Department of Zoology, University of British Columbia, Vancouver, BC, Canada.
Department of Biological Sciences, University of Alberta, Edmonton, AB, Canada.
Glob Chang Biol. 2018 Jan;24(1):517-525. doi: 10.1111/gcb.13852. Epub 2017 Sep 1.
Ecological stressors (i.e., environmental factors outside their normal range of variation) can mediate each other through their interactions, leading to unexpected combined effects on communities. Determining whether the net effect of stressors is ecologically surprising requires comparing their cumulative impact to a null model that represents the linear combination of their individual effects (i.e., an additive expectation). However, we show that standard additive and multiplicative null models that base their predictions on the effects of single stressors on community properties (e.g., species richness or biomass) do not provide this linear expectation, leading to incorrect interpretations of antagonistic and synergistic responses by communities. We present an alternative, the compositional null model, which instead bases its predictions on the effects of stressors on individual species, and then aggregates them to the community level. Simulations demonstrate the improved ability of the compositional null model to accurately provide a linear expectation of the net effect of stressors. We simulate the response of communities to paired stressors that affect species in a purely additive fashion and compare the relative abilities of the compositional null model and two standard community property null models (additive and multiplicative) to predict these linear changes in species richness and community biomass across different combinations (both positive, negative, or opposite) and intensities of stressors. The compositional model predicts the linear effects of multiple stressors under almost all scenarios, allowing for proper classification of net effects, whereas the standard null models do not. Our findings suggest that current estimates of the prevalence of ecological surprises on communities based on community property null models are unreliable, and should be improved by integrating the responses of individual species to the community level as does our compositional null model.
生态胁迫因子（即在正常变化范围之外的环境因素）可以通过相互作用来调节彼此，从而对群落产生意想不到的综合影响。确定胁迫因子的净效应是否具有生态学意义，需要将其累积效应与代表其个体效应线性组合的零模型（即加性预期）进行比较。然而，我们表明，基于单一胁迫因子对群落属性（如物种丰富度或生物量）影响的标准加性和乘性零模型并不能提供这种线性预期，从而导致对群落拮抗和协同响应的错误解释。我们提出了一种替代方案，即组成零模型，它不是基于胁迫因子对单个物种的影响来进行预测，而是基于胁迫因子对个别物种的影响，并将其汇总到群落水平。模拟表明，组成零模型能够更准确地提供胁迫因子净效应的线性预期。我们模拟了受影响物种呈纯加性方式的成对胁迫因子对群落的响应，并比较了组成零模型和两个标准群落属性零模型（加性和乘性）在预测物种丰富度和群落生物量随不同组合（正、负或相反）和胁迫因子强度的线性变化方面的相对能力。组成模型几乎可以预测所有情况下的多个胁迫因子的线性效应，从而可以正确分类净效应，而标准零模型则不行。我们的研究结果表明，基于群落属性零模型对群落中生态意外现象的普遍估计是不可靠的，应该通过整合单个物种对群落水平的响应来改进，正如我们的组成零模型所做的那样。
