i a + s 38 3199.5 15.9 0.0001 2 a.s. on other
aSymbol interpretation: age (a), sex (s), subpopulation (p), harvest pressure (i).
Table 2. Estimates of cause-speciﬁc mortality for brown bears monitored in Sweden between 1984 and 2006. Parameter estimates are
from the best-ﬁtting candidate multistate model, with the following effects on mortality transition probability in m-surge notation:
Ψfrom(12)to3(intensity subpop+subpop age+subpop sex)+from(12)to(4)(sex age)+others. The age categories are deﬁned as follows: yearlings = 1y, subadults = 2 − 4y,
adults = 5y +. The vulnerability of yearling brown bears to legal hunting in the north was estimated to be 0 (not shown here), but no conﬁdence
interval could be constructed due to the small sample size and lack of hunting mortalities in that group. Nonetheless, legal hunting mortality
for yearling bears in the north can be expected to be relatively small, for reasons outlined in the main text. The top-performing model for
mortalities other than legal hunting did not distinguish between subpopulations and periods of harvest intensity.
Cause Subpop. Age category Sex Harvest intensity Estimate 95% lCI 95% uCI SE
Hunting North Subadult f low 0.036 0.014 0.089 0.017
f high 0.023 0.009 0.058 0.011
m low 0.103 0.052 0.193 0.035
m high 0.068 0.033 0.136 0.025
Adult f low 0.019 0.007 0.051 0.010
f high 0.012 0.005 0.031 0.006
m low 0.067 0.027 0.154 0.030
m high 0.043 0.018 0.100 0.019
South Yearling f low 0.019 0.008 0.045 0.008
f high 0.054 0.028 0.103 0.018
m low 0.034 0.015 0.073 0.013
m high 0.092 0.051 0.163 0.028
Subadult f low 0.021 0.010 0.043 0.008
f high 0.058 0.034 0.097 0.016
m low 0.023 0.011 0.045 0.008
m high 0.063 0.038 0.102 0.016
Adult f low 0.031 0.017 0.057 0.010
f high 0.086 0.058 0.126 0.017
m low 0.040 0.023 0.071 0.012
m high 0.109 0.075 0.157 0.021
Other North/south Yearling f high/low 0.177 0.121 0.251 0.033
m 0.086 0.039 0.179 0.034
Subadult f 0.060 0.036 0.099 0.016
m 0.183 0.134 0.244 0.028
Adult f 0.066 0.047 0.092 0.012
m 0.107 0.076 0.148 0.018
Cause-speciﬁc vulnerability in bears 661
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Journal of Animal Ecology, 78, 656–665
The general pattern for vulnerability to causes other than
legal hunting was one of greater risk for young individuals,
particularly males (Fig. 2, Table 2). Subadult males and year-
ling females were most vulnerable. Subadult male bears were
more vulnerable than subadult females and adults of both
sexes, whereas among females, yearlings were the most
vulnerable. Depending on population and age/sex group,
individuals were between 1·6 and 9·1 times more vulnerable to
the combination of other mortalities than to legal hunting.
However, during the period of high harvest quotas, legal
hunting mortality estimates in the south, with the exception
of subadult males and yearling females, were similar to the
mortality estimates associated with other causes (Fig. 2,
In addition to the top model, two other candidate models
received plausible support based on AICc (ΔAIC 0–2; Burnham
& Anderson 2002); one included an effect of subpopulation
on mortality due to causes other than legal hunting (slightly
lower in the south), and the other did not include a sub-
population:sex interaction on legal hunting mortality. Aside
from these differences, all top-performing candidate models
showed similar results in terms of structure and effect sizes.
Recapture probability estimates (Fig. 3) were at or near 1
for instrumented bears alive inside the study area but were
substantially lower for bears alive outside the study area.
Recapture probabilities for live bears without radiotransmitters
were at or near 0, regardless of location. The probability of
detecting a newly dead bear due to mortality causes other
than legal hunting was higher for instrumented bears than
bears without transmitters and higher for animals in the
south than the north (with yearling bears having the highest
detection probability among the three age categories). The
top-performing candidate models did not make a distinction
between the sexes in terms of capture probability, regardless
of the state (Table 3).
