R goes fishing: Analyzing fisheries data using AD Model Builder and R

Arni Magnusson (University of Washington)

Fisheries stock assessment is mainly about two questions: what is the 
current stock status, and what are the consequences of alternative harvest 
levels. The data provide indirect information in the form of annual landed 
catch, age distribution, index of relative abundance, and biological 
features like length, weight, and maturity at age. Other possible data 
include tag recoveries, juvenile surveys, and acoustic estimates of 
absolute abundance. Due to these diverse data types, as well as economic 
importance, fisheries stock assessment has pushed the limits of applied 
statistics over the last fifty years. The dynamic models are highly 
nonlinear, include dozens of parameters, and the objective function 
consists of several likelihood components. The most efficient algorithm to 
fit these models is automatic differentiation, as implemented with AD 
Model Builder, a dialect of C++. To diagnose the model fit, trellis plots 
in R have proven very useful, and the coda package is used to diagnose 
MCMC convergence. Here, I demonstrate the functions I have implemented to 
make these tasks easier, both for newcomers to R and old dogs.