Alyssa M. Budd^*,1,2, Benjamin Mayne², Oliver Berry², and Simon Jarman^{1, 2}

¹ School of Biological Sciences, The University of Western Australia, Perth, WA, Australia

² Environomics Future Science Platform, Indian Ocean Marine Research Centre, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Crawley, WA, Australia

* Correspondence: Alyssa M. Budd, School of Biological Sciences, Indian Ocean Marine Research Centre, University of Western Australia, Crawley, WA, Australia.

Email: alyssa.budd@csiro.au Abstract

Lifespan is a key attribute of a species’ life cycle and varies extensively among major lineages of animals. In fish, lifespan varies by several orders of magnitude, with reported values ranging from less than one year to approximately 400 years. Lifespan information is particularly useful for species management, as it can be used to estimate invasion potential, extinction risk and sustainable harvest rates. Despite its utility, lifespan is unknown for most fish species. This is due to the difficulties associated with accurately identifying the oldest individual(s) of a given species, and/or deriving lifespan estimates that are representative for an entire species. Recently it has been shown that CpG density in gene promoter regions can be used to predict lifespan in mammals and other vertebrates, with variable accuracy across taxa. To improve accuracy of lifespan prediction in a non-mammalian vertebrate, here we develop a fish-specific genomic lifespan predictor. Addressing previous issues of low sample size and sequence dissimilarity, we incorporate more than eight times the number of fish species used previously (n = 442) and use fish-specific gene promoters as reference sequences. Our model predicts fish lifespan from genomic CpG density alone (measured as CpG observed/expected ratio), explaining 64 % of the variance between known and predicted lifespans. The results demonstrate the value of promoter CpG density as a universal predictor of fish lifespan that can applied where empirical data are unavailable, or impracticable to obtain.