ArticlePDF Available
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
Richard Bischof
*, 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 (
Ursus arctos
) 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
et al.
, 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
et al.
, 2003; Kuparinen
& Merilä, 2007). It is thus not surprising that science dealing
*Corresponding author. E-mail:
Cause-specific vulnerability in bears
© 2009 The Authors. Journal compilation © 2009 British Ecological Society,
Journal of Animal Ecology
, 656–665
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
et al.
, 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
Cervus elaphus
) on the island of Rum (Clutton-Brock, Guin-
ness & Albon, 1982) and Soay sheep (
Ovis aries
) 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 (
Ursus arctos
), 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
et al.
, 1999). In Bischof
et al.
(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
et al.
, 1997; Swenson & Sandegren, 1999; Swenson,
Dahle & Sandegren, 2001; Sahlén
et al.
, 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
E) encompasses
12 000 km
, the other site (‘south’, 61
N, 18
E) is 11 500 km
in size.
These areas are based on genetically distinct subpopulations that
match geographical clusters of bears with no or very little interchange
of females (Manel
et al.
, 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
et al.
, 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
et al
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
et al.
, 1993). Bears designated for radiotelemetry (
= 388)
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
R. Bischof
et al.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society,
Journal of Animal Ecology
, 656–665
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
et al.
(2006), Zedrosser
et al.
(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
et al.
(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):
Natural (
= 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
et al.
, 1993). Their
usefulness for modelling transitions between other types of states,
e.g. behavioural and reproductive states (Barbraud & Weimerskirch,
2005; Weladji
et al.
, 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
et al.
, 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):
eqn 1
being the probability of dying due to legal hunting during the
time period
+ 1,
the probability of dying due to causes other than
legal hunting during the same time period, and 1
the probability
of surviving.
, 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
, 656–665
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
et al.
(1999) pointed out, in cases where recoveries are obtained from
specific causes of death (with associated cause-specific mortality
), hence
, 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 [
, (1
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
capture occasion
+ 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
capture occasion
+ 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
to occasion
+ 1. Whereas direct survival estimates at
are interpreted as having survived from occasion
to occa-
+ 1, transition probabilities at occasion i are interpreted as
having made a transition during the interval between
1 to
Animals not encountered alive at occasion
+ 1 and not discovered
dead between the end of occasion i and the end of occasion
+ 1
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
et al.
, 2004; Choquet
et al.
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
et al.
, 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
Full model:
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,
Table 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
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Journal of Animal Ecology, 78, 656–665
traditions and management regimes for harvesting as a
selective force.
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.
© 2009 The Authors. Journal compilation © 2009 British Ecological Society, Journal of Animal Ecology, 78, 656–665
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
wild populations.
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
Council of Norway, and several private foundations.
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Received 24 September 2008; accepted 5 January 2008
Handling Editor: Stan Boutin
... There is no limit to the number of bears an individual can shoot, as long as the county-level quota has not been reached. Because there is little incentive for hunters to pass on an opportunity to kill a bear, bear hunting in Sweden is mostly considered as nonselective with regard to age, sex, and size (Bischof et al., 2009), although recent estimates show that hunting may now be slightly biased toward older males and larger individuals (Bischof et al., 2018;Leclerc et al., 2016). However, since 1986, all members of a family group of bears, that is, a female accompanied by dependent offspring of any age, have been afforded legal protection from hunting . ...
... In Sweden, all bears killed legally (e.g., legal hunting, management kills, defense of life and property) must be reported to the management authorities. Death due to other reasons (e.g., natural deaths, vehicle and train collisions, illegal hunting) has also to be reported, although an unknown proportion of mortalities remains undetected (Bischof et al., 2008(Bischof et al., , 2009). ...
... Third, we assumed males and females as similarly vulnerable to hunting in our calculations of hunting mortality. This assumption is sensible in our study population considering that male and female brown bears are not discernable at a distance for hunters (Bischof et al., 2009), but can be challenged in other populations. Our results would potentially be reinforced in a male-biased harvest system. ...
