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Age‐based demography is fundamental to management of wild fish populations. Age estimates for individuals can determine rates of change in key life‐history parameters such as length, maturity, mortality and fecundity. These age‐based characteristics are critical for population viability analysis in endangered species and for developing sustainable harvest strategies. For teleost fish, age has traditionally been determined by counting increments formed in calcified structures such as otoliths. However, the collection of otoliths is lethal and therefore undesirable for threatened species. At a molecular level, age can be predicted by measuring DNA methylation. Here, we use previously identified age‐associated sites of DNA methylation in zebrafish (Danio rerio) to develop two epigenetic clocks for three threatened freshwater fish species. One epigenetic clock was developed for the Australian lungfish (Neoceratodus forsteri) and the second for the Murray cod (Maccullochella peelii) and Mary River cod (Maccullochella mariensis). Age estimation models were calibrated using either known‐age individuals, ages derived from otoliths or bomb radiocarbon dating of scales. We demonstrate a high Pearson's correlation between the chronological and predicted age in both the Lungfish clock (cor = .98) and Maccullochella clock (cor = .92). The median absolute error rate for both epigenetic clocks was also low (Lungfish = 0.86 years; Maccullochella = 0.34 years). This study demonstrates the transferability of DNA methylation sites for age prediction between highly phylogenetically divergent fish species. Given the method is nonlethal and suited to automation, age prediction by DNA methylation has the potential to improve fisheries and other wildlife management settings.

Identifying local adaptation in bottlenecked species is essential for effective conservation management. Selection detection methods are often applied to bottlenecked species and have an important role in species management plans, assessments of the species’ adaptive capacity, and looking for responses to major threats like climate change. Yet, the allele frequency changes driven by selection and exploited in selection detection methods, are similar to those caused by the strong neutral genetic drift expected during a bottleneck. Consequently, it is often unclear what accuracy selection detection methods may offer within bottlenecked populations. In this study, we used simulations to explore if signals of selection could be confidently distinguished from genetic drift across 23 bottlenecked and reintroduced populations of Alpine ibex (Capra ibex). We used the meticulously recorded demographic history of the Alpine ibex to generate a comprehensive simulated SNP data. The simulated SNPs were then used to benchmark the confidence we could place in putative outliers identified through selection scans on empirical Alpine ibex SNP data. Within the simulated dataset, the false positive rates were high for all selection detection methods but fell substantially when two or more selection detection methods were combined. However, the true positive rates were consistently low and became essentially negligible after this increased stringency. Despite the detection of many putative outlier loci in the empirical Alpine ibex RADseq data, none met the threshold needed to distinguish them from genetic drift-driven false positives. Unfortunately, the low true positive rate also creates a paradox, by preventing the exclusion of recent local adaptation within the Alpine ibex.