Discussion
We applied a pedigree reconstruction approach to estimate the breeding
and adult population size of brown bears on the Shiretoko Peninsula,
Japan. Large-scale, intensive genetic sampling enabled a high rate of
parentage assignment, which allowed us to raise the minimum size of the
breeder/adult populations. The adults (≥4 years old as of 2019)
accounted for 47.1% of the total unique bears identified in 2019–2020,
which was comparable to the percentage of adults (43.0%; ≥5 years old,
defined in Craighead, Sumner and Mitchell, 1995) in Yellowstone bears
monitored at Trout Creek, 1960–1968. This suggests that the current
method is effective enough to detect breeders/adults among bears without
information on age. The estimated breeding/adult population size,
although that was the minimum value, was higher than among other brown
bear populations in the world, suggesting that this population, which
inhabits a small area, has a very high reproductive potential (Schwartz,
Miller and Haroldson, 2003). The breeding/adult population size is a
very useful indicator for determining population dynamics and set
harvest/hunting quotas (Swenson, et al. , 1994), which is
essential for the development of wildlife management and conservation
policies. Kohira et al. (2009) estimated the population growth rate to
be >1 under the conditions where ≥81 adult females ≥5 years
old (among 150 females in total) existed in the Shiretoko Peninsula,
excluding Shibetsu Town (which accounts for 31% of the total forest
cover in the current study area), with eight adult female
mortalities/year (7.2 adult [≥5 years old] female mortalities/year
in the same area during 2011–2020). Our results suggest that the
current harvest rates are below the sustainable level; however, careful
attention is still required because some of the parameters used in
Kohira et al. (2009) were extrapolated from data obtained from other
brown bear populations.
To estimate the maximum breeding population, we made an assumption that
the breeding population would not exceed the total number of parents
that produced bears identified in 2019–2020. This assumption was
unreliable if the sampling efforts were insufficient or if the sampling
area was too limited. In these circumstances, the maximum population
size would be severely underestimated. Although most of the hair-trap
sites were placed in coastal areas for ease of access, the combination
of hair-trapping and scat collection enabled intensive genetic sampling
in the current study, which was supported by the accumulative curve of
unique bears shown in Figure 3. Brown bears in this population range
from high elevations (e.g., to eat alpine stone pine cones in summer) to
coastal areas (e.g., to eat salmon in autumn) depending on seasonal
changes in food availability (Shirane, et al. , 2021), suggesting
that most of the bears on the peninsula had the potential to be sampled.
In addition, one of the advantages of the current method is that it was
possible to infer the presence of the parents without sampling if their
offspring were sampled. Male bears disperse from their birthplace at
around three years of age (Shirane, et al. , 2019), which allowed
mothers living in the area with a low sampling probability to be
detected by the pedigree reconstruction. Therefore, it was unlikely that
the true breeding population exceeded the current estimation, but there
is a need to give careful attention to the possibility of
overestimation. One of the disadvantages of this method is that with an
increase in the number of bears whose parent(s) are unknown, the number
of hypothetical parents increases, which raises the ceiling of the
estimate. This concern was partially mitigated by the use of COLONY
software, which allowed each hypothetical parent to be assigned to
multiple bears based on the promiscuous mating ecology of bears
(Steyaert, et al. , 2012). However, because it is not always
possible to know whether they are alive or dead, this leads to an
overestimation, particularly in short-term surveys, as discussed in
Creel and Rosenblatt (2013) and Spitzer et al. (2016), in which a
population estimation was conducted based on a similar method. In the
present study, more than two-thirds and over 90% of the bears were
assigned for both parents and either parent, respectively. This rate of
parentage assignment is high compared to other studies targeting brown
bears (Norman and Spong, 2015; Sawaya, Kalinowski and Clevenger, 2014;
Spitzer, et al. , 2016) and other bear species (Zeyl, et
al. , 2009), which allowed us to reduce the generation of hypothetical
parents in this study.
This “alive or dead problem” holds true not only for hypothetical
parents but also for existing ones. Although the parentage assignment
rate was high, the lack of information regarding their survival also
leads to overestimations. In this study, among the 295 existing parents
(170 females and 125 males) assigned as the parents of the 492 unique
bears identified in the 2-year period, 222 bears (113 females and 109
males) had already been identified by 2018, of which 196 (97 females and
99 males) were confirmed to be dead. This enabled us to reduce the
number of breeders without information on their survival, which in turn
reduced the difference between the minimum and maximum breeding
populations. This was mainly achieved by the accumulation of over 20
years of genetic information preceding large-scale sampling events.
Furthermore, information on age for dead bears (obtained mainly by an
analysis of their teeth) and the date of first identification for living
bears were very useful to assign the minimum age, which helped improve
the accuracy of estimates of the minimum population size as of 2019. We
suggest that the current method based on pedigree reconstruction offers
less advantage in terms of estimating breeder/adult population sizes
based on genetic data obtained by limited sampling events, but works
well for populations where continuous genetic surveys, particularly
targeting harvested bears, have been conducted in advance.
