Terceiro: The statistical properties of recreational catch data off the northeastern U.S. coast 



671 



but usually best characterized by the Poisson or nega- 

 tive binomial distribution, depending on the manner in 

 which the catch rate is configured. This finding suggests 

 that standardization methods for MRFSS catch-rate data 

 where Poisson (in the case of per hour rates) or negative 

 binomial (for per trip rates) error structures are assumed 

 would usually be more appropriate than methods where 

 normal or lognormal error structures are assumed. 



The modeling of both the simulated and empirical 

 MRFSS catch rates indicates that one may draw errone- 

 ous conclusions about stock trends by assuming the wrong 

 error distribution in procedures used to developed stan- 

 dardized indices of abundance. The results demonstrate 

 the importance of considering not only the overall model 

 fit and significance of classification effects, but also the pos- 

 sible effects of model misspecification, when determining 

 the most appropriate model construction. In particular, the 

 simulation exercise indicates that assuming a lognormal 

 model in the calculation of indices of abundance from 

 recreational fishery catch-per-trip data with a true under- 

 lying negative binomial distribution will provide indices 

 that will strongly underemphasize the true trends in the 

 indices, and therefore in stock abundance. This underesti- 

 mation applies equally to populations that may be declin- 

 ing or increasing faster than the lognormally standardized 

 indices might indicate. 



The MRFSS catch-per-trip indices standardized with the 

 negative binomial model, which the descriptive statistics 



and goodness-of-fit results suggest should be the appropri- 

 ate model, differ relatively little from the unstandardized 

 indices, indicating that the model effects accounted for a 

 low percentage of the variation in mean catch rate. The 

 classification categories recorded in the general MRFSS 

 sampling are broad, and even measures of angling avidity 

 such as "angler-reported days of saltwater fishing during 

 the previous 12 months" may not be adequate proxies for 

 the real factors (besides stock abundance) that account for 

 variation in recreational fishery mean catch rates. To make 

 standardization analysis of MRFSS catch rate data poten- 

 tially more useful, by accounting for a significantly larger 

 part of the unexplained variance and thus providing more 

 accurate indices of abundance, more information on the 

 characteristics of individual fishing trips may be needed. 

 Such information might include details on the type of 

 equipment used, the skills, experience, avidity, and identity 

 of the individual fishermen, and detailed temporal and spa- 

 tial information about fishing trips. In the future, collection 

 of detailed trip data for general recreational fisheries may 

 be best accomplished by the identification and sampling of 

 "test fleets" of known, individual fishermen. 



Acknowledgments 



I thank Vic Crecco of the Connecticut Department of Envi- 

 ronmental Protection, for raising questions about the best 

 way to calculate indices of abundance from recreational 

 fishery catch rate data during debates over the bluefish 

 assessments; Paul Rago of the Northeast Fisheries Science 

 Center, for numerous discussions about statistical distribu- 

 tions and tests; and two anonymous Fishery Bulletin ref- 

 erees, whose comments helped improve the quality of the 

 analyses and therefore the usefulness of the results. 



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