Transferable Ecology for a changing world (TREE)

迫切需要全球范围的可持续性变革来应对灾难性的气候变化和生物多样性的丧失。然而，在实践中，应对这些危机的保护、恢复和管理行动将在当地范围内实施：个别森林、田野、湖泊和草原。从业者、政策制定者和生态学家很早就认识到了“一刀切”战略的徒劳无益，以及针对特定或个别环境制定行动计划、为特定物种或个别地点制定行动计划的重要性。例如，英国政府雄心勃勃的植树计划强调了在正确的地方种植正确的树木对实现净零的重要性，因为在错误的地方种植错误的树木可能无法建立，或者更糟糕的是，导致持续数十年的净碳排放。然而，尽管需要个别地点的特定背景信息，但生态学传统上侧重于平均效应，而不是个体效应，破坏了应用生态学的最终目标：将生态科学应用于现实世界。为了做真正能为实地政策提供信息的科学，生态学可以利用快速发展的“精确医学”领域开发的方法，该领域同样基于这样的前提，即给定的药物或治疗对每个患者的效果可能不同，这就需要个性化的医疗计划。由于来自多个来源的大量生态数据的可用性越来越高，包括远程传感器、公民科学家和环境监测网络在内的精准医学方法学的应用现在成为可能。在这个探索性项目中，我们将把精准医学方法用于应用生态问题，并开发有助于引导下一代应用生态学家进入大数据时代的新方法。我们将开发大数据采样和建模的框架和工具，以产生准确和实用的知识，为地面行动提供信息。该项目将使用现有的计算机模型--iland--模拟欧洲、美国和日本的真实森林景观，使用虚拟生态方法来实现这一点。首先，我们将使用iLand对这些景观中的个别森林实施虚拟森林恢复行动。然后，这些景观中的森林将受到虚拟采样，从而模仿生态学家、公民科学家和遥感技术收集数据的过程。例如，我们将模仿测量森林生物多样性的测量员，作为国家环境监测活动的一部分。然后，我们将应用不同的建模方法来分析虚拟样本数据，包括在精确医学领域开发的方法以及生态学中常用的方法。通过将这些分析的结果与“真实的”模拟数据进行比较，我们将能够评估不同的抽样和建模决策如何影响我们结果的准确性。具体地说，我们将评估在一系列环境条件(即海拔、气候、土壤类型)下，个别森林对恢复行动的预测的准确性。我们将利用这一理解，为设计未来的环境监测调查和大数据建模制定指导方针，以最大限度地提高这些预测的准确性。通过将应用生态学的重点从平均影响转向可操作的、个体层面的影响，我们的指导方针将使政策制定者能够针对预期影响最大的地区开展恢复行动，从而最大限度地发挥有限资源的功效，应对气候变化和生物多样性丧失。

Global-scale sustainability transformations are urgently required to address catastrophic climate change and biodiversity loss. In practice, however, the conservation, restoration and management actions to combat these crises will be applied at local scales: to individual forests, fields, lakes, and grasslands. Practitioners, policymakers and ecologists have long-recognised the futility of silver bullet, 'one-size-fits-all' strategies, and the importance of targeting and tailoring actions to specific or individual contexts, and developing action plans for specific species, or individual sites. For example, the UK government's ambitious tree planting initiatives emphasise the importance of 'the right tree in the right place' to achieving Net Zero, because the wrong trees planted in the wrong place may fail to establish, or worse still, lead to net carbon emissions that persist for decades. However, despite the need for context-specific information for individual sites, ecology has traditionally focused on average effects, rather than individual effects, undermining the ultimate goal of applied ecology: the application of ecological science to the real-world. To do science that can truly inform on-the-ground policies, ecology can capitalise on methods developed by the rapidly advancing field of 'precision medicine', which similarly rests on the premise that the effect of a given drug or treatment may be different for each individual patient, necessitating personalised healthcare plans. The application of precision medicine methodologies, which require 'big data', is now possible due to the increasing availability of vast quantities of ecological data from multiple sources, including remote sensors, citizen scientists and environmental monitoring networks. In this exploratory project, we will adapt the use of precision medicine approaches to applied ecological problems and develop novel approaches that help guide the next generation of applied ecologists into the big-data era. We will develop frameworks and tools for sampling and modelling big data in a way that yields accurate and practicable knowledge that can inform on-the-ground actions.This project will achieve this using a 'virtual ecology' approach using an existing computer model - 'iLand' - that simulates real, forested landscapes in Europe, the U.S.A and Japan. First, we will use iLand to virtually implement forest restoration actions to individual forests across these landscapes. The forests in these landscapes will then be subjected to virtual sampling, thus mimicking the collection of data by ecologists, citizen scientists and remote sensing technologies. For example, we will mimic surveyors that measure forest biodiversity as part of a national environmental monitoring campaigns. We will then apply different modelling methods to analyse the virtually sampled data, including methods developed in the field of precision medicine as well as methods that are commonly used in ecology. By comparing the results of these analyses against the 'true' simulated data, we will be able to evaluate how different sampling and modelling decisions influence the accuracy of our results. Specifically, we will assess the accuracy of predictions individual forest responses to restoration actions across a range of environmental conditions (i.e., elevation, climate, soil types). We will use this understanding to develop guidelines for designing future environmental monitoring surveys, and modelling of big data, that maximises the accuracy of these predictions. By shifting applied ecology's focus away from average effects, towards actionable, individual-level effects, our guidelines will enable policymakers to target and prioritise restoration actions to areas where desired effects are greatest, thus maximising efficacy of limited resources for addressing climate change and biodiversity loss.