Predicting reference points and associated uncertainty from life histories for risk and status assessment. (3rd November 2015)

• Record Type: Journal Article
• Authors: García-Carreras, Bernardo; Jennings, Simon; Le Quesne, Will J. F.
• Is Part Of: ICES journal of marine science. Volume 73:Number 2(2016)
• Page Start: 483
• Page End: 493
• Publication Date: 2015-11-03
• Subjects: conservation -- life history -- management -- reference points -- selectivity -- sensitivity -- uncertainty; Ocean -- Periodicals; Fisheries -- Periodicals; Fishes -- Periodicals; Marine biology -- Bibliography -- Periodicals; 551.4605
• DOI: 10.1093/icesjms/fsv195
• Languages: English
• ISSNs: 1054-3139

Abstract

Abstract: To assess status of fish populations and the risks of overexploitation, management bodies compare fishing mortality rates and abundance estimates with reference points (RP). Generic, "data-poor" methods for estimating RP are garnering attention because they are faster and cheaper to implement than those based on extensive life history data. Yet data-poor RP are subject to many unquantified uncertainties. Here, we predict fishing mortality RP based on five levels of increasingly comprehensive data, to quantify effects of parameter and structural uncertainty on RP. Level I RP (least data) are estimated solely from species' maximum size and generic life history relationships, while level V RP (most data) are estimated from population-specific growth and maturity data. By estimating RP at all five data levels, for each of 12 North Sea populations, we demonstrate marked changes in the median RP values, and to a lesser extent uncertainty, when growth parameters come from data rather than life history relationships. As a simple rule, halving the median level I RP gives almost 90% probability that a level V median RP is not exceeded. RP and uncertainty were substantially affected by assumed gear selectivity; plausible changes in selectivity had a greater effect on RP than adding level V data. Calculations of RP using data for successive individual years from 1984 to 2014 showed that the median RP based on data for any given year would often fall outside the range of uncertainty for RP based on data from earlier or later years. This highlighted the benefits of frequent RP updates when suitable data are available. Our approach provides a quantitative method to inform risk-based management and decisions about acceptable targets for data collection and quality. Ultimately, however, the utility and extent of adoption of data-poor methods for estimating RP will depend on the risk aversion of managers.