首页> 外文期刊>Canadian Journal of Fisheries and Aquatic Sciences >Simultaneous identification and correction of systematic error inbioenergetics models: demonstration with a white crappie (Pomoxisannularis) model.
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Simultaneous identification and correction of systematic error inbioenergetics models: demonstration with a white crappie (Pomoxisannularis) model.

机译:同时识别和校正生物能学模型中的系统误差:用白色可丽饼(Pomoxisannularis)模型进行演示。

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摘要

Recent evidence indicates that important systematic error exists in many fish bioenergetics models (BEMs). An approach for identifying and correcting this error is demonstrated with a white crappie (Pomoxis annularis) BEM. Model-predicted trajectories of growth and cumulative consumption for 39 individual white crappie obtained from six 60-day laboratory experiments diverged from observed values by up to 42.5% and 227%, respectively, indicating systematic error in the BEM. To evaluate correlates of the systematic error, model prediction errors were regressed against three major input /output variables of BEMs that were covered by the laboratory experiments: fish body weight (80-341 g), temperature (23-30 degree C), and consumption level (0.5%-6.2% daily). Consumption level explained 80% of the prediction error for growth and consumption. Two multiple regression equations containing body weight, temperature, and consumption variables were developed to estimate growth prediction error (R super(2) = 0.96) and consumption prediction error (R super(2) = 0.86), and incorporated into the white crappie BEM to correct its predictions. Cross-validation indicated that growth and consumption prediction error was reduced 2- to 4-fold by correction. Given recent evidence of widespread systematic error and increasing application rates of BEMs, the efficient error-identification and -correction approach described appears broadly applicable and timely.
机译:最近的证据表明,许多鱼类生物能学模型(BEM)中存在重要的系统误差。用白色薄饼(Pomoxis ringis)BEM演示了一种识别和纠正此错误的方法。从六个60天的实验室实验中获得的39个白色小饼的模型预测的生长轨迹和累积消耗量,与观测值相差分别高达42.5%和227%,表明BEM中存在系统误差。为了评估系统误差的相关性,将模型预测误差针对实验室实验涵盖的BEM的三个主要输入/输出变量进行了回归:鱼体重(80-341 g),温度(23-30摄氏度)和消费水平(每天0.5%-6.2%)。消费水平解释了增长和消费预测误差的80%以上。开发了两个包含体重,温度和消耗变量的多元回归方程来估计生长预测误差(R super(2)= 0.96)和消耗预测误差(R super(2)= 0.86),并将其纳入白饼BEM中纠正其预测。交叉验证表明,通过修正,增长和消费预测误差减少了2到4倍。鉴于最近证据表明广泛的系统错误和BEM的应用率不断提高,所描述的有效的错误识别和纠正方法似乎广泛适用且及时。

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