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首页> 外文期刊>ICES Journal of Marine Science >Catch per unit effort standardization using spatio-temporal models for Australia's Eastern Tuna and Billfish Fishery
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Catch per unit effort standardization using spatio-temporal models for Australia's Eastern Tuna and Billfish Fishery

机译：使用时空模型对澳大利亚东部金枪鱼和比尔菲什渔业进行单位工作量捕捞标准化

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摘要

The majority of catch per unit effort (cpue) standardizations use generalized linear models (GLMs) or generalized additive models (GAMs). We develop geostatistical models that model catch locations as continuous Gaussian random fields (GRFs) and apply them to standardizing cpue in Australia's Eastern Tuna and Billfish Fishery (ETBF). The results are compared with the traditional GLMs currently used in ETBF assessments as well as GAMs. Specifically, we compare seven models in three groups: two GLMs, two GAMs, and three GRF models. Within each group, one model treats spatial and temporal variables independently, while the other model(s) treats them together as an interaction term. The two spatio-temporal GRF models differ in treating the spatial-temporal interaction: either as a random process or as an autoregressive process. We simulate catch rate data for five pelagic species based on real fishery catch rates so that the simulated data reflect real fishery patterns while the "true" annual abundance levels are known. The results show that within each group, the model with a spatial-temporal interaction term significantly outperforms the other model treating spatial and temporal variables independently. For spatial-temporal models between the three groups, prediction accuracy tends to improve from GLM to GAM and to the GRF models. Overall, the spatio-temporal GRF autoregressive model reduces mean relative predictive error by 43.4% from the GLM, 33.9% from the GAM, and reduces mean absolute predictive error by 23.5% from the GLM and 3.3% from the GAM, respectively. The comparison suggests a promising direction for further developing the geostatistical models for the ETBF.
机译：单位捕获量（cpue）的大多数捕获标准化使用广义线性模型（GLM）或广义加性模型（GAM）。我们开发了地统计学模型，将捕获位置建模为连续的高斯随机场（GRF），并将其应用于澳大利亚东部金枪鱼和比尔菲什渔业（ETBF）的cpue标准化。将结果与当前在ETBF评估中使用的传统GLM以及GAM进行比较。具体来说，我们将三组中的七个模型进行了比较：两个GLM，两个GAM和三个GRF模型。在每个组中，一个模型独立地处理空间和时间变量，而其他模型则将它们一起作为交互项。两种时空GRF模型在处理时空相互作用方面有所不同：作为随机过程还是作为自回归过程。我们根据实际渔业捕捞率模拟了五个中上层鱼类的捕捞率数据，以便模拟数据反映真实的渔业模式，而已知“真实”的年度丰度水平。结果表明，在每个组中，具有时空交互作用项的模型明显优于其他独立处理时空变量的模型。对于三组之间的时空模型，从GLM到GAM以及GRF模型，预测准确性趋于提高。总体而言，时空GRF自回归模型分别使GLM的平均相对预测误差降低了43.4％，GAM的平均相对预测误差降低了33.9％，GLM的平均绝对预测误差降低了23.5％，GAM的平均绝对预测误差降低了3.3％。比较结果为进一步开发ETBF的地统计学模型提供了一个有希望的方向。

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