Estimating population size and trends of the Swedish brown bear Ursus arctos population
ABSTRACT Estimating population size and trends are key issues in the conservation and management of large carnivores. The rebounding brown bear Ursus arctos population in Sweden is monitored by two different systems, both relying on voluntary resources. Population estimates have been calculated using Capture-Mark-Recapture methods, based on DNA-based scat surveys in five of the six Swedish counties with established bear populations. A total of 1,358 genotypes were identified using DNA extracted from collected scats. An independent ongoing programme, the Large Carnivore Observation Index (LCOI), was initiated in 1998. The LCOI uses effort-corrected observations of bears by moose Alces alces hunters during the moose hunt (> 2 million observation hours/year) and has shown a good correlation with relative population density of bears using the DNA-based method. From this, we have calculated population trends during the period 1998-2007. Using an exponential model, we estimated the yearly increase in the bear population to be 4.5% at the national level, varying between 0 and 10.2% in different counties. We used the regional population estimates and the trends from the LCOI, taking the variation from both systems into account using parametric bootstrapping, to calculate the regional as well as the national population size in Sweden in fall 2008. In one case (the northernmost county; Norrbotten) a DNA-scat survey was lacking, so we used assumptions based on data from the neighbouring county to estimate population size. We estimated the Swedish brown bear population to be 3,298 individuals (2,968-3,667; 95% confidence intervals) in 2008. Our results suggest that reliable information, necessary for the management of the brown bear population can be obtained from volunteers using standardised methods.
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ABSTRACT: Aim: Brown bear populations in Scandinavia show a strong mitochondrial DNA (mtDNA) phylogeographic structure and low diversity relative to other parts of Europe. Identifying the timing and origins of this mtDNA structure is important for conservation programs aimed at restoring populations to a natural state. Therefore, it is essential to identify whether contemporary genetic structure is linked to post-glacial recolonisation from divergent source populations or an artefact of demographic impacts during recent population bottlenecks. We employed ancient DNA techniques to investigate the timing and potential causes of these patterns. Location: Scandinavia and Europe. Methods: Ancient mtDNA sequences from 20 post-glacial Scandinavian bears were used to investigate phylogeographic structure and genetic diversity over the last 6000 years. MtDNA from 19 Holocene Norwegian bears was compared with 499 sequences from proximate extant populations in Sweden, Finland, Estonia and western Russia. A single mtDNA sequence from a Holocene Denmark sample was compared with 149 ancient and modern bears from Western Europe. Results: All nineteen Holocene Norwegian samples are identical to or closely related to the most common mtDNA haplotype found in northern Europe today. MtDNA diversity was low and not significantly different from extant populations in northern Europe. In Denmark, we identified a single mtDNA haplotype that is previously unrecorded from Scandinavia. Main conclusions: The current discrete phylogeographic structure and lack of mtDNA diversity in Scandinavia is attributed to serial founder effects during post-glacial recolonisation from divergent source populations rather than an artefact of recent anthropogenic impacts. In contrast to previous interpretations, the recolonisation of southern Scandinavia may not have been limited to bears from a single glacial refugium. Results highlight the importance of conserving the long-term evolutionary separation between northern and southern populations and identify southern Scandinavia as an important reservoir of mtDNA diversity that is under threat in other parts of Europe.Diversity and Distributions 01/2012; · 6.12 Impact Factor
