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

我们如何利用机器学习来预测生物多样性的未来变化？

基本信息

批准号：
2890191
负责人：
金额：
--
依托单位：
University of Southampton
依托单位国家：
英国
项目类别：
Studentship
财政年份：
2023
资助国家：
英国
起止时间：
2023 至无数据
项目状态：
未结题

来源：
https://gtr.ukri.org/projects?ref=studentship-2890191
关键词：
How well predict future changes

项目摘要

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

海洋浮游生物群落是地球系统的基本组成部分。它们支持食物网，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。这些生态系统功能是由极其多样化的微生物群落发挥的，因此，生物多样性和生态地理学的预测变化可能对地球系统产生重要影响。物种分布模型（SDM）已被证明是一个强大的方法来估计当代浮游生物地理和生物多样性。然而，它们提出了一些重要但有些不切实际的假设，2特别是在海洋微生物群落方面。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。随着空间数据模型越来越多地被用于预测海洋群落对环境变化的未来反应，量化这些误差和不确定性的来源非常重要。该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、扩散和一系列其他潜在的重要因素可能如何影响空间碎片监测系统的预测能力。这将提高我们了解海洋微生物生物多样性和海洋地理学将如何继续受到持续气候变化影响的能力