Crustaceans are notoriously difficult to age because of their indeterminate growth and the moulting of their exoskeleton throughout life. The poor knowledge of population age structure in crustaceans therefore hampers accurate assessment of population dynamics and consequently sustainable fisheries management. Quantification of DNA methylation of the evolutionarily conserved ribosomal DNA (rDNA) may allow for age prediction across diverse species. However, the rDNA epigenetic clock remains to be tested in crustaceans, despite its potential to inform both ecological and evolutionary understanding, as well as conservation and management practices. Here, patterns of rDNA methylation with age were measured across 5154 bp of rDNA corresponding to 355 quality‐filtered loci in the economically important European lobster (Homarus gammarus). Across 0‐ to 51‐month‐old lobsters (n = 155), there was a significant linear relationship between age and percentage rDNA methylation in claw tissue at 60% of quality‐filtered loci (n = 214). An Elastic Net regression model using 46 loci allowed for the accurate and precise age estimation of individuals (R2 = 0.98; standard deviation = 1.6 months). Applying this ageing model to antennal DNA from wild lobsters of unknown age (n = 38) resulted in predicted ages that are concordant with estimates of minimum size at age in the wild (mean estimated age = 40.1 months; range 32.8–55.7 months). Overall, the rDNA epigenetic clock shows potential as a novel, nonlethal ageing technique for European lobsters. However, further validation is required across a wider range of known‐age individuals and tissue types before the model can be used in fisheries management.
Background
Transposable elements are significant components of most organism’s genomes, yet the reasons why their abundances vary significantly among species is poorly understood. A recent study has suggested that even in the absence of traditional molecular evolutionary explanations, transposon proliferation may occur through a process known as ‘transposon engineering’. However, their model used a fixed beneficial transposon insertion frequency of 20%, which we believe to be unrealistically high.
Results
Reducing this beneficial insertion frequency, while keeping all other parameters identical, prevented transposon proliferation.
Conclusions
We conclude that the author’s original findings are better explained through the action of positive selection rather than ‘transposon engineering’, with beneficial insertion effects remaining important during transposon proliferation events.
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