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ABSTRACT: Large terrestrial carnivores, e.g. wolves or bears, often play a key ecological role from their position at the apex of trophic systems. Changes to their populations reverberate through ecological communities; consequently their widespread decline in numbers and shrinking distribution due to human persecution has brought about a loss and reconfiguration of biological diversity in many systems. Although many large carnivore populations are now under conservation-minded management, political and economic constraints make compromises necessary. A common compromise is to permit limited harvests, with the premise of sustainability and the objective to increase tolerance and funding for carnivore recovery and conservation. Here we question whether a large carnivore that has to “look over its shoulder” for human hunters can still fully perform its ecological role at the apex of a trophic system. We use information about carnivore behavior, ecology, trophic interactions, and the effects of human exploitation to argue that exploitation of large carnivores, even if sustainable numerically, undermines the commonly expressed rationale for their conservation, namely the restoration and preservation of ecosystem functionality. Our argument centers around (i) the necessity of behavioral adjustments in large carnivores to anthropomorphic risk, which may limit their contribution to the “landscape of fear”, and (ii) the observation that many of the same features that put large carnivores at the apex of trophic systems also make them vulnerable to human exploitation and persecution, with implicit consequences for their ecological functionality and evolution. Although hunting large carnivores can improve public acceptance, managers must be aware of the trade-offs.Biological Conservation 01/2013; 168:128–133. · 3.79 Impact Factor
Wildl. Biol. 17: 114-123 (2011)
? Wildlife Biology, NKV
Estimating population size and trends of the Swedish brown bear
Ursus arctos population
Jonas Kindberg, Jon E. Swenson, Go ¨ ran Ericsson, Eva Bellemain, Christian Miquel & Pierre Taberlet
Estimating population size and trends are key issues in the conservation and management of large carnivores. The
rebounding brown bear Ursus arctos population in Sweden is monitored by two different systems, both relying on
voluntary resources. Population estimates have been calculated using Capture-Mark-Recapture methods, based on
were identified using DNA extracted from collected scats. An independent ongoing programme, the Large Carnivore
alces hunters during the moose hunt (. 2 million observation hours/year) and has shown a good correlation with
relative population density of bears using the DNA-based method. From this, we have calculated population trends
calculate the regional as well as the national population size in Sweden in fall 2008. In one case (the northernmost
county; Norrbotten) a DNA-scat survey was lacking, so we used assumptions based on data from the neighbouring
county to estimate population size. We estimated the Swedish brown bear population to be 3,298 individuals (2,968-
the brown bear population can be obtained from volunteers using standardised methods.
Key words: brown bear, DNA, faeces, genetic, monitoring, observations, population size, survey, Ursus arctos, volunteers
Jonas Kindberg & Go¨ran Ericsson, Department of Wildlife, Fish and Environmental Studies, Swedish University of
email@example.com (Go¨ran Ericsson)
5003, NO-1432 A˚s, Norway, and Norwegian Institute for Nature Research, NO-7485 Trondheim, Norway - e-mail: jon.
Eva Bellemain*, Christian Miquel & Pierre Taberlet, Laboratoire d’Ecologie Alpine (LECA), CNRS UMR 5553,
Universite´ Joseph Fourier, BP 53, F-38041 Grenoble Cedex 9, France - e-mail addresses; firstname.lastname@example.org (Eva
Bellemain); email@example.com (Christian Miquel); firstname.lastname@example.org (Pierre Taberlet)
*Present address: National Centre for Biosystematics, Natural History Museum, University of Oslo, P.O. Box 1172
Blindern, NO-0318 Oslo, Norway
Corresponding author: Jonas Kindberg
Received 17 September 2010, accepted 23 March 2011
Associate Editor: Olivier Gimenez
Population size and trends are important parameters
for the management and conservation of large
carnivore species (Kendall et al. 1992, Mowat &
Strobeck 2000). These parameters are used to assess
population status, decide quotas for harvested
populations, evaluate the effects of management
measures or decisions (Wilson & Delahay 2001), or
to obtain parameters for conservation principles
? WILDLIFE BIOLOGY 17:2 (2011)
including the IUCN criteria for Red-listing evalua-
tions(Vie ´ etal.2009).Thisisevenmoreimportantfor
low-density populations of rare and elusive animals
that are long-lived and with relatively low reproduc-
tion rates; in addition these species are particularly
difficult to monitor (Thompson 2004). The brown
bear Ursus arctos is a typical example of a cryptic
animal with these characteristics, it occurs at rel-
atively low densities, even within established areas,
and it avoids humans (Nielsen et al. 2004, Nellemann
et al. 2007). The public often demands an accurate
knowledge of population size. Thus, managers are
faced with challenges of estimating population sizes
and trends and evaluating the population’s response
to hunting and other management measures, often
within short time spans.
