The Spatial Evolutionary and Ecological Vicariance Analysis (SEEVA) methodology has been developed by Lena Struwe, Richard Lathrop, Scott Haag, and Peter Smouse at Rutgers University, USA, and Einar Heiberg at Lund’s University, Sweden.

Fig. 1. Showing the complex interaction between geography (distribution. space), phylogeny (evolution, ancestry), and ecology (environment, climate) through time for a lineage.

Please see Struwe et al. (submitted) for a detailed overview and justification of SEEVA methodology and mathematical formulas. 

In contrast to vicariance biogeography, which assumes geographic separation of populations, the Spatial Evolutionary and Ecological Vicariance Analysis approach allows researchers also to look at ecological vicariance (differences) of sympatric and allopatric species and clades. This method can utilize GIS-derived dataset of collection-associated ecological and environmental data in combination with phylogenetic data to investigate trends in speciation using statistical methods with spatial interpretations. The method can also be used for other kinds of comparisons between groups and clades, in areas such as coevolution, diseases, morphological evolution, and niche comparisons.

Generally, SEEVA works by using measurements gathered from individuals of species or populations, and these measurements are then analyzed statistically for differences between groups (species) and/or clades. Two statistical test are being employed, the Divergence Index (to measure differences between groups or clades) and Fisher’s Exact test (the latter to provide a p-value for tests with small sample sizes).

Environmental variables (data columns) are divided into categories either as non-ordered, qualitative categories (e.g., soil types) or ordered, quantitative sections representing subsets of the total variation in the variable (e.g., precipitation amounts). A statistical test is performed to investigate if the distribution of species or monophyletic clades in different categories of environmental variables shows a random or non-random pattern, with taxonomic group vs. counts for collections for the categories for one variable in an X x Y multi-way table.

Example of X x Y multiway table showing skewed character state distributions for two different groups using 4 categories (states) for one variable.  The numbers inside the table are number of observations, i.e., collections.

A non-random (skewed) pattern is a stronger association for an environmental character state(s) with a specific group/clade, both historically (evolutionary) and presently. The classification of the environmental variables is rather coarse, but these tests provide a way of looking for broad patterns.

The Divergence Index (D) is a measure from 0 to 1 on how skewed a distribution is between two groups or clades, and D is independent of sample-size and can therefore be compared between groups/clades as well as variables.  D is also independent from p-values, which indicates if the data distribution at a node is statistically significantly different between the two groups/clades, or not.  We suggest that measures are taken to adjust the significance value of the p-value (usually 0.05) to adjust for multiple sampling from the same dataset, using for example the Bonferroni correction.

We have previously used another index of skewness, the Impact Index (I), but the Index of Divergence (D) is superior to I and replaces this. However, the Impact Index is still listed in the result outputs from the SEEVA software (as are chi-square statistics).

The SEEVA method can be enhanced when combined with biogeographic and phylogeographic spatial analysis, ancestral area analysis, dating methods, geographic mapping of populations, endangered species analysis, and ecological niche analysis.  The method will work on any kind of data including absence/presence of diseases, morphological or phytochemical measurements, pollinator type, color morphs – as long as individuals are measured.

The species can be grouped in two ways for the SEEVA analysis: 1) species-by-species (Manual Analysis); and 2) by sister clades (Tree-based). The latter approach includes phylogenetic information, since species data from two (or more, if a polytomy) clades will be compared and analyzed, and environmental trends and reactions over the time of the evolution of a group can be assessed by comparing impact values between nodes.

Caveats and notes: The method works with a minimum of one record for each species, however, results based on such a small sample should be evaluated with caution. In general, correlations between divergence and environmental variables can be inferred as trends and tendencies within phylogenetic lineages, and not as the definite cause for the divergence until further research. It should not be assumed that environmental variables are independent, in fact, many of them are not, but an assumption of independency is not necessary for this analysis.

ADDITIONAL LINKS

Need more information? Contact Einar Heiberg (software; einar@heiberg.se) & Lena Struwe (methodology; struwe@aesop.rutgers.edu)

Get SEEVA software and example files here: http://seeva.heiberg.se

The most current Software manual for SEEVA: download pdf (this has more information about the method)

Join the SEEVA mailing list for information about updates and new versions. Subscribe here: https://email.rutgers.edu/mailman/listinfo/seeva_list

Macro written by Scott Haag for ArcGIS to export environmental data into existing Excel spreadsheets with collection data: link (text file)

How to cite the SEEVA methodology:
Heiberg, E. 20XX. SEEVA ver. X.XX. Software for Spatial Evolutionary and Ecological Vicariance Analysis. Available from the author at http://seeva.heiberg.se. [citation for the software]

Heiberg, E. & L. Struwe. 20XX. SEEVA manual, ver X.XX. On-line publication, Rutgers University. Available from the authors at http://www.rci.rutgers.edu/~struwe/seeva [citation for the manual]

Struwe, L., P. E. Smouse, E. Heiberg, S. Haag, & R. G. Lathrop. MS (2010). Spatial evolutionary and ecological vicariance analysis (SEEVA), a novel approach to biogeography and speciation research, with an example from Brazilian Gentianaceae. Journal of Biogeography, October 2010). [official citation for software and methodology]

Studies that utilize SEEVA:

Struwe, L., S. Haag, E. Heiberg, & J. R. Grant. 2009. Andean speciation and vicariance in neotropical Macrocarpaea (Gentianaceae-Helieae). Annals of Missouri Botanical Garden 96: 450-469.

Presentations and posters:

Struwe, L., R. G. Lathrop, & P. E. Smouse. 2006. Spatial Evolutionary and Ecological Vicariance Analysis of Biodiversity – a new interdisciplinary approach. Botany 2006 conference, Chico, CA, 28 July-3 Aug, 2006.

Struwe, L., R. Lathrop, & P. Smouse. 2007. Biogeography and environmental response through time using spatial evolutionary ecological vicariance analysis (SEEVA) in neotropical gentians. Botany 2007 meeting, Chicago, 7-11 July 2007.

Struwe, L., E. Heiberg, S. Haag, & J. Grant. 2008. Comparative ecological analysis of sympatric and allopatric species and clades in the Andes. Botany 2008 meeting, Vancouver, 26-30 July 2008.

Struwe, L., E. Heiberg, S. Haag, R. G. Lathrop, & P. E. Smouse. 2010. Tracking evolutionary clades across the space-time-environmental continuum. Botany 2010 Meeting, Providence, RI, Aug 2010