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Harvest, through its intensity and regulation, often results in selection on female reproductive traits. Changes in female traits can have demographic consequences, as they are fundamental in shaping population dynamics. It is thus imperative to understand and quantify the demographic consequences of changes in female reproductive traits to better understand and anticipate population trajectories under different harvest intensities and regulations. Here, using a dynamic, frequency‐dependent, population model of the intensively hunted brown bear (Ursus arctos) population in Sweden, we quantify and compare population responses to changes in four reproductive traits susceptible to harvest‐induced selection: litter size, weaning age, age at first reproduction, and annual probability to reproduce. We did so for different hunting quotas and under four possible hunting regulations: (i) no individuals are protected, (ii) mothers but not dependent offspring are protected, (iii) mothers and dependent offspring of the year (cubs) are protected, and (iv) entire family groups are protected (i.e., mothers and dependent offspring of any age). We found that population growth rate declines sharply with increasing hunting quotas. Increases in litter size and the probability to reproduce have the greatest potential to affect population growth rate. Population growth rate increases the most when mothers are protected. Adding protection on offspring (of any age), however, reduces the availability of bears for hunting, which feeds back to increase hunting pressure on the non‐protected categories of individuals, leading to reduced population growth. Finally, we found that changes in reproductive traits can dampen population declines at very high hunting quotas, but only when protecting mothers. Our results illustrate that changes in female reproductive traits may have context‐dependent consequences for demography. Thus, to predict population consequences of harvest‐induced selection in wild populations, it is critical to integrate both hunting intensity and regulation, especially if hunting selectivity targets female reproductive strategies.
... On the other hand, good mortality records are available on an annual basis for many terrestrial and aquatic species that are harvested, allowing the reconstruction of population dynamics and assessment of harvest effects (Jerina et al., 2003;Milner et al., 2006;Carruthers et al., 2014;Gwinn et al., 2015). Mortality records are also used to estimate the magnitude and selectivity of different causes of mortality in large carnivores (Linnell et al., 2010;Raithel et al., 2017), including brown bears in different ecosystems (Bischof et al., 2009;Lamb et al., 2017). ...
... population size estimates, sex structure and litter size). For age and sex-specific survival probabilities, which were not available for our study area, we used values from the Scandinavian brown bear population (Table 2), which is the most similar to the Slovenian population in terms of its demographic trend and management system (both are hunted populations; Bischof et al., 2009;Swenson et al., 2017). The population size was calculated for each year after 1998 by a) subtracting recorded (mostly anthropogenic) mortality for the current year (sex-and agespecific), b) multiplying the matrix of surviving individuals by the matrix of sex-and age-specific natural relative mortality (survival) to remove unrecorded mortality, c) calculating the number of reproductive females before denning and number of born cubs in the next year and d) ageing all individuals by one year and adding newborn cubs to move the population into the next year ( Fig. 1). ...
... Values that matched the population size and age structure criteria were on average smaller than the average of the initial values. This was expected because we used estimates from the Scandinavian brown bear population as initial values of natural (background) mortality (Bischof et al., 2009). In Scandinavia, winters are much longer, growing seasons are shorter and living conditions are likely harsher than in Slovenia; accordingly, the expected natural mortality may be higher in Scandinavia. ...
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Reliable data and methods for assessing changes in wildlife population size over time are necessary for management and conservation. For most species, assessing abundance is an expensive and labor-intensive task that is not affordable on a frequent basis. We present a novel approach to reconstructing brown bear population dynamics in Slovenia in the period 1998-2019, based on the combination of two CMR non-invasive genetic estimates (in 2007 and 2015) and long-term mortality records, to show how the latter can help the study of population dynamics in combination with point-in-time estimates. The spring (i.e. including newborn cubs) population size estimate was 383 (CI: 336-432) bears in 1998 and 971 (CI: 825-1161) bears in 2019. In this period, the average annual population growth rate was 4.5 %. The predicted population size differed by just 7 % from the non-invasive genetic size estimate after eight years, suggesting that the method is reliable. It can predict the evolution of the population size under different management scenarios and provide information on key parameters, e.g. background mortality and the sex- and age-structure of the population. Our approach can be used for several other wildlife species, but it requires reliable mortality data over time.
... Harvest intensity (proportion of marked bears killed by hunters) in the study population was low (mean: 0.13) during 1990-2005 and high (mean: 0.28) during 2006-2011 (Gosselin et al. 2015, Van de Walle et al. 2018, which corresponds to the first observed decrease in population size (Supporting Information, Fig. S1; Swenson et al. 2017). Harvest selectivity of sex and age is low; the sex ratio (M:F) of the harvest was ~1:1 (Bischof et al. 2009). ...
