Delimiting areas of endemism through kernel interpolation.

Area of Science:

• Biogeography
• Spatial Ecology
• Conservation Biology

Background:

• Identifying areas of endemism is crucial for understanding biodiversity patterns and evolutionary history.
• Existing methods often rely on grid cells or may not capture the continuous nature of species distributions.

Purpose of the Study:

• To introduce and validate a new spatial interpolation method, Geographical Interpolation of Endemism (GIE), for identifying areas of endemism.
• To compare GIE with traditional methods like Parsimony Analysis of Endemism and NDM using Brazilian spider data.

Main Methods:

• Geographical Interpolation of Endemism (GIE) based on kernel spatial interpolation of species distribution centroids.
• Estimation of overlap between species distributions using areas of influence defined by distance to the farthest occurrence point.
• Comparative analysis with Parsimony Analysis of Endemism and NDM.

Main Results:

• GIE identified 101 areas of endemism for Brazilian spiders, independent of grid cell limitations.
• The method revealed areas with fuzzy edges and a higher number of synendemic species compared to other methods.
• Identified areas showed congruence with endemism areas of other taxonomic groups.

Conclusions:

• GIE is an effective tool for delimiting areas of endemism across multiple scales.
• The congruence of GIE-identified areas with other taxa suggests shared biogeographic processes.
• The method provides a continuous and nuanced approach to mapping biodiversity hotspots.