A Hierarchical Model of Persistent and Transient Growth Variation Applied to Lake Superior Lake Trout

Variability in individual fish growth both within and among populations can interact with mortality to affect variation in size-at-age and other critical features of populations. Herein, we developed and applied mixed-effects, hierarchical growth models to back-calculated length-at-age data from six lake trout (Salvelinus namaycush) populations in Lake Superior to quantify how growth variation was attributable to persistent sources within and among populations and transient (short-term and not consistent over time for an individual) sources. Persistent variation in growth among individuals explained more variability in length-at-age than transient variation, and most of this variation was within populations rather than among populations. Simulations showed that the modeling approach could robustly estimate most growth function parameters, even with mismatches between true and assumed among-population covariation, although a higher number of populations enabled better estimation of certain population-level parameters. An implicit assumption to our interpretation was that lake trout populations in Lake Superior had not experienced substantial size-selective mortality, so their length-at-age patterns largely reflected growth variation rather than size-selective mortality. This assumption should be tested when interpreting future applications of this growth modeling approach.