Assessing the magnitude and selectivity of cause-speciﬁc mor-
tality in managed populations is crucial for understanding their
population dynamics and the evolutionary forces acting upon
them. Legal hunting, in addition to being the most convenient
to assess, is also the most easily managed component of
mortality in many wild populations. Although it is the single-most
Fig. 2. Estimates of cause-speciﬁc mortality
(thick bars) and 95% CI boundaries (thin
bars) for female (black) and male (grey)
brown bears monitored in Sweden between
1984 and 2006. Parameter estimates are from
the best-ﬁtting candidate multistate model,
with the following effects on mortality
transition probability in m-surge notation:
Ψfrom(12)to3(intensity subpop+subpop age+subpop sex)+from(12)to
(4)(sex age)+others. The vulnerability of yearling
brown bears to legal hunting in the north was
estimated to be 0 (not shown here), but no
conﬁdence interval could be constructed due to
the small sample size and lack of hunting
mortalities in that group. Nonetheless, legal
hunting mortality for yearling bears in the
north can be expected to be relatively small,
for reasons outlined in the main text. The
graph for mortalities other than legal hunting
does not distinguish between subpopulations
and periods of harvest intensity because these
terms were not included in the top-performing
multistate capture–recapture model.
662 R. Bischof et al.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Journal of Animal Ecology, 78, 656–665
important cause of mortality for bears in Sweden (Sahlén
et al., 2006), we found that the combined mortality from
other causes is as great, and for several demographic groups
greater than legal hunting. In addition to being of consider-
able magnitude, mortalities other than legal hunting also show
greater demographic selectivity than legal hunting. Interest-
ingly, although only a fraction of the ‘other’ mortality cate-
gory was natural mortality, these nonharvest mortalities still
showed a general pattern of demographic selectivity that we
would expect from a natural mortality regime in long-lived
species, namely greater vulnerability of young animals. We
cannot say whether this comparison also holds quantitatively,
as no similar brown bear population has been studied under
purely natural conditions. Nonetheless, it is clear that this
combination of natural and human-caused mortalities is an
equally important contributor to this brown bear population’s
dynamics and potentially evolution as is hunting. The low
selectivity of harvesting mortality, on the other hand, contrasts
clearly with results obtained in marine ecosystems (Olsen
et al., 2004) and trophy hunting cultures (Coltman et al.,
2003) with a very strict size-selective harvesting regime.
Therefore, one should not underestimate the role of hunting
Fig. 3. Recapture probability estimates
(large horizontal bars) for brown bears in
Sweden with 95% CI boundaries from the
top MCR model for states 1 (alive inside the
study area), 2 (alive outside the study area),
and 4 (newly dead due to causes other than
legal hunting). Recapture probability for
animals newly dead due to legal hunting was
set to 1 (because of the reporting requirement
of legally harvested bears) and is not shown.
Black and grey bars represent estimates for
instrumented and non-instrumented bears,
respectively. Parameters without standard
error boundaries indicate that all individuals
in that group either had 0% or 100% recapture
probability. The recapture probability
component of the MCR model in m-surge
notation is: Pto(1,4) (age+radio+pop)+to(2) (age+radio)+others.
Probability State Full model Top ranking
model (see table 1)
capture alive inside a + s + p + r a + p + r
alive outside a + s + p + r a + r
newly dead: legal hunting 1 1
newly dead: other a + s + p + r a + p + r
transition alive inside -> alive outside a + s + p + a.s a + s
alive outside -> alive inside a + s + p + a.s a + s + p + a.s
aSymbol interpretation: age (a), sex (s), subpopulation (p), radio-marked (r).
Table 3. Comparison of model termsa and
interactions with respect to state-speciﬁc capture
probabilities and conditional movement in
and out of the study areas in the full MCR
model and those used in the best performing
overall models (see also Table 1). Because o
the reporting requirement of legally harvested
bears, capture probability for animals newly
dead due to legal hunting was set to 1.
Cause-speciﬁc vulnerability in bears 663
traditions and management regimes for harvesting as a
Demographically selective harvesting is receiving growing
attention from ecologists and evolutionary biologists, as it
has the potential to affect population dynamics (Langvatn &
Loison, 1999; Mysterud, Coulson & Stenseth, 2002; Milner,
Nilsen & Reassen, 2006) and evolutionary processes
(Coltman et al., 2003; Garel et al., 2007; Proaktor, Coulson &
Milner-Gulland, 2007). Males have generally been found to
be more vulnerable to hunting than females, with young
males being the most vulnerable age/sex class, both in bears
(Derocher et al., 1997; Noyce & Garshelis, 1997; McLellan
et al., 1999) and in other large mammals, such as cervids (e.g.
