Estimating gillnet selectivity using nonlinear response surface regression

• Published in: Canadian journal of fisheries and aquatic sciences 1998-06, Vol.55 (6), p.1328-1337
• Main Authors: Helser, Thomas E, Geaghan, James P, Condrey, Richard E
• Format: Article
• Language: English
• Subjects: Animal, plant and microbial ecology; Applied ecology; Biological and medical sciences; Cynoscion nebulosus; Exploitation and management of natural biological resources (hunting, fishing and exploited populations survey, etc.); Fundamental and applied biological sciences. Psychology; Marine
• ISSN: 0706-652X; 1205-7533
• DOI: 10.1139/f98-036

We present a method of estimating the selectivity of experimental gill nets in which type A and type B curves are solved for simultaneously as a response surface using nonlinear regression. The modeling approach provides a general statistical framework for estimating selectivity parameters, evaluating different functional forms of the selectivity model, and testing for differences between models. We applied this approach to the gillnet catches of Louisiana spotted seatrout (Cynoscion nebulosus) in a five-panel experimental net from data collected from 1988 to 1995. The selectivity of the experimental gill nets for female and male spotted seatrout could be described by a common response surface that was based on a four-parameter normal probability density function (r 2 = 0.95). By estimating type A and type B curves simultaneously the model was able to explain 74% more variation in the data than when compared with methods that estimate type B curves for each size-class individually. Statistical comparison of annual response surfaces was not significantly different (p > 0.05) and suggests that the estimation approach was relatively insensitive to interannual variation in population size composition.