Current methods for monitoring brown bears
of material for identification of individual bears by
used both for estimation of population size and
trends (Mattson 1997, Kojola et al. 2006, Schwartz
et al. 2008, Kindberg et al. 2009), and most ob-
servation-based methods focus on females with
cubs (Knight et al. 1995, Eberhardt & Knight 1996,
Harris et al. 2007, Ordiz et al. 2007, Schwartz et al.
2008), because they are easier to recognise in the
field and are the most important segment for
in combination with other methods, e.g. in Mark-
Resight studies, where radio-marked bears are used
in combination with aerial surveys (Swenson et al.
1994, Miller et al. 1997, Solberg et al. 2006). The
introduction of non-invasive DNA-based methods
in the 1990s (Taberlet & Bouvet 1992, Ho ¨ ss et al.
1992, Taberlet et al. 1996, 1999, Mills et al. 2000,
Paetkau 2003) made it possible to accurately dis-
tinguish individuals in an area without the need to
capture and handle them. The prevailing methods
for non-invasive DNA sampling are hair snagging
using baits (Boulanger et al. 2002, Kendall et al.
2009) and collection of scats (Bellemain et al. 2005).
Population size estimations have benefitted from
the development of analysis methods using Cap-
ture-Mark-Recapture (CMR) software (White &
Burnham 1999) and better field methods for im-
proving capture rates (Woods et al. 1999, Mowat &
Strobeck 2000, Kendall et al. 2009).
After centuries of persecution, the brown bear
population in Sweden was reduced to a few remote
areas in the 1930s with an estimated lowest pop-
After effective conservation measures were imple-
mented in the early 20th century, various methods
of estimation indicated a steady increase in popu-
lation numbers and distribution (Swenson et al.
1995). However, these population estimates were
made using different methods, mainly based on
inquiries of presence or various methods of estima-
tion and also lacked variance estimates, so they
could not be compared or used to determine
population trends. The first population estimate
made by the Scandinavian Brown Bear Research
Project (SBBRP) used several methods, including
observations of marked and unmarked bears from
helicopter surveys during the mating season and
among harvested bears (Swenson et al. 1994). To-
day population estimates are based on DNA sur-
troduced in 2001 (Bellemain et al. 2005), and mon-
itoring of population trend is based on systematic
effort-corrected observations of bears by moose
al. 2009). These methods, together with data from
main sources of information used for the manage-
ment of the bear population in Sweden.
In this article, we describe how we estimated
population trends and population size of brown
bears in Sweden and in the individual counties in
2008 using the methods described above. We have
also used these data to estimate population size,
even in areas where DNA surveys had not been
conducted. This has been extremely useful for bear
managers in Sweden and perhaps it can be used as a
model for wildlife managers in other countries or
jurisdictions as well.
Material and methods
Brown bears are unevenly distributed in the
is expanding from four former relict core areas
(Swenson et al. 1998, Manel et al. 2004), which
results in a skewed sex ratio in the expansion areas
(Swenson et al. 1998, Kindberg et al. 2009). The
habitat is mainly boreal forest with the Scandina-
vian mountain range in the west and the more
populated areas along the eastern coast. Large
carnivore management is mainly administrated at
the county level, and therefore most surveys are
conducted and reported at this scale.
3 ofSweden (Fig.1).Thepopulation
? WILDLIFE BIOLOGY 17:2 (2011) 115
Collection of scats and genetic analysis
We collected bear scat samples within the counties
throughout the study area, except for the northern-
most county (i.e. Norrbotten; see Fig. 1). We
conducted these collections even where bears were
very rare or non-existent. We collected samples
opportunistically by cooperating with moose hunt-
ers, volunteers and personnel from the SBBRP.
