Knowledge Exchange Fellowship: developing and sharing mathematical tools to protect urban trees and woodland from invasive pests

知识交流奖学金：开发和共享数学工具，以保护城市树木和林地免受入侵性害虫侵害

• 批准号: NE/X000478/1
• 负责人: Laura Wadkin
• 金额: $ 20.17万
• 依托单位: Newcastle University
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=NE%2FX000478%2F1
• 关键词: Knowledge; Exchange; Fellowship; developing; sharing

The loss of biodiversity due to the spread of tree diseases and harmful invasive pests within our native forests is having an enormous environmental, economic, and social impact. The threat has been escalating in recent years due to increased accidental international imports and climate change creating a more favourable environment for many pathogens and pests. In the '25 Year Environment Plan' the UK government highlights enhancing biosecurity as a key priority, through the control of existing diseases and pests, and by building forest resilience against new ones.A key invasive pest of concern is the oak processionary moth (OPM), a destructive pest harmful to both oak trees and humans. Despite great efforts to contain the outbreak to the original infested area of south-east England, OPM continues to spread. Trees within public parks and urban areas are most affected, risking further exacerbating both the socio-economic and ethnic divides in the access to quality green space. The prediction and control of the future dynamics of the OPM population is the priority of the governmental OPM Control Programme (OPM-CP: including researchers from the Department for Environment, Food and Rural Affairs (DEFRA), the Forestry Commission (FC), and Southampton GeoData, among others). Increasing the use of computational modelling and statistical epidemiology are priority measures identified by DEFRA to protect plant health for environmental and social wellbeing. Despite the potential impact of such interdisciplinary methods, there is a gap in translating these theoretical techniques into widespread usage by environmental organisations for developing robust forestry management and protection plans. This Fellowship aims to bridge this gap to develop mathematical models for the spread of OPM through dynamic collaboration with members of the OPM-CP.Aligned with NERC's priority delivery areas of 'healthy environment' through protecting current woodland health and 'resilient environment' through informing strategies to ensure future woodland resilience, this Fellowship will drive the development of both a local agent-based model, and a national stochastic differential continuum model for the spread of OPM. The models will be underpinned by rigorous statistical analysis and parameter inference of real data shared by OPM-CP and used to inform policies to control OPM, particularly in deprived urban areas at high risk of infestation. The results will not only provide an insight into managing the control of OPM but also establish a framework for knowledge exchange between mathematicians and forestry partners transferable to the management of future pests and diseases.

由于树木疾病和有害的入侵性害虫在我们的原生森林中的传播，生物多样性的丧失正在产生巨大的环境，经济和社会影响。近年来，由于意外的国际进口增加以及气候变化为许多病原体和害虫创造了更有利的环境，这种威胁不断升级。在“25年环境计划”中，英国政府强调通过控制现有疾病和害虫以及建立森林抵御新疾病和害虫的能力来加强生物安全是一个关键优先事项。一个关键的入侵害虫是橡树步行蛾（OPM），这是一种对橡树和人类都有害的破坏性害虫。尽管做出了巨大努力，将疫情控制在英格兰东南部的原始疫区，但OPM仍在继续蔓延。公园和城市地区的树木受影响最大，有可能进一步加剧在获得优质绿色空间方面的社会经济和种族鸿沟。预测和控制OPM种群的未来动态是政府OPM控制方案的优先事项（OPM-CP：包括来自环境、食品和农村事务部（DEFRA）、林业委员会（FC）和南安普顿地理数据的研究人员）。增加使用计算模型和统计流行病学是DEFRA确定的保护植物健康以促进环境和社会福祉的优先措施。尽管这种跨学科方法具有潜在影响，但在将这些理论技术转化为环境组织的广泛使用以制定强有力的林业管理和保护计划方面还存在差距。该奖学金旨在弥合这一差距，通过与OPM-CP成员的动态合作，为OPM的传播开发数学模型。与NERC的优先交付领域“健康环境”保持一致，通过保护当前的林地健康和“弹性环境”，通过制定战略，以确保未来的林地弹性，该奖学金将推动基于当地代理人的模型，和一个国家随机微分连续模型的传播OPM。这些模型将以严格的统计分析和对OPM-CP共享的真实的数据的参数推断为基础，并用于为控制OPM的政策提供信息，特别是在面临虫害高风险的贫困城市地区。研究结果不仅将为管理OPM的控制提供深入的见解，而且还将为数学家和林业合作伙伴之间的知识交流建立一个框架，可用于未来的病虫害管理。

项目成果

Accelerating Bayesian inference for stochastic epidemic models using incidence data

• DOI: 10.1007/s11222-023-10311-6
• 发表时间: 2023-12-01
• 期刊: STATISTICS AND COMPUTING
• 影响因子: 2.2
• 作者: Golightly，Andrew;Wadkin，Laura E.;Kypraios，Theodore
• 通讯作者: Kypraios，Theodore

Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model

通过二节点流行病网络模型的推理量化入侵害虫动态

• DOI: 10.3390/d15040496
• 发表时间: 2023
• 期刊: Diversity
• 作者: Wadkin L

Inference for epidemic models with time-varying infection rates: Tracking the dynamics of oak processionary moth in the UK.

• DOI: 10.1002/ece3.8871
• 发表时间: 2022-05
• 期刊: ECOLOGY AND EVOLUTION
• 影响因子: 2.6
• 作者: Wadkin, Laura E.;Branson, Julia;Hoppit, Andrew;Parker, Nicholas G.;Golightly, Andrew;Baggaley, Andrew W.
• 通讯作者: Baggaley, Andrew W.