Investigations in nutritional ecology often require the identification of animal feeding patterns in natural conditions (what, where, and when do animals eat). Thus, methods are needed to trace not only individual resource uptake but also the relative use of different resources in a population or community. Recent biochemical developments allow predicting the use of sugar‐rich resources from insects in the field. Individual feeding status (feeding history, food sources) is inferred by comparing insect sugar profiles with those of individuals fed on controlled diets. Individual assignations are then used to predict the relative consumption of different resources at the population or community level. As both steps may generate error, accurate prediction rules are needed. However, research from other domains (e.g., protein‐marking studies) suggests that classical decision rules used for such tasks may sometimes induce bias. This study evaluated the performance of these rules and compared them to alternative methods on simulated, realistic datasets. It tested different methods for individual classification but also introduced methods for prevalence estimation, whose specific purpose is to estimate the relative frequency of different classes. Alternative methods substantially outperformed the traditional algorithms to predict insect individual feeding status and population class distribution (relative frequency of insects with different feeding status). This study provided a simple decision tool to choose a method according to dataset size, variance, and biochemical method used. Alternative methods should increase prediction confidence in future studies. Such approaches should easily be generalized to a wider range of systems.

中文翻译：

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从糖谱推断昆虫进食模式：统计方法的比较

营养生态学调查通常需要确定自然条件下的动物饲养模式（动物吃什么、在哪里吃、什么时候吃）。因此，不仅需要追踪个体资源的吸收，还需要追踪不同资源在人口或社区中的相对使用的方法。最近的生化发展可以预测田间昆虫对富含糖分的资源的利用。通过比较昆虫糖谱与以受控饮食喂养的个体的糖谱来推断个体进食状态（进食历史、食物来源）。然后使用个人分配来预测人口或社区级别的不同资源的相对消耗。由于这两个步骤都可能产生错误，因此需要准确的预测规则。然而，来自其他领域的研究（例如，蛋白质标记研究）表明，用于此类任务的经典决策规则有时可能会引起偏差。本研究评估了这些规则的性能，并将它们与模拟真实数据集上的替代方法进行了比较。它测试了不同的个体分类方法，但也介绍了流行率估计方法，其具体目的是估计不同类别的相对频率。替代方法在预测昆虫个体摄食状态和种群类别分布（具有不同摄食状态的昆虫的相对频率）方面明显优于传统算法。本研究提供了一个简单的决策工具，可以根据数据集大小、方差和所使用的生化方法来选择方法。替代方法应增加未来研究的预测信心。