Details of Award
NERC Reference : NE/M010481/1
Test of a mechanistic general model of global ecosystems: improving prediction by increasing simplicity?
Training Grant Award
- Lead Supervisor:
- Professor DJ Murrell, University College London, Genetics Evolution and Environment
- Grant held at:
- University College London, Genetics Evolution and Environment
- Science Area:
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Terrestrial
- ENRIs:
- Biodiversity
- Science Topics:
- Complexity Science
- Community Ecology
- Ecosystem Scale Processes
- Abstract:
- The Madingley Model (MM) is the first attempt to couple all of the key biological processes that underpin the life cycle and behaviour of all of the millions of trillions of organisms on Earth (photosynthesis, feeding, metabolism, reproduction, dispersal, and death) in order to attempt to capture how such processes lead to the structure and function of whole ecosystems. In this way, the model addresses the central questions in ecology: how do interactions among plants and animals lead to the ecosystems that we see around us; how do these ecosystems vary across the world; what will happen to these ecosystems in the future in response to various human pressures; and how can we mitigate or reverse any damage? The current MM is very much a starting point and whilst the model shows a number of predictions that seem to match patterns and relationships observed in the natural world, the model has not been tested for robustness to variations in parameter values and changes in assumptions about component processes such as herbivory and predation. A number of important questions remain: can the emergent properties of the current model be matched by a simplified version that strips away some of the biological complexity? Are some of the emergent patterns insensitive to the key biological assumptions of the model? Simpler models are easier to analyse and have fewer parameters to be estimated, reducing the chances that the model is fitted to noisy, unreliable data. On the other hand, recent ecological research using so-called neutral models that avoid much biological detail have shown how some commonly measured patterns are unable to differentiate between candidate hypotheses put forward to explain the origin of the pattern. Are there such patterns in the predictions of the MM? For example the MM currently predicts the inverse biomass pyramid (with highest biomass in the upper trophic levels) observed in marine ecosystems, and reproduces the empirically derived relationship between growth rate and body size; but shows a weaker match to data for the relationship between body mass and mortality. Are the former predictions highly sensitive to model assumptions, or do they occur even in grossly simplified versions of the model? Can the latter mismatch be improved with different assumptions regarding consumer responses to food availability and intelligent behaviour whilst maintaining the good matches with data found in other predictions? This project will first seek to reduce the complexity of the MM and test the robustness of current results to changes in parameter values and model assumptions. In so-doing we will provide a deeper understanding of the emergent properties and important processes that determine ecosystem structure and function. The project will then go on to ask if the addition of some well-established theory for predator and herbivore consumption and intelligent behaviour will improve the match of current model predictions to empirical patterns. Making the MM more relevant to the ecosystems it is trying to capture has a significant contribution to managers and policy makers because the MM represents a tool to explore the potential effects of their decisions on the environment, in a computer simulated setting, before the decisions are rolled out in the real world. In this light, the Madingley Model was cited as an exemplary development at the plenary meeting of the Intergovernmental Platform on Biodiversity and Ecosystem Services (IPBES) in 2013. Development of the MM requires a high level of mathematical and computer modelling skills and the supervisory team and partner institutes have an excellent and internationally recognised track record in this area. The project will result in novel insight into how ecosystems function and are structured, but will also train a researcher in the key skills that are widely acknowledged to be lacking in the environmental sciences.
- NERC Reference:
- NE/M010481/1
- Grant Stage:
- Completed
- Scheme:
- DTG - directed
- Grant Status:
- Closed
- Programme:
- Industrial CASE
This training grant award has a total value of £99,068
FDAB - Financial Details (Award breakdown by headings)
| Total - Fees | Total - RTSG | Total - Student Stipend |
|---|---|---|
| £17,981 | £11,000 | £70,090 |
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