Ecosystem services can be altered dramatically when the ecosystem is invaded by invasive plant species. Such species often facilitate their own invasion through a change of the local ecosystem conditions around them that is beneficial for their spread. This is called a self-reinforcing feedback effect. To avoid undesirable ecosystem shifts, management strategies aimed at stopping the invasion have to be developed in an early stage. To this purpose, one needs: 1) information on the current distribution of the invasive species, and 2) projections of the expected spread of the species under different future conditions.
Dr. André Große-Stoltenberg (Justus-Liebig-University Gießen, Germany) has done his PhD on the detection and impact of the invasive species Acacia longifolia (Andrews) in a Mediterranean dune system in Portugal. A. longifolia is an N2-fixing woody plant, which increases the nitrogen level in its surroundings in the originally nutrient-poor dune system. Methods were developed to distinguish A. longifolia from other species, using field spectroscopy and remote sensing techniques. Furthermore, factors that are of importance for the change in the local ecosystem conditions were identified. Although this work has delivered many valuable insights in the spatial distribution of A. longifolia and the factors that might influence the spread, it did not yet deliver a simulation model that can be used to make future projections of the invasion under different conditions.
Cellular automata (CA) simulate discrete changes over space and time. CA consist of: a grid of cells, a neighborhood definition, a finite set of discrete states, a finite set of transition rules, an initial state, and discrete time. The unique property of CA is that the state of a cell at time t is a function of only the states of the cell itself and its neighbors at time t-1. Because of this property, cellular automata are suitable for modelling systems in which discrete entities (such as plants) spread by means of neighborhood effects (such as seed dispersal and self-reinforcing feedbacks).
The aim of this thesis is to develop a CA model to project the past and future spread of A. longifolia in the described dune ecosystem in Portugal. You can build upon existing literature on cellular automata for vegetation modelling in arid Mediterranean ecosystems (e.g. by Sonia Kéfi and co-authors) and use the maps classified by Dr. André Große-Stoltenberg.
This thesis is performed in collaboration with Dr. André Große-Stoltenberg (Justus-Liebig-University Gießen, Germany).
- To define Cellular Automata (CA) transition rules that represent the spread of Acacia longifolia, and implement them.
- To calibrate and/or validate the CA model based on Acacia longifolia occurance data for a study area in Portugal, obtained by André Große-Stoltenberg from remote sensing data.
- To define and run a set of future scenarios to project the spread of Acacia longifolia under different future conditions.
- Große-Stoltenberg A., Hellmann C., Thiele J., Werner C., Oldeland J. (2018). Early detection of GPP-related regime shifts after plant invasion by integrating imaging spectroscopy with airborne LiDAR. Remote Sensing of Environment 209, 780-792. DOI: 10.1016/j.rse.2018.02.038.
- Kéfi S., Rietkerk M., Alados A.L., et al. (2007). Spatial vegetation patterns and imminent desertification in Mediterranean arid ecosystems. Nature 449 (7159), 213-217. DOI: 10.1038/nature06111.
- Huang H., Zhang L., Guan Y, Wang D. (2008). A cellular automata model for population expansion of Spartina alterniflora at Jiuduansha Shoals, Shanghai, China. Estuarine, Coastal and Shelf Science 77(1), 47-55, DOI: 10.1016/j.ecss.2007.09.003.
- Passed the course Spatial Modelling & Statistics (30306), or another course in which cellular automata are taught.
- Conceptual-thinking skills.
Theme(s): Modelling & visualisation; Integrated Land Monitoring