Bayesian prediction of multivariate ecology from phenotypic data yields new insights into the diets of extant and extinct taxa
Bayesian prediction of multivariate ecology from phenotypic data yields new insights into the diets of extant and extinct taxa
复制标题
DOI：
10.1086/725055
复制
发表时间：
2023-03
期刊：
影响因子：
--
通讯作者：
Anna L. Wisniewski;Jonathan A. Nations;G. Slater
中科院分区：
文献类型：
--
作者：
Anna L. Wisniewski;Jonathan A. Nations;G. Slater
形态学通常反映生态学，使缺乏直接观察的分类群（如化石）能够预测生态作用。在比较分析中，生态特征，如饮食，往往被视为分类，这可能有助于预测和简化分析，但忽视了生态位的多元性质。然而，用于定量和预测多变量生态的方法仍然很少。在这里，我们排名的相对重要性的13个食物项目的样本88现存的食肉类哺乳动物，然后使用贝叶斯多层次模型来评估这些排名是否可以预测牙齿形态和身体大小。传统的饮食类别未能捕捉到真正的多变量性质的食肉动物的饮食，但贝叶斯回归模型来自生活类群有很好的预测精度的重要性排名。使用我们的模型来预测单个食物项目的重要性，多变量饮食生态位，以及一组数据不足的现存和灭绝的食肉动物物种的最接近的现存类似物，证实了一些分类群的长期想法，但产生了关于其他人的基本饮食生态位的新见解。我们的方法提供了一个有前途的替代传统的饮食分类。重要的是，这种方法不需要局限于饮食，但作为一个通用的框架，从表型性状预测多变量生态。
Morphology often reflects ecology, enabling the prediction of ecological roles for taxa that lack direct observations such as fossils. In comparative analyses, ecological traits, like diet, are often treated as categorical, which may aid prediction and simplify analyses but ignores the multivariate nature of ecological niches. Futhermore, methods for quantifying and predicting multivariate ecology remain rare. Here, we ranked the relative importance of 13 food items for a sample of 88 extant carnivoran mammals, and then used Bayesian multilevel modeling to assess whether those rankings could be predicted from dental morphology and body size. Traditional diet categories fail to capture the true multivariate nature of carnivoran diets, but Bayesian regression models derived from living taxa have good predictive accuracy for importance ranks. Using our models to predict the importance of individual food items, the multivariate dietary niche, and the nearest extant analogs for a set of data-deficient extant and extinct carnivoran species confirms long-standing ideas for some taxa, but yields new insights about the fundamental dietary niches of others. Our approach provides a promising alternative to traditional dietary classifications. Importantly, this approach need not be limited to diet, but serves as a general framework for predicting multivariate ecology from phenotypic traits.