Hunters picked up each scat sample using a stick of
wood and put 1 cm3of the sample into a 20-ml
collection bottle. They used a different stick and
bottle for each sample. For each scat sample the
volunteers recorded the sampling date, the geo-
graphical location, the names of the hunting teams
and the coordinates (Swedish RT90 2.5 gon V).
and amplifications were performed at the Labora-
toire d’Ecologie Alpine, Grenoble, France, as de-
was first screened for species-diagnostic amplifica-
& Strobeck 1994). After this, six microsatellite loci
& Strobeck 1994, Taberlet et al. 1997) and a sex
following the multiplex preamplification method
detection and sizing of fragments was performed in
an ABI Prism 3100 DNA sequencer (Applied Bio-
systems, Foster City, California, USA). Amplifica-
least twice among the four replicates, and as ho-
mozygous if all the replicates showed identical
homozygous profiles. If neither of those cases
occurred, the alleles were treated as missing data.
The gels were analysed using the Genemapper
(version 3.0) software package (Applied Biosys-
tems, Foster City, California, USA). We grouped
samples according to their genotypes and identified
the unique genotypes.
Since 1998, the Swedish Association for Hunting
and Wildlife Management has collected observa-
tions of bears annually through the LCOI pro-
gramme during the moose hunt, as complementary
information to their ’moose observation’ survey
(Ericsson & Wallin 1999, Sylve ´ n 2000, Liberg et al.
2010). The LCOI is based on bear observations
made by moose hunters during the first seven days
of the hunt and is corrected for effort using man-
hours. The index has been evaluated and the
observations corrected for effort are closely corre-
lated with relative bear density, as determined from
individuals genetically identified during the popu-
lation censuses (see below; Kindberg et al. 2009).
Because the monitoring is carried out yearly, it has
been used to calculate population growth rate in
each county, as well as for Sweden as a whole.
We calculated population growth rate as the
’instantaneous rate of increase’ (r) for the period
1998-2007 using an exponential growth model,
except for the county of Ga ¨ vleborg, where we used
the period 1998-2006. The reason for the shorter
Figure 1. The different Swedish counties and the distribution of
bears as an index of average bear density from the LCOI (1998-
2006) in gray shading (higher densities have darker nuances).
? WILDLIFE BIOLOGY 17:2 (2011)
period was that data collection changed in parts of
the county of Ga ¨ vleborg in 2007. Thus, we cannot
be certain that the 2007 datum is comparable with
earlier data. With regard to the national growth
rate, these areas in the county of Ga ¨ vleborg were
removed (for the entire period 1998-2007), so that
only areas which continuously submitted reports
using the same protocol were used for the national
Counties with DNA-based estimates
The counties of Dalarna and Ga ¨ vleborg were
and were followed by Va ¨ sternorrland and Va ¨ ster-
botten in 2004 and Ja ¨ mtland in 2006 (Bellemain et
al. 2005, Solberg et al. 2006). To estimate total
population size in each county, we identified in-
dividual bears using DNA analysis from the scats
collected by volunteers (Table 1), and we analysed
the data using CMR methods available in program
MARK (White & Burnham 1999), with each week
used as a session for capture and recapture (11
weeks for Dalarna and Ga ¨ vleborg and 12 weeks for
the other surveys). We used closed population
models and model selection using Akaike’s Infor-
mation Criterion (AICc) values and model averag-
Table 2). For the counties of Ja ¨ mtland and Va ¨ ster-
2002). For Va ¨ sternorrland two models were includ-
ed in the model averaging and, because the lower
bound confidence limit was less than the number of
identified individuals, confidence limits were calcu-
lated byhandasdescribed byWilliams etal. (2002).
All high-ranking models included individual het-
erogeneity in capture probabilities and time effects.
