Knowledge Exchange Fellowship: developing and sharing mathematical tools to protect urban trees and woodland from invasive pests
Lead Research Organisation:
Newcastle University
Department Name: Sch of Maths, Statistics and Physics
Abstract
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.
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.
Publications
Wadkin L
(2023)
Quantifying Invasive Pest Dynamics through Inference of a Two-Node Epidemic Network Model
in Diversity
Golightly A
(2023)
Accelerating Bayesian inference for stochastic epidemic models using incidence data
in Statistics and Computing
Wadkin L
(2024)
Estimating the reproduction number, R 0 , from individual-based models of tree disease spread
in Ecological Modelling
| Description | This award has supported the development of several open-source computational models for the spread of invasive pests in the UK, in close collaboration with environmental stakeholders and ecologists. These models allow us to simulate the spread of an invasive pest (e.g., the oak processionary moth, which we have considered as a case study) with the goal of predicting its future expansion. With collaborators at Durham University, we developed a state-of-the-art Bayesian inference scheme to estimate the parameters for a stochastic compartmental SIR (susceptible, infested, removed) model with a time-varying infestation rate to describe the spread of OPM, finding the infestation rate and subsequent basic reproduction number (R0) have remained constant since 2013. This shows further controls must be taken to reduce the reproduction rate of the moth below one and stop the advance of OPM into other areas of England. Building upon this temporal work, we developed spatial models, including a two node network modelling connecting two neighbouring London parks, showing that one of the parks was the primary driver of the infestation. We have also developed individual-based models and explored different methods of calculating the effective reproduction number (R0) from such simulations. These findings demonstrate the applicability of such modelling techniques to describing the spread of invasive pest populations within the UK, with the statistical methodology a powerful tool for parameter estimation to ensure models are representative of reality. |
| Exploitation Route | The computational models developed are of interest to a broad range of applied mathematicians, ecologists, forest management teams and policy makers. They can currently be used to predict the population numbers of the invasive oak processionary moth within the UK, and with further development, have the potential to be able to provide detailed spatial predictions which can guide evidence based management decisions. The underlying mathematical and statistical frameworks used are transferable to a wide range of other case studies within invasive pest and plant health modelling. |
| Sectors | Agriculture Food and Drink Digital/Communication/Information Technologies (including Software) Environment |
| Description | This award has enabled a deepening collaboration between academics (in applied mathematics, statistics, and ecology) and partners in government and industry, including Defra, The Forestry Commission, Fera Science Ltd, and Forest Research. Knowledge exchange has formed the basis of the project - facilitating data and expertise sharing . This has supported model development which have been used to provide reports and updates to the project partners, predicting the spread of the invasive oak processionary moth. Presentations at stakeholder events (e.g., the OPM Stakeholder Annual Meeting) have enabled us to highlight the usefulness of such computational techniques to answer practical invasive pest management questions, and highlighted where further work could improve predictions (e.g., in systematic data collection or enhanced statistical techniques to overcome partial datasets). |
| First Year Of Impact | 2025 |
| Sector | Agriculture, Food and Drink,Digital/Communication/Information Technologies (including Software),Environment |
| Description | Climate change effects on the spread of wildfires: A mathematical approach |
| Amount | £120,000 (GBP) |
| Funding ID | 2883580 |
| Organisation | Natural Environment Research Council |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2023 |
| End | 03/2027 |
| Description | Future Proofing Plant Health - emulator project |
| Amount | £140,000 (GBP) |
| Organisation | Department For Environment, Food And Rural Affairs (DEFRA) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2024 |
| End | 02/2026 |
| Description | Spatiotemporal modelling the spread of invasive pests across Great Britain |
| Amount | £120,000 (GBP) |
| Funding ID | 2876001 |
| Organisation | Engineering and Physical Sciences Research Council (EPSRC) |
| Sector | Public |
| Country | United Kingdom |
| Start | 08/2023 |
| End | 09/2027 |
| Title | Software supporting 'Quantifying invasive pest dynamics through inference of a two-node epidemic network model' |
| Description | Invasive woodland pests have substantial ecological, economic, and social impacts, harming biodiversity and ecosystem services. Mathematical modelling informed by Bayesian inference can deepen our understanding of the fundamental behaviours of invasive pests and provide predictive tools for forecasting future spread. A key invasive pest of concern in the UK is the oak processionary moth (OPM). OPM was established in the UK in 2006; it is harmful to both oak trees and humans, and its infestation area is continually expanding. Here, we use a computational inference scheme to estimate the parameters for a two-node network epidemic model to describe the temporal dynamics of OPM in two geographically neighbouring parks (Bushy Park and Richmond Park, London). We show the applicability of such a network model to describing invasive pest dynamics and our results suggest that the infestation within Richmond Park has largely driven the infestation within Bushy Park. |
| Type Of Material | Data analysis technique |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | This research dataset provides a computational inference scheme to estimate the parameters for a network model of the invasive oak processionary moth. It suggests the infestation within Richmond Park has largely driven the infestation in Bushy Park which has been shared with our project partners in forestry management. |
| URL | https://data.ncl.ac.uk/articles/software/Quantifying_invasive_pest_dynamics_through_inference_of_a_t... |
| Title | Software supporting the manuscript "Estimating the reproduction number, R0, from individual-based models of tree disease spread" |
