Similarity indices for spatial ecological data

Area of Science:

• Ecology
• Spatial analysis
• Biodiversity informatics

Background:

• Accurate assessment of species distribution patterns is crucial for ecological modeling and conservation.
• Traditional similarity indices often overemphasize local variations, potentially obscuring broader spatial relationships.
• Developing robust methods to compare species distribution maps is essential for ecological research.

Purpose of the Study:

• To present a novel method for assessing similarity between species distribution maps.
• To develop similarity measures that prioritize global spatial features over local discrepancies.
• To demonstrate the utility of these new indices for ecological model selection.

Main Methods:

• The proposed method groups sites into cliques to allow controlled adjustments.
• This technique minimizes the impact of minor local dissimilarities on the overall similarity measure.
• The method generates similarity indices that are visually more interpretable than traditional ones.

Main Results:

• The new similarity indices provide visually more satisfactory comparisons of species distribution maps.
• The method effectively reduces the influence of local discrepancies, highlighting global patterns.
• The indices proved useful in comparing observed spatial patterns with model predictions.

Conclusions:

• The developed method offers a more robust way to assess similarity between species distribution maps.
• This approach enhances the visual assessment of spatial patterns and aids in model selection.
• The technique is applicable to various species, including oribatid mites, woodlarks, and red deer.