Introduction
Age is an important factor in the study of wildlife ecology. Age information is essential to establishing life history characteristics such as growth rate, age of maturity, and age of death (Morris 1972). In addition, survival and fecundity rates are inextricably linked to age; as animals age, they reach actuarial (i.e., decreasing survival probability with age) and reproductive senescence (Williams 1957; Monaghan et al. 2008; Nussey et al. 2013; Gaillard & Lemaître 2017; Gaillard & Lemaître 2020). These factors are closely related to population dynamics (Oli & Armitage 2004) and in turn to the conservation and management of animal species (Robert et al. 2015; Colchero et al. 2019; Tidière et al. 2021).
Consequently, biologists must determine the ages of animals, although estimating age based on appearance is difficult for many species. Therefore, the otoliths and scales of fish (Kimura et al. 1979) and the wax plugs of baleen whales (Purves 1955) have been used for age estimation. In other cases, teeth have been used to assess age in various wildlife species. The method of counting the annual rings of tooth cementum has been used with pinnipeds (Scheffer 1950; Laws 1952; Scheffer & Myrick 1980), and subsequently, the number of laminations in teeth was employed to estimate age in toothed whales (Nishiwaki et al. 1958). In addition, cementum annuli have been widely adopted to determine the age of terrestrial mammals (Thomas 1977). However, some difficulties face age estimation using cementum annuli. First, study animals must be captured to remove a tooth, which limits the target to dead or anesthetized individuals. Moreover, pulling teeth from living animals is highly invasive. Second, accuracy may differ between skilled and less-experienced workers due to the precision required for this technique. Third, cementum annuli become more difficult to read in older individuals. Fourth, cementum annuli are thought to form at different rates depending on climate and nutritional stress, and these informations are not always available for target species or regions (Rolandsen et al. 2008).
Brown bears (Ursus arctos ) live for 20–30 years (Interagency Grizzly Bear Committee 1987). Pregnant female bears give birth from late January to early February (Friebe et al. 2014) during winter hibernation, which lasts for 3–7 months (González-Bernardo et al. 2020). Offspring become independent from their mothers at 1.5 or 2.5 years of age (Shimozuru et al 2017). The minimum age at first parturition was 4 years (Mano & Tsubota 2002), and physical growth terminated around 5 and 8 years of age for females and males, respectively (Shirane et al. 2020). Individual and seasonal variations in body size make identification of bear age by appearance almost impossible (Shirane et al. 2020; Shirane et al. 2021) except for cubs-of-the-year, and therefore tooth-based age estimation has been used to determine bear ages. In Europe and the United States, brown bears have been protected or carefully managed after dramatic population decreases (Zedrosser et al. 2001; Mattson & Merrill 2002). On the other hand, conflicts such as crop depredation, intrusion into human residential areas, and attacks on livestock and humans have become serious problems, and management agencies have developed policies to reduce these conflicts (Can et al. 2014; Bombieri et al. 2019). Controlling bear populations via legal hunting and culling is one such policy. Bears are vulnerable to over-harvesting due to their low reproductive rate, and reduced populations require many years to recover (Miller 1990). Therefore, knowledge of the age structure is crucial to understanding their ecology, as well as to the development of appropriate strategies for conservation and management of bears.
Recently, as an alternative method for age estimation, DNA methylation levels have been employed as an indicator (Bocklandt et al. 2011; Koch & Wagner 2011). DNA methylation is an epigenetic mechanism involving the transfer of a methyl group onto the C5 position of cytosine to form 5-methylcytosine, which occurs predominantly on cytosines located within cytosine–guanine dinucleotide (cytosine-phosphate-guanine; CpG) sites in vertebrates (Bogdanović & Veenstra 2009). DNA methylation regulates gene expression by inhibiting the binding of transcriptional activators to DNA or altering chromatin states to inhibit transcription factor binding (Moore et al. 2013; Rose & Klose 2014). In addition to the relationship between DNA methylation and gene expression, research has demonstrated that the degree of DNA methylation changes with age (Jones et al. 2015), providing a foothold for its application to age estimation (De Paoli-Iseppi et al. 2017). In early epigenetic research efforts for age estimation, the focus was on forensic research in humans using various biological samples, including blood, muscle, saliva, buccal swabs, and semen (Horvath 2013; Lee et al. 2015; Bekaert et al. 2015). Subsequently, similar techniques have been established for laboratory animals such as mice (Wang et al., 2017; Petkovich et al., 2017; Stubbs et al., 2017) and naked mole rats (Heterocephalus glaber ; Lowe et al. 2020), companion animals such as dogs and cats (Thompson et al. 2017; Qi et al. 2021; Raj et al. 2021), and wild animals such as humpback whales (Megaptera novaeangliae ; Polanowski et al. 2014), bottlenose dolphins (Tursiops truncatus ; Beal et al. 2019), long-lived seabirds (Ardenna tenuirostris ; De Paoli-Iseppi et al. 2019), green turtles (Chelonia mydas ; Mayne et al. 2022), chimpanzees (Pan troglodytes ; Ito et al. 2018), wolves (Canis lupus ; Thompson et al. 2017), and roe deer (Capreolus capreolus ; Lemaître et al. 2022). Moreover, body condition and life history factors such as obesity, social status, and hibernation are reportedly associated with the DNA methylation level (Biggar & Storey 2014; Alvarado et al. 2015; Yamazaki et al. 2021).
The main purpose of this study was to establish a novel age estimation method for brown bears based on methylation levels in blood-derived DNA collected from captive and wild bears. Differences in sex and growth environment, including diet, frequency of interactions with conspecifics, hibernation status, and risk of exposure to pathogens, may contribute to epigenetic aging. Therefore, we assessed such influences on epigenetic aging in bears.