Landscape Simulation Modelling

This is my fifth contribution to JustScience week.

The last couple of days I’ve discussed some techniques and case studies of statistical model of landscape processes. Monday and Tuesday I looked at the power-law frequency-area characteristics of wildfire regimes in the US, Wednesday and Thursday I looked at regression modelling for predicting and explaining land use/cover change (LUCC). The main alternative to these empirical modelling methods are simulation modelling techniques.

When a problem is not analytically tractable (i.e. equations cannot be written down to represent the processes) simulation models may be used to represent a system by making certain approximations and idealisations. When attempting to mimic a real world system (for example a forest ecosystem), simulation modelling has become the method of choice for many researchers. This may have become the case since simulation modelling can be used when data is sparse. Also, simulation modelling overcomes many of the problems associated with the large time and space scales involved in landscapes studies. Frequently, study areas are so large (upwards of 10 square kilometres – see photo below of my PhD study area) that empirical experimentation in the field is virtually impossible because of logistic, political and financial constraints. Experimenting with simulation models allows experiments and scenarios to be run and tested that would not be possible in real environments and landscapes.

Spatially-explicit simulation models of LUCC have been used since the 1970s and have dramatically increased in use recently with the growth in computing power available. These advances mean that simulation modelling is now one of the most powerful tools for environmental scientists investigating the interaction(s) between the environment, ecosystems and human activity. A spatially explicit model is one in which the behaviour of a single model unit of spatial representation (often a pixel or grid cell) cannot be predicted without reference to its relative location in the landscape and to neighbouring units. Current spatially-explicit simulation modelling techniques allow the spatial and temporal examination of the interaction of numerous variables, sensitivity analyses of specific variables, and projection of multiple different potential future landscapes. In turn, this allows managers and researchers to evaluate proposed alternative monitoring and management schemes, identify key drivers of change, and potentially improve understanding of the interaction(s) between variables and processes (both spatially and temporally).

Early spatially-explicit simulation models of LUCC typically considered only ecological factors. Because of the recognition that landscapes are the historical outcome of multiple complex interactions between social and natural processes, more recent spatially-explicit LUCC modelling exercises have begun to integrate both ecological and socio-economic process to examine these interactions.

A prime example of a landscape simulation model is LANDIS. LANDIS is a spatially explicit model of forest landscape dynamics and processes, representing vegetation at the species-cohort level. The model requires life-history attributes for each vegetation species modelled (e.g. age of sexual maturity, shade tolerance and effective seed-dispersal distance), along with various other environmental data (e.g. climatic, topographical and lithographic data) to classify ‘land types’ within the landscape. Previous uses of LANDIS examined the interactions between vegetation-dynamics and disturbance regimes , the effects of climate change on landscape disturbance regimes , and simulated the impacts of forest management practices such as timber harvesting.

Recently, LANDIS-II was released with a new website and a paper published in Ecological Modelling;

LANDIS-II advances forest landscape simulation modeling in many respects. Most significantly, LANDIS-II, 1) preserves the functionality of all previous LANDIS versions, 2) has flexible time steps for every process, 3) uses an advanced architecture that significantly increases collaborative potential, and 4) optionally allows for the incorporation of ecosystem processes and states (eg live biomass accumulation) at broad spatial scales.

During my PhD I’ve been developing a spatially-explicit, socio-ecological landscape simulation model. Taking a combined agent-based/cellular automata approach, it directly considers:

  1. human land management decision-making in a low-intensity Mediterranean agricultural landscape [agent-based model]
  2. landscape vegetation dynamics, including seed dispersal and disturbance (human or wildfire) [cellular automata model]
  3. the interaction between 1 and 2

Read more about it here. I’m nearly finished now, so I’ll be posting results from the model in the near future. Finally, some other useful spatial simulation modelling links:

Wisconsin Ecosystem Lab – at the University of Wisconsin

Center for Systems Integration and Sustainability – at Michigan State University

Landscape Ecology and Modelling Laboratory – at Arizona State University

Great Basin Landscape Ecology Lab – at the University of Nevada, Reno

Baltimore Ecosystem Study – at the Institute of Ecosystems Studies

The Macaulay Institute – Scottish land research centre

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