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Evaluation of procedures to reduce bias in fish growth parameter estimates resulting from size-selective sampling

机译:评估程序以减少大小选择抽样导致的鱼类生长参数估计值的偏差

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Size-selective fish sampling can strongly bias von Bertalanffy growth parameter estimates. Bias-correction methods have been developed, but they often require previous knowledge of selectivity pattern, capture-recapture data or intensive age-growth sampling over multiple consecutive years. When corrective measures are not feasible, investigators have attempted a number of biologically based procedures to reduce this bias. We evaluated several existing biologically based procedures that could potentially remove bias from growth parameter estimates. We built an age and length structured population model and tested the utility of four procedures to remove bias: 1) fixing to at zero, 2) deleting data associated with ages that are not fully vulnerable to sampling, 3) deleting less than fully vulnerable ages and fixing to at zero, and 4) fixing L-similar to, at the maximum value observed in the data. We considered sampling gears that had no size selectivity, asymptotic selectivity. and dome-shaped selectivity patterns for all procedures. Results suggested that none of these procedures would eliminate bias in growth parameters across all three gear selectivity patterns. Investigators should attempt to use methods to correct growth parameters for size selectivity of sampling gears (e.g., mark recapture methods). If such corrections are not feasible, prior information about the size selectivity pattern of sampling gears is necessary for informed selection of biologically based von Bertalanffy fitting procedures
机译:大小选择鱼的采样可以强烈地偏离冯·贝塔朗菲的生长参数估计值。偏差校正方法已经被开发出来,但是它们通常需要在连续多年中具有选择性模式,捕获-捕获数据或密集的年龄增长采样的先前知识。当纠正措施不可行时,研究人员已尝试了许多基于生物学的程序来减少这种偏倚。我们评估了几种现有的基于生物学的程序,这些程序可能会消除生长参数估计值的偏差。我们建立了年龄和长度结构化的人口模型,并测试了四个程序的效用,以消除偏差:1)固定为零,2)删除与并非完全易受抽样影响的年龄相关的数据,3)删除少于完全易受伤害的年龄并固定为零,以及4)将L相似值固定为数据中观察到的最大值。我们考虑了没有大小选择性,渐近选择性的采样齿轮。和用于所有程序的圆顶形选择性模式。结果表明,这些程序都不能消除所有三个齿轮选择性模式中的生长参数偏差。研究人员应尝试使用各种方法来校正生长参数,以提高采样齿轮的尺寸选择性(例如标记重获方法)。如果这样的校正不可行,则必须事先获得有关采样齿轮尺寸选择性模式的信息,以进行基于生物学的冯·贝塔朗菲拟合程序的知情选择

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