MartiTracks: a geometrical approach for identifying geographical patterns of distribution.

PLoS One

Laboratorio de Sistemática y Biogeografía, Escuela de Biología, Universidad Industrial de Santander, Bucaramanga, Santander, Colombia.

Published: April 2011

AI Article Synopsis

  • Panbiogeography uses a methodical approach to understand species distribution through locality data, but there are limited quantitative and automated methods available.
  • A new algorithm is proposed that employs a geometric framework to analyze biogeographical data, calculating minimum spanning trees for species and assessing spatial consistency among these.
  • The results from studying species of the genus Bomarea and various taxa in the Northern Andes show that this geometrical method effectively simplifies data management and reveals general distribution patterns in a quantitative manner.

Article Abstract

Panbiogeography represents an evolutionary approach to biogeography, using rational cost-efficient methods to reduce initial complexity to locality data, and depict general distribution patterns. However, few quantitative, and automated panbiogeographic methods exist. In this study, we propose a new algorithm, within a quantitative, geometrical framework, to perform panbiogeographical analyses as an alternative to more traditional methods. The algorithm first calculates a minimum spanning tree, an individual track for each species in a panbiogeographic context. Then the spatial congruence among segments of the minimum spanning trees is calculated using five congruence parameters, producing a general distribution pattern. In addition, the algorithm removes the ambiguity, and subjectivity often present in a manual panbiogeographic analysis. Results from two empirical examples using 61 species of the genus Bomarea (2340 records), and 1031 genera of both plants and animals (100118 records) distributed across the Northern Andes, demonstrated that a geometrical approach to panbiogeography is a feasible quantitative method to determine general distribution patterns for taxa, reducing complexity, and the time needed for managing large data sets.

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Source
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3075252PMC
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0018460PLOS

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