Granger-causality analysis of integrated-model outputs, a tool to assess external drivers in fishery
| Autor: | Margarita M. Rincón, Javier Ruiz, Rachele Corti, Bjarki Thor Elvarsson, Fernando Ramos |
|---|---|
| Přispěvatelé: | European Commission, Junta de Andalucía |
| Jazyk: | angličtina |
| Rok vydání: | 2019 |
| Předmět: |
0106 biological sciences
bepress|Physical Sciences and Mathematics Computer science Population Granger-causality Aquatic Science bepress|Life Sciences|Marine Biology Centro Oceanográfico de Cádiz 01 natural sciences Gadget model bepress|Life Sciences Granger causality Goodness of fit European anchovy bepress|Physical Sciences and Mathematics|Environmental Sciences Pesquerías Causation Predictability education MarXiv|Life Sciences|Marine Biology Stock (geology) Ecosystem based fisheries management education.field_of_study Granger causality analysis 010604 marine biology & hydrobiology 04 agricultural and veterinary sciences Fishery 040102 fisheries Predictive power MarXiv|Physical Sciences and Mathematics 0401 agriculture forestry and fisheries Environmental drivers MarXiv|Life Sciences MarXiv|Physical Sciences and Mathematics|Environmental Sciences |
| Zdroj: | e-IEO. Repositorio Institucional Digital de Acceso Abierto del Instituto Español de Oceanografía instname Digital.CSIC. Repositorio Institucional del CSIC |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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