Universal prediction of vertebrate species age at maturity

Hoffmann, M. et al. The impact of conservation on the status of the world’s vertebrates. Science (1979) 330, 1503–1509 (2010).CAS 

Google Scholar 
Wilder, A. P. et al. The contribution of historical processes to contemporary extinction risk in placental mammals. Science (1979) 380, eabn5856 (2023).CAS 

Google Scholar 
Hutchings, J. A., Myers, R. A., García, V. B., Lucifora, L. O. & Kuparinen, A. Life-history correlates of extinction risk and recovery potential. Ecol. Appl. 22, 1061–1067 (2012).Article 
PubMed 

Google Scholar 
Reynolds, J. D., Dulvy, N. K., Goodwin, N. B. & Hutchings, J. A. Biology of extinction risk in marine fishes. Proc. R. Soc. B: Biol. Sci. 272, 2337–2344 (2005).Article 

Google Scholar 
Oli, M. K. & Dobson, F. S. The relative importance of life‐history variables to population growth rate in Mammals: Cole’s prediction revisited. Am. Nat. 161, 422–440 (2003).Article 
PubMed 

Google Scholar 
Healy, K., Ezard, T. H. G., Jones, O. R., Salguero-Gómez, R. & Buckley, Y. M. Animal life history is shaped by the pace of life and the distribution of age-specific mortality and reproduction. Nat. Ecol. Evol. 3, 1217–1224 (2019).Article 
PubMed 

Google Scholar 
Rogers, T. D., Cambiè, G. & Kaiser, M. J. Determination of size, sex and maturity stage of free swimming catsharks using laser photogrammetry. Mar. Biol. 164, 213 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Levasseur, K. E., Stapleton, S. P. & Quattro, J. M. Precise natal homing and an estimate of age at sexual maturity in hawksbill turtles. Anim. Conserv. 24, 523–535 (2021).Article 

Google Scholar 
Valenzuela-Molina, M., Atkinson, S., Mashburn, K., Gendron, D. & Brownell, R. L. Fecal steroid hormones reveal reproductive state in female blue whales sampled in the Gulf of California, Mexico. Gen. Comp. Endocrinol. 261, 127–135 (2018).Article 
CAS 
PubMed 

Google Scholar 
Heydenrych, M. J., Budd, A. M., Mayne, B. & Jarman, S. A genomic predictor for age at sexual maturity for mammalian species. Evol. Appl. 17, e13635 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Budd, A. M., Mayne, B., Berry, O. & Jarman, S. Fish species lifespan prediction from promoter cytosine-phosphate-guanine density. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13774, 1–14 (2023).Mayne, B., Berry, O. & Jarman, S. Redefining life expectancy and maximum lifespan for wildlife management. Austral Ecol. 45, 855–857 (2020).Article 

Google Scholar 
McLain, A. T. & Faulk, C. The evolution of CpG density and lifespan in conserved primate and mammalian promoters. Aging (Albany, NY) 10, 561 (2018).Article 
CAS 
PubMed 

Google Scholar 
Weber, M. et al. Distribution, silencing potential and evolutionary impact of promoter DNA methylation in the human genome. Nat. Genet. 39, 457–466 (2007).Article 
CAS 
PubMed 

Google Scholar 
Saxonov, S., Berg, P. & Brutlag, D. L. A genome-wide analysis of CpG dinucleotides in the human genome distinguishes two distinct classes of promoters. Proc. Natl Acad. Sci. USA 103, 1412–1417 (2006).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Tian, H., He, Y., Xue, Y. & Gao, Y. Q. Expression regulation of genes is linked to their CpG density distributions around transcription start sites. Life Sci. Alliance 5, e202101302 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Gardiner-Garden, M. & Frommer, M. CpG Islands in vertebrate genomes. J. Mol. Biol. 196, 261–282 (1987).Article 
CAS 
PubMed 

