Department of Ecology and Evolutionary Biology, University of California Santa Cruz, Santa Cruz, California, United States of America.
PLoS One. 2011;6(7):e22015. doi: 10.1371/journal.pone.0022015. Epub 2011 Jul 7.
Bayesian mixing models have allowed for the inclusion of uncertainty and prior information in the analysis of trophic interactions using stable isotopes. Formulating prior distributions is relatively straightforward when incorporating dietary data. However, the use of data that are related, but not directly proportional, to diet (such as prey availability data) is often problematic because such information is not necessarily predictive of diet, and the information required to build a reliable prior distribution for all prey species is often unavailable. Omitting prey availability data impacts the estimation of a predator's diet and introduces the strong assumption of consumer ultrageneralism (where all prey are consumed in equal proportions), particularly when multiple prey have similar isotope values.
We develop a procedure to incorporate prey availability data into bayesian mixing models conditional on the similarity of isotope values between two prey. If a pair of prey have similar isotope values (resulting in highly uncertain mixing model results), our model increases the weight of availability data in estimating the contribution of prey to a predator's diet. We test the utility of this method in an intertidal community against independently measured feeding rates.
Our results indicate that our weighting procedure increases the accuracy by which consumer diets can be inferred in situations where multiple prey have similar isotope values. This suggests that the exchange of formalism for predictive power is merited, particularly when the relationship between prey availability and a predator's diet cannot be assumed for all species in a system.
贝叶斯混合模型允许在使用稳定同位素分析营养相互作用时纳入不确定性和先验信息。在纳入饮食数据时,构建先验分布相对简单。然而,使用与饮食相关但不成比例的数据(例如猎物丰度数据)通常是有问题的,因为这种信息不一定能预测饮食,而且为所有猎物物种构建可靠先验分布所需的信息通常不可用。省略猎物丰度数据会影响对捕食者饮食的估计,并引入消费者超广义主义的强烈假设(所有猎物以相等的比例被消耗),特别是当多种猎物具有相似的同位素值时。
我们开发了一种程序,将猎物丰度数据纳入贝叶斯混合模型中,条件是两种猎物的同位素值之间存在相似性。如果一对猎物具有相似的同位素值(导致混合模型结果非常不确定),我们的模型会增加丰度数据在估计猎物对捕食者饮食贡献方面的权重。我们在潮间带群落中针对独立测量的摄食率测试了这种方法的效用。
我们的结果表明,在多种猎物具有相似同位素值的情况下,我们的加权程序可以提高消费者饮食推断的准确性。这表明,在不能为系统中所有物种假设猎物丰度与捕食者饮食之间的关系的情况下,形式主义与预测能力的交换是值得的,特别是在不能为系统中所有物种假设猎物丰度与捕食者饮食之间的关系的情况下。