| Description | This code allows the user to recreate the figures from the Ecological Modelling paper: "Estimating the reproduction number, R0, from individual-based models of tree disease spread", DOI: https://doi.org/10.1016/j.ecolmodel.2024.110630, including model simulations and the inference scheme. Tree populations worldwide are facing an unprecedented threat from a variety of tree diseases and invasive pests. Their spread, exacerbated by increasing globalisation and climate change, has an enormous environmental, economic and social impact. Computational individual-based models are a popular tool for describing and forecasting the spread of tree diseases due to their flexibility and ability to reveal collective behaviours. In this paper we present a versatile individual-based model with a Gaussian infectivity kernel to describe the spread of a generic tree disease through a synthetic treescape. We then explore several methods of calculating the basic reproduction number $R_0$, a characteristic measurement of disease infectivity, defining the expected number of new infections resulting from one newly infected individual throughout their infectious period. It is a useful comparative summary parameter of a disease and can be used to explore the threshold dynamics of epidemics through mathematical models. We demonstrate several methods of estimating R_0 through the individual-based model, including contact tracing, inferring the Kermack-McKendrick SIR model parameters using the linear noise approximation, and an analytical approximation. As an illustrative example, we then use the model and each of the methods to calculate estimates of R_0 for the ash dieback epidemic in the UK. |
| Type Of Material | Computer model/algorithm |
| Year Produced | 2023 |
| Provided To Others? | Yes |
| Impact | This research database and model form the basis of the academic research paper: https://doi.org/10.1016/j.ecolmodel.2024.110630 which explores methods of calculating the basic reproduction number R0 from individual-based models. |
| URL | https://data.ncl.ac.uk/articles/software/An_individual-based_model_with_an_infectious_kernel_describ... |
| Description | Department for Food Environment and Rural Affairs (Defra) |
| Organisation | Department For Environment, Food And Rural Affairs (DEFRA) |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | I (as PI) have been liaising with the Plant Health Team throughout the course of this project, establishing a strong collaboration and partnership. I have contributed my skills in applied mathematics and computational modelling to develop tools that give insights into the spread of the oak processionary moth. |
| Collaborator Contribution | The Plant Health Team at Defra have provided data from the oak processionary moth survey and shared valuable expertise on invasive pest ecology, along with information on the practical forestry management techniques which have informed our model development. |
| Impact | A multi-disciplinary collaboration between Newcastle University mathematicians and the Defra Plant Health Team. |
| Start Year | 2022 |
| Description | Fera Science Ltd |
| Organisation | Fera Science Limited |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | This award has facilitated a collaboration between applied mathematicians at Newcastle University and ecologists and modellers at Fera Science Limited. We have provided mathematical and statistical expertise through our invasive pest case study, developing several open-source models for predicting invasive pest spread. |
| Collaborator Contribution | Our collaborators at Fera Science have provided data and landscape maps, crucial for our model development. They also share valuable expertise in invasive pest management strategies and other modelling techniques. |
| Impact | This award has facilitated a collaboration between applied mathematicians at Newcastle University and ecologists and modellers at Fera Science Limited. This has supported the results of this award and also led to new research proposals and collaboration on a Future Proofing Plant Health Project. |
| Start Year | 2022 |
| Description | Forestry Commission OPM collaboration |
| Organisation | Forestry Commission |
| Country | United Kingdom |
| Sector | Public |
| PI Contribution | As part of the award we have been able to strengthen our relationship with the Forestry Commission, particularly with the OPM Control Programme. Regular meetings and discussions with the OPM Project Manager Andrew Hoppit resulted in my invitation to the OPM stakeholders meeting in January 2023 to network and discuss recent research results. We have also collaborated on a further research publication (https://doi.org/10.3390/d15040496). |
| Collaborator Contribution | Forestry Commission have supported us through regular research meetings and data sharing, culminating in regular invitations to join the annual OPM stakeholders meeting and two research publications (https://doi.org/10.1002/ece3.8871 and https://doi.org/10.3390/d15040496). |
| Impact | OPM Stakeholder Meeting (January 2023) - Engagement Output. Academic publications (all attributed to this award). |
| Start Year | 2022 |
| Description | Mathematical Ecology Day (Newcastle University) |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Professional Practitioners |
| Results and Impact | We hosted a Mathematical Ecology Workshop Day in the School of Mathematics, Statistics and Physics at Newcastle University in July of 2024. The event attracted a diverse pool of academics from applied mathematics, statistics and ecology, alongside policymakers and forestry management teams (~30 attendees). The day consisted of several invited talks from researchers and government partners, and programmed networking time for knowledge exchange and future research planning. The feedback from the day was overwhelmingly positive with attendees citing the opportunity to make new collaborations and driving research momentum. We plan to turn this into an annual event. |
| Year(s) Of Engagement Activity | 2024 |
| Description | OPM Stakeholders Meeting |
| Form Of Engagement Activity | Participation in an activity, workshop or similar |
| Part Of Official Scheme? | No |
| Geographic Reach | National |
| Primary Audience | Other audiences |
| Results and Impact | My role as PI on this award has allowed me to develop ongoing collaborations with governmental forestry groups, including the Forestry Commission. Through this ongoing relationship I was invited to join the OPM Stakeholders Meeting in London in January 2023, a one day event bringing together policymakers, foresters, land owners and researchers across all sectors to share their findings and experiences of the OPM problem within the UK. This knowledge-sharing activity provided valuable up-to-date information about the current OPM situation which will be used in my ongoing research, as well as a valuable networking opportunity to promote the research of this award and initial new collaborations (e.g., Observatree group). |
| Year(s) Of Engagement Activity | 2023 |