Generalized Additive Models (a.k.a., GAM) comprises a collection of nonparametric and semi-parametric regression techniques for exploring relationships between response and predictor variables. The peculiarity of GAM is that there is no need to make any a priori assumption on the functional form linking the two set of variables, as these relationships are modeled with smooth functions. GAM have recently found wide applications in ecological analysis and particularly in fisheries.


A greater scientific understanding of the mechanisms affecting fish population abundance and distribution is needed to develop sustainable management strategies of our renewable ocean resources. Both single-species (e.g., stock and recruitment models) and ecosystem-based (e.g., marine protected areas) management of fish populations rely on key assumptions regarding ecological traits of fish early life stages. The egg, larval, and early juvenile stages have been considered critical periods in the entire a fish’s life history. For example, the dispersal and survival of fishes during early life will determine their distribution and abundance during adult stages. Many fish species produce an enormous amount of eggs compared to terrestrial vertebrate species, however, only an infinitesimal percentage of these eggs survive to the adult stage. Hence, fishes face many challenges during their journey from the egg to the adult stage, and exhibit tremendous variability in the number of offspring that survive to reproduce.