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.