Ecological Network Inference for Resilient Food Systems: A Mathematical Approach

要满足不断增长的全球粮食需求，同时减轻气候变化带来的不确定性，防止生物多样性进一步受到侵蚀，就需要有弹性的粮食系统。应对这些挑战的关键是认识到农业是更广泛生态系统的一部分。农药可以对一系列非目标物种产生连锁反应，而更广泛的农业环境中不同元素之间的相互作用对于了解整个系统的行为方式至关重要。理解这些相互作用的一个重要方法是将其视为一个物种网络，这些物种与代表生态相互作用的环节相连，如竞争、捕食、寄生。这些网络的大小不一，但可能由数百或数千种物种组成，它们相互作用，对生态系统的整体状态和功能做出贡献。该项目的目的是创建新的基于网络的方法来研究农业生态系统及其对气候变化和人类世其他严重破坏的反应。我们特别寻求回答以下关键研究问题：*我们如何在相关的时空尺度上构建农业生态网络？*我们如何利用这些复杂的网络为农业生态系统对破坏和干扰的反应模型提供信息？生态学的一个基本问题是如何从观察中构建物种相互作用网络。虽然可以通过直接观察现场的相互作用来实现构建，但这种方法是耗时的，局部的，并且不能扩展到更全局的系统。目前的网络推理方法将跨空间和时间的二元“存在-不存在”数据中的物种共现作为输入（Volkov et al. 2009）。由于以下几个原因，这种方法是不充分的：它们无法区分不同的交互类型；它们可以将相互作用与环境协方差混为一谈，并且不能适当地解释间接相互作用。该项目将基于最大熵（MaxEnt）的统计力学原理开发新的网络推理技术。MaxEnt之前已经在上下文中提出过（Volkov et al. 2009, Emary et al. 2021），但我的目标是扩展这种技术，使用延迟相关性来正确重建交互类型，而不仅仅是幅度。这对于正确理解生态系统的功能显然是必不可少的。经过一段时间的方法开发，我将通过计算机模拟，探索在信息收集有限的复杂生态系统中的推理质量。接下来，我将使用NCL作为合作伙伴的H2020 EcoStack项目，演示该方法对现实世界生物监测数据的适用性。因此，我将构建与英国粮食系统相关的生态系统服务网络。了解和减轻农业生态系统如何应对气候变化和物种丧失造成的迅速和可能剧烈的变化是研究的重点。我认为做到这一点的唯一方法是通过网络方法，考虑在不同生态系统组成部分中干扰的级联效应。

Resilient food systems are required to meet the ever-increasing global demand for food, whilst mitigating against the uncertainties wrought by climate change and preventing the further erosion of biodiversity. Essential to meeting these challenges is the appreciation that agriculture takes place as part of a wider ecosystem. Pesticides can have knock-on effects on a range of non-target species, and that it is the interactions between the different elements of the wider agricultural environment that are critical to understand how the whole system behaves.An important way of understanding these interactions is as a network of species connected with links that represent ecological interactions such as competition, predation, parasitism. These networks can range in size, but might consist of hundreds or thousands of species, all interacting and contributing to the overall state and functioning of the ecosystem.The aim of this project is to create new network-based ways of studying agro-ecosystems and their response to climate change and other severe disruptions of the Anthropocene. In particular we seek to answer the following key research questions:* How can we construct agro-ecological networks over the relevant temporal and spatial scales?* How can we use these complex networks to inform models of the response of agro-ecosystems to disruption and disturbance?A fundamental question in ecology is how to construct species-interaction networks from observations. Whilst construction can be achieved through directly observing interactions in the field, this method is time-consuming, localised and does not scale to a more global system. Current approaches to network inference take as their input the co-occurence of species in binary "presence-absence" data across space and time (Volkov et al. 2009). Such approaches are inadequate for several reasons: they are unable to distinguish different interaction types; they can conflate interactions with environmental covariance, and do not properly account for indirect interactions.This project will develop new network-inference techniques based on the statistical-mechanical principle of Maximum Entropy (MaxEnt). MaxEnt has been proposed in the context before (Volkov et al. 2009, Emary et al. 2021), but my goal is to extend this technique to use time-delayed correlations to correctly reconstruct interaction type and not just the magnitude. This is clearly essential for a proper understanding of ecosystem functioning. After a period of method development I will, through computer simulations, explore the quality of inference in complex ecosystems with limited information gathering. Next I shall demonstrate the applicability of the method to real-world biomonitoring data, using the H2020 EcoStack project, of which NCL is a partner. I will thus construct ecosystem services networks relevant to UK food systems.Understanding and mitigating how agro-ecosystems respond to the rapid and potentially drastic changes wrought by climate change and species loss is a research priority. I contend that the only way to do this is through a network approach that considers the cascading effects of a disturbance throughout different ecosystem components.