Bayesian state-space surplus production models are commonly applied in fisheries stock assessment when the only information available is an index of relative abundance. However, even relatively simple models such as these can be computationally …
For fisheries where both fishery-independent and -dependent catch observations are available, spatiotemporally standardized indices of abundance currently use only the fishery-independent data because it is difficult to reliably standardize …
A discussion of [Bayesian spatio-temporal modelling of anchovy abundance through the SPDE approach](https://www.sciencedirect.com/science/article/pii/S2211675317302816) by Quiroz and Prates for the Winter 2019 Space-time reading group at the …
Bayesian state space models have played an important role in fisheries for many years. These models are typically fit using Markov chain Monte Carlo, often using general-purpose software such as BUGS, JAGS, and more recently Stan. Stan uses a sampler …
A discussion of [Chapter 1 in Advanced Spatial Modeling with SPDEs using R and INLA](https://becarioprecario.bitbucket.io/spde-gitbook/ch-INLA.html) for the Autumn 2018 Space-time reading group at the University of Washington. This chapter focuses on …
Many fish species occur in areas with complicated geography. Natural barriers such as islands and coastlines mean that the spatial structure of the population is unlikely to be stationary. Here I develop and fit a spatiotemporal model that accounts …
Sustainable fisheries management requires the best stock assessments available in order to determine the current state of a stock and its trend over time. These estimates are typically based on fishery-independent surveys conducted specifically to …
Spatially-explicit catch and effort data is increasingly available, and may provide an important source of information for estimating abundance. There is potential to improve abundance estimates by combining fishery-independent and fishery-dependent …