Developing strategies to prevent collapse of the Amazon rainforest

通过在区域内产生自己的降雨并抑制当地野火的发生，亚马逊雨林促进了其自身稳定所需的条件。因此，砍伐森林会降低剩余森林的稳定性。研究估计，20%-25%的森林砍伐加上气候变化可能会导致剩余的森林大规模崩溃，变成热带稀树草原。这将导致其在全球生物多样性10%的份额中损失很大一部分，扰乱区域水循环，并进一步加速全球气候变化。目前的森林砍伐量为17%，最近的干旱被视为可能出现崩溃的第一个迹象。与推动条件变化不成比例的快速状态变化通常被称为临界点。临界点的识别和分类是基于对控制方程的分析。然而，人们只能通过所谓的平均场近似来获得这些方程，平均场近似用系统的宏观量值(如森林覆盖率)的平均值来表示系统的动态。当系统单元(如植物斑块)的数量较大时，环境条件在空间上近似均匀，随机影响平均，使波动较小。在亚马逊雨林这样的生态系统中，这些假设被违反：植物扩散或火灾等传播过程会产生巨大的波动，环境条件(如降雨模式)通常是高度不均匀的。该项目将引入一种方法，可以从具有随机相互作用的大型耦合单元系统的模拟中提取倾倒准则，即使模型是不均匀的或平均场近似背后的假设被打破(在所谓的连续相变)。该方法以非传统的方式运行模拟，引入人工反馈控制，然后从受控系统的观测中提取原始未受控系统的信息。这种新方法将在概率元胞自动机上开发和测试。然后，我们将把新方法应用于亚马逊雨林的模型，该模型具有现实的异质性(包括气候梯度、土壤质量和人类影响)。我们将研究在各种砍伐森林的情况下其崩溃的趋势，并确定需要哪些重新造林战略来防止崩溃或从崩溃中恢复。

By generating its own rainfall regionally and suppressing occurrence of wildfires locally, the Amazon rainforest promotes the conditions required for its own stability. Hence, removal of forest reduces the stability of the remaining forest. Studies estimate that a 20-25% deforestation combined with climate change could induce a large scale collapse of the remaining forest to tropical savanna. This would cause a loss of much of its 10% share of global biodiversity, disrupt the regional water cycle and further accelerate global climate change. The current amount of deforestation is 17%, with recent droughts seen as possible first signs of an approaching collapse.Rapid state shifts that are disproportionate to the driving changes in conditions are commonly called tipping points. Identification and classification of tipping points is based on the analysis of governing equations. Yet, one can only obtain these equations by taking a so-called mean-field approximation, which expresses the system's dynamics in terms of the means of its macro-scale quantities (such as forest cover fraction).This works when the number of system units (such as plant patches) is large, the environmental conditions are approximately spatially homogeneous, and random influences average out, making fluctuations small. In an ecosystem such as the Amazon rainforest these assumptions are violated: spreading processes such as plant dispersal or fire can generate large fluctuations and environmental conditions (such as rainfall patterns) are often highly heterogeneous.This project will introduce a method that can extract tipping criteria from simulations of large systems of coupled units with random interactions, even when the model is heterogeneous or the assumptions behind mean field approximations break down (at so-called continuous phase transitions).The method runs the simulations in a non-conventional way by introducing artificial feedback control and then extracting information about the original uncontrolled system from observations of the controlled system.This new approach will be developed and tested on probabilistic cellular automata. We will then apply the new method to a model of the Amazon rainforest with realistic heterogeneities (including climatic gradients, soil quality and human impact). We will study its tendency to collapse under various deforestation scenarios and determine which reforestation strategies would be required for prevention of or recovery from collapse.