Modelling fish growth: Model selection, multi-model inference and model selection uncertainty
Modelling fish growth: Model selection, multi-model inference and model selection uncertainty
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DOI：
10.1016/j.fishres.2006.07.002
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发表时间：
2006-11-01
影响因子：
2.4
通讯作者：
Katsanevakis, Stelios
中科院分区：
文献类型：
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作者：
Katsanevakis, Stelios
基于信息论的模型选择是生物科学中一个相对较新的范式，与经典方法相比具有许多优点。本研究的目的是将信息论应用于鱼类生长建模领域，并说明在估计生长参数时如何考虑模型选择的不确定性。该方法适用于四种鱼的长度年龄数据，取自文献。每个数据集拟合五个候选模型：von Bertalanffy增长模型（VBGM），广义VBGM，Gompertz增长模型，Schnute Richards增长模型和logistic。在每种情况下，“最佳”模型的选择，通过最小化的小样本，偏差校正形式的赤池信息准则（AIC）。为了量化每个模型的可解释性，给定数据和五个模型的集合，计算每个模型的“赤池权重”ω（i）。基于wi估计每个病例的平均模型。采用多模型推断（MMI）方法，得到了模型平均渐近长度L。对每个物种的估计，使用所有五个模型，通过模型平均估计L。并通过wi对每个模型的预测进行加权。在本研究的示例中，模型选择的不确定性导致最佳模型的渐近长度的标准误差放大（高达3.9倍），因此在所有四种情况下估计L。从最好的模型会导致高估的渐近长度的精度。VBGM在用于推理时，如果不是最佳模型，可能会导致有偏的点估计和错误的精度评估。即使VBGM是最佳模型，模型选择的不确定性也不应被忽视。建议基于赤池权重通过模型平均进行多模型推断，以进行稳健的参数估计并处理模型选择中的不确定性。(c)2006 Elsevier B.V.保留所有权利。
Model selection based on information theory is a relatively new paradigm in biological sciences with several advantages over the classical approaches. The aim of the present study was to apply information theory in the area of modelling fish growth and to show how model selection uncertainty may be taken into account when estimating growth parameters. The methodology was applied for length-age data of four species of fish, taken from the literature. Five-candidate models were fitted to each dataset: von Bertalanffy growth model (VBGM), generalized VBGM, Gompertz growth model, Schnute-Richards growth model, and logistic. In each case, the 'best' model was selected by minimizing the small-sample, bias-corrected form of the Akaike information criterion (AIC). To quantify the plausibility of each model, given the data and the set of five models, the 'Akaike weight' omega(i) of each model was calculated. The average model was estimated for each case based on wi. Following a multi-model inference (MMI) approach, the model-averaged asymptotic length L. for each species was estimated, using all five models, by model-averaging estimations of L. and weighting the prediction of each model by wi. In the examples of this study, model selection uncertainty caused a magnification of the standard error of the asymptotic length of the best model (up to 3.9 times) and thus in all four cases estimating L. from just the best model would have caused overestimation of precision of the asymptotic length. The VBGM, when used for inference, without being the best model, could cause biased point estimation and false evaluation of precision. Model selection uncertainty should not be ignored even if VBGM is the best model. Multi-model inference by model-averaging, based on Akaike weights, is recommended for making robust parameter estimations and for dealing with uncertainty in model selection. (c) 2006 Elsevier B.V. All rights reserved.