Biomass limit reference points are sensitive to estimation method, time‐series length and stock development

Research output: Contribution to journalJournal articleResearchpeer-review

105 Downloads (Pure)

Abstract

Biomass limit reference points are widely used in fisheries management and defines the biomass threshold below which stock productivity (i.e. recruitment) is likely to be impaired. Scientifically sound and transparent methods for estimating biomass thresholds are therefore needed together with ways of quantifying uncertainties. The main focus of the study was placed on two methods currently applied to several small-bodied pelagic species in the Northeast Atlantic. These methods have not formerly been described in the scientific literature and are in the present study being compared to some already described methods, of which, one is broadly applied outside the Northeast Atlantic. Using a combination of data simulations and data from 51 small-bodied pelagic fish stocks, we analyzed the sensitivity of estimated biomass thresholds to (i) the choice of method, (ii) time-series length, and (iii) stock development (e.g. rebuilding or declining). It was demonstrated that estimated biomass thresholds are associated with considerable uncertainty not previously quantified. Furthermore, the level of the estimated threshold and the amount of uncertainty depended on choice of method, time-series length, and stock development trends. Hence, this study contributes to improving the quality of future biomass limit reference points by providing guidance regarding choice of method and how to demonstrate stock-specific uncertainties.
Original languageEnglish
JournalFish and Fisheries
Volume22
Issue number1
Pages (from-to)18-30
Number of pages13
ISSN1467-2960
DOIs
Publication statusPublished - 2021

Keywords

  • Fisheries management
  • Stock-recruitment
  • Statistical uncertainty
  • Small pelagics
  • RAM legacy
  • Sustainable exploitation

Fingerprint

Dive into the research topics of 'Biomass limit reference points are sensitive to estimation method, time‐series length and stock development'. Together they form a unique fingerprint.

Cite this