... Heterogeneity in female reproductive success due to differences in lifespan could ultimately affect matrilineal genetic structure (Rosenbaum et al. 2002). Although sex and age harvest selectivity is considered low in this population (Bischof et al. 2009), high harvest appears to select for longer maternal care duration and smaller adult female size , Van de Walle et al. 2018). If such selectivity has a genetic basis and is asymmetric among matrilines, then increased FGS can arise, but FGS was lower in our study and not suggestive of this effect. ...
... However, hunter-killed bears are representative of the population , and mean age of shot female bears is ~5 years in our study population (Frank et al. 2017b), effectively truncating many females' reproductive lifespans (Zedrosser et al. 2013). There is strong evidence that harvest is additive in this population and increasingly so across time (Bischof et al. 2009), and the relative risk of harvest compared with natural mortality generally increases with age (Bischof et al. 2018). Human access and proximity to roads has been linked to risk of harvest for bears (Penteriani et al. 2018) and other mammals (Hill et al. 2019). ...
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Harvest can disrupt wildlife populations by removing adults with naturally high survival. This can reshape sociospatial structure, genetic composition, fitness, and potentially affect evolution. Genetic tools can detect changes in local, fine-scale genetic structure (FGS) and assess the interplay between harvest-caused social and FGS in populations. We used data on 1614 brown bears, Ursus arctos, genotyped with 16 microsatellites, to investigate whether harvest intensity (mean low: 0.13 from 1990 to 2005, mean high: 0.28 from 2006 to 2011) caused changes in FGS among matrilines (8 matrilines; 109 females ≥4 years of age), sex-specific survival and putative dispersal distances, female spatial genetic autocorrelation, matriline persistence, and male mating patterns. Increased harvest decreased FGS of matrilines. Female dispersal distances decreased, and male reproductive success was redistributed more evenly. Adult males had lower survival during high harvest, suggesting that higher male turnover caused this redistribution and helped explain decreased structure among matrilines, despite shorter female dispersal distances. Adult female survival and survival probability of both mother and daughter were lower during high harvest, indicating that matriline persistence was also lower. Our findings indicate a crucial role of regulated harvest in shaping populations, decreasing differences among “groups,” even for solitary-living species, and potentially altering the evolutionary trajectory of wild populations. © 2020 The Authors. Evolutionary Applications published by John Wiley & Sons Ltd
... For all three species, we considered two competing sources of mortality: 1) legal culling, which is always detected (e.g., legal hunting, management kills, defense of life and property), and 2) all other mortalities, which are not always detected (e.g., natural deaths, vehicle and train collisions, illegal hunting). By distinguishing these two main causes of death and accounting for imperfect detection, the model can produce estimates of total mortality, as well as separate estimates for each mortality cause (29,30). Humans are the dominant force driving the dynamics of all three species. ...
... For all three species, we considered two competing sources of mortality: legal culling (h), which is always detected (e.g., legal hunting, management kills, defense of life and property), and all other mortalities (w), which may not always be detected (e.g., natural deaths, vehicle and train collisions, illegal hunting). By distinguishing between these two causes of mortality in the model and accounting for imperfect detection, the OPSCR model can produce estimates of total mortality, as well as separate estimates for each mortality cause (29,30). For wolves and wolverines, vital rates were allowed to vary between years, yielding annual estimates of recruitment and cause-specific mortality. ...
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The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the world's largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimes.
... Carnivores conflict with humans for numerous reasons which often result in intentional or accidental killing (Bischof et al., 2009;Swanepoel et al., 2015). Intentional killing may occur as a result of legal hunting or when people perceive carnivores as a direct threat to human life or property (e.g. ...
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Human-caused mortality is the main cause of death for large carnivores worldwide and has had serious adverse effects on their populations. Detailed quantitative information on potential causes and patterns of mortalities are vital for development of effective conservation strategies. We investigated human-caused large carnivore mortalities across Iran using reports provided by Iran’s Department of the Environment (DOE) during January 1980–January 2021, which comprised 399 mortality instances involving 443 carnivore deaths. Brown bears (Ursus arctos) had the highest frequency of occurrence (30%), followed by striped hyenas (Hyaena hyaena; 24%), and Persian leopards (Panthera pardus saxicolor; 17%). Overall, mortalities related to agricultural (i.e. livestock, or crops including plants, fruits, beehives) loss occurred more frequently (31%) than mortality related to illegal trade (21%) and risk to humans (7%). Specifically, brown bears were killed more frequently due to potential threats to human life and crops, whereas leopards and wolves were killed more often because of livestock depredations. Additionally, leopards were killed more frequently for illegal trade of their skins. We recommend the DOE improve local communities’ attitudes toward large carnivores by promoting conservation education programs and incentive compensation schemes, as well as implement mitigation measures (e.g. wildlife crossing structures or fencing) at road mortality hotspots to prevent unnecessary deaths of large carnivores in Iran.
