2024-03-29T07:04:19Zhttp://digital.csic.es/dspace-oai/requestoai:digital.csic.es:10261/2093262022-06-14T10:45:54Zcom_10261_14com_10261_8col_10261_267
Granger-causality analysis of integrated-model outputs, a tool to assess external drivers in fishery
Rincón, M.M.
Corti, Rachele
Elvarsson, Bjarki Thor
Ramos, Fernando
Ruiz Segura, Javier
European Commission
Junta de Andalucía
Gadget model
Environmental drivers
Granger-causality
European anchovy
Ecosystem based fisheries management
Integrated models are able to combine several sources of data into a single analysis using joint likelihood functions, fostering the consistency of assumptions among analyses and the ability to diagnose goodness of fit and model-misspecification. Owing to their capacity to consistently combine diverse information, integrated models could detect the variability induced by external drivers, such as various environmental drivers, on key components of the stock dynamics (e.g. recruitment) in cases where these external drivers are relevant but not yet identified or incorporated into the modelling exercise. This diagnosing power could then be used to explore causality between fishery dynamics, as estimated by the integrated model, and external drivers. To achieve this aim, a correlation analysis is neither necessary nor sufficient to prove causation. An alternative statistical concept, Granger-causality, provides a framework that uses predictability, rather than correlation, to give more evidence of causation between time-series variables. A two-step procedure to investigate external forcings in stock dynamics is proposed. First, an integrated model is implemented to detect anomalies that cannot be explained by the internal dynamics of the stock. Then, in a second step, Granger-causality is used to detect the external origin of these anomalies. This two-step procedure is explored using the European anchovy in the Gulf of Cádiz as an example population where the external (environmental) drivers are well documented. The fishery dynamics is first estimated through an age-length model (Gadget). Then Granger-causality is used to assess the predictive power of different environmental drivers on recruitment. The results indicate that this is a powerful procedure, although also with important limitations, to determine predictability and that it can be implemented in a wide variety of stocks and external drivers. Moreover, once Granger-causality has been identified, it is shown that it can be used to forecast by making few modifications of the integrated model used for diagnosis.
The research leading to these results has received funding from the European Union Seventh Framework Programme (FP7-KBBE-2013) under the grant agreement 613571/MAREFRAME project and Margarita M. Rincón was funded by P09-RNM-5358 of the Junta de Andalucía and CEIJ-018, CEIMAR young researchers project.
2020-04-28T08:05:28Z
2020-04-28T08:05:28Z
2019-05
2020-04-28T08:05:28Z
artículo
http://purl.org/coar/resource_type/c_6501
doi: 10.1016/j.fishres.2019.01.001
issn: 0165-7836
Fisheries Research 213: 42-55 (2019)
http://hdl.handle.net/10261/209326
10.1016/j.fishres.2019.01.001
http://dx.doi.org/10.13039/501100000780
http://dx.doi.org/10.13039/501100011011
#PLACEHOLDER_PARENT_METADATA_VALUE#
info:eu-repo/grantAgreement/EC/FP7/613571
http://doi.org/10.1016/j.fishres.2019.01.001
Sí
none
Elsevier