Circularity in fisheries data weakens real world prediction
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Published version
Date
2020-12
Authors
Giron-Nava, Alfredo
Munch, Stephan B.
Johnson, Andrew F.
James, Chase C.
Saberski, Erik
Pao, Gerald M.
Aburto-Oropeza, Octavio
Sugihara, George
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OA Version
Published version
Citation
Alfredo Giron-Nava, Stephan B Munch, Andrew F Johnson, Ethan Deyle, Chase C James, Erik Saberski, Gerald M Pao, Octavio Aburto-Oropeza, George Sugihara. 2020. "Circularity in fisheries data weakens real world prediction." Scientific Reports, Volume 10, Issue 1, https://doi.org/10.1038/s41598-020-63773-3
Abstract
The systematic substitution of direct observational data with synthesized data derived from models during the stock assessment process has emerged as a low-cost alternative to direct data collection efforts. What is not widely appreciated, however, is how the use of such synthesized data can overestimate predictive skill when forecasting recruitment is part of the assessment process. Using a global database of stock assessments, we show that Standard Fisheries Models (SFMs) can successfully predict synthesized data based on presumed stock-recruitment relationships, however, they are generally less skillful at predicting observational data that are either raw or minimally filtered (denoised without using explicit stock-recruitment models). Additionally, we find that an equation-free approach that does not presume a specific stock-recruitment relationship is better than SFMs at predicting synthesized data, and moreover it can also predict observational recruitment data very well. Thus, while synthesized datasets are cheaper in the short term, they carry costs that can limit their utility in predicting real world recruitment.
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© The Author(s) 2020. Open Access. This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/.