... No information is available on the death rates for the Cantabrian Mountains, with the exception of that relating to cubs between them leaving their dens and family break-up (i.e., during their first 16 months of life;Planella et al. 2019). For this reason, the mortality has been the only parameter which we have varied between the different scenarios, employing the ranges defined in the other European populations(Bischof et al. 2009; Gervasi & Ciucci 2018;Wiegand et al. 1998). We execute each of the simulation scenarios table 1. demographic parameters and principal results of the different projection model scenarios for the Cantabrian Mountains brown bear population analysed with the VortEX 10 program. ...
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This chapter addresses how the population recovery of the brown bear in the Cantabrian Mountains affects the consideration of its current state of conservation and its legal protection. In accordance with the criteria of the IUCN Red List of Threatened Species, the population has gone from “Critically Endangered” to “Endangered”, and it will presumably move to the category of “Vulnerable” during the course of this decade. The population viability analysis of the Cantabrian bear population suggests that it will continue to grow, provided that a high survival rate of adult females is guaranteed, which is the most relevant factor in population dynamics. However, it is still far from being considered a non-threatened population and the periodic evaluations carried out in compliance with the Habitats Directive (92/43/EEC) consider that it is still in an unfavourable state of conservation. From a legal point of view, the Cantabrian bear is considered “Endangered”, although the criteria for including species in the current Spanish Catalogue for Threatened Species contain inconsistencies which may affect the legal coverage of the brown bear and other species undergoing recovery processes, but whose populations are still low in number. The increase in the bear population poses new challenges that make it necessary to update the Recovery Plans, which are the specific legal instruments for the conservation of the bear and its habitat, as well as the expansion of the Natura 2000 Network, considering the expansion of the distribution area of the Cantabrian population. These processes must be carried out with adequate environmental governance and social participation.
... No information is available on the death rates for the Cantabrian Mountains, with the exception of that relating to cubs between them leaving their dens and family break-up (i.e., during their first 16 months of life; Planella et al. 2019). For this reason, the mortality has been the only parameter which we have varied between the different scenarios, employing the ranges defined in the other European populations(Bischof et al. 2009; Gervasi & Ciucci 2018;Wiegand et al. 1998). We execute each of the simulation scenariostable 1. demographic parameters and principal results of the different projection model scenarios for the Cantabrian Mountains brown bear population analysed with the VortEX 10 program.described ...
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The current climate change scenario may produce different impacts on species, ranging from on their genes to their physiology and behaviour, and for all possible interactions between all these. As a result of global warming, the scientific literature suggests that the brown bear will be more active during the winter (spending less time hibernating) and that it will forage in more humanised areas. To what point these changes may influence its reproductive success, despite its phenotypic plasticity, is a question which needs to be addressed. Similarly, areas protected for the species may see a decline in their effectiveness, as the extent and quality of habitats adequate for the species reduce. Climate change is only considered as a threat in 11 of the 49 management or conservation documents covering the brown bear in the world. Of these, only two suggest management measures and neither of these provide indicators for these measures. Alterations in the bear’s feeding pattern in the Cantabrian Mountains, related to climate change, have been observed over the past decades. Recent projections predict a drastic population reduction, caused by the loss of large areas of the distribution of various plant species key for their feeding and cover. However, the limitations of such models, the capacity of adaptation of the species, non-linear effects of climate change and the great uncertainty about these predicted effects should all be taken into account. Additionally, the brown bear was widely distributed across the Iberian Peninsula until a few centuries ago, even as far south as Huelva and Murcia. Beyond the important necessity of favouring the conservation and restoration of habitats, the ecological connectivity between them and their bear populations, management of the human factor as the principal threat to the conservation of the Cantabrian bear in a climate change context, is essential.
... during the day, is the most important cause of mortality for brown bears in Scandinavia, e.g. >80% of bear deaths in Sweden between 1984and 2006(Bischof et al., 2009. Thus, nocturnal habits help bears to avoid encounters with people in general and, more specifically, to reduce mortality risk in areas where human activities like hunting are practiced. ...
