Department of Integrative Biology, Oregon State University, Corvallis, Oregon, 97331, USA.
School of Forestry and Environmental Studies, Yale University, New Haven, Connecticut, 06511, USA.
Ecology. 2017 Jun;98(6):1535-1547. doi: 10.1002/ecy.1802.
Intraspecific variation in ecologically relevant traits is widespread. In generalist predators in particular, individual diet specialization is likely to have important consequences for food webs. Understanding individual diet specialization empirically requires the ability to quantify individual diet preferences accurately. Here we compare the currently used frequentist maximum likelihood approach, which infers individual preferences using the observed prey proportions to Bayesian hierarchical models that instead estimate these proportions. Using simulated and empirical data, we find that the approach of using observed prey proportions consistently overestimates diet specialization relative to the Bayesian hierarchical approach when the number of prey observations per individual is low or the number of prey observations vary among individuals, two common features of empirical data. Furthermore, the Bayesian hierarchical approach permits the estimation of point estimates for both prey proportions and their variability within and among levels of organization (i.e., individuals, experimental treatments, populations), while also characterizing the uncertainty of these estimates in ways inaccessible to frequentist methods. The Bayesian hierarchical approach provides a useful framework for improving the quantification and understanding of intraspecific variation in diet specialization studies.
种内生态相关特征的变异普遍存在。特别是在一般捕食者中，个体的饮食特化可能对食物网有重要影响。从经验上理解个体饮食特化需要准确量化个体饮食偏好的能力。在这里，我们比较了目前使用的频率主义最大似然方法，该方法使用观察到的猎物比例来推断个体偏好，而贝叶斯层次模型则估计这些比例。使用模拟和经验数据，我们发现当每个个体的猎物观察数量较少或个体之间的猎物观察数量不同时，使用观察到的猎物比例的方法相对于贝叶斯层次方法，始终高估了饮食特化程度，这是经验数据的两个常见特征。此外，贝叶斯层次方法允许估计猎物比例及其在组织内部和组织之间（即个体、实验处理、种群）的变异性的点估计值，同时以频率方法无法达到的方式描述这些估计值的不确定性。贝叶斯层次方法为改善饮食特化研究中的种内变异的量化和理解提供了一个有用的框架。
