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Spatial Congruence Analysis (SCAN): A method for detecting biogeographical patterns based on species range

Cassiano A F R Gatto1, Mario Cohn-Haft2

  • 1Pós Graduação em Ecologia-PPG-ECO, Instituto Nacional de Pesquisas da Amazônia-INPA, Manaus, Brazil.

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Spatial Congruence Analysis (SCAN) identifies species groups (chorotypes) by mapping spatial relationships. This method reveals complex biogeographical patterns and offers new insights into ecological and historical processes driving species distributions.

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Area of Science:

  • Biogeography
  • Spatial Ecology
  • Computational Biology

Background:

  • Chorotypes, defined by congruent geographical distributions from shared processes, are key biogeographical units.
  • The degree of spatial range congruence is an underutilized parameter in defining chorotypes.
  • Existing methods for identifying shared ranges often face scale bias or fail to capture complex distribution patterns.

Purpose of the Study:

  • To introduce a novel analytical method, Spatial Congruence Analysis (SCAN), for identifying chorotypes.
  • To assess chorotypes using spatial congruence as an explicit numerical parameter.
  • To explore the complexity of spatial relationships among species distributions.

Main Methods:

  • SCAN constructs a one-layered network connecting species (vertices) via pairwise spatial congruence estimates (edges).
  • An algorithm analyzes this network to identify spatial relationships relative to a reference species.
  • The method was validated using simulated range gradients and real bird distribution data.

Main Results:

  • SCAN accurately describes distribution gradients in simulated data with high detail.
  • Analysis of bird distributions revealed that only a small fraction of range overlap is biogeographically significant.
  • Species distributions exhibit diverse patterns, including convergence, nesting within larger chorotypes, and complex overlaps.

Conclusions:

  • SCAN provides a detailed, quantitative approach to defining chorotypes and understanding species distribution patterns.
  • The method highlights significant variation in chorotype complexity and composition.
  • Metrics like congruence, depth, and richness enable detailed chorotype description and cross-taxa/region comparisons.