Circularity in fisheries data weakens real world prediction

Giron-Nava, Alfredo; Munch, Stephan B.; Johnson, Andrew F.; Deyle, Ethan; James, Chase C.; Saberski, Erik; Pao, Gerald M.; Aburto-Oropeza, Octavio; Sugihara, George

doi:10.1038/s41598-020-63773-3

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Circularity in fisheries data weakens real world prediction

2020

By Giron-Nava, Alfredo ; Munch, Stephan B. ; Johnson, Andrew F. ; ...

Source: Sci Rep. 2020 Apr 24;10(1):6977.

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Giron-Nava, Alfredo ; Munch, Stephan B. ; Johnson, Andrew F. ; Deyle, Ethan ; James, Chase C. ; ... More +

NOAA Program & Office:

NMFS (National Marine Fisheries Service) ; SWFSC (Southwest Fisheries Science Center) ;

Description:

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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Source:

Sci Rep. 2020 Apr 24;10(1):6977.

DOI:

https://dx.doi.org/10.1038/s41598-020-63773-3

Pubmed ID:

32332835

Pubmed Central ID:

PMC7181812

Document Type:

Journal Article ;

Rights Information:

CC BY

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