Multiscale Diversity in the Marshes of the Georgia Coastal Ecosystems LTER
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Factors that maintain genetic and species diversity may act in concert in natural ecosystems. Here, we investigate correlations between genetic diversity (in eight salt marsh species) and community species diversity. A significant positive correlation existed between genetic diversity and species richness, although the relationship was not significant for any species individually. Nonetheless, four of the eight comparisons showed strong positive relationships between genetic and species diversity. Additionally, several abiotic variables were used in a model selection procedure to determine what site-level characteristics might drive differences in genetic diversity in this system. The rate of larval influx, as measured by barnacle abundance on Spartina alterniflora, was the strongest predictor of site-level genetic diversity in our samples. Our results suggest that estuarine management efforts should consider recruitment rates when selecting areas for protection.
KeywordsGenetic diversity Species–genetic diversity correlation SGDC Salt marsh GCE-LTER Settlement rate
This material is based upon work supported by the National Science Foundation under grant numbers OCE-9982133 and OCE-0620959. Additional funding was provided by the University of Georgia and the National Geographic Society (NGS grant #8351-07). Thanks to R. Miller, M. Cozad, D. Saucedo, M. S. Pankey, the GCE-LTER Schoolyard program, and field researchers at the University of Georgia Marine Institute (UGAMI) for help with field collections. D. Patel helped with DNA amplification and sequencing. Special thanks to T. Bell, S. Small, and C. Zakas for help in the lab and comments on the manuscript. K. F. Robinson was a great help in data analysis and also provided comments on the paper. Additional helpful comments were graciously provided by two anonymous reviewers. This is a contribution of the Georgia Coastal Ecosystems Long-Term Ecological Research program, and contribution number 985 of the University of Georgia Marine Institute.
- Akaike, H. 1973. Information theory and an extension of the maximum likelihood principle. In Second International Symposium on Information Theory, ed. B.N. Petrov and F. Csaki, 267–281. Budapest: Akademiai Kiado.Google Scholar
- Antonovics, J. 1976. The input from population genetics: “The new ecological genetics”. Systematic Biology 1: 233–245.Google Scholar
- Bishop, T.D. 2007. Mollusc population size distribution monitoring: Fall 2004 mid-marsh and creekbank infaunal and epifaunal mollusc size distributions based on collections from GCE marsh, monitoring sites 1-10. http://gce-lter.marsci.uga.edu/lter/data/gce_data.htm. GCE Dataset: INV-GCEM-0705a.
- Bruno, J.F. and M.D. Bertness. 2001. Habitat modification and facilitation in benthic marine communities. In Marine community ecology, ed. M.D. Bertness, S.D. Gaines, and M.E. Hay, 201–218. Sunderland: Sinauer.Google Scholar
- Burnham, K.P. and D.R. Anderson. 2002. Model selection and inference: An information-theoretic approach. New York: Springer.Google Scholar
- Croarkin, C.M., O. Tobias, J.J. Filliben, B. Hembree, W.F. Guthrie, J. Prins, C. Zey, N.A. Heckert, and L. Trutna. 2003. NIST/SEMATECH e-Handbook of Statistical Methods, NIST Handbook 151.Google Scholar
- Dabney, A., J.D. Storey, and G.R. Warnes. 2008. qvalue: Q-value estimation for false discovery rate control. R package version 1.1.1.Google Scholar
- Doyle, J. and J.L. Doyle. 1987. Genomic plant DNA preparation from fresh tissue—CTAB method. Phytochemical Bulletin 19: 11.Google Scholar
- Ewing, B., L. Hillier, M.C. Wendl, and P. Green. 1998. Base-calling of automated sequencer traces using Phred. I. Accuracy assessment. Genome Research 8: 175–185.Google Scholar
- Folmer, O., M. Black, W. Hoeh, R. Lutz, and R. Vrijenhoek. 1994. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Molecular Marine Biology and Biotechnology 3: 294–299.Google Scholar
- Hodson, R.E. 2005. Surface water bacterial productivity at ten Georgia Coastal Ecosystems LTER sampling sites. http://gce-lter.marsci.uga.edu/lter/data/gce_data.htm. GCE datasets: BCT-GCEM-0511-a-e.
- Hubbell, S.P. 2001. The unified theory of biodiversity and biogeography. Princeton: Princeton University Press.Google Scholar
- Kunza, A.E., and S.C. Pennings. 2008. Patterns of plant diversity in Georgia and Texas salt marshes. Estuaries and Coasts 31: 673-681. GCE dataset: PLT-GCET-0608.Google Scholar
- MacArthur, R.H. and E.O. Wilson. 1967. The theory of island biogeography. Princeton: Princeton University Press.Google Scholar
- Magurran, A.E. 1988. Ecological diversity and its measurement. Princeton: Princeton University Press.Google Scholar
- Neter, J., W. Wassreman, and M.H. Kutner. 1990. Applied linear statistical models, 3rd ed. Homewood: R.D. Irwin.Google Scholar
- Peakall, R. and P.E. Smouse. 2006. GENALEX 6: Genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6: 288–295.Google Scholar
- Pennings, S.C. 2000-2006. Fall plant monitoring survey -- biomass calculated from shoot height and flowering status of plants in permanent plots at GCE sampling sites 1-10. http://gce-lter.marsci.uga.edu/lter/data/gce_data.htm. GCE datasets: PLT-GCEM-0303a-c, -0311b, -0501b, -0511b, and -0612b.
- R Development Core Team. 2007. R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. http://www.R-project.org.
- Smith, J.M. and R.W. Frey. 1985. Biodeposition by the ribbed mussel Geukensia demissa in a salt marsh, Sapelo Island, Georgia. Journal of Sedimentary Petrology 55: 817–828.Google Scholar