Loading…
Loading grant details…
| Funder | Natural Environment Research Council |
|---|---|
| Recipient Organization | University of Sheffield |
| Country | United Kingdom |
| Start Date | Sep 30, 2024 |
| End Date | Mar 30, 2028 |
| Duration | 1,277 days |
| Number of Grantees | 2 |
| Roles | Student; Supervisor |
| Data Source | UKRI Gateway to Research |
| Grant ID | 2926094 |
The Arctic is experiencing rapid climate change, and as a consequence tundra ecosystems are showing large increases in plant growth and rapid expansion of larger stature plants. This change is recognised by the IPCC as one of the clearest examples of ecosystem change in response to warming.
Understanding the consequences of this "greening of the Arctic" is of great importance, because the changes in ecosystem biodiversity effect ecosystem functioning, and hence the capacity of tundra to mitigate climate change through uptake of CO2.
However, Arctic ecosystems can be highly heterogeneous, so it is difficult to identify general, broadly applicable rules of how tundra change will alter ecosystem function and feedback to climate.
This PhD will therefore use the powerful solution of spatial analysis utilising the natural heterogeneity of tundra landscapes to understand the linkages between plant community composition and tundra ecosystem function.
By sampling across hundreds of contrasting point locations, and collecting data on multiple plant and soil traits, the relationships between above ground plant biodiversity and traits, biophysical properties of the soil, and soil carbon and nutrient cycling, will be established.
At each location, hyperspectral reflectance data will also be collected to find out which wavelengths best predict the changes in above and below ground structure and function, and which may offer the opportunity to scale up these changes to make predictions from large scale remotely sensed data (such as satellites).
This approach also allows the natural landscape variation to be used as a proxy for climate-driven tundra change to predict future changes in ecosystem functioning.
University of Sheffield
Complete our application form to express your interest and we'll guide you through the process.
Apply for This Grant