This seemed to be reasonable, because search effort
varied among capture sessions, with the highest
effort attained during the first week of the moose
hunt (Bellemain et al. 2005). Heterogeneity among
individuals can arise due to factors that cannot be
recognised from DNA, such as age and reproduc-
tive status (Boulanger et al. 2008). We modelled
analysed, the number of samples successfully genotyped for 5-7 loci (including the sex locus) and the number of unique genotypes
County Survey year
Number of unique
Dalarna & Ga ¨ vleborg
Dalarna & Ga ¨ vleborg
Va ¨ sternorrland
Va ¨ sterbotten
Ja ¨ mtland
* 3,000 scats were randomly selected to be analysed among the 5,185 collected scats.
Table 2. The top three ranked closed population Capture-Mark-Recapture models used to estimate brown bear population size in the
different counties. For a summary of the counties of Dalarna and Ga ¨ vleborg in 2001, see Bellemain et al. (2005).
WeightsAverage capture probability
Va ¨ sternorrland 2004Mth2
Va ¨ sterbotten 2004Mth2*sex
Ja ¨ mtland 2006Mth2*sex
* Mth2*sex¼Heterogeneity and temporal variation in detection probabilities for each sex;
Mth2¼Heterogeneity and temporal variation in detection probabilities;
Mt¼Temporal variation in detection probabilities.
? WILDLIFE BIOLOGY 17:2 (2011) 117
heterogeneity using the Pledger model with two
mixtures (Pledger 2000, White 2008).
The estimate for Dalarna and Ga ¨ vleborg in 2001
(Bellemain et al. 2005) also had time and heteroge-
neity in the selected model. For that survey, it was
which had been determined for the two counties
combined, to obtain the population size for each of
the counties. We assumed that the population was
distributed between the two counties in the same
way as the identified genotypes, which gave a 52/
48% split of the joint population of 550 bears (52%
in Dalarna). Only 4% of the genotypes were found
in both counties.
Because the surveys were conducted in different
years over a 5-year period, it was not possible to
different surveys. However, an analysis of all
available genotypes (including the 2002 survey in
Dalarna and Ga ¨ vleborg) in 2008 showed that 2.8%
of the genotypes were present in more than one
county (E. Bellemain, unpubl. data).
County without a DNA-based estimate
At the time of our analysis, no DNA-based
population estimate had been conducted in the
county of Norrbotten, which is also the region with
available yet. We estimated the brown bear popu-
compare with the adjacent county of Va ¨ sterbotten
in 2004 when the scat survey was made there), the
relationship between the LCOI and the relative
density of bears previously found for the county of
Va ¨ sterbotten (Kindberg et al. 2009) and the
estimated density of brown bears in the county of
Va ¨ sterbotten from the DNA-based population
estimate. We calculated the estimate of the 2004
to the registered moose areas determined by the
Norrbotten County Board (ca 78,800 km2). This
method entailed the assumption that the relation
between the LCOI and bear density was similar to
an adjacent county with comparable forest density
and structure. This is an important assumption,
because the slope of the relationship varies within
Sweden (Kindberg et al. 2009). To improve our
confidence in using the relationship from Va ¨ ster-
botten as a proxy for Norrbotten, we compared the
relationshipinJa ¨ mtlandwiththerelationshipfound
in Dalarna-Ga ¨ vleborg and Va ¨ sternorrland. These
counties also have a similar forest density and
structure, but the relationship between the LCOI
and population density in Ja ¨ mtland was not
included in the study by Kindberg et al. (2009). If
the slopes of the relationships were similar in these
would also be similar for Norrbotten and Va ¨ ster-
To calculate the uncertainty in the estimates that
on DNA and population growth rates according to
the LCOI, we used parametric bootstrapping to
create 10,000 values of both population estimates
and growth rate, using the same mean and variance
as our results. We used the rlnorm and rnorm
functions in the statistical software R 2.8.1 (R
Development Core Team 2008). We averaged the
standard errors from the somewhat skewed confi-
dence limits from the MARK estimates, which
population cannot be lower than the number of
identified individuals. We used the lognormal
distribution to improve the variance estimate of
the population size, as it has been suggested to be
close to this type of confidence limits (Chao 1989).
estimates will be the same and this only minimally
affects the size of the variance. We derived the
estimated population for each county in 2008 from
the mean of the 10,000 population values and the
95% confidence limits calculated using the 2.5 and
97.5 percentiles. We estimated the total number for
Sweden by randomly adding the 10,000 county
estimates and calculating the mean and confidence
as described above.
and the minimum density of bear from the DNA-
scat surveys differed among counties, we tested the
regression slopes between Ja ¨ mtland and Va ¨ ster-
norrland, as well as among all the five counties
(Dalarna and Ga ¨ vleborg were combined) using a-
nalysis of covariance.
