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The role of environmental filtering on terrestrial plant trait distributions at large spatial scales
Group: Radboud University Nijmegen, Environmental Science
Promotors: Prof. Dr. M.A.J. Huijbregts
Promotors: Dr. L. Santini
In this time of global change, there is a need to better understand how the environment affects global vegetation distributions. The main goal of my thesis was to determine the role of environmental filtering on the distribution of terrestrial plant traits at large spatial scales. First, I provide a better understanding of how traits vary across large-scale environmental gradients, where I highlight the importance of root traits. Second, I test the application of environmental filtering in order to predict more informative aspects of vegetation, and found that with few plant trait distributions future large-scale vegetation patterns can be predicted. Third, I discuss the reliability of trait-based models in terms of uncertainties based on model predictive performance, applicability domain, model uncertainty and realism of predicted trait combinations. I highlight that a systematic and careful selection of input data and modelling techniques will improve the interpretability and reliability of trait-based modelling results.
By September we will present an overview of SENSE dissertations on this page, with links to the full texts of the dissertations.