Langvatn & Loison, 1999). Such selectivity may arise due to
direct management actions (e.g. selective quotas), active
choice by the hunter (e.g. trophy hunting), or differential vul-
nerability caused by differences in individual characteristics
(e.g. behaviour, morphology). We found an overall pattern of
weak demographic selectivity of legal hunting, with a trend
towards greater male vulnerability, at least in the north.
Although only a trend, a difference in vulnerability between
the sexes (at least among adults) could in part be due to the
legal protection that females receive in Sweden during the
time they are with dependent young. Another contributing
factor may be passive selectivity as a result of behavioural
differences between male and female bears, rather than active
hunter selectivity (see also Bischof et al., 2008a). With respect
to the ﬁrst argument, lower cub-of-the-year mortality (Swen-
son et al., 2001) and higher average age at weaning (Dahle &
Swenson, 2003a) in the north means that females spend a
greater proportion of their time with dependent young than in
the south, which could explain the trend towards a gender
effect on legal hunting mortality in the north, but not in the
With the exception of yearling bears in the north, we found
no clear indication of age-speciﬁc vulnerability to legal
hunting among Swedish brown bears. The vulnerability of
yearling bears to legal hunting in the north was estimated to
be 0, but no conﬁdence interval could be constructed due to
the small sample size and lack of hunting mortalities in that
group. Nonetheless, legal hunting mortality for yearling bears
in the north can be expected to be relatively small, mainly for
two reasons: (i) because in the north 46% of litters are weaned
at 2·5, thus a smaller proportion of yearlings are available for
legal harvest than in the south, where almost all litters are
weaned at age 1·5 (Dahle & Swenson 2003a) and (ii) about one-
third of the northern study area is made up of national parks,
where bears are protected by law and most yearlings born in
those areas have not yet dispersed to be available to hunters
on the periphery of the protected areas (Støen et al., 2006).
Several studies on bears have found age-speciﬁc vulnerabilities
to hunting (e.g., brown bears: McLellan & Shackleton, 1988;
Bunnell & Tait, 1985; black bears, Ursus americanus: Noyce &
Garshelis, 1997; Czetwertynski, Boyce & Schmiegelow, 2007;
polar bears, Ursus maritimus: Derocher et al., 1997). The lack
of consistent and pronounced age effects on vulnerability to
legal hunting in our study is therefore somewhat surprising.
Analysis of the composition of the harvest revealed relatively
little demographic bias between hunting methods in the
Swedish harvest (Bischof et al., 2008a), and we suggested that
differences in the hunting system (no bag limit, few guided
hunts, quota-limited season, etc.) are partially responsible for
the limited effect of sex and age on relative vulnerability,
compared with North American bear populations. It is worth
stressing again that a quota-limited harvest without individual
bag limits provides little incentive for a hunter to pass up a
shot at a legal brown bear. We note that active hunter selectivity
may increase in the future should the brown bear population
continue to grow, thus increasing encounter probabilities and
therefore harvesting opportunities for hunters. An increase in
active selectivity, although not necessarily desirable, is more
likely to be brought on by a change in the hunting system, for
example, a shift from the current quota-limited hunt to one
with a single bear tag assigned to individuals hunters.
Although biased harvest can cause demographic and
evolutionary side effects, so can unbiased harvest. In an ungulate
population model, Proaktor et al. (2007) noted that harvest
pressure played a greater role in the selection for lighter weight
at ﬁrst reproduction than the degree of harvest selectivity. An
increase in overall mortality can lead to a discounting of
future reproduction, which may eventually result in the
beneﬁts of earlier reproduction outweighing its cost, such as
lower offspring survival (Bischof, Mysterud & Swenson,
2008b). Thus, an elevated total mortality of Swedish brown
bears as a consequence of growing harvest quotas may not
only directly reduce the population growth rate in the long
run, but cause additional indirect effects if a reduced age (and
body mass) at primiparity is favoured.
