ABSTRACT
The age of an individual is an essential demographic parameter but is difficult to estimate without long-term monitoring or invasive sampling. Epigenetic approaches are increasingly used to age organisms, including nonmodel organisms such as cetaceans. Maui dolphins (Cephalorhynchus hectori maui) are a critically endangered subspecies endemic to Aotearoa New Zealand, and the age structure of this population is important for informing conservation. Here we present an epigenetic clock for aging Maui and Hector's dolphins (C. h. hectori) developed from methylation data using DNA from tooth aged individuals (n = 48). Based on this training data set, the optimal model required only eight methylation sites, provided an age correlation of .95, and had a median absolute age error of 1.54 years. A leave-one-out cross-validation analysis with the same parameters resulted in an age correlation of .87 and median absolute age error of 2.09 years. To improve age estimation, we included previously published beluga whale (Delphinapterus leucas) data to develop a joint beluga/dolphin clock, resulting in a clock with comparable performance and improved estimation of older individuals. Application of the models to DNA from skin biopsy samples of living Maui dolphins revealed a shift from a median age of 8-9 years to a younger population aged 7-8 years 10 years later. These models could be applied to other dolphin species and demonstrate the ability to construct a clock even when the number of known age samples is limited, removing this impediment to estimating demographic parameters vital to the conservation of critically endangered species.
ABSTRACT
DNA methylation data facilitate the development of accurate molecular estimators of chronological age or "epigenetic clocks." We present a robust epigenetic clock for the beluga whale, Delphinapterus leucas, developed for an endangered population in Cook Inlet, Alaska, USA. We used a custom methylation array to measure methylation levels at 37,491 cytosine-guanine sites (CpGs) from skin samples of dead whales (n = 67) whose chronological ages were estimated based on tooth growth layer groups. Using these calibration data, a penalized regression model selected 23 CpGs, providing an R 2 = 0.92 for the training data; and an R 2 = 0.74 and median absolute age error = 2.9 years for the leave one out cross-validation. We applied the epigenetic clock to an independent dataset of 38 skin samples collected with a biopsy dart from living whales between 2016 and 2018. Age estimates ranged from 11 to 27 years. We also report sex correlations in CpG data and describe an approach of identifying the sex of an animal using DNA methylation. The epigenetic estimators of age and sex presented here have broad applications for conservation and management of Cook Inlet beluga whales and potentially other cetaceans.