We considered results to be significant at P ?
Genetic identification of individuals
The number of scat samples collected and analysed
? WILDLIFE BIOLOGY 17:2 (2011)
in the laboratory, the number of samples success-
are shown in Table 1.
Relationships between the LCOI and minimum
There was no significant difference between the
slopes from the regression of observations/1,000
hours and minimum bear density from the DNA-
scat survey in Ja ¨ mtland and Va ¨ sternorrland (P ¼
0.46; Fig. 2). We also tested the slopes from all the
counties and only the slope of Va ¨ sterbotten was
significantly different (P , 0.001).
The LCOI indicated that the bear populations had
a significantly positive growth rate during the
period 1998-2007 in all but two counties: Va ¨ ster-
botten and Dalarna, which showed no significant
trend (Table 3). The highest growth rates were
found in Ga ¨ vleborg (r ¼ 0.097, P , 0.007), based
on data from 1998-2006, and Va ¨ sternorrland (r ¼
0.095, P , 0.017). Both counties are considered to
be expansion areas. In the counties of Ja ¨ mtland
and Norrbotten, which include several core areas
of reproduction, the growth rates were lower (r ¼
0.054, P , 0.007 and r ¼ 0.050, P , 0.004, re-
spectively), but were still judged to be relatively
high. The total Swedish bear population had a
positive trend with an instantaneous rate of in-
crease (r) of 0.045 (P , 0.008), as calculated from
The counties of Dalarna and Va ¨ sterbotten had no
significant population trend, and therefore the
population estimates and confidence limits (95%)
for 2008 were assumed to be the same as calculated
from the scat surveys, 286 (range: 251-337) for
Dalarna in 2001 and 309 (range: 265-401) for
Va ¨ sterbotten in 2004. The other populations had a
significant observed population trend and boot-
used the trends in an exponential model to estimate
the population size for 2008 with 95% confidence
limits.TheJa ¨ mtlandpopulationwasestimatedtobe
1,009 (range: 878-1,151) bears in 2008. Ga ¨ vleborg
and Va ¨ sternorrland, which had the highest ob-
served growth rates, were estimated to have 529
(range: 352-759) and 255 (range: 171-364) bears,
respectively, in 2008. The estimated population size
713-1,152) in 2008, using the relationship between
observation rate and population density in Va ¨ ster-
The total population estimate for Sweden was
calculated by randomly adding the 10,000 county
estimates. The total estimate was a brown bear
population of 3,298 (range: 2,968-3,667) for 2008.
Figure 2. The relationship between the LCOI and density of
ThecountyofVa ¨ sternorrlandisshownwiththeslopeasasolidline
and dark gray prediction interval (95%) and the county of
Ja ¨ mtland is shown with a dotted line and light gray prediction
Table 3. Brown bear population estimates based on DNA in scats and trends estimated from the Large Carnivore Observation Index
(LCOI) surveys in the different Swedish counties. The trends are based on data from 1998-2007 (1998-2006 for Ga ¨ vleborg) and figures
within brackets show confidence limits (95%).
County DNA survey yearCMR population estimate Growth rate (r)P Population estimate 2008
Ga ¨ vleborg
Va ¨ sternorrland
Ja ¨ mtland
Va ¨ sterbotten
? WILDLIFE BIOLOGY 17:2 (2011) 119
In this article, we have used two independent
methods which we have developed and tested for
monitoring the Swedish brown bear population.
