Hidden Markov models with serial correlation for identifying stock-recruitment regime shifts

Resolving productivity changes is crucial to effective fisheries management as shifts in the stock-recruitment (SR) relationship redefine levels of sustainable removals. Hidden Markov models (HMMs) and state-space models are complementary methodologies for capturing discrete (regime-like) and continuous variations, respectively, in fisheries stock productivity. In this work, we combine the strengths of both approaches by developing HMMs with serial correlation and implementing them efficiently. To account for interactions between SR parameters and other components of stock assessment models, we embed hidden Markov SR models within the broader stock assessment framework. Additionally, we incorporate covariates into the transition probabilities of the HMM to address nonstationarity and substantially reduce model complexity. Simulation and case studies demonstrate the strong performance of this novel methodology., , , # Hidden Markov models with serial correlation for identifying stock-recruitment regime shifts

[https://doi.org/10.5061/dryad.31zcrjdxn](https://doi.org/10.5061/dryad.31zcrjdxn)

## Description of the data and file structure

**Files and variables**

#### File: HMSM_AR_1_Data_and_Codes.zip

Description: This zip file includes the TMB codes and data used for the simulation studies presented in the paper:

Hidden Markov models with serial correlation for identifying stock-recruitment regime shifts by Shajitha Shahul Hameed, Noel G. Cadigan, Candemir Cigsar, Paul Regular, and Nan Zheng.

This zip file contains two main folders: Simulations and External_Fitting. It also contains an R script to implement the Viterbi algorithm.

1\. Simulations

This folder includes the TMB (Template Model Builder)codes and data used for the simulation studies presented in the paper:

Hidden Markov models with serial correlation for identifying stock-recruitment regime shifts by Shajitha Shahul Hameed, Noel ...,