Artificial food supplementation of wildlife is an increasing practice for species conservation, as well as for hunting and viewing tourism. Yet, our understanding of the implications of wildlife supplementary feeding is still very limited. Concerns have been raised over the potential negative impact of artificial feeding, but the effects of this practice on animal movements and rhythms of activity are just beginning to be investigated. Here, with the aim of studying whether the artificial feeding of brown bears may affect their behaviour, we analysed (1) the probability and intensity of feeding site use at different temporal scales, (2) how the use of artificial feeding sites is related to the bear's age and sex, main periods of the bear's annual cycle (i.e. mating and hyperphagia) and characteristics of the feeding sites, and (3) how the use of artificial feeding may be affecting bear movement patterns. We analysed the movements of 71 radio-collared brown bears in southern-central Finland and western Russian Karelia. Artificial feeding sites had several effects on brown bears in boreal habitats. The probability of a feeding site being used was positively correlated to the stability of this food resource over time, whereas sexes and bear classes (subadults, adults and females with cubs) did not show significant differences in the use of feeding sites, which were visited predominantly at night and slightly more during hyperphagia. The probability of using an artificial feeding site affected the daily net distance only (bears using feeding sites: 3.5 ± 4.5 km, range: 0–29 km; bears not using feeding sites: 4.4 ± 4.9 km, range: 0–47 km). Those brown bears using artificial feeding more intensively moved shorter distances at a lower speed within smaller home ranges compared to bears that used this food sources less. Highly predictable and continuously available anthropogenic food may therefore have substantial impacts on brown bear movement patterns, ecology and health. The recorded changes in movement patterns support the evidence that artificial feeding may have important implications for bear ecology and conservation.
... These areas are characterized by the widespread presence of people and infrastructures, which potentially have ecological impacts on bears. The close coexistence of brown bears and humans generates multiple human-driven disturbances (Ordiz et al. 2017) and causes bear mortality (Bischof et al. 2009), affecting the distribution, demography, behavior, and viability of bear populations (Penteriani et al. 2018a;Zarzo-Arias et al. 2018). ...
Brown bears Ursus arctos were historically persecuted and almost eradicated from southern Europe in the twentieth century as a result of hunting and direct persecution. The effects of human-induced mortality were exacerbated by other threats, such as habitat loss and fragmentation, due to the expansion of human populations. As a result, nowadays there are only small fragmented populations of bears in southern Europe. Brown bears in the Cantabrian (north-western Spain), Apennine (central Italy), and Pindos (north-western Greece) mountains represent three examples of small and threatened bear populations in human-modified landscapes. Most of their range is characterized by high human densities, widespread agricultural activities, livestock raising and urban development, connected by dense networks of transport infrastructures. This has resulted in a reduction of continuous habitat suitable for the species. Here, we summarize the past and present histories and fates of these three populations as examples on how the coexistence of bears and people in human-modified landscapes can take different turns depending on human attitudes.
... The overall human-caused mortality rate was significantly higher in males than in females (52% vs. 18%; P < 0.01; Tables 2 and 3), which supports our first hypothesis (i.e., sex-biased mortality hypothesis). This tendency is in accordance with observations made in previous studies conducted on the Shiretoko Peninsula 28 and in most brown bear populations 27,38,39 , in which the subadult mortality rate is highest among all age-sex classes. The sex difference was significant in groups 2 and 3 (males vs. females: 38% vs. 7% for group 2; 71% vs. 0% for group 3; both P < 0.01), but not in group 1 (P > 0.05), which supports our third hypothesis (i.e., sex × birthplace hypothesis). ...
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Human habituation of large carnivores is becoming a serious problem that generates human-wildlife conflict, which often results in the removal of animals as nuisances. Although never tested, human habituation potentially reduces the fitness of adult females by reducing their offspring's survival as well as their own, due to an increased likelihood of human-caused mortality. Here, we tested this hypothesis in brown bears inhabiting Shiretoko National Park, Japan. We estimated the frequency of human-caused mortality of independent young (aged 1-4 years) born to mothers living in areas with different maternal levels of human habituation and different proximities to areas of human activity. The overall mortality rate was higher in males than in females, and in females living near a town than those in a remote area of park. Surprisingly, more than 70% of males born to highly habituated mothers living around a remote wildlife protection area were killed by humans; this proportion is greater than that for males born to less-habituated mothers living in almost the same area. The current study clarified that interactions among maternal human habituation, birthplace (proximity to town), age, and sex determine the likelihood of human-caused mortality of brown bears at an early stage of life.
