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Predicting the vertical profiles of anchovy ( Engraulis mordax ) and sardine ( Sardinops sagax ) eggs in the California Current System
Author(s) -
ALEXANDRA CURTIS K.,
CHECKLEY DAVID M.,
PEPIN PIERRE
Publication year - 2007
Publication title -
fisheries oceanography
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.016
H-Index - 80
eISSN - 1365-2419
pISSN - 1054-6006
DOI - 10.1111/j.1365-2419.2006.00414.x
Subject(s) - engraulis , anchovy , sardine , pelagic zone , stock assessment , oceanography , hydrography , environmental science , fishery , geology , biology , fish <actinopterygii> , fishing
Several published models exist for simulating vertical profiles of pelagic fish eggs, but no one has rigorously assessed their capacity to explain observed variability. In this study, we applied a steady‐state model, with four different formulations for vertical diffusivity, to northern anchovy ( Engraulis mordax ) and Pacific sardine ( Sardinops sagax ) eggs in the California Current region. Vertical mixing profiles, based on wind speed and hydrography, were combined with estimated terminal ascent velocities of the eggs based on measurements of egg buoyancy and size, to simulate the vertical profiles of the eggs. We evaluated model performance with two data sets: (1) vertically stratified tows for both species and (2) paired samples for sardine eggs from 3‐m depth and in vertically integrated tows. We used two criteria: whether the model predicted individual observed vertical profiles (1) as well as the observed mean and (2) better than the observed mean. Model predictions made with the formulation producing the most gradual profile of vertical diffusivity provided the best match to observations from both data sets and for both species. Addition of a random error term to the terminal ascent velocity further improved prediction for anchovy eggs, but not sardine. For the paired data, model prediction of integrated abundance from abundance at 3‐m depth had significantly lower mean square error than prediction based on a linear regression of 3 m on integrated abundance. Our results support the feasibility of using data from the Continuous Underway Fish Egg Sampler quantitatively as well as qualitatively in stock assessments.