They provide management authorities with an
index to follow the population trends in different
areas over time, as well as distribution, and with
statistically robust estimates of population size. All
the counties in Sweden with an established bear
population, except the northernmost county of
Norrbotten, were surveyed between 2001 and 2006
scats and CMR methods. These surveys cover
. 160,000 km2and encompass almost all areas
range. In addition, the LCOI has provided yearly
indices of bear density since 1998, covering all
counties in Sweden with bears, as well as the
counties without a current bear population.
is difficult to obtain large enough sample sizes for
CMR estimations, due to resource limitations and
the huge areas which must be covered (Mills et al.
2000). In this connection, the role of volunteers is
important to keep costs low, but also to allow the
survey of the huge areas needed (Newman et al.
2003). Another important advantage of using vol-
unteers is that involving them in the monitoring
process increases their knowledge and understand-
ing of the procedures (Newman et al. 2003). The
feedback as one of the most important factors, for
obtaining the long data series, 8-10 years, required
for trend estimations (Maxwell & Jennings 2005,
Harris et al. 2007). The use of LCOI as an addition
module to the ongoing moose observations system
will help to keep this interest as the moose is the
means thatthe system issomewhatdependentupon
the moose hunting situation.
in Sweden was in 2001 set at 100 yearly reproduc-
tions equalling about 1,000 individuals. This was
supplemented in a governmental decision in 2008
stating that the population should be maintained at
local situation regarding conflicts, e.g. livestock de-
predation (including semi-domestic reindeer Rangi-
fer tarandus), competition for game and problem
Our objective was to provide estimates of brown
bear population size by county, because bear
management operates at that scale in Sweden. That
means that the spatial structure of bear population
management does not correspond to the current
bear population distribution (see Fig. 1). This will
have an impact on the results, i.e. violation of
population closure assumptions, as parts of the
same population will be surveyed at different times
in different areas.
There are a number of confounding factors for
both methods. For scat surveys, they can occur
during the collection of scat samples (unevenly
distributed or that some areas are completely
missing; see Bellemain et al. 2005), the handling of
samples, the analysis of DNA and the choice of
models for estimating population size. We used
closed capture models for estimating population
size, as these are better suited for estimating the
number of individuals (Amstrup et al. 2005), but
bears move across large areas and therefore some
bears can appear in several counties, violating
closure assumptions (Miller et al. 1997, Schwartz
et al. 2003). We believe that this is a minor issue, as
the sampling takes place within a limited period
(maximum 12weeks)during the hyperphagiaperiod
when there is little immigration/emigration and also
that the surveys cover large areas, thus encompass-
ing most of the individuals (Kendall et al. 2009).
Using observations for estimating population
these to underestimate population size (Swenson et
al. 1995, Schwartz et al. 2008, Kendall et al. 2009),
but for estimating trends in population size, they
seem to give estimates comparable to demographic
methods (Harris et al. 2007, Brodie & Gibeau 2007;
but also see Fernandez-Gil et al. 2010). This, in
al. 2009), supports our assumption that we can use
the LCOI to obtain trends in the population. The
conditions in each area should also be as similar as
possible throughout the period. If changes occur in
e.g. the manner in which the hunting is conducted,
the reporting protocol, the areas involved or the
composition of the brown bear population, it could
affect the LCOI.
We calculated population growth (r) as exponen-
does not include density-dependent effects; on the
other hand it does not require any other assump-
tions. The difference between different growth
models should be minor in our case, and we only
included the years for which we have data.
? WILDLIFE BIOLOGY 17:2 (2011)
Harvest rates have increased rapidly in recent
years (quotas increased from 55 in 1999 to 233 in
2008) and all the effects of the most recent quotas
cannot be fully seen in the calculated growth rate,
which is based on data from the entire period. This
means that the projected growth that we have used
may be somewhat higher than it actually was at the
end of period. The estimated sustainable harvest in
the Swedish population of brown bear has been
estimated at 11.2% of the females (C.I: 8.2-13.5%)
based on long-term monitoring data from the study
area in central Sweden (Bischof & Swenson 2009).
