Linear model analysis of net catch data using the negative binomial distribution

Sampling with nets or trawls remains a common technique for determining the comparative abundances of aquatic organisms, and the objective of such stu dies is frequently to evaluate relationships among the counts of individual s caught and exogenous variables. Analysis of such data is often done with a general linear model (e.g., ANOVA, ANCOVA, regression), assuming an under lying normal probability distribution. Such analyses are not fully satisfac tory because of the symmetry and continuous nature of the assumed normal pr obability distribution and the high variance to low mean value relationship s common to counts of biological populations. The negative binomial is a di screte probability distribution that is recognized as a suitable descriptor of organism count data. We present an approach for undertaking linear mode l analyses of net catch data that permits estimation of model parameters (i ncluding the negative binomial k parameter) and hypothesis testing of both continuous and discrete model effects and their interactions using bootstra p replication. The analysis incorporates adjustment for varying element siz es, such as differences in the amounts of water filtered during sampling.