如何在不完善的知识和网络复杂性面前预测社区对扰动的反应
How to predict community responses to perturbations in the face of imperfect knowledge and network complexity
摘要：
预测大型复杂系统对扰动的响应是一个挑战。最近对食物网行为进行预测的尝试表明，理解一个系统所需的努力随着其复杂性的增加而迅速增长，因为需要越来越精确的系统元素信息。在这里，我们表明，并不是系统的所有元素都需要同样好地测量。这表明，更有效地分配努力来理解复杂系统是可能的。我们开发了一种迭代技术，以确定一个有效的测量策略。最后，在我们对模型食物网的评估中，我们发现最重要的是精确测量长寿、多面手、顶级捕食者的死亡率和捕食率。优先研究这些物种将使人们更容易理解复杂的食物网对扰动的反应。
英文摘要：
It is a challenge to predict the response of a large, complex system to a perturbation. Recent attempts to predict the behaviour of food webs have revealed that the effort needed to understand a system grows quickly with its complexity, because increasingly precise information on the elements of the system is required. Here, we show that not all elements of the system need to be measured equally well. This suggests that a more efficient allocation of effort to understand a complex systems is possible. We develop an iterative technique for determining an efficient measurement strategy. Finally, in our assessment of model food webs, we find that it is most important to precisely measure the mortality and predation rates of long-lived, generalist, top predators. Prioritizing the study of such species will make it easier to understand the response of complex food webs to perturbations.
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