Circularity in fisheries data weakens real world prediction.

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 approac

SUBMITTER: Giron-Nava A 

PROVIDER: S-EPMC7181812 | biostudies-literature | 2020 Apr

REPOSITORIES: biostudies-literature