Details of Award
NERC Reference : NE/T006579/1
Timeline to collapse
Grant Award
- Principal Investigator:
- Dr CF Clements, University of Bristol, Biological Sciences
- Co-Investigator:
- Professor D Childs, University of Sheffield, School of Biosciences
- Grant held at:
- University of Bristol, Biological Sciences
- Science Area:
- Atmospheric
- Earth
- Freshwater
- Marine
- Terrestrial
- Overall Classification:
- Panel C
- ENRIs:
- Biodiversity
- Global Change
- Natural Resource Management
- Pollution and Waste
- Science Topics:
- Community Ecology
- Biodiversity
- Community structure
- Environmental stressors
- Population dynamics
- Conservation Ecology
- Conservation management
- Population Ecology
- Biodiversity
- Extinction
- Population dynamics
- Abstract:
- Biological systems on which humanity relies for food, fresh water, and clean air are becoming increasingly stressed. One way in which such stress manifests is through loss of resilience: ecosystems are increasingly at risk of rapid change, characterised by sudden collapses in the abundance of populations. Increasing anthropogenic stressors thus leave us at a critical point for ecosystem management, where to preserve biodiversity and the services on which society relies, we need to reliably detect what systems are at most risk of collapse, and thus where conservation effort should be targeted. Such predictions are hampered not only by the nature of ecological systems, which are inherently high dimensional and non-linear, but also by a lack of long-term high-resolution data for many populations and communities. Such limitations mean that process-to-pattern methods such as mechanistic modelling approaches - whereby detailed models on a specific system are parameterised and perturbed to assess whether that system is at risk - are unsuitable for predicting the fates of most systems. An alternative school of thought has suggested a pattern-to-process approach, whereby signals observed in data are used to infer changes in the structure of a system which may lead to its collapse. These pattern-to-process phenomenological approaches required fewer data and make fewer assumptions about the structure of the system, providing generalisable rule-of-thumb methods to warn of approaching disaster. Were they to be reliable, such methods would be an invaluable tool to help manage and conserve biodiversity in a rapidly changing world. However, the promise of such phenomenological methods is negated by the lack of basic testing. For example, whilst many warning methods have been developed (based fluctuations in population abundances, declines in body size, and changes in the spatial distribution of populations) thus far no work has compared the relative predictive efficacy of these methods. Moreover, we know nothing about how key drivers of stability such as community and landscape complexity affect our ability to make reliable predictions, catastrophic failings if such methods are to be used to inform the management of heterogeneous populations and communities in the real world. In this project, we will take a bold new approach to the advancement and testing of phenomenological warning signals by developing a spatially-explicitly multi-species experimental system which will allow high-resolution, high-dimensional data to be generated on multiple species at the landscape scale. We will use this system to (i) provide the first simultaneous test of currently proposed phenomenological warning methods, (ii) develop and test novel warning signals derived from ecological theory based on changes in the behaviour of individuals and assess how these perform in relation to previously proposed methods, and (iii) assess the reliability of warning signal methods across various spatial and community complexities, and thus their suitability for informing conservation decision making. The work we are proposing is fundamental not only to or understanding of resilience loss in biological systems, but also to practical on-the-ground management of key resources on which human society relies. Our focus during this work will be not only to understand how integral drivers of population dynamics such as spatial and community complexity affect our ability to make reliable predictions, but how reliable the developed methods will be when subjected to the vagaries and inconsistencies seen in real-world conservation data. Thus, this project spans a range of disciplines, generating important insights in fields including demography and community ecology, whilst targeting significant downstream socio-economic impact by developing robust predictive frameworks to help minimise biodiversity loss in the face of anthropogenic forcing.
- NERC Reference:
- NE/T006579/1
- Grant Stage:
- Awaiting Event/Action
- Scheme:
- Standard Grant FEC
- Grant Status:
- Active
- Programme:
- Standard Grant - NI
This grant award has a total value of £542,945
FDAB - Financial Details (Award breakdown by headings)
| DI - Other Costs | Indirect - Indirect Costs | DA - Investigators | DA - Estate Costs | DI - Staff | DI - T&S | DA - Other Directly Allocated |
|---|---|---|---|---|---|---|
| £37,275 | £226,451 | £26,051 | £52,012 | £176,353 | £6,570 | £18,234 |
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