Dynamics of community composition

自世纪初以来，群落组成的演替或随时间变化的概念一直是生态学的基础，并支撑着应对环境变化后果所需的科学。然而，直到最近，获得足够数据的困难意味着预测即使是10个或20个物种的群落的动态几乎是不可能的。这种情况在过去几年中发生了巨大的变化，随着大型生态数据集的日益可用，现在是重新审视社区动态基本问题的正确时机。我们将使用两种重要的和受威胁的社区类型的数据来开发我们的方法：珊瑚礁（使用最近的监测数据）和温带森林（使用过去10000年的花粉记录）。长远来说，我们的方法亦适用于其他类型的社区。我们会首先研究如何量度社区组成的变化率。生态学家目前使用的测量方法没有通过一个基本的测试：如果一个群落中的每个物种都以恒定的速度生长，那么这些测量方法就不是恒定的。因此，它们无法对人类活动是否影响演替速度等简单问题给出有意义的答案。我们将开发一种基于几何原理的新方法，解决这一困难，并为我们进一步的工作提供严格的基础。在大多数情况下，我们只能观察到社区组成的变化，其时间尺度远远短于确定过程的最终结果所需的时间尺度。因此，我们将开发新的方法来模拟社区组成的短期变化。我们将使用大量的珊瑚礁和森林的短期变化的数据来构建成分动态的随机模型。在我们提出的几何框架中，根据群落的当前组成和环境条件，预测未来组成的变化相对简单，然后我们将分析这些模型的长期行为。这将告诉我们在我们无法直接观察的时间尺度上的继承结果。例如，我们可以预测海洋表面温度升高对珊瑚礁的长期影响，或人类活动增加对森林的长期影响。我们的模型是随机的，因此从长期来看，结果将是可能和不可能成分的概率分布。因此，我们需要以新的方式思考社区的稳定。而不是平衡状态，一个社区将返回干扰后，我们将确定的组合，可能会持续一段时间。我们的研究解决基本的生态问题，并有很大的相关性，目前面临的社会挑战。海洋表面温度和人类活动强度等环境条件正在迅速变化。我们的工作将为预测这些变化的可能后果提供严格的方法。

The idea of succession, or change over time in the composition of a community, has been fundamental to ecology since the early 20th century, and underpins the science needed to tackle the consequences of environmental change. However, until very recently, the difficulties of obtaining sufficient data meant that predicting the dynamics of communities of even ten or twenty species was almost impossible. This situation has changed dramatically in the last few years, with increasing availability of large ecological data sets making this the right time to revisit basic questions about community dynamics. We will develop our approach using data on two important and threatened community types: coral reefs (using recent monitoring data) and temperate forests (using pollen records from the last 10000 years). In the longer term, our approach will be relevant to many other types of community.We will first examine the measurement of rate of change in community composition. The measures currently used by ecologists fail a basic test: if every species in a community is growing at a constant rate, these measures are not constant. Thus they cannot give meaningful answers to simple questions such as whether human activity affects the rate of succession. We will develop a new approach based on geometrical principles, which resolves this difficulty and provides the rigorous foundations for our further work.In most cases, we can only observe changes in community composition over time scales much shorter than those needed to determine the eventual outcome of the process. We will therefore develop new ways of modelling short-term change in the composition of a community. We will use large amounts of data on short-term change in coral reefs and forests to construct stochastic models of compositional dynamics. In the geometrical framework that we propose, it is relatively simple to project future changes in composition, based on the current composition of a community and the environmental conditions.We will then analyze the long-term behaviour of these models. This will tell us about the outcome of succession over time scales that we cannot observe directly. For example, we can project the long-term consequences of increased sea surface temperature on coral reefs, or of increased human activity on forests. Our models are stochastic, and so in the long term the outcome will be a probability distribution of likely and unlikely compositions. We therefore require new ways of thinking about the stability of communities. Rather than equilibrium states to which a community will return after disturbance, we will identify sets of compositions that are likely to persist over time.Our research addresses basic ecological questions, and has great relevance to the challenges currently facing society. Environmental conditions such as sea surface temperature and the intensity of human activity are changing rapidly. Our work will provide rigorous methods for predicting the likely consequences of these changes.