Date

2009

Document Type

Master's Thesis

Degree Name

Master of Science (M.S.)

Department

Moss Landing Marine Laboratories

Abstract

The paucity of fish abundance data has resulted in management decisions in nearshore rocky reef areas of central California based on data-poor stock assessments or none at all. The accuracy of fish stock assessment models can be improved with the inclusion of time-series fish counts and sampling effort data. This study informs fisheries managers on the potential of existing abundance survey data for understanding trends in nearshore rocky reef fishes of central California (Cape Mendocino and Pt. Conception). I included 18 fish species commonly targeted by fisheries in this study area. Nine abundance surveys were analyzed to compare trends, intra-annual precision and sources of variability I used the generalized linear model (GLM) to create yearly abundance indices from fish count, effort and explanatory variable data collected for each study species. To assess the direction and significance of trends, I used linear regressions based on yearly index values. S. mystinus, S. miniatus, S. caurinus and O. elongatus were analyzed in greater detail. I found that different abundance survey methodologies often indicated different trends for species. When comparisons could be made, surveys linear trends were statistically different for each species over set time periods. Survey biases likely explain these differences. Significant species trends for surveys were mostly downward in the most recent time-period examined (2004-07). Survey types also sampled species with varying degrees of precision, but each survey has the potential to be useful for management in some way. This analysis of abundance patterns is useful for designing future surveys of these species, and informing management of nearshore rocky reef fishes in a time of increasing fishing pressure on this assemblage.

Comments

Thesis (M.S.) Division of Science and Environmental Policy. Moss Landing Marine Laboratories

Download

Share

COinS