When more data are available, future trend esti-
mations should include curvilinear models in com-
petition with the current model as used elsewhere
(see Harris et al. 2007).
The estimate for Norrbotten was largely depen-
dent on assumptions based on the survey in
Va ¨ sterbotten. It is most likely that the relationship
between the number of bears and the LCOI is more
similar to the situation in the adjacent county of
Va ¨ sterbotten than in the other counties. But
Norrbotten contains two former core areas for
female bears, whereas Va ¨ sterbotten only shares one
with Ja ¨ mtland (Manel et al. 2004). We were able to
verify the assumption that this relationship was
statistically the same between Ja ¨ mtland and the
adjacent Va ¨ sternorrland (see Fig. 2). Nevertheless,
we must await the results of the 2010 scat survey in
Norrbotten to test the assumptions used here. Until
then, this population estimation for Norrbotten
should be used carefully.
The LCOI shows a clear linear relationship when
compared to the relative density of bears in the
DNA surveys (Kindberg et al. 2009), but the re-
lationships are different in different areas, as with
moose (Ericsson & Wallin 1999). However, these
relationships might change both over time and with
changing bear density. One should therefore peri-
odically, perhaps every 5-7 years, correct bear ob-
servations with other surveys (DNA scats) to reduce
the risk of over- or underestimates. The need for a
complementary DNA-survey can be accelerated if
the LCOI indicates a major change in the popula-
tion trend, especially a decline (Hauser et al. 2006).
It is also important that the method of the collection
of bear observations is constant over time.
It is important to conduct new population es-
timates in the counties on a regular basis. At this
time, Dalarna and Ga ¨ vleborg have the oldest esti-
mates. However, it would probably be more valid
biologically to conduct the surveys to include entire
subpopulations of bears (Manel et al. 2004), rather
than at the county level, to avoid violations of the
assumptions of closed population models. It is im-
portant to continuously evaluate our models
against estimates from future DNA-based scat sur-
veys. This will allow us to test the accuracy of the
estimates, verify the trend calculations from the
LCOI and learn from the process, i.e. adaptive
Our calculations have not taken immigration,
emigration or deaths into account, but they should
database has recently been constructed, containing
all bears that have been sampled and analysed,
which will allow this to be evaluated. Bears that are
located south of the counties of Ga ¨ vleborg and
Dalarna have not been considered in our national
estimate, because they represent a relatively small
should be included in future analysis (as discussed
above) are the effects on growth rate caused by
hunting, both in the short- and long-term, various
growth models and diffusion and density. Hetero-
geneity in capture probabilities may occur because
someindividuals arelocated in less accessible areas.
A solution to this might be to model distance to
roads as acovariate for individuals (Huggins 1989).
The sex ratio of the population should be followed,
expansion area (with fewer females; Swenson et al.
1998) to a more stable population structure with
more reproducing females.
The use of the two independent methods we have
developed and tested for monitoring the Swedish
brown bear population provides management au-
statistically robust estimates of population size. It is
possible that these methods are suitable for other
large brown bear populations where volunteers are
available and willing to contribute.
Acknowledgements - we thank the participating hunters
who collected the scats and reported their observations.
Funding came from the county boards of Ja ¨ mtland,
Va ¨ sterbotten and Va ¨ sternorrland, the Swedish Associa-
tion for Hunting and Wildlife Management, WWF
Sweden, FORMAS, the Swedish Environmental Protec-
tion Agency, the Norwegian Directorate for Nature
Management and the programme for Adaptive Manage-
ment of Fish and Wildlife Populations. We thank C.
Maudet, A. Durand, O. Alibeu and C. Poillot for their
valuable contribution to the scat genotyping. Finally, we
also thank G. Bergqvist, C-G. Thulin and F. Widemo for
? WILDLIFE BIOLOGY 17:2 (2011) 121
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