Community- and ecosystem-level effects of multiple environmental change drivers: Beyond null model testing
Community- and ecosystem-level effects of multiple environmental change drivers: Beyond null model testing
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DOI：
10.1111/gcb.14382
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发表时间：
2018-11-01
影响因子：
11.6
通讯作者：
De Laender, Frederik
中科院分区：
文献类型：
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作者：
De Laender, Frederik
了解环境变化的多种驱动因素的共同影响是一项关键的科学挑战。今天的主要方法是将观察到的联合效应与各种类型的零模型的预测进行比较。当观察到的联合效应大于（小于）零模型预测的联合效应时，驱动程序被称为协同（拮抗）联合收割机。在这里，我认为，这种方法并没有促进对重要的社区和生态系统层面的变量，如生物多样性和生态系统功能的影响的理解。我用生态学理论表明，不同的机制可以导致相同的偏离零模型的预测。因此，我表明，相同的机制可以导致不同的偏差从零模型的预测。这些例子说明，不可能从空模型中做出强有力的机械推论。接下来，我提出了一个替代框架来研究这种影响。该框架明确区分了两种不同类型的驱动因素（资源比例变化和多种压力源），并通过将压力源效应纳入资源摄取理论来整合两者。我表明这个框架可以促进理解，原因有三个。首先，它迫使“多重压力”正规化，使用的因素，描述压力的数量和种类，他们的选择性和动态行为，以及物种之间的初始性状多样性和容忍度。其次，它对这些因素如何影响生物多样性和生态系统功能产生了可检验的预测，单独和与资源比例变化相结合。第三，它可能在信息方面失败。也就是说，它的假设是明确的，因此预测和观察到的效果之间的不同类型的偏差可以指导新的实验和理论改进。我的结论是，这一框架将更有效地了解全球变化对社区和生态系统的影响，比目前的做法零模型测试。
Understanding the joint effect of multiple drivers of environmental change is a key scientific challenge. The dominant approach today is to compare observed joint effects with predictions from various types of null models. Drivers are said to combine synergistically (antagonistically) when their observed joint effect is larger (smaller) than that predicted by the null model. Here, I argue that this approach does not promote understanding of effects on important community- and ecosystem-level variables such as biodiversity and ecosystem function. I use ecological theory to show that different mechanisms can lead to the same deviation from a null model's prediction. Inversely, I show that the same mechanism can lead to different deviations from a null model's prediction. These examples illustrate that it is not possible to make strong mechanistic inferences from null models. Next, I present an alternative framework to study such effects. This framework makes a clear distinction between two different kinds of drivers (resource ratio shifts and multiple stressors) and integrates both by incorporating stressor effects into resource uptake theory. I show that this framework can advance understanding because of three reasons. First, it forces formalization of "multiple stressors," using factors that describe the number and kind of stressors, their selectivity and dynamic behaviour, and the initial trait diversity and tolerance among species. Second, it produces testable predictions on how these factors affect biodiversity and ecosystem function, alone and in combination with resource ratio shifts. Third, it can fail in informative ways. That is, its assumptions are clear, so that different kinds of deviations between predictions and observed effects can guide new experiments and theory improvement. I conclude that this framework will more effectively progress understanding of global change effects on communities and ecosystems than does the current practice of null model testing.