Advantages and insights from a hierarchical Bayesian growth and dynamics model based on salmonid electrofishing removal data

Abstract

Growth is a fundamental ecological process of stream-dwelling salmonids which is strongly interrelated to critical life history events (emergence, mortality, sexual maturity, smolting, spawning). The ability to accurately model growth becomes critical when making population predictions over large temporal (multi-decadal) and spatial (meso) scales, e.g., investigating the e ect of global change. Body length collection by removal sampling is a widely-used practice for monitoring sh populations over such large scales. Such data can be e ciently integrated into a Hierarchical Bayesian Model (HBM) and lead to interesting ndings on sh dynamics. We illustrate this approach by presenting an integrated HBM of brown trout (Salmo trutta) growth, population dynamics, and removal sampling data collection processes using large temporal and spatial scales data (20 years; 48 sites placed along a 100 km latitudinal gradient). Growth and population dynamics are modelled by ordinary di erential equations with parameters bound together in a hierarchical structure. The observation process is modelled with a combination of a Poisson error, a binomial error, and a mixture of Gaussian distributions. Absolute t is measured using posterior predictive checks, which results indicate that our model ts the data well. Results indicate that growth rate is positively correlated to catchment area. This result corroborates those of other studies (laboratory, exploratory) that identi ed factors besides water temperature that are related to daily ration and have a signi cant e ect on stream-dwelling salmonid growth at a large scale. Our study also illustrates the value of integrated HBM and electro shing removal sampling data to study in situ sh populations over large scales.