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首页> 外文期刊>Philosophical Transactions of the Royal Society of London, Series B. Biological Sciences >Comparison of Bayesian and numerical optimization-based diet estimation on herbivorous zooplankton
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Comparison of Bayesian and numerical optimization-based diet estimation on herbivorous zooplankton

机译：贝叶斯和数值优化的饮食估计对食草浮游生物的比较

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Consumer diet estimation with biotracer-based mixing models provides valuable information about trophic interactions and the dynamics of complex ecosystems. Here, we assessed the performance of four Bayesian and three numerical optimization-based diet estimation methods for estimating the diet composition of herbivorous zooplankton using consumer fatty acid (FA) profiles and resource library consisting of the results of homogeneous diet feeding experiments. The method performance was evaluated in terms of absolute errors, central probability interval checks, the success in identifying the primary resource in the diet, and the ability to detect the absence of resources in the diet. Despite occasional large inconsistencies, all the methods were able to identify the primary resource most of the time. The numerical optimization method QFASA using chi(2)(QFASA-CS) or Kullback--Leibler (QFASA-KL) distance measures had the smallest absolute errors, most frequently found the primary resource, and adequately detected the absence of resources. While the Bayesian methods usually performed well, some of the methods produced ambiguous results and some had much longer computing times than QFASA. Therefore, we recommend using QFASA-CS or QFASA-KL. Our systematic tests showed that FA models can be used to accurately estimate complex dietary mixtures in herbivorous zooplankton. This article is part of the theme issue 'The next horizons for lipids as 'trophic biomarkers': evidence and significance of consumer modification of dietary fatty acids'.
机译：使用基于生物游客的混合模型的消费者饮食估算提供了有关营养互动的有价值信息和复杂生态系统的动态。在这里，我们评估了四种贝叶斯和三个数值优化的饮食估算方法，用于使用消费者脂肪酸（FA）曲线和由均质饮食饲料实验结果组成的消费者脂肪酸（FA）曲线和资源文献来估算食草浮游植物的饮食成分。在绝对错误，中央概率间隔检查方面评估方法性能，在饮食中识别主要资源的成功以及检测饮食中没有资源的能力。尽管偶尔的较大不一致，但所有方法都能够识别大部分时间的主要资源。使用CHI（2）（QFASA-CS）或Kullback - 雷布勒（QFASA-KL）距离测量的数值优化方法具有最小的绝对错误，最常见的是主要资源，并充分检测到没有资源。虽然贝叶斯方法通常表现良好，但一些方法产生了模棱两可的结果，并且一些比QFASA的计算时间更长。因此，我们建议使用QFASA-CS或QFASA-KL。我们的系统测试表明，FA模型可用于准确地估算食草浮游植物中的复杂膳食混合物。本文是主题问题的一部分，是脂质的下一个视野作为“营养生物标志物”：消费者修饰膳食脂肪酸的证据和意义。

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