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.

In contrast to traditional vicariance biogeography, which assumes geographic separation of populations, the Spatial Evolutionary and Ecological Vicariance Analysis approach allows researchers 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 many other kinds of comparisons between groups and clades, in areas such as co-evolution, 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 have been employed, the chi-square test and Fisher’s Exact test (the latter to provide a better p-value for tests with small sample sizes).

Environmental variables are divided into categories either as non-ordered, qualitative sections (e.g., soil types) or ordered, quantitative sections representing quartiles of the total amount of data (e.g., precipitation categories). 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.

Table. 1. Example of X x Y multiway table showing skewed character state distributions for two different groups using 4 states for one particular variable.  The numbers are number of observations. i.e., collections.

A non-random (skewed) pattern 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.

Examples of questions that can be analyzed with the SEEVA method include:

·        Which environmental variable show the biggest difference between two sister groups, or two sympatric species?

·        What patterns in soil types do you see as you move up in the phylogeny of a group? 

·        Are species in dry-season areas derived from wet-season areas?

·        Is long-distance dispersal associated with changes in ecological traits?

·        What came first, higher rainfall or higher elevation, in a particular clade?

·        Do allopatric sister species show larger ecological divergence than sympatric sister species?

The SEEVA method can be enhanced when combined with dispersal-vicariance analysis (DIVA), 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.

Using the chi-square value, an impact index (i) that is independent of sample size and degrees of freedom is being calculated (i = square root of [(chi-square value / (df x sample size N)]). This index provides a measurement for the skewness for each node and variable, and therefore a possibility to compare different nodes and different variables.

Table 2. Example of results table for one node (with one species vs. 3 species in the sister clades) and one variable, showing to the right the character state distributions for two clades and their observed data (colletions). then to the left the P-value from the chi-swuare analysis, the degrees of freedom, and the impact index number; all from the SEEVA analysis. Note that in this particular case a Fisher’s Exact analysis is needed to get an appropriate p-value since the sample sizes are so small. 

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. The generic null hypothesis for all tests is that the cross-classified factors are completely independent, and the hypothesis driving the work is that: “Species or clade distributions are influenced by specific environmental variables, as shown by non-random associations of particular species or clades and their environmental situations”. It should not be assumed that environmental variables are independent, in fact, many of them are not, and an assumption of independency is not necessary for this analysis.

Fig. 2. Differences in distribution of 4 soil categories (one variable) throughout a phylogenetic tree. The numbers are impact indices for each  node, the bars indicate percentage of categories present in each sister group.

Fig. 3. Differences in impact indices for seven environmental variables throughout a phylogenetic tree.

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

Software manual for SEEVA: download pdf

Become a beta-tester of the SEEVA software. Join the SEEVA mailing list for information about updates and new versions. Subscribe here: https://email.rutgers.edu/mailman/listinfo/seeva_list

How to cite the SEEVA methodology:

Struwe, L., P. Smouse, S. Haag, E. Heiberg, & R. G. Lathrop. (Manuscript). SEEVA – Spatial Evolutionary and Ecological Vicariance Analysis: a new interdisciplinary approach to historical biogeography and niche changes. J. Biogeography (to be submitted).

How to cite the software:

Heiberg, E. 2008. SEEVA ver. X. Software for Spatial Evolutionary and Ecological Vicariance Analysis. Available from the author at http://seeva.heiberg.se.

How to cite the manual:

Heiberg, E. & L. Struwe. 2008. SEEVA manual.  On-line publication, Rutgers University. Available at http://seeva.heiberg.se and http://www.rci.rutgers.edu/~struwe/seeva.

Studies that utilize SEEVA:

Struwe, L., S. Haag, E. Heiberg, and J. R. Grant. Andean Speciation and Vicariance in Neotropical Macrocarpaea (Gentianaceae-Helieae). Annals of Missouri Botanical Garden (accepted).

Presentations:

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.