Simulating the spatial and temporal dynamics of landscapes using generic and complex models

Landscape patterns originate from exogenous (e.g., climate) and endogenous (e.g., competition) processes and feedbacks that interact spatially and temporally. The resulting dynamics can be analyzed and quantified using spatio-temporal models. Various approaches are currently in use, ranging from generic to process-oriented models. However, strict classifications are difficult as models can be characterized by many different criteria. One important distinction is structural complexity. This may manifest itself in: (1) conceptual complication of the modelling approach, (2) the translation of the system complexity into model formalism, and (3) the level of detail of the simulated output. Thus, process models that mirror systems by quantifying individual biotic and/or abiotic processes may be referred to as complex models since their simulated output usually identifies explicit system details that require many input parameters mirroring the system bottom-up. Generic models, on the other hand, tend to be structurally parsimonious, usually not accounting for specific system details. They are often applied to study topics of complex systems theory such as emergence, self-organization, scaling, and chaos theory, and involving techniques used in non-linear dynamical systems theory. This special issue identifies concepts and methods used by models to represent spatially dynamic landscape patterns. It assesses relationships between landscape and model complexity, and discusses approaches to quantify the spatio-temporal pattern dynamics resulting from model simulations. The models presented here address a variety of different research topics, including climate change, urban development, ecological engineering, landscape classification concepts, spatial population dynamics, habitat fragmentation, and conservation. The models account for different biotic levels (individual, population, vegetation patterns), and are driven by both exogenous and endogenous processes. Identification and quantification of the landscape patterns produced by the simulated output relies on various indicators. Discrete-static indicators include landscape metrics. Static pattern descriptions involve fractal dimensions, whereas dynamic indicators include power spectra, entropy, or indicators of patch aggregation. Thematically, this special issue contributes to a variety of key topics in landscape ecology, including consideration of anthropogenic impacts on landscapes, or holistic approaches such as self-organization, or the governing role of connectivity in shaping ecological systems. (c) 2005 Elsevier B.V. All rights reserved.