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Project properties |
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| Title | Global tree trait data coverage |
| Group | Plant Ecology and Nature Conservation Group |
| Project type | thesis |
| Credits | 36 |
| Supervisor(s) | Coline Boonman |
| Examiner(s) | Coline Boonman |
| Contact info | coline.boonman@wur.nl |
| Begin date | 2024/09/01 |
| End date | 2025/12/31 |
| Description | Trees are pivotal to global biodiversity, acting as ecosystem engineers and generating habitat to half the world’s known terrestrial flora and fauna. Accelerating global changes are threatening these tree species, where widespread species tend to be less vulnerable than local, rare species. But what determines the spread of a species? Traits may be the aspect that can solve this question. However, trait data is highly scattered, biased to certain regions and towards more common species. This thesis topic aims to understand what trait data is available for tree species, where the observations come from, describing the distributions across space and with vegetation types. Regarding the global changes and how species can cope/adapt to these, intraspecific trait variation may also be extremely important. How much do we know about this variation? The amount of data is limited, but is it enough to allow for models to predict species’ intra-specific trait variation based on the environment they occur in?
The questions you will be working on, specifically on one trait that is of importance to trees and has a good data coverage (and possibly on one region): - What is the spread of trait data for tree species? - Can we quantify intra-specific trait variation? - Is intra-specific trait variation related to environment? - Can intra-specific trait variation explain the geographic spread of tree species? Related papers: https://www.nature.com/articles/s41467-022-30888-2 https://nph.onlinelibrary.wiley.com/doi/full/10.1111/nph.18999 Be aware that this is a data heavy project, and does not include fieldwork |
| Used skills | Data analysis, Spatial analysis, Empirical/Statistical Modelling, R, GoogleEarthEngine/GIS |
| Requirements | Experience in R and affinity with large datasets and modelling. |