CALCULATION OF CATCH RATE AND TOTAL CATCH IN ROVING SURVEYS OF ANGLERS

Citation
Jm. Hoenig et al., CALCULATION OF CATCH RATE AND TOTAL CATCH IN ROVING SURVEYS OF ANGLERS, Biometrics, 53(1), 1997, pp. 306-317
Citations number
18
Categorie Soggetti
Statistic & Probability","Statistic & Probability
Journal title
ISSN journal
0006341X
Volume
53
Issue
1
Year of publication
1997
Pages
306 - 317
Database
ISI
SICI code
0006-341X(1997)53:1<306:COCRAT>2.0.ZU;2-8
Abstract
To estimate the total catch in a sport fishery sampled by a roving cre el survey, we multiply an estimate of the total fishing effort by the estimated catch rate (i.e., catch per unit of fishing effort). While t he statistical theory for estimating the fishing effort from instantan eous or progressive counts is well established, there is much confusio n about the appropriate way to estimate the catch rate. Most studies h ave used the ratio of means or the mean of the ratios of individual ca tches and efforts. We analyzed the properties of these estimators of c atch rate under the assumption that fishing is a stationary Poisson pr ocess. The ratio of means estimator has a finite second moment, while the mean ratio estimator has infinite variance. Simulation studies sho wed that the mean of ratios estimator tends to have high and unstable mean squared error relative to the ratio of means estimator and this i s in accordance with empirical observations. We also studied the prope rties of the mean of ratios estimator when all interviews with people fishing for less than epsilon minutes duration were disregarded for va lues of epsilon up to 60 minutes. There was typically a marked reducti on in mean squared error when the shorter trips were not included. We recommend that the mean of ratios estimator, with all trips less than 30 minutes disregarded, be used to estimate catch rate and hence total catch under the roving creel survey design. It has the correct expect ation (at least approximately after the truncation) and almost always had smaller mean squared error than the ratio of means estimates in ou r simulations.