Data from: SIDER: an R package for predicting trophic discrimination factors of consumers based on their ecology and phylogenetic relatedness

Stable isotope mixing models (SIMMs) are an important tool used to study species’ trophic ecology. These models are dependent on, and sensitive to, the choice of trophic discrimination factors (TDF) representing the offset in stable isotope delta values between a consumer and their food source when they are at equilibrium. Ideally, controlled feeding trials should be conducted to determine the appropriate TDF for each consumer, tissue type, food source, and isotope combination used in a study. In reality however, this is often not feasible nor practical. In the absence of species-specific information, many researchers either default to an average TDF value for the major taxonomic group of their consumer, or they choose the nearest phylogenetic neighbour for which a TDF is available. Here, we present the SIDER package for R, which uses a phylogenetic regression model based on a compiled dataset to impute (estimate) a TDF of a consumer. We apply information on the tissue type and feeding ecology of the consumer, all of which are known to affect TDFs, using Bayesian inference. Presently, our approach can estimate TDFs for two commonly used isotopes (nitrogen and carbon), for species of mammals and birds with or without previous TDF information. The estimated posterior probability provides both a mean and variance, reflecting the uncertainty of the estimate, and can be subsequently used in the current suite of SIMM software. SIDER allows users to place a greater degree of confidence on their choice of TDF and its associated uncertainty, thereby leading to more robust predictions about trophic relationships in cases where study-specific data from feeding trials is unavailable. The underlying database can be updated readily to incorporate more stable isotope tracers, replicates and taxonomic groups to further increase the confidence in dietary estimates from stable isotope mixing models, as this information becomes available.

稳定同位素混合模型（Stable Isotope Mixing Models, SIMMs）是研究物种营养生态学的重要工具。这类模型的构建与结果敏感性均依赖于营养判别因子（Trophic Discrimination Factors, TDF）的选择——该因子代表处于同位素平衡状态时，消费者与其食物源之间的稳定同位素δ值偏移。理想情况下，应开展控制饲喂试验，以确定研究中针对每种消费者、组织类型、食物源及同位素组合的适宜TDF。但在实际操作中，这往往既不可行也不具备实践可行性。在缺乏物种特异性数据的情况下，许多研究者要么默认采用其研究对象所属主要分类类群的平均TDF值，要么选择已有TDF数据的系统发育近缘物种作为替代。本文介绍一款面向R语言的SIDER包，该工具基于汇编数据集构建系统发育回归模型，可用于估算消费者的TDF。研究中借助贝叶斯推断（Bayesian inference），纳入了已知会影响TDF的消费者组织类型与摄食生态等信息。目前，该方法可针对哺乳类与鸟类，在有无既往TDF数据的情况下，为两种常用同位素（氮与碳）估算TDF。估算得到的后验概率（posterior probability）可同时给出均值与方差，以反映估算的不确定性，后续可直接应用于现有SIMM软件套件中。SIDER包可帮助用户为其选择的TDF及其伴随的不确定性赋予更高的置信度，从而在无法获取针对特定研究的饲喂试验数据时，得到关于营养关系的更可靠预测。其底层数据库可轻松更新，纳入更多稳定同位素示踪剂、重复样本与分类类群信息，随着此类数据的积累，将进一步提升基于稳定同位素混合模型的食性估计的可信度。