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Simulation Study of Biological Reference Points for Summer Flounder
Author(s) -
Rothschild Brian J.,
Jiao Yue,
Hyun SaangYoon
Publication year - 2012
Publication title -
transactions of the american fisheries society
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.696
H-Index - 86
eISSN - 1548-8659
pISSN - 0002-8487
DOI - 10.1080/00028487.2012.667041
Subject(s) - maximum sustainable yield , fishing , stock (firearms) , flounder , stock assessment , mathematics , fishery , econometrics , statistics , population , fisheries management , economics , geography , biology , fish <actinopterygii> , demography , archaeology , sociology
The biological reference point F x % (i.e., the fishing mortality rate that maintains the spawning stock biomass per recruit at x % of its unfished value [where x is usually set to 40]) is a commonly used proxy for F MSY (the fishing mortality rate that results in the maximum sustainable yield). However, F x % is not in general equivalent to F MSY . To investigate the difference between F x % and F MSY , we developed a simple simulation model capable of representing the relationship between yield and fishing mortality, maximum spawning potential (%MSP), and the curvature of the stock–recruitment (S–R) curve (parameterized as β ) for a stock similar to summer flounder Paralichthys dentatus (a high‐ β species). The model demonstrates that the dynamic trajectories of the stock are heavily dependent on β . The model confirmed the dependence of equilibrium yield on β and produced a specific relationship between the magnitude of β and yield. A decision‐theoretic approach was used to suggest that setting x to 40 reduces yield and that smaller values of x produce greater yields for high‐ β stocks. The analysis focuses attention on the fact that the choice of F x % as a management tool places extreme reliance on the least known and understood component of fish population dynamics: the S–R relationship. Our conclusion (to use a value of x considerably less than 40 to obtain MSY) was supported by (1) our simulation results, (2) averaging in a decision‐theoretic approach, (3) the correspondence of the traditionally computed biomass at the maximum sustainable yield with high values of β , and (4) the values of x reported in the literature.