Nested areas of endemism analysis

Aim  To develop and evaluate a nested clade approach to aid in the determination of areas of endemism (AoE) in biogeographical studies.
Methods  We adapted the nested clade analysis (NCA) to studies of areas of endemism. For this purpose we adapted several of the programs currently in use. Two data sets were examined using this approach – one involving Sciobius in southern Africa and the other involving terrestrial mammals in Mexico.
Results  Nested clade analysis as applied to areas of endemism produced results similar to those of previous analyses of Sciobus in southern Africa. An analysis of terrestrial mammals in Mexico supports the designation of some biogeographical provinces as areas of endemism while suggesting that other provinces may comprise composite distributions that should be subdivided.
Main conclusions  The nested clade analysis approach utilized primarily in genetic analysis of phylogeographical patterns in population biology studies can be adapted to understanding AoE in the realm of biogeography. This approach offers a statistical paradigm to evaluate AoE suggested by parsimony analysis of endemicity (PAE) trees.     

Parsimony analysis of endemicity and analysis of endemicity: A fair comparison

Parsimony Analysis of Endemicity (PAE) has been compared with other methods regarding its performance to identify areas of endemism. It is frequently compared with the Analysis of Endemicity (AE), which seems to perform better than PAE to identify these areas. Here I compare PAE and AE considering the sympatric taxa diagnosed as endemic, being as strictly close as possible to sympatry, and using previously published data of Sciobius (Coleoptera: Curculionidae). AE identified more candidate areas of endemism than PAE, but the number of highly restricted endemic taxa to these areas was insufficient to support them as areas of endemism. Considering strictly sympatry (homopatry), PAE performed better than AE; however, both methods may identify areas with some grade of sympatry, but the recognition of which areas constitute real areas of endemism in the strict sense depends on the interpretation of the researcher.     

An improved method for the identification of areas of endemism using species co-occurrences

Aim To develop a simple method that (1) combines the notions of biotic elements (groups of taxa with ranges significantly more similar to each other than to the ranges of other taxa) and of areas of endemism (AoE, areas of non‐random distributional congruence among taxa), and (2) overcomes the constraints of a previously suggested null model‐based method that cannot deal with disjunctions and is strictly grid‐dependent. Location We used test data sets from southern Africa and Crete. Methods First, we used a null‐model approach to detect pairs of species that have a significant degree of co‐occurrence, in order to determine biotic elements. Subsequently, we used a parsimony analysis of endemicity to delineate candidate AoE, and multivariate analysis to define groups of biotic elements on the basis of species interactions (co‐occurrence, mutual exclusion, neutral) using only the species detected in the previous step. We applied this method to the well known data set for Sciobius in southern Africa, as well as to endemic invertebrates of Crete (Greece), in order to evaluate its performance. Results Our results are very similar to those of previous analyses, and produce meaningful delineation of AoE and biotic elements in both data sets. The method is flexible regarding null models and significance levels, and eliminates noise in the data. Main conclusions We offer a simple method that provides reasonable identification of both biotic elements and AoE, produces good‐fit statistics, reduces uninformative or junk output, and reduces computational time.