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In large-herbivore populations, environmental variation and density dependence co-occur and have similar effects on various fitness components. Our review aims to quantify the temporal variability of fitness components and examine how that variability affects changes in population growth rates. Regardless of the source of variation, adult female survival shows little year-to-year variation [coefficient of variation (CV<10%)], fecundity of prime-aged females and yearling survival rates show moderate year-to-year variation (CV<20%), and juvenile survival and fecundity of young females show strong variation (CV>30%). Old females show senescence in both survival and reproduction. These patterns of variation are independent of differences in body mass, taxonomic group, and ecological conditions. Differences in levels of maternal care may fine-tune the temporal variation of early survival. The immature stage, despite a low relative impact on population growth rate compared with the adult stage, may be the critical component of population dynamics of large herbivores. Observed differences in temporal variation may be more important than estimated relative sensitivity or elasticity in determining the relative demographic impact of various fitness components.
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We examined the number, sex, and age composition of polar bears (Ursus maritimus) killed by harvest, destroyed as problem bears, relocated to zoos, and killed during handling from western Hudson Bay between 1966 and 1992. Harvest and removal of problem bears were biased towards males (66.7-70.1%) with most bears (71.7%) taken under a managed quota, but destruction of problem bears (13.6%) was also an important component of removal. An average of 42 bears per year were removed from the population with a mean age of 5.3 years for females and 6.1 years for males. Females were most vulnerable to harvest at 1-4 years of age and males at 2-4 years. Number of bears removed each year averaged 6% of the population and adult females removed represented 1% of the population. The harvest appeared sustainable due to the male bias and young age of harvested bears. Male-biased harvest was the most likely explanation for the preponderance of females in the population.
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The age structures of 39 populations of three species of North American bears were analyzed. Estimated mortality rates of cubs in their first year were 30-40% for brown bears and 25-30% for black bears. Apparent subadult mortality rates derived from living animals (15 -35% an-nually) were higher than those of adults. Apparent mean annual mortality rates of subadult and adult females combined were 17.2, 16.8, and 18.8% for black, brown, and polar bears respectively. Comparable values for males were 25.5, 23.0, and 22.6% annually. Because hunting appears to be the major mortality factor in most North American bear populations, interpretation of age structures is facilitated by explicitly incorporating the effects of hunting and its associated biases in the analyses. The simple model proposed to accommodate the hunter-bear interaction clarifies differences in age distributions between species and between sexes within species. Most of the differences in sex-specific mortali-ty rates are a product of differential vulnerability related to home range size and method of hunting. &SUMÉ. Les auteurs ont analysé la structure d'âge de 39 groupes d'ours représentant trois esfices nord-américaines. Les taux de mortalité calculés pour les oursons au cours de leur premikre année étaient de 30 a 40% chez les ours bruns et de 25 a 30% chez les ours noirs. Les taux de mortalité apparents parmi les jeunes, calculés d'aprts les animaux en vie (15 a 35% chaque année) 6taient plus 6levés que ceux des adultes. Les taux de mortalit6 moyens annuels apparents pour les femelles jeunes et adultes Ctaient de 17.2% chez les ours noirs, de 16.8% chez les ours bruns et de 18.8% chez les ours blancs. Les valeurs comparables pour les mâles étaient respectivement de 25.5%, 23.0% et 22.6%. Puisque la chasse comporte les plus important facteur de mortalit6 dans la plupart de populations d'ours en Amérique du Nord, ]'interpretation des structures d'âge est facilitée par l'inclusion dans les analyses des effets de la chasse et de ses tendances associ6e.s. Le modkle simple propos6 en vue d'accomoder l'interaction entre chasseurs et ours signale plus clairement les diff6rences dans la portée des groupes d'âge entre les esptces et entre les sexes d'une même esptce. Les différences dans les taux de mortalité calculés par sexe sont d'ordinaire un produit d'une vulnerabilité différentielle reliée a l'étendue du domaine et à la méthode de chasse. Mots clés: distribution d'âge, ours, taux de mortalité, Amérique du Nord, proportion par sexe, espkce Vrsus Traduit pour le journal par Maurice Guibord.
