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.