I have three research lines that are described below in more detail:
First, to understand plants' responses in an enviroment when challenged by neigbouring plants and pests and diseases. I want to unravel these processes and relationships through the application of models, field and greenhouse experiments and analysis of large datasets.
Here a couple of examples of the work I did in this context:
- Ratios of red to far-red light are used by the plant as indicator of upcoming shade. A lowered R:FR ratio leads to shoot elongation and makes the plant more competitive in dense stands. Interestingly, lowered R:FR also downregulates plant defence. Why? In this paper we explored through 3D plant modelling the benefit of reducing defence in response to R:FR.
- Plant subjected to insect herbivory emit a blend of volatile organic compounds. The natural enemies of these herbivores (e.g. parasitoids) can use this blend to locate their host. However, they appear to use only a small number of compounds to locate their host. In this study, we model the emission of these compounds and their fate in the canopy. We show that the physico-chemical characteristics of volatile organic compounds determine for a large part whether they can serve as reliable indicators of herbivory.
Secondly, I am interested in the introduction, establishment and spread of alien plant pests. Through increasing trade and travel, alien plant pests and pathogens arrive at unprecedented rates. For risk managers, it is important to know when where new species will arrive. The models that I have developed in this context are used by the European Food and Safety Authority for risk assessment (see e.g. here and here). I am co-leading a modelling work package of a European Horizon Research program, HOMED on invasive plant pests and pathogens.
Finally, I love the application of models and novel statistical tools to answer intruiging ecological questions. Historically, statistics has focussed on univariate analysis, while multivariate techniques are less well developed. Those techniques out there, such a ordination techniques (PCA, RDA), hammer down the multivariate structure to a number of dimensions that we can visualize. I am interested in stastistical techniques that deal with the multivariate of the data, withouth collapsing this multidimensionality.
- For example, here I have described with Dr. James Weedon (VU University Amsterdam) the use of Dirichlet regression to deal with proportions partitioned over multiple categories, such as %leaf,%stem,%root biomass.
- With Prof. Bill Shipley (University of Sherbrooke, Canada) I work on causal inference techniques (structural equation modelling). See examples here.