Records of bountied brown bears Ursus arctos in Norway and Sweden were analysed to estimate population size in the mid-1800's, and changes in population size and distribution in relation to the bear management policies of both countries. In the mid-1800's about 65% of the bears in Scandinavia were in Norway (perhaps 3,100 in Norway and 1,650 in Sweden). Both countries tried to eliminate the bear in the 1800's; Sweden was more effective. By the turn of the century, the numbers of bears were low in both countries. The lowest population level in the population remnants that have subsequently survived occurred around 1930 and was estimated at 130 bears. Sweden's policy was changed at the turn of the century to save the bear from extinction. This policy was successful, and the population is now large and expanding. Norway did not change its policy and bears were virtually eliminated by 1920-30. Since 1975, bear observations increased in Norway. This coincided temporally with an abrupt increase in the Swedish bear population, and bears reappeared sooner in areas closer to the remnant Swedish populations. Both conditions support our conclusion that the bear was virtually exterminated in Norway and suggest that bears observed now are primarily immigrants from Sweden, except for far northern Norway, which was recolonised from Russia and Finland. Today, we estimate that the Scandinavian bear population numbers about 700, with about 2% in Norway (on average about 14 in Norway, 650-700 in Sweden). This is a drastic reduction in the estimate of bears in Norway, compared with earlier studies. The trends in bear numbers responded to the policies in effect. The most effective measures used in Scandinavia to conserve bears were those that reduced or eliminated the economic incentive for people to kill them. Our analysis also suggests that population estimates based on reports from observations made by the general public can be greatly inflated.
Trends of grizzly bear (Ursus arctos) populations are most sensitive to female survival; thus, understanding rates and causes of grizzly bear mortality is critical for their conservation. Survival rates were estimated and causes of mortalities investigated for 388 grizzly bears radiocollared for research purposes in 13 study areas in the Rocky and Columbia mountains of Alberta, British Columbia, Montana, Idaho, and Washington between 1975 and 1997. People killed 77-85% of the 99 grizzly bears known or suspected to have died while they were radiocollared. In jurisdictions that permitted grizzly bear hunting, legal harvest accounted for 39-44% of the mortalities. Other major causes of mortality included control killing for being close to human habitation or property, self-defense, and malicious killings. The mortality rate due to hunting was higher (P = 0.006) for males than females, and subadult males had a higher probability (P = 0.007) of being killed as problem animals than did adult males or females. Adult females had a higher (P = 0.009) mortality rate from natural causes than males. Annual survival rates of subadult males (0.74-0.81) were less than other sex-age classes. Adult male survival rates varied between 0.84 and 0.89 in most areas. Survival of females appeared highest (0.95-0.96) in 2 areas dominated by multiple-use land and were lower (0.91) in an area dominated by parks, although few bears were killed within park boundaries. Without radiotelemetry, management agencies would have been unaware of about half (46-51%) of the deaths of radiocollared grizzly bears. The importance of well-managed multiple-use land to grizzly bear conservation should be recognized, and land-use plans for these areas should ensure no human settlement and low levels of recreational activity.
This update of the 1st edition (1978) presents 14 models for analysis of bird banding or fish tagging studies. Maximum likelihood estimators of survival and recovery rates are given with their estimators of sampling variances and covariances. Goodness of fit tests are presented as well as log-likelihood ratio tests of hypothesis. Two computer software packages are illustrated in the handbook and are available. -from Authors
Many agencies use data on hunter success and harvest composition to guide decisions about black bear (Ursus americanus) management, despite well-known limitations of such data. The likely influence of natural food abundance on harvests has been acknowledged, yet few studies have examined this relation. We conducted a simple survey, employing subjective ratings by experienced observers, to monitor food abundance across Minnesota's bear range, and used these data to interpret a 12-year record of hunting success and harvest composition. Percent females in the harvest, mean age of females killed, and hunting success were related inversely to fall food abundance, particularly hazelnuts (Corylus spp.) and acorns (Quercus spp.). Percent females in the harvest, mean age of females killed, and estimated harvest rates for most sex-age classes, particularly adult females, also increased with increased number of hunters. After accounting for fall food and number of hunters, the estimated size of the bear population appeared to have little effect on hunting success and harvest sex ratio; that is, bear harvest data apparently yielded little insight into population status. Despite the simple format and subjective nature of our food survey, it adequately explained most of the year-to-year variation in hunting success and the sex-age composition of the harvest.