The field of fisheries science and, in particular, fish population dynamics and stock assessment modeling has rapidly progressed over the last decade, largely due to advances in computing power, statistical theory, and data collection technology (Maunder and Punt 2013). Higher resolution data (e.g., catch locations or animal tracks collected via global positioning systems) along with continually improving computational ability have enabled new explorations into how best to model the complex biological and anthropological processes driving fish populations. The result has been a reemergence of modeling tools previously deemed too complex or data intensive, as well as the development of new methods to handle multidimensional, spatiotemporal parameter estimation in statistically rigorous ways.