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Why use landscape models? Models allow us to generate and test hypotheses on systems

Why use landscape models? Models allow us to generate and test hypotheses on systems Collect data, construct model based on assumptions, observe behavior of the model Identify areas of understanding Identify range of variability Identify sensitive parameters Management applications

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Why use landscape models? Models allow us to generate and test hypotheses on systems

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  1. Why use landscape models? • Models allow us to generate and test hypotheses on systems • Collect data, construct model based on assumptions, observe behavior of the model • Identify areas of understanding • Identify range of variability • Identify sensitive parameters • Management applications • Test different management scenarios • E.g., alternatives for a National Forest Plan

  2. Landscape ecology poses particular challenges to modeling applications • High degree of complexity in ecological systems that we have to account for • nonequilibrium systems • spatial heterogeneity • complex feedbacks through time • relevant processes that operate at a variety of scales

  3. Spatial and temporal constraints on landscape studies • Experiments on large areas are difficult • Even more difficult to replicate experiments; or even "sample" and analyze replicates • Many large-scale processes operate slowly, so landscape change does also • Even with good data, systems too complex to predict behavior

  4. Operationally, useful to think of three general types of landscape models • Landscape change models • Land cover classes, ecosystem types, or habitats • Influenced by natural or anthropogenic processes • Landscape process models • Simulate a process that depends on landscape states and changes • E.g., hydrological change, or nutrient movement through the soil • Individual-based models

  5. Individual-Based population models - models of how organisms move through, use, and interact with the landscape • Can be analytical or simulation models • Collections of individuals • Advantages • Can be highly mechanistic • Testable • Disadvantages • Complexity • Poor generality

  6. Landscape change models often include simulation of disturbance and a response • landscape pattern - three basic things, within climatic framework • 1) an abiotic or geomorphic template • 2) disturbance • 3) biotic responses, e.g., succession • Landscape change models a way of simulating pattern change in pattern in a landscape • Most landscape models are different ways of conceptualizing these three general areas • Depending on needs, may need to include in a model processes operating within any of these three areas • Questions, scales, determine processes to include

  7. Modeling approaches - Baker (1989) • Distinguished between distributional landscape models and spatial landscape models • Distributional models - model the different values of a variable in a landscape. E.g., the area of a landscape in different land use classes or elements • Spatial models - Model spatial location and configuration of landscape elements or classes • All landscape change models contain basic components of • 1) initial configuration • 2) change processes or dynamics • 3) output configuration

  8. Spatial models as defined by Baker, are what we usually think of as landscape models • Include location and configuration of landscape elements • Often use maps or a matrix representation as input and output • Raster or grid cell format most common • Other ways of classifying models • E.g., further in Baker 1989 • Sklar and Costanza, QMLA • Turner and Dale, same volume

  9. From He and Mladenoff 1999

  10. From Pastor and Johnston

  11. What are problems confronted in developing spatial landscape models? • Stochastic models (probabilistic) simulate changes in the state of polygons cells based on a matrix of transition probabilities • Based on observed or inferred rates of change between possible states on a landscape - Markov transition models. • Assume several things that typically may not be true: • Transition rates are constant over time • Rates due to current state • Must have data to define states and derive transition rates

  12. http://www.env.duke.edu/landscape/classes/env214/le_mod1.htmlhttp://www.env.duke.edu/landscape/classes/env214/le_mod1.html

  13. http://www.env.duke.edu/landscape/classes/env214/le_mod2.htmlhttp://www.env.duke.edu/landscape/classes/env214/le_mod2.html

  14. Cellular automata models: "systems of cells interacting in a simple way but displaying complex overall behavior" (Phipps 1992) • System of cell networks or grids • Has specified initial configuration • Cells interact with neighborhood (transitions) • Each cell adopts one of m possible states • Follows discrete time dynamic • Transition rules for each state can be simple, deterministic or stochastic • Cartographic Models • Binary, Categorical • Weightings, Quantitative

  15. http://www.env.duke.edu/landscape/classes/env214/le_mod2.htmlhttp://www.env.duke.edu/landscape/classes/env214/le_mod2.html

  16. http://www.env.duke.edu/landscape/classes/env214/le_mod0.htmlhttp://www.env.duke.edu/landscape/classes/env214/le_mod0.html

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