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 …
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 …
Spatially varying catchability for fishery-dependent CPUE standardization.
Accounting for preferential sampling in spatial models of catch and effort data
Incorporating nonstationary spatial processes by allowing for spatially varying covariance parameters.
Eliminating HMC divergences when fitting Bayesian state-space models for fish populations.