Estimating the Size-Selectivity of Fishing Gear by Conditioning on the Total Catch
提出一种条件最大似然模型，将渔具选择性数据转化为二项或区间尺度多项数据，从而可用广义线性模型拟合选择曲线，为渔业评估提供合法统计推断。
Abstract A conditional maximum likelihood model is used to estimate the size-selectivity of trawls, gillnets, and hooks when the data are obtained by simultaneous fishing with meshes or hooks of different size and/or shape. Size-selectivity is expressed here by the selection curve, r(l), the probability that a fish of length l, if contacting the gear, will be retained (caught). In many selectivity studies r(l) is fitted either by eye, by heuristic means, or by improper application of generalized linear models. Then it is not possible to make legitimate statistical inference about r(l), or about assessments of the state of the fishery if those assessments use r(l). It is shown here that by conditioning on the total catch, selectivity data can be modeled as binary data, or polytomous data on interval scales. Application of the model to trawl and hook data demonstrates that selection curves can be fitted using generalized linear models, which may require nonstandard link functions or link functions with parameters. Key Words: Conditional maximum likelihoodFishing gearGeneralized linear modelsLogistic curvesPoisson totalsSize-selectivity