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Spatial Evolutionary and Ecological Vicariance Analysis<\/h2>\nSpatial Evolutionary and Ecological Vicariance Analysis<\/h2>\nThe Spatial Evolutionary and Ecological Vicariance Analysis (SEEVA) methodology was first developed by Lena Struwe, Richard Lathrop, Scott Haag, Peter Smouse (Rutgers University, NJ, USA), and Einar Heiberg (Lund University, Sweden).<\/p>\n
Further methodological improvements were developed by Lena Struwe, Peter Smouse, Marcelo Reginato (The New York Botanical Garden), and Donald Walker (Tennessee Tech University).<\/p>\n
This website is under revision… please be patient with us while we update it with our improved methodology.<\/em><\/p>\n This 2011 software organizes quantitative data through separation into quartiles. It provides D-values and p-values. The method is described in Struwe et al. (2001).<\/p>\n Please cite this software as:<\/em> This 2016 release organizes qualitative data in the same way as in the Matlab version, but includes the new treatment of quantitative data with directed Z-values. This method has been submitted as part of the Walker et al. manuscript.<\/p>\n Please cite this software as:<\/em> <\/p>\n References and sources:<\/strong><\/p>\n Struwe, L., P. E. Smouse, E. Heiberg, S. Haag, & R. G. Lathrop. 2011. Spatial evolutionary and ecological vicariance analysis (SEEVA), a novel approach to biogeography and speciation research, with an example from Brazilian Gentianaceae. Journal of Biogeography 38: 1841-1854.<\/p>\n Struwe, L., S. Haag, E. Heiberg, & J. R. Grant. 2009. Andean speciation and vicariance in neotropical\u00a0Macrocarpaea\u00a0<\/em>(Gentianaceae-Helieae). Annals of Missouri Botanical Garden 96: 450-469.<\/p>\n Walker, D. M., L. A. Castlebury, A. Y. Rossman, & L. Struwe. 2013. Host conservatism or host specialization? Patterns of fungal diversification are influenced by host specificity in\u00a0Ophiognomonia<\/em>\u00a0(Gnomoniaceae, Diaporthales). Biological Journal of Linnean Society. 111: 1-16.<\/p>\n Walker, D. M, P. E. Smouse, M. Reginato, & L. Struwe. 2017. Cladal divergence in fungal Ophiognomonia (Gnomoniaceae: Diaporthales) shows evidence of climatic niche vicariance. Biological Journal of Linnean Society.<\/p>\n <\/p>\n <\/p>\n Fig. 1. Shows the complex interaction between geography (distribution, space), phylogeny (evolution, ancestry), and ecology (environment, climate) through time for a lineage.<\/p>\n In contrast to vicariance biogeography, which assumes geographic separation of populations, the SEEVA approach allows researchers to also look at ecological vicariance (differences) of sympatric and allopatric species and clades. This method utilizes a GIS-derived data set 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 comparisons between groups and clades, in areas such as coevolution, pathology, morphological evolution, and niche comparisons.<\/p>\n 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 tests are employed, the Divergence Index (to measure differences between groups or clades) and Fisher\u2019s Exact test (to provide a p<\/em>-value for tests with small sample sizes).<\/p>\n 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. In an X x Y multi-way table, taxonomic group vs. counts for collections for the categories of one variable are compared.<\/p>\n Example of X x Y multi-way table showing skewed character state distributions for two different groups using 4 categories (states) for one variable.\u00a0 The numbers inside the table are number of observations, i.e., collections.<\/p>\n <\/p>\n <\/p>\n 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.<\/p>\n The Divergence Index (D<\/em>) is a measure from 0 to 1 on how skewed a distribution is between two groups or clades, D<\/em> is independent of sample-size, and can therefore be compared between groups\/clades as well as variables.\u00a0\u00a0D<\/em>\u00a0is 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.\u00a0 We suggest that measures are taken to adjust the significance value of the\u00a0p<\/em>-value (usually 0.05), to adjust for multiple sampling from the same data set, using for example the Bonferroni correction.<\/p>\n In the past, we used another index of skewness, the Impact Index (I<\/em>), but the Index of Divergence (D<\/em>) is superior to\u00a0I\u00a0<\/em>and has replaced this. However, the Impact Index is still listed in the result outputs from the SEEVA software (as are chi-square statistics).<\/p>\n 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, or color morphs \u2013 as long as individuals are measured.<\/p>\n 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. Additionally, environmental trends and reactions over the time of the evolution of a group can be assessed by comparing impact values between nodes.<\/p>\n Caveats and Notes: <\/strong>This 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, but not as the definite cause for the divergence, until further research. It should not be assumed that environmental variables are independent, because in fact, many of them are not, but an assumption of independency is not necessary for this analysis.<\/p>\n Macro<\/b> written by Scott Haag for ArcGIS to export environmental data into existing Excel spreadsheets with collection data: (Arc-macro-SEEVA-2010-SHaag.doc<\/a> Word file).<\/p>\n","protected":false},"excerpt":{"rendered":"SEEVA software for Matlab<\/strong> by Einar Heiberg<\/h3>\n
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\nHeiberg, E. 2011. SEEVA ver. 1.00. Software for Spatial Evolutionary and Ecological Vicariance Analysis. Available from the author at\u00a0<\/span>http:\/\/seeva.heiberg.se\/<\/a>.<\/span><\/p>\nSEEVA software for R<\/strong>\u00a0by Marcelo Reginato<\/h3>\n
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\nReginato, M. 2016 onwards. SEEVA R package. Available from the author, please contact\u00a0Dr. Lena Struwe<\/a> by email.<\/p>\nShort Introduction to SEEVA<\/h2>\n
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\n number of observations<\/em><\/td>\n category 1<\/td>\n category 2<\/td>\n category 3<\/td>\n category 4<\/td>\n<\/tr>\n \n Group 1\/clade A<\/td>\n 0<\/td>\n 10<\/td>\n 16<\/td>\n 21<\/td>\n<\/tr>\n \n Group 2\/ clade B<\/td>\n 10<\/td>\n 15<\/td>\n 9<\/td>\n 0<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n ADDITIONAL LINKS<\/h2>\n