Optimising UK Landscapes for Agroecosystem Resilience

优化英国景观以增强农业生态系统的弹性

• 批准号: 2888103
• 金额: --
• 依托单位: CRANFIELD UNIVERSITY
• 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-2888103
• 关键词: Optimising; UK; Landscapes; Agroecosystem; Resilience

The UK's withdrawal from the EU's Common Agricultural Policy (CAP) provides an opportunity to enhance the resilience of agricultural landscapes. Agroecosystem resilience is interlinked with the provision of non-market ecosystem services, such as soil health, pest regulation and pollination, and the functional biodiversity that underpins them. However, the interactive effects of agricultural practices and landscape complexity on functionally important biological communities are not well known. This project will develop a mechanistic landscape-scale model for several bioindicators (earthworms, collembola, hoverflies) of agroecosystem multifunctionality. These bioindicators represent diverse life histories, behaviours, and dispersal capabilities, and so exhibit varying sensitivities to environmental conditions, agricultural management practices and landscape features. The model allows us to test the central hypothesis that while different bioindicators show differing sensitivities to specific landscape (configuration of land use types) and management practices, agricultural landscapes can be designed to benefit each bioindicator simultaneously. We expect this new understanding of the interplay between populations, agricultural practices, and landscapes to inform optimisation management plans, such as the upcoming Environmental Land Management (ELM) scheme that replaces the CAP Ecological Focus Area's (EFA's).The specific objectives of the project include: 1) Identify the most representative species for each bioindicator, giving weight to their functional importance in agroecosystems, existing models for each species, and data availability for model development and validation. Individual life history and behavioural traits for each species will be synthesised from the literature, the majority of which can be found in existing models. 2) Develop the standardised mechanistic submodels that inform the population models. These include established energy budget modelling approaches (the parameters for which are available for many species, and for others can be easily obtained from individual-level data) and behavioural submodels which will use a state-space modelling approach to identify key drivers of movement patterns at their relevant spatial scales. Individual-level movement data is limited in the literature, and so here we envisage a need to collect new data for hoverflies from ongoing experiments at Sonning Farm.3) Design the agent-based modelling framework, which represents spatially explicit landscapes, heterogeneous exposure to agricultural practices, individual-environment interactions, and the individual traits from which species population dynamics emerge. In the first instance, the model environment will replicate the time-series of conditions (e.g. weather, management) of the validation datasets together with land cover and soil maps held by CEH (LCMs, open source) and Cranfield (LandIS). 4) Validate the model, using extensive datasets held by Reading, Cranfield, and Syngenta together with published datasets such as the UK Environmental Change Network and the National Biodiversity Network atlas. Approximate Bayesian Computation, a powerful tool for developing complex ecological models, will be used to streamline model analysis. Model validation across diverse agroecosystems, for which population data is available, will enable quantification of the interactive effects (e.g. antagonistically or synergistically) of landscape composition and agricultural management on the bioindicator populations. 5) Simulate novel landscape composition and management scenarios, using the Cranfield high-performance computer, to quantify the effect of each scenario on each bioindicator population. Scenarios will compare the current tiered ELM scheme in the UK and EFA's under the EU's CAP.

英国退出欧盟共同农业政策（CAP）为增强农业景观的恢复力提供了机会。农业生态系统的复原力与非市场生态系统服务的提供相互关联，例如土壤健康、害虫防治和授粉，以及支撑这些服务的功能性生物多样性。然而，农业实践和景观复杂性对功能重要的生物群落的相互作用影响尚不清楚。该项目将为农业生态系统多功能性的几种生物指示剂（蚯蚓、跳虫、食蚜蝇）开发机械景观尺度模型。这些生物指示剂代表了不同的生活史、行为和扩散能力，因此对环境条件、农业管理实践和景观特征表现出不同的敏感性。该模型使我们能够检验中心假设，即虽然不同的生物指标对特定景观（土地利用类型的配置）和管理实践表现出不同的敏感性，但农业景观可以设计为同时使每个生物指标受益。我们期望对人口、农业实践和景观之间相互作用的这种新理解能够为优化管理计划提供信息，例如即将取代 CAP 生态重点区域 (EFA) 的环境土地管理 (ELM) 计划。该项目的具体目标包括： 1) 确定每个生物指标最具代表性的物种，重视其在农业生态系统中的功能重要性、每个物种的现有模型以及数据可用性 模型开发和验证。每个物种的个体生活史和行为特征将从文献中综合得出，其中大部分可以在现有模型中找到。 2) 开发为群体模型提供信息的标准化机械子模型。其中包括已建立的能量预算建模方法（其参数可用于许多物种，而其他物种可以从个体水平的数据中轻松获得）和行为子模型，该模型将使用状态空间建模方法来识别相关空间尺度上运动模式的关键驱动因素。文献中个体水平的运动数据有限，因此我们设想需要从桑宁农场正在进行的实验中收集食蚜蝇的新数据。3) 设计基于代理的建模框架，该框架代表空间明确的景观、农业实践的异质暴露、个体与环境的相互作用以及物种种群动态出现的个体特征。首先，模型环境将复制验证数据集的条件时间序列（例如天气、管理）以及 CEH（LCM，开源）和克兰菲尔德（LandIS）持有的土地覆盖和土壤图。 4) 使用雷丁、克兰菲尔德和先正达持有的广泛数据集以及英国环境变化网络和国家生物多样性网络图集等已发布的数据集来验证模型。近似贝叶斯计算是开发复杂生态模型的强大工具，将用于简化模型分析。在可获得种群数据的不同农业生态系统中进行模型验证，将能够量化景观组成和农业管理对生物指示剂种群的相互作用（例如拮抗或协同）。 5) 使用克兰菲尔德高性能计算机模拟新的景观组成和管理场景，以量化每种场景对每个生物指示剂群体的影响。情景将比较英国当前的分层 ELM 计划和欧盟 CAP 下的 EFA。