Details of Award
NERC Reference : NE/T010355/1
Simulating UK plant biodiversity under climate change to aid landscape decision making
Grant Award
- Principal Investigator:
- Professor R Reeve, University of Glasgow, College of Medical, Veterinary, Life Sci
- Co-Investigator:
- Professor C Cobbold, University of Glasgow, School of Mathematics & Statistics
- Co-Investigator:
- Professor G Marion, The James Hutton Institute, BioSS
- Co-Investigator:
- Dr NA Brummitt, The Natural History Museum, Life Sciences
- Grant held at:
- University of Glasgow, College of Medical, Veterinary, Life Sci
- Science Area:
- Atmospheric
- Earth
- Terrestrial
- Overall Classification:
- Unknown
- ENRIs:
- Biodiversity
- Global Change
- Natural Resource Management
- Science Topics:
- Biodiversity
- Climate change
- Earth & environmental
- Ecosystems
- Plant ecology
- Earth & environmental
- Ecosystem impacts
- Climate & Climate Change
- Uncertainty in complex systems
- Complexity Science
- Anthropogenic pressures
- Ecosystem services
- Land use change
- Conservation Ecology
- Population dynamics
- Biodiversity
- Population Ecology
- Abstract:
- Landscapes are composed of multiple habitats as well as the biodiversity that resides within them, and are a product of interactions between species present, climate, geography and human use. They provide many ecosystem services, such as provision of food and water, regulation of climate and carbon cycling, which are vital for a stable future for our society, economy, health and wellbeing. Plants form the basis of all terrestrial ecosystems and are fundamental to providing these ecosystem services. Landscape decisions should therefore be underpinned by tools that enable prediction of plant responses to global change and landscape management. However, current approaches to modelling plant species distributions are deficient for this purpose as they focus on individual, or a small number of, species; ignore interactions between species; or only model a small number of plant functional types. A systems approach will be used to address this significant gap in current real-world landscape decision support by developing tools to predict (including uncertainty quantification) current and future distribution of all ~1,800 UK plant species in a manner that accounts for competitive interactions between species. This will enable effective assessment of the impacts of landscape decisions and/or climate change, e.g. in specific locations or on important habitat types such as peatlands. Invasive non-natives are considered a growing threat to ecosystem services and through extension to ~200,000 plant species worldwide this tool also enables assessment of the impact of invasive non-native plant species on current and potential future UK landscapes. Pests and diseases also represent a significant challenge and tools developed by this project will be a valuable resource for managing landscapes for plant health, for example, by providing distributions of at-risk populations - i.e. the distribution of plant hosts for any disease or pest of interest. Future work could explore the potentially critical feedbacks between the dynamics of plant community distributions and the transmission of pests and diseases by coupling models of these processes. This project builds on an existing coarse spatial scale model for all plant biodiversity on Earth and an ongoing NERC-funded project developing a higher resolution version for UK plant species. The latter project makes use of the more detailed climate, land use and plant coverage records available for the UK. However, further refinements are needed to properly quantify structural and process uncertainty within this framework. Without such work predictions of the effect of climate change and land use decisions that emerge from these models could be misleading. Currently niche preferences are parameterised by observational data with no uncertainty assessment. In terms of structural uncertainty, it is critical to account for between-species heterogeneity better by establishing how each species grows and reproduces (its functional type). Building on existing digitisation expertise at the Natural History Museum we therefore propose to extract relevant functional type information from existing taxonomic descriptions to create a more extensive trait database for all UK native and non-native plant species. As well as being a valuable resource in its own right and extensible to all global plant records, this work will be used within the project to enhance the simulation model to capture the relative differences in growth, competition and dispersal between species. Comparison with the current model based on a limited number of functional types will highlight the role of structural complexity and the impact of non-linearities on model output. We will also develop tools to quantify uncertainty in these models using available plant species distribution data so that we can correctly capture the impact of planned and expected land use and climate change, and ultimately guide future landscape decision making.
- NERC Reference:
- NE/T010355/1
- Grant Stage:
- Completed
- Scheme:
- Directed (Research Programmes)
- Grant Status:
- Closed
- Programme:
- Landscape Decisions
This grant award has a total value of £297,987
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DI - Staff | DA - Estate Costs | DA - Other Directly Allocated | DI - T&S |
|---|---|---|---|---|---|---|
| £8,503 | £116,693 | £34,570 | £99,703 | £29,638 | £379 | £8,503 |
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