Combining fishery data through integrated species distribution models
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Species Distribution Models are pivotal for fisheries management. There has been an increasing number of fishery data sources available, making data integration an attractive way to improve model predictions. A wide range of methods have been applied to integrate different datasets in different disciplines. We focus on the use of Integrated Species Distribution Models (ISDMs) due to their capacity to formally accommodate different types of data and scale proportional gear efficiencies. ISDMs use joint modelling to integrate information from different data sources to improve parameter estimation by fitting shared environmental, temporal and spatial effects. We illustrate this method first using a simulated example, and then apply it to a case study that combines data coming from a fishery-independent trawl survey and a fishery-dependent trammel net observations on Solea solea. We explore the sensitivity of model outputs to several weightings for the commercial data and also compare integrated model results with ensemble modelling to combine population trends in the case study. We obtain similar results but discuss that ensemble modelling requires both response variables and link functions to be the same across models. We conclude by discussing the flexibility and requirements of ISDMs to formally combine different fishery datasets.
Paradinas , I , Illian , J B , Alonso-Fernändez , A , Pennino , M G & Smout , S 2023 , ' Combining fishery data through integrated species distribution models ' , ICES Journal of Marine Science . https://doi.org/10.1093/icesjms/fsad069
ICES Journal of Marine Science
Copyright The Author(s) 2023. Published by Oxford University Press on behalf of International Council for the Exploration of the Sea. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.
DescriptionIP would like to thank the European Commission for the funding (GAP-847014). IP is grateful to the MSCA fellowship that supported his research. MGP thanks the project IMPRESS (RTI2018-099868-B-I00), ERDF, Ministry of Science, Innovation, and Universities - State Research Agency.
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