To assume the mortality of hypothetical parents and bears identified
only before 2019, we defined three criteria, i.e., a maximum number of
generations, maximum age as a breeder, and long-term absence of
observation records in the areas with high survey activity. This enabled
us to exclude 33% (37/114) of those bears from the maximum population
size. The adoption of these criteria was a realistic approach on the
basis of previous studies; however, it may be too conservative. For
example, the minimum ages of some parents were estimated based on the
age of the oldest daughter/son in the offspring list, but it was
unlikely that the daughter/son was the first offspring that they raised
successfully. In fact, among bears included in the maximum breeding
number (N = 49 and 28, for females and males, respectively), the
minimum age for 16 females and 11 males was estimated to be 20 years of
age or older, but their real ages may have exceeded the threshold
criteria as a breeder. In addition, opportunistic hair-trapping and scat
collection has been conducted throughout the peninsula over the last
decade; thus, those older bears should have had a higher possibility of
being sampled. Therefore, it is reasonable to think that the maximum
breeding size still included a certain number of bears that were already
dead. This suggests that the true breeding population size was closer to
the minimum than maximum number, which is supported by the accumulative
curve of unique adult bears that almost reached a plateau at the end of
the 2-year period.
The sex ratio of breeders was more than two-fold (2.04) biased in favor
of females, which is unusual compared to other brown bear populations
(e.g., 1.20–1.30 in Swedish population; Spitzer, et al. , 2016).
It is generally accepted that there are no sex biases at birth in brown
bears (Schwartz, et al. , 2003), and this result therefore raises
some issues. It was most likely due to sex differences in reproductive
opportunities; male reproduction is competitive (Steyaert, et
al. , 2012), and breeding opportunities tend to be biased toward
physically mature males, which reduces the possibility for young males
with limited breeding experience to be assigned as a father in a
parentage analysis. This is consistent with a previous report showing
that the frequency of breeding was low in 5- to 9-year old males but
high in 10- to 14-year old bears in the Rusha area of the Shiretoko
Peninsula (Shimozuru, et al. , 2020). However, if the bias were
solely due to this reason, the number of males assigned as ≥4 years old
based on a pedigree reconstruction should have been larger than that of
females, which was not true (27 females vs. 18 males). In addition, the
number of bears of unknown age was not very different (49 females vs. 47
males) in the minimum population. Furthermore, the number of bears whose
father was unknown (47) was fewer than that of bears whose mother was
unknown (76), which reduced the possibility that males had a lower
probability of sampling than females did in the current field survey.
This suggests that the female-biased breeding population (128 vs. 66) or
adult (≥4 year) population (155 vs. 84) was not strongly influenced by
procedural matters in the current analysis. The adult sex ratio has been
shown to vary in other brown bear populations, but is more or less
biased to females (Schwartz, et al. , 2003), similarly to this
population. This was partially supported by the male-biased probability
of human-caused death in this population, particularly for 2- to
3-year-old bears when males initiate natal dispersal (Kohira, et
al. , 2009; Shimozuru, et al. , 2020). In addition, the high
mortality rate in males due to natural causes, e.g., starvation due to
the high energy demand during development in males (predicted by Mattson
and Reid, 1991) or intraspecific killing (Schwartz, et al. ,
2003), may have accelerated this tendency, although the sex differences
in the natural survival rate are still unknown in this population.
The minimum population size (449 individuals as of 2019) in the study
area (total area of three towns: 1,760 km2; total
forest cover in the area: 1,378 km2) indicated that
the Shiretoko Peninsula has one of the highest brown bear populations
area in the world. The minimum density (25.5–32.6 bears/100
km2) was much higher than the estimated brown bear
density in the interior populations of Europe (e.g., Swedish population:
0.8–1.2 bears/100km2; Bellemain, et al. , 2005)
and North America (0.4–8.0 bears/100km2;
Haroldson, et al. , 2021; Schwartz, et al. , 2003), and also
higher than or comparable to the coastal populations in Alaska
(18.4–40.0 bears/100km2; Schwartz, et al. ,
2003), where a high-nutrient diet (e.g., salmon) is available in the
hyperphagia period. In this study, genetic sampling conducted in two
consecutive years (2019–2020) allowed us to increase the minimum
population by 28% compared to the number obtained solely in the first
year (2019). This was partially achieved by the minimum age assignment
for bears identified for the first time in 2020, based on pedigree
reconstruction and also on body size assessment in cases where video
data were allowed to specify the donor bear. This suggests that a simple
count of the detected genotypes, a very classic method, can still
provide practicable data through a combination of long-term, continuous
genetic monitoring for dead/alive bears and a subsequent multi-year
large-scale sampling event. We still need to ascertain how close the
minimum value is to the true population size through the use of more
sophisticated statistical methods, e.g., SECR approaches. However,
population estimates using statistical models sometimes have wide
confidence limits (Lukacs and Burnham, 2005). Therefore, a precise
estimation of the minimum population size sometimes provides more
applicable and conservative information for wildlife management and
conservation, and can be a useful indicator to select the best-fit model
(Solberg, et al. , 2006), thereby helping to refine population
estimates.
In conclusion, our study suggests that pedigree reconstruction is a very
useful tool for estimating breeding/adult populations and minimum
population size in elusive wildlife species. This approach is also
applicable to wildlife populations under circumstances where population
estimation using statistical models, e.g., the SECR approach, is
difficult for various reasons, e.g., geographical limitations and the
behavioral characteristics of study animals. It should be emphasized
that not only the sampling intensity for large-scale sampling events but
also the preceding accumulation of information on the genotypes and ages
of dead individuals are essential to maximize the utility of this
approach. The current study indicates how important an accurate
knowledge of animal mortality (due to management culls, hunting,
accidents, poaching, and natural deaths) and secured recovery of samples
are for monitoring populations of wildlife. A large-scale, intensive
genetic survey is very costly, and therefore it is not often conducted.
In preparation for the opportunity of such surveys, continuous genetic
monitoring efforts are needed to maximize the amount and quality of the
information regarding demographic parameters.