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"Dynamic Landscape Modelling: The Quest for a Unifying Theory"

Cédric Gaucherel and François Houllier and Daniel Auclair and Thomas Houet

	Abstract
1	Introduction
2	Landscape processes: Current approaches
	2.1	Agricultural landscape
	2.2	Forest landscape
	2.3	Arid landscape
	2.4	Urban landscape
3	Landscape-model properties: Current practices
	3.1	Explicit versus neutral landscape models: the “spirit” of models
	3.2	Raster versus patchy landscape models: the “space” of models
	3.3	Mono- versus multi-scale models: the “scale” of models
	3.4	Modelling networks or not: the “veins” of models
4	What model characteristics for what landscape type: Research priorities
5	Discussion: Towards a dynamic landscape theory
	5.1	Universality behind case studies
	5.2	Complexity theory as a conceptual basis
	5.3	Mathematical formalism and modelling as a toolkit
6	Conclusion
	Acknowledgements
A	Appendix
	A.1	Neutral landscape models
	A.2	Landscape self-organization
	References
	Figures
	Tables

List of Figures

Figure 1:
Illustration of the two main landscape models. There are: process-explicit models, using specific processes (a), and neutral landscape models, using random functions (b). Process-explicit models often simulate landscape structure dynamics in terms of land cover (top-right), as well as bio-ecological and socio-economical processes (top-left) hosted by these structures. In addition, these processes are interacting (dashed arrows) through neighbouring fluxes that likely modify their dynamics (dashed curves). Neutral landscape models often simulate fixed landscape structures in raster mode (bottom-left), here adapted from Saura and Martínez-Millán (2000). To study agricultural landscapes some patchy neutral models (bottom-right), associated to linear networks (in black), have recently been built on the basis of Voronoï diagrams. Colours are representing hypothetical land covers.

Figure 2:
Scheme illustrating the principle of a modelling platform (a) and the architecture of an example: the DYPAL landscape modelling platform (b). The principle of a platform aims at finding a compromise (the greatest common divisor GCD) between a range of models dedicated to various objectives and a universal model addressing all possible objectives (as the least common multiple LCM). A platform is built on the basis of a generic kernel (bottom, in blue) dedicated to the common object modelled (here patchy landscapes of the DYPAL platform), and then developed at low cost in various dedicated models (in warm colours).