Google Scholar 
Li, C. Z. et al. Epigenetic predictors of species maximum life span and other life-history traits in mammals. Sci. Adv. 10, eadm7273 (2024).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Bergeron, L. A. et al. Evolution of the germline mutation rate across vertebrates. Nature 615, 285–291 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Herculano-Houzel, S. Longevity and sexual maturity vary across species with number of cortical neurons, and humans are no exception. J. Comp. Neurol. 527, 1689–1705 (2019).Article 
PubMed 

Google Scholar 
de Magalhães, J. P., Costa, J. & Church, G. M. An analysis of the relationship between metabolism, developmental schedules, and longevity using phylogenetic independent contrasts. J. Gerontology: Ser. A 62, 149–160 (2007).
Google Scholar 
Aylwin, C. F., Toro, C. A., Shirtcliff, E. & Lomniczi, A. Emerging genetic and epigenetic mechanisms underlying pubertal maturation in adolescence. J. Res. Adolesc. 29, 54–79 (2019).Article 
PubMed 

Google Scholar 
Li, C. et al. MKRN3 regulates the epigenetic switch of mammalian puberty via ubiquitination of MBD3. Natl Sci. Rev. 7, 671–685 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Ball, G. F. & Wade, J. The value of comparative approaches to our understanding of puberty as illustrated by investigations in birds and reptiles. Horm. Behav. 64, 211–214 (2013).Article 
PubMed 

Google Scholar 
MacLean, J. A. et al. Rhox: a new homeobox gene cluster. Cell 120, 369–382 (2005).Article 
CAS 
PubMed 

Google Scholar 
Yang, N. et al. A hyper-quiescent chromatin state formed during aging is reversed by regeneration. Mol. Cell 83, 1659–1676.e11 (2023).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Choi, Y., Ballow, D. J., Xin, Y. & Rajkovic, A. Lim homeobox gene, Lhx8, is essential for mouse oocyte differentiation and survival1. Biol. Reprod. 79, 442–449 (2008).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Fan, G. et al. Initial data release and announcement of the 10,000 Fish Genomes Project (Fish10K). Gigascience 9, giaa080 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Lewin, H. A. et al. The Earth BioGenome Project 2020: starting the clock. Proc. Natl Acad. Sci. USA 119, e2115635118 (2022).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Dennis, B., Munholland, P. L. & Scott, J. M. Estimation of growth and extinction parameters for endangered species. Ecol. Monogr. 61, 115–143 (1991).Article 

Google Scholar 
Reid, J. M., Bignal, E. M., Bignal, S., McCracken, D. I. & Monaghan, P. Identifying the demographic determinants of population growth rate: a case study of red-billed choughs Pyrrhocorax pyrrhocorax. J. Anim. Ecol. 73, 777–788 (2004).Article 

Google Scholar 
Jones, O. R. et al. Diversity of ageing across the tree of life. Nature 505, 169–173 (2014).Article 
CAS 
PubMed 

Google Scholar 
Staerk, J. et al. Performance of generation time approximations for extinction risk assessments. J. Appl. Ecol. 56, 1436–1446 (2019).Article 

Google Scholar 
Supple, M. A. & Shapiro, B. Conservation of biodiversity in the genomics era. Genome Biol. 19, 131 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
Theissinger, K. et al. How genomics can help biodiversity conservation. Trends Genet. 39, 545–559 (2023).Article 
CAS 
PubMed 

Google Scholar 
Mayne, B., Berry, O., Davies, C., Farley, J. & Jarman, S. A genomic predictor of lifespan in vertebrates. Sci. Rep. 9, 17866 (2019).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
De Magalhães, J. P. & Costa, J. A database of vertebrate longevity records and their relation to other life-history traits. J. Evol. Biol. 22, 1770–1774 (2009).Article 
PubMed 

Google Scholar 
Myhrvold, N. P. et al. An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96, 3109 (2015).Article 

Google Scholar 
Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals. Ecology 90, 2648 (2009).Article 

Google Scholar 
Périer, R. C., Praz, V., Junier, T., Bonnard, C. & Bucher, P. The Eukaryotic Promoter Database (EPD). Nucleic Acids Res. 28, 302–303 (2000).Article 
PubMed 
PubMed Central 

