Parrack: Estimating stock abundance from size data 



323 



of estimated, abundance estimation bias is assured 

 because stock size is a function of that rate. It thus 

 seems prudent to include the rate in the vector of 

 estimates to avoid abundance estimation bias even if 

 it is not useful. When necessary, Monte Carlo methods 

 can be used to establish interval estimates on e"^. This 

 study indicates that estimates of e"^ are often biased, 

 yet precise. The estimate of error variance over the 97 

 trials of e xample 1 wa s 0.0084, so the 95% CI width 

 is ±1.96\/(0.0084^97) or ±0.0182, and the bias ad- 

 justment is 0.6422. 



Acknowledgments 



I express very sincere appreciation to Douglas G. Chap- 

 man for his knowledgeable and diligent guidance of this 

 research and William G. Clark whose critical sugges- 

 tions and encouragement significantly broadened the 

 scope of this study. I am indebted to Bradford E. 

 Brown, of the Southeast Fisheries Center of the Na- 

 tional Marine Fisheries Service, who provided com- 

 puter resources and other critical support. I especially 

 thank Nancie J. Parrack for many helpful suggestions 

 as the study progressed and Stephen B. Mathews for 

 technical recommendations as well as a critical review 

 of the original manuscript. 



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