How well can we predict future changes in biodiversity using machine learning? Experiments in an eco-evolutionary testbed.

海洋浮游生物群落是地球系统的基本组成部分。它们支持食物链，推动全球生物地球化学循环，并在调节地球气候方面发挥核心作用1。这些生态系统功能是由一个极其多样化的微生物群落执行的，因此，生物多样性和生物地理的预测变化很可能在地球系统内产生重要影响。物种分布模型(SDMS)已被证明是估算当代浮游生物地理学和生物多样性的有效方法。然而，他们提出了一些重要的、有些不切实际的假设，特别是与海洋微生物群落有关的假设。最值得注意的是，它们忽视了已知的微生物物种快速进化的能力，也没有考虑洋流的扩散。随着SDMS越来越多地被用于预测海洋生物对环境变化的未来反应，必须量化这些误差和不确定性的来源。这个项目的目的是使用一个模拟的‘虚拟生态系统’3来评估进化、扩散和其他一系列潜在的重要因素如何影响SDMS的预测技能。这将提高我们理解海洋微生物多样性和生物地理学将如何继续受到持续气候变化的影响的能力

Marine plankton communities are a fundamental component of the Earth system. They support food webs, drive global biogeochemical cycles, and play a central role in the regulation of Earth's climate1. These ecosystem functions are carried out by an extremely diverse microbial community, and predicted changes in biodiversity and biogeography are therefore likely to have important impacts within the Earth system. Species distribution models (SDMs) have proved to be a powerful method for estimating contemporary plankton biogeography and biodiversity. However, they make a number of important and somewhat unrealistic assumptions2, especially in relation to marine microbial communities. Most notably, they neglect the known capacity of microbial species to rapidly evolve, and they do not account for dispersal by ocean currents. With SDMs increasingly being applied to predict the future response of marine communities to environmental change it is important that these sources of error and uncertainty are quantified. The aim of this project is to use a simulated 'virtual ecosystem'3 to assess how evolution, dispersal and a range of other potentially important factors may impact the predictive skill of SDMs. This will improve our ability to understand how marine microbial biodiversity and biogeography will continue to be impacted by ongoing climate change