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The effect of cost surface parameterization on landscape resistance estimates.
Erin L Koen1, Jeff Bowman, Aaron A Walpole
1Environmental and Life Sciences, Trent University, 1600 West Bank Drive, Peterborough, Ontario K9J 7B8, Canada. erinkoen@trentu.ca
Molecular Ecology Resources
|February 23, 2012
Summary
Understanding landscape resistance is key for functional connectivity. This study reveals how different methods respond to cost weights, with linear responses improving accuracy for model fitting and asymptotic responses offering stability when weights are unknown.
Area of Science:
- Landscape ecology
- Spatial genetics
- Conservation planning
Background:
- Cost or resistance surfaces model landscape permeability for animal movement and gene flow.
- Parameterizing these surfaces with accurate weights is challenging due to unknown true costs, often relying on expert opinion.
- Sensitivity analysis is crucial to understand how varying cost weights impact landscape resistance estimates.
Purpose of the Study:
- To analyze the sensitivity of different cost surface parameterization methods and landscape permeability models to variations in relative cost weights.
- To identify which methods yield linear versus asymptotic responses to cost weight changes.
- To provide guidance on selecting appropriate methods based on data availability and analysis goals.
Main Methods:
- Conducted a sensitivity analysis on three cost surface parameterization methods.
- Evaluated two landscape permeability models: least cost path and effective resistance.
- Varied cost weights assigned to landscape elements to observe changes in resistance estimates.
Main Results:
- Identified two distinct responses to cost weight variation: linear and asymptotic changes.
- Accumulated least cost and effective resistance estimates on resistance-coded surfaces showed a linear response.
- Other cost surface scenarios exhibited asymptotic changes in resistance estimates.
- Linear responses were most sensitive to cost weight variations.
Conclusions:
- Cost surfaces producing linear responses enhance functional connectivity accuracy, particularly with statistical model fitting.
- Asymptotic response methods are more robust when cost weights are unknown or model selection is not employed.
- Choosing the appropriate method based on sensitivity response improves the reliability of landscape connectivity studies.
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