Measuring differences between overlapping but unpaired spatial surveys using a geographic information system

Area of Science:

• Ecology
• Geographic Information Systems (GIS)

Background:

• Comparing overlapping synoptic survey data presents challenges for traditional statistical methods.
• Existing methods like ANOVA and ANCOVA may be inappropriate for non-normally distributed spatial survey data.

Purpose of the Study:

• To propose and evaluate a novel GIS-based method for comparing unpaired synoptic survey data.
• To assess the spatial distribution of differences between survey datasets.

Main Methods:

• Utilized GIS to create minimum convex polygons (MCP) and Voronoi tesselations for area weighting.
• Overlaid MCP and Voronoi maps to create a mosaic of polygons, assigning data from each survey.
• Computed area-weighted means, variances, and z-statistics for paired differences.
• Employed a cross-product autocorrelation statistic to analyze the spatial distribution of differences.

Main Results:

• The proposed GIS method identified significant differences in only one macrobenthic taxon between two Lake Erie surveys.
• Conventional methods (ANOVA, ANCOVA) detected numerous significant differences, deemed inappropriate for spatial survey data.
• Contour overlays allowed detection but hindered assessment of differences.

Conclusions:

• The novel GIS method provides a statistically sound and appropriate approach for comparing synoptic spatial survey data.
• This method is more suitable than ANOVA, ANCOVA, or contour overlays for assessing differences in ecological survey data.