Do the biological characteristics of trout (<scp><i>Salmo trutta</i></scp>) smolts influence their spring migration timing and maiden marine sojourn duration?

Anadromous salmonids migrate seaward to exploit feeding and growth opportunities in marine habitats, yet how smolt biological characteristics influence their marine migratory behavior remains poorly understood. This study used 9 years of trout (Salmo trutta) population monitoring data from 15,595 tagged age-0+ parr, 1033 smolts detected migrating downstream in spring, and 99 adults detected returning from their first marine migration to the River Frome (Dorset, UK) to investigate the influence of smolt biological characteristics on their migration timing and maiden marine sojourn duration. Age-specific differences in the influence of smolt length on migration timing were found, with longer 1-year-old smolts emigrating later than their shorter counterparts within the same age class, but the opposite association existed for 2-year-old smolts. A bespoke integrated statistical model quantified the effects of smolt emigration day of year, age, sex, and length on the probability of first-time migrants returning to the river after one or more sea winters. Younger, later migrating smolts had a longer marine sojourn duration than their older, earlier migrating counterparts, and females remained at sea for longer periods than males. Although the statistical model was designed to maximize the use of information available in the data, it revealed only weak effects of smolt biological characteristics on the maiden marine sojourn duration. A complementary simulation study suggested that detecting more spring migrating smolts and analyzing longer time series of trout population monitoring data would increase the ability to detect statistically significant effects. Therefore, a strategic review of the trout population monitoring program, including more long-term biological data collection, is recommended. The modelling work presented here can provide guidance on the size of the required dataset and how to maximize the power of imperfect data.