Spatial neighborhood effect and scale issues in the calibration and validation of a dynamic model of Phragmites australis distribution – A cellular automata and machine learning approach

• We develop a dynamic model of Phragmites distribution using cellular automata.
• We investigate patterns of distribution and spread at different scale settings.
• We obtain cellular automata transition rules using boosted regression trees.
• We present a model of neighborhood effect that captures directional influences.

We developed a dynamic model of the distribution of Phragmites australis, a plant that has spread intensively on Finnish coasts. The model employs cellular automata and utilizes machine learning to provide the transition rules. We examined the effects that various cell sizes and neighborhood extents had on pattern detection and model behavior. We obtained the transition probabilities using boosted regression trees in a way that accounts for the spatial arrangement of the neighboring cells. The results show the influence of the scale settings on the ability to detect and simulate patterns of Phragmites dynamics. The introduced method of quantifying the neighborhood effect, based on the spatial arrangement of the neighboring cells, displayed potential for capturing directional influences within the neighborhood. Our study addresses the close-range effect on the distribution of Phragmites, and it can be linked with models of water quality to predict future distributions under various scenarios of land-cover change.