Wolf Christopher, Novak Mark, Gitelman Alix I
Department of Statistics, Oregon State University, Corvallis, OR, 97331, USA.
Department of Integrative Biology, Oregon State University, Corvallis, OR, 97331, USA.
Oecologia. 2017 Jun;184(2):327-339. doi: 10.1007/s00442-017-3867-7. Epub 2017 Apr 19.
Considerable effort has been devoted to the estimation of species interaction strengths. This effort has focused primarily on statistical significance testing and obtaining point estimates of parameters that contribute to interaction strength magnitudes, leaving the characterization of uncertainty associated with those estimates unconsidered. We consider a means of characterizing the uncertainty of a generalist predator's interaction strengths by formulating an observational method for estimating a predator's prey-specific per capita attack rates as a Bayesian statistical model. This formulation permits the explicit incorporation of multiple sources of uncertainty. A key insight is the informative nature of several so-called non-informative priors that have been used in modeling the sparse data typical of predator feeding surveys. We introduce to ecology a new neutral prior and provide evidence for its superior performance. We use a case study to consider the attack rates in a New Zealand intertidal whelk predator, and we illustrate not only that Bayesian point estimates can be made to correspond with those obtained by frequentist approaches, but also that estimation uncertainty as described by 95% intervals is more useful and biologically realistic using the Bayesian method. In particular, unlike in bootstrap confidence intervals, the lower bounds of the Bayesian posterior intervals for attack rates do not include zero when a predator-prey interaction is in fact observed. We conclude that the Bayesian framework provides a straightforward, probabilistic characterization of interaction strength uncertainty, enabling future considerations of both the deterministic and stochastic drivers of interaction strength and their impact on food webs.
人们在估计物种相互作用强度方面投入了大量精力。这项工作主要集中在统计显著性检验以及获取有助于确定相互作用强度大小的参数的点估计值上，而未考虑与这些估计值相关的不确定性的特征描述。我们考虑一种通过将估计捕食者特定猎物的人均攻击率的观测方法构建为贝叶斯统计模型，来描述泛化捕食者相互作用强度不确定性的方法。这种构建方式允许明确纳入多种不确定性来源。一个关键的见解是，在对捕食者捕食调查中典型的稀疏数据进行建模时所使用的几种所谓非信息先验的信息性质。我们向生态学中引入一种新的中性先验，并为其优越性能提供证据。我们通过一个案例研究来考察新西兰潮间带蛾螺捕食者的攻击率，并且我们不仅说明了贝叶斯点估计可以与频率论方法得到的估计值相对应，还说明了使用贝叶斯方法时，用95%区间描述的估计不确定性更有用且在生物学上更现实。特别是，与自助置信区间不同，当实际观察到捕食者 - 猎物相互作用时，贝叶斯后验区间对于攻击率的下限不包括零。我们得出结论，贝叶斯框架为相互作用强度的不确定性提供了一种直接的概率特征描述，使得未来能够同时考虑相互作用强度的确定性和随机性驱动因素及其对食物网的影响。
