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The importance of topographically corrected null models for analyzing ecological point processes
Philip McDowall1, Heather J Lynch1
1Department of Ecology and Evolution, Stony Brook University, 650 Life Sciences Building, Stony Brook, New York, 11794, USA.
Ecology
|April 29, 2017
Summary
Ecological analyses of point process data can be biased by ignoring surface topography. This study introduces methods to correct for topography, improving the accuracy of spatial pattern analysis and ecological modeling.
Area of Science:
- Ecology
- Spatial Statistics
- Geospatial Analysis
Background:
- Point process analyses commonly assume data lie on a 2D plane or 3D volume.
- Many ecological point patterns are generated on surfaces within 3D space (e.g., landscapes).
- Projecting surface data to a 2D plane distorts area and distance, violating analysis assumptions.
Purpose of the Study:
- To investigate the impact of uncorrected topography on point process analyses.
- To develop and demonstrate methods for incorporating topographic corrections in spatial ecology.
Main Methods:
- Simulating point processes on surfaces with varying topography.
- Analyzing projected data versus topographically corrected data.
- Comparing statistical test results and model coefficients between approaches.
Main Results:
- Failure to account for topography can bias spatial clustering detection.
- Topographic effects can significantly skew coefficients in ecological models using slope covariates.
- Proposed simulation methods allow for topographically corrected null models.
Conclusions:
- Topographic corrections are crucial for accurate ecological point process analysis.
- Ignoring surface geometry can lead to erroneous conclusions about spatial patterns.
- Accurate spatial modeling requires accounting for the true surface on which data occur.
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