Identifiable state-space models: A case study of the Bay of Fundy sea scallop fishery

State-space models (SSMs) are now popular tools in fisheries science for providing management advice when faced with noisy survey and commercial fishery data. Such models are often fitted within a Bayesian framework requiring both the specification of prior distributions for model parameters and simulation-based approaches for inference. Here we present a frequentist framework as a viable alternative and recommend using the Laplace approximation with automatic differentiation, as implemented in the R package Template Model Builder, for fast fitting and reliable inference. Additionally we highlight some identifiability issues associated with SSMs that fisheries scientists should be aware of and demonstrate how our modelling strategy surmounts these problems. Using the Bay of Fundy sea scallop fishery we show that our implementation yields more conservative advice than that of the reference model. The Canadian Journal of Statistics 47: 27-45; 2019 (c) 2018 Statistical Society of Canada