Impacts of different types of data integration on the predictions of spatio-temporal models: A fishery application and simulation experiment Fisheries Research

Integrated spatio-temporal models, which enable the sharing of information across locations, time and data sources, are gaining traction for their potential to generate more precise and more accurate estimations compared to models fitted to single data sources. Standard integrated spatio-temporal models combine multiple data sources via a catchability factor. Recently, spatially varying catchability (SVC) integrated spatio-temporal models were developed to implement data integration via the estimation of an SVC term for the least reliable data sources. Expanded-domain integrated spatio-temporal models are models integrating data from different spatial areas. Spatio-temporal models can combine standard or SVC integrated modelling with expanded-domain integrated modelling. The above-mentioned types of data integration have never been evaluated through a comparative analysis. Here, we investigate the impacts of these different types of data integration on the predictions of spatio-temporal models, via an application to the southern hake (Merluccius australis) HAK4 stock, where the bottom trawl data collected within the New Zealand observer programme are integrated with data from five different bottom trawl research surveys, and a simulation experiment. In total, six models were compared in the present study, where the three last models constitute expanded-domain integrated models