Identifying spawner biomass per‐recruit reference points from life‐history parameters. Issue 4 (24th March 2020)

• Record Type: Journal Article
• Authors: Zhou, Shijie; Punt, André E.; Lei, Yeming; Deng, Roy Aijun; Hoyle, Simon D.
• Is Part Of: Fish and fisheries. Volume 21:Issue 4(2020)
• Page Start: 760
• Page End: 773
• Publication Date: 2020-03-24
• Subjects: Bayesian measurement error -- FishBase -- productivity -- RAM Legacy database -- resilience -- spawning potential ratio -- steepness; Fisheries -- Periodicals; Fishes -- Periodicals; 639.2
• DOI: 10.1111/faf.12459
• Languages: English
• ISSNs: 1467-2960

Abstract

Abstract: Analysis of spawning biomass per‐recruit has been widely adopted in fisheries management. Fishing mortality expressed as spawning potential ratio (SPR) often requires a reference point as an appropriate proxy for the fishing mortality that supports a maximum sustainable yield— F MSY . To date, a single generic level between F 30% and F 40% is routinely used. Using records from stock assessments in the RAM Legacy Database (RAMLD), we confirm that SPR at MSY (SPRMSY ) is a declining function of stock productivity quantified by F MSY . We then use general linear models (GLM) and Bayesian errors‐in‐variables models (BEIVM) to show that SPRMSY can be predicted from life‐history parameters (LHPs, including maximum lifespan, age‐ and length‐at‐maturation, growth parameters, natural mortality, and taxonomic Class ) as well as gear selectivity. The calculated SPRMSY ranges from about 13% to 95% with a mean of 47%. About 64% of the stocks in the RAMLD require SPRMSY > 40%. Modelling SPRMSY reveals that LHPs plus Class explain 61% of the deviance in SPRMSY . Faster‐growing, low‐survival, and short‐lived species generally require a high SPR. With equal LHPs, elasmobranchs require about 20% higher SPRMSY than teleosts. When F MSY is estimated from fisheries that harvest older fish, increasing the vulnerable age by one year leads to about an 8% increase in SPRMSY . The BEIVM yields smaller variance and bias than the GLM. The models developed in this study could be used to predict SPRMSY reference points for new stocks using the same LHPs for calculating F x%, but without knowledge of the stock‐recruitment parameters.