Introduction
The identification of demographic parameters is fundamental for
understanding behavioral ecology (Roy, et al. , 2012; Stoen,
et al. , 2006) and is essential for the effective management and
conservation (Katzner, et al. , 2011) of wild animals. This
includes quantity-related factors, such as population size/density and
the number of reproductively active individuals, and quality-related
factors, such as sex ratios, age structures, survival/mortality rates,
reproductive rate, and population growth rate. Reliable estimates of
these parameters are of particular importance for endangered animals or
populations, but are usually difficult to obtain. This is particularly
true for rare or elusive species, including large carnivores, most of
which have declining population trends (Wolf and Ripple, 2018). In
addition to habitat loss and fragmentation by deforestation
(Zemanova, et al. , 2017), human-caused mortality, including
culling for management purposes and hunting have become a serious threat
to populations (Collins and Kays, 2011). On the other hand, an increase
in the population of large carnivores presents a potential threat to
human populations and livestock (Hristienko and McDonald, 2007).
Therefore, population monitoring of wild carnivores inhabiting areas
close to human populations is indispensable for the development of
wildlife management and conservation policies, such as a determining
harvest quotas (Kohira, et al. , 2009).
In the last two decades, DNA-based statistical models have been
developed and used to estimate population sizes and trends. Most are
based on noninvasive sampling methods. In large carnivore studies this
includes the collection of hair (Rounsville, et al. , 2022;
Woods, et al. , 1999), feces (Kindberg, et al. , 2011;
Kohn, et al. , 1999), and their combination (Ciucci, et
al. , 2015). Hair and fecal samples allow DNA-based individual
identification without capturing and handling the animals, which is of
great advantage in terms of cost-effectiveness (Kindberg, et al. ,
2011), and animal welfare (Cattet, et al. , 2008). Several
estimators have been developed for population size estimation based on
noninvasive genetic data, including capture-mark-recapture (CMR) methods
(Seber, 1986), rarefaction analysis (Kohn et al., 1999), and, more
recently, spatially explicit capture-recapture (SECR) methods (Efford,
2004). These methods have been applied to several large carnivore
species, including brown bears (Ursus arctos ) (Kindberg, et
al. , 2011; Morehouse and Boyce, 2016), wolves (Canis lupus )
(Caniglia, et al. , 2012), coyotes (Canis latrans )
(Kohn, et al. , 1999; Morin, Kelly and Waits, 2016), and mountain
lions (Puma concolor ) (Russell, et al. , 2012). These
methods use an individual’s genotype as a molecular tag (Schwartz,
Luikart and Waples, 2007). Genotypes can be a unique and permanent mark,
which is superior to classic CMR approaches that use physical tags, such
as ear-tags and leg bands. However, genotypic data are more than just
tags; they contain further information, such as parent-offspring
relationships and population structures, which sometimes improve the
accuracy of estimates of population sizes and trends (Pearse, et
al. , 2001).
As an alternative method for estimating demographic parameters, a
DNA-based pedigree reconstruction approach has been developed (Creel and
Rosenblatt, 2013). This approach has been widely used to estimate the
number of breeding individuals in a population (Israel and May, 2010;
Koch, et al. , 2008; Pearse, et al. , 2001; Quinn, Alden and
Sacks, 2019), as well as to investigate many aspects of animal behavior,
including population structure (Calboli, et al. , 2008;
Hudy, et al. , 2010), breeding ecology (Levine, et al. ,
2019; Shimozuru, et al. , 2019), and dispersal (Arora, et
al. , 2012). Because population estimations based on statistical models
do not provide age-related information, breeding population size
estimates can offer more practical information regarding the
reproductive potential of a population. One of the advantages of this
method is that it enables the presence of breeders that were not
directly sampled to be inferred if their offspring have been sampled,
although it remains uncertain whether they were dead or alive at the
time of sampling. Therefore, this method is particularly useful for
estimating the number of breeding individuals under the circumstances
where the inferred breeders can be determined to be alive or dead. For
example, in a previous study in painted turtles (Chrysemys
picta ), Pearse et al. (2001) targeted hatchlings as offspring in a
candidate parentage analysis, in addition to their mothers attending the
nest, which enabled them to determine the number of male breeders that
existed at the copulating period. In most mammals it is not possible to
selectively sample newborns. In addition, it is almost impossible to
obtain information on age by noninvasive genetic sampling, which makes
it more difficult to know whether the breeders inferred by pedigree
reconstruction are dead or alive. Such uncertainty over the
survival/mortality of the breeders raises the ceiling of the maximum
estimates and thereby impairs its accuracy. This holds particularly true
for large carnivores that are relatively long-lived, for which
multiple-generations can exist in a population, and mortality is
difficult to detect. Therefore, studies of breeding populations based on
the pedigree reconstruction approach are challenging and remain rare in
large carnivore populations (Creel and Rosenblatt, 2013; Spitzer,
et al. , 2016).
In this study, we estimated the breeding and adult population size, as
well as the minimum population size, in a brown bear (Figure 1)
population in the Shiretoko Peninsula, Japan, based on a pedigree
reconstruction approach. The Shiretoko Peninsula is located in eastern
Hokkaido, Japan (Figure 2). An area extending from the middle to the tip
of the peninsula has been designated a UNESCO World Natural Heritage
Site, as well as a national park, where the habitat of the brown bear is
protected. However, human–bear conflict, including agricultural crop
damage and intrusion into human residential areas, has become a serious
problem on the peninsula. As many as 20–70 bears have been killed
annually over the past decade (total 373 bears in 2011–2020), mainly
for management purposes. This small peninsula consists of coastal area
and precipitous mountains, and most of the area has limited
accessibility, which makes it difficult to conduct a population
estimation survey based on a systematic genetic sampling targeting all
areas of the peninsula. As an alternative, a harvest-based method, based
on the mortality records of brown bears, has estimated a population size
as 559, although the wide confidence intervals (±440) give little
credibility to the estimates (Ministry of the Environment Government of
Japan, 2017). The precise estimation of the population and/or breeding
population is required for the appropriate management and conservation
of brown bears. On the peninsula, information on genotypes, sex, and
ages of dead bears (due to management culls, hunting, accidents, or
natural causes) has been accumulated for the past three decades. Due to
the strong relationship between park managers and hunters on the
peninsula, poaching or hunting without a report are very unlikely to
have occurred over the past two decades. In addition, opportunistic
noninvasive genetic sampling (hairs and feces) has been performed in
some areas (Shirane, et al. , 2018), and continuous bear
monitoring surveys (including DNA sampling) have been conducted for a
decade or more in the Rusha area (Figure 2; Shimozuru, et al. ,
2017). The accumulated information, if combined with large-scale genetic
sampling, may be able to identify reliable demographic parameters,
although other methods (e.g., the CMR method, a rarefaction analysis and
the SECR method) are difficult due to geographical limitations. In the
current study, we applied a pedigree reconstruction approach to this
small but highly populated bear habitat. The population size of breeders
and adults, and the minimum population size, were estimated based on
large-scale genetic sampling events conducted in two consecutive years.