why use landscape models? models allow us to generate and test hypotheses on systems collect data,...

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

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

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

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

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

• AdvantagesCan be highly mechanistic

Testable

• DisadvantagesComplexity

Poor generality

Landscape change models often include simulation of disturbance and a responselandscape 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

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 of1) initial configuration

2) change processes or dynamics

3) output configuration

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

From He and Mladenoff 1999

From Pastor and Johnston

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

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

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

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

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

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