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.