Google Scholar 
Dreos, R., Ambrosini, G., Périer, R. C. & Bucher, P. The Eukaryotic Promoter Database: expansion of EPDnew and new promoter analysis tools. Nucleic Acids Res. 43, D92–D96 (2015).Article 
CAS 
PubMed 

Google Scholar 
Lenhard, B., Sandelin, A. & Carninci, P. Metazoan promoters: emerging characteristics and insights into transcriptional regulation. Nat. Rev. Genet. 13, 233–245 (2012).Article 
CAS 
PubMed 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2013).Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
Ooi, H. glmnetUtils: utilities for’Glmnet’. R package version 1.1. 5. https://cran.r-project.org/web/packages/glmnetUtils/glmnetUtils.pdf (2020).Breiman, L. Bagging predictors. Mach. Learn. 24, 123–140 (1996).Article 

Google Scholar 
Stock, A., Gregr, E. J. & Chan, K. M. A. Data leakage jeopardizes ecological applications of machine learning. Nat. Ecol. Evol. 7, 1743–1745 (2023).Article 
PubMed 

Google Scholar 
Kapoor, S. & Narayanan, A. Leakage and the reproducibility crisis in machine-learning-based science. Patterns 4, 100804 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Taquet, V., Blot, V., Morzadec, T., Lacombe, L. & Brunel, N. MAPIE: an open-source library for distribution-free uncertainty quantification. arXiv preprint arXiv:2207.12274 (2022).Kim, B., Xu, C. & Barber, R. Predictive inference is free with the jackknife+-after-bootstrap. Adv. Neural Inf. Process. Syst. 33, 4138–4149 (2020).
Google Scholar 
Yong, S. Y. & Ong, C. S. Uncertainty quantification of the virial black hole mass with conformal prediction. Mon. Not. R. Astron Soc. 524, 3116–3129 (2023).Article 
CAS 

Google Scholar 
Quenouille, M. H. Problems in plane sampling. Ann. Math. Stat. 20, 355–375 (1949).Article 

Google Scholar 
Khosravi, A., Nahavandi, S. & Creighton, D. Construction of optimal prediction intervals for load forecasting problems. IEEE Trans. Power Syst. 25, 1496–1503 (2010).Article 

Google Scholar 
Kolberg, L., Raudvere, U., Kuzmin, I., Vilo, J. & Peterson, H. gprofiler2—an R package for gene list functional enrichment analysis and namespace conversion toolset g: Profiler. F1000Res 9, ELIXIR–709 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Wickham, H. ggplot2. Wiley Interdiscip. Rev. Comput. Stat. 3, 180–185 (2011).Article 

Google Scholar 
Yu, G., Smith, D. K., Zhu, H., Guan, Y. & Lam, T. T.-Y. ggtree: an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol. Evol. 8, 28–36 (2017).Article 

Google Scholar 
Xu, S. et al. ggtreeExtra: compact visualization of richly annotated phylogenetic data. Mol. Biol. Evol. 38, 4039–4042 (2021).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Wickham, H. et al. Welcome to the Tidyverse. J. Open Source Softw. 4, 1686 (2019).Article 

Google Scholar 
Kuhn, M. et al. Package ‘caret’. R. J. 223, 7 (2020).
Google Scholar 
Harris et al. Array programming with NumPy. Nature 585, 357–362 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
McKinney, W. Data structures for statistical computing in Python. SciPy 445, 51–56 (2010).
Google Scholar 
Virtanen, P. et al. Fundamental algorithms for scientific computing in Python and SciPy 1.0 contributors. SciPy 1.0. Nat. Methods 17, 261–272 (2020).Article 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Pedregosa, F. et al. Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).
Google Scholar 
Budd, A. M. & Yong, S. Y. Code and Data for ‘Universal Prediction of Vertebrate Species Age at Maturity’ https://doi.org/10.5281/zenodo.13637779 (2024).