Our results conﬁrm that the increase in harvest pressure
coincided with elevated vulnerability to hunting for individuals
in the affected subpopulation. Whereas a positive effect of
hunting pressure on vulnerability is intuitive, the quantitative
effect of increased harvest pressure and how this may affect
the level of compensation has rarely been evaluated. We
found that the 3·4-fold increase in average annual quotas in
the south was comparable to the estimated 2·8-fold increase in
average vulnerability to hunting over the same time periods.
The change in harvest pressure in conjunction with the
availability of cause-speciﬁc mortality estimates presented an
opportunity to evaluate the assumption of additivity in
mortalities implemented in the matrix of transition probabilities
(equation 1). This assumption was motivated by the precau-
tionary principle and the following considerations: (i) hunting
mortality occurs over a relatively short time frame (1–2
months), (ii) it takes place after much of the other mortalities
have already been experienced (see below), and (iii) as
Lebreton (2005) suggested, strong compensation can rarely
be expected as a consequence of density dependence or
heterogeneity in survival, and should be less likely in long-
lived species than short-lived ones. The assumption of
additivity was supported by the ﬁnding that vulnerability to
natural mortality did not change as a result of increasing
harvest pressure in the south. In the case of complete or
partial compensation, we would have expected a depressing
664 R. Bischof et al.
effect of increasing harvest intensity on the risk due to
mortalities other than legal hunting. Nonetheless, overall
population densities increased during the study period, so we
concede that some caution is advised when interpreting
changes in risk between periods of high and low harvest
Sex and age effects were most pronounced for mortality
causes other than legal hunting and showed patterns of greater
vulnerability of young animals and greater vulnerability of
males than females, at least among subadults. These effects
are similar to ﬁndings from brown bear populations in North
America (McLellan et al., 1999; Haroldson, Schwartz &
White, 2006), with the exception that in our study population
female yearlings were the most vulnerable female age class to
mortality causes other than legal hunting, rivaling the vulner-
ability of male subadults. As mentioned earlier, the relatively
high vulnerability of subadult males can be explained by
increased mobility and dispersal behaviour of males, as well
as their propensity to be less cautious (Blanchard & Knight,
1991; McLellan et al., 1999). The causes of elevated vulnera-
bility of yearling females, compared with the other two female
age classes and even adult males, are less clear. Swenson et al.
(2001) reported mortality rates due to intraspeciﬁc predation
for female yearlings in Sweden that were several times higher
than that of male yearlings, but the reason for this sex bias is
unknown and warrants further investigation.
In addition to the differences in magnitude and selectivity,
legal hunting and other mortalities also differ in the timing
during the biological year. Whereas legal hunting is concen-
trated in a relatively short time period in late summer and
early fall, the combined other mortalities are spread over the
entire out-of-den period, albeit unevenly. The strong temporal
focus of hunting mortality, compared with other mortalities,
is likely to have consequences in terms of selectivity, for
example if there is seasonal variation in the manifestation of
life-history strategies (e.g. if some individuals were to wean
their young after, rather than before the hunting season). This
issue goes beyond the scope of our current analysis, but
should be explored in future empirical and theoretical work.
An obvious information gap that remains for our study
population is an assessment of the spatial and temporal
patterns of harvest effort. Bischof et al. (2008a) explored and
described harvest patterns and the demography of the harvest
in the Swedish brown bear population. In the present study,
we examined individual vulnerability to cause-speciﬁc risks in
the same population, over roughly the same time frame.
Estimates of cause-speciﬁc risk, harvest patterns, and harvest
effort should be considered an essential information triage
that can give ecologists and managers a comprehensive
picture of the implications of harvest and other mortalities for
We thank R. Pradel for helpful discussions and A. Ordiz and A. Zedrosser for
review and constructive criticism. S. Brunberg coordinated ﬁeld activities. We
are also grateful for critical comments on earlier versions of the manuscript by
J. Boulanger, J. M. Gaillard, H. Sauer, and an anonymous reviewer. Funding for
this project came from the Norwegian University of Life Sciences (R.B.) and
the Research Council of Norway (YFF to A.M.). The Scandinavian Brown
Bear Research Project has been supported by the Swedish Environmental
Protection Agency, Norwegian Directorate for Nature Management, Swedish
Association for Hunting and Wildlife Management, WWF-Sweden, Research
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Received 24 September 2008; accepted 5 January 2008
Handling Editor: Stan Boutin