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An area of endemism is defined by the spatial congruence among two or more species with distributions that are limited by barriers. In this study, we explored and discussed the use of the network analysis method (NAM) and neighbor-joining (NJ) to analyze the areas of endemism of Quercus sect. Lobatae (red oak species) in Mexico and Central America. We compared the NAM and NJ with other methods commonly used in biogeographic studies to show the advantages of these new approaches and to identify the shortcomings of other approaches. The NAM used in this study is based on notions of centrality measures, such as betweenness. We incorporated the strength of the ties within the internal networks through p-cores and aggregate constraints in iterative analyses. The NAM based on betweenness is ideal for recognizing completely allopatric areas of endemism. The iterative NAMs increase the number of possible areas of endemism because they minimize the effect of minimal overlap, and the p-core is efficient at identifying the closest relationships among species in the cases in which betweenness is not informative. The number of areas of endemism increases when the sympatry matrix minimizes the dispersal effect and the sample effort is maximized, allowing the identification of the greatest number of these areas. The NJ method supports the idea that areas diverge among themselves in a differential way; the long branches correspond to zones with high speciation rates and complex histories (biotic and tectonic), and the short branches correspond to zones with low speciation rates and simple histories. In a classification scheme, NJ was capable of identifying the areas that are considered biotically complex because of their high speciation rates. The results obtained with the NAM and NJ showed that the physiographic regions of Mexico are not natural units and that many of them are composed of at least two different biotic components.

New Approaches to the Biogeography and Areas of Endemism of Red Oaks (Quercus L., Section Lobatae)