A meta-analysis of coastal populations’ genetic diversity of species throughout their range Brittany Boribong1, Michelle Cruz2, Fangyuan Hong3, Julia Earl4, Sean Hoban4 1University

2California

3Mount

of Scranton, Pennsylvania State University, San Marcos Holyoke College, South Hadley, Massachusetts 4National Institute for Mathematical and Biological Synthesis (NIMBioS)

Introduction Along a species' distribution, its location in either the center or edge of the range may affect various characteristics of the population, such as fitness, genetic diversity, and adaptability. The abundant-center hypothesis states that populations found in the center of their natural distribution are more abundant than those found at the edges, which is thought to contribute to differences in genetic diversity along the range. Despite its prevalence in population studies, this hypothesis has rarely been tested in a rigorous way across many taxa. Eckert et al. (2008) tested for the genetic variation in plants and animals along its species’ range but did not account for variation in sample sizes among studies. Similarly, this project focuses on populations along the coast and how genetic diversity is affected by location within a species' range. By focusing only on coastal populations, range shapes are often less complex, and thus the genetic signal is likely clearer (Sagarin et al. 2006). We use a metaanalysis, a statistical technique for comparing the results of similar studies (Harrison 2011).

One-dimensional vs. Two-dimensional Range Map

Figure 2. Distance between sample sites and the center of the range of Malaclemys terrapin (Diamondback terrapin) (Hauswaldt and Glenn 2005)

Single-factor Tests  We built linear models of the genetic response variables against the categorical predictive variables to test our hypothesis Genetic Variables versus Methodological Factors

Methods We used the following criteria to include 21 papers in our meta-analysis:     

Appropriate sample size Recorded sample site (map or coordinates) Microsatellite markers Coastal species Non invasive, freshwater, migratory, or from hatcheries  Species has a documented range map  Expected and observed heterozygosity, and number of alleles To conduct our meta-analysis, we calculated an effect size as the correlation coefficient for each genetic measurement of each species versus relative distance to center. We also performed correlation tests to determine the independence of our genetic variables.

Genetic Variables versus Biological Factors

Figure 6. . Distance between sample sites and the center of the range of Epinephelus coioides (Orange-spotted grouper) (Antoro et al. 2006; Pumitinsee et al. 2009)

Multi-factor Models  Alleles per locus vs. 1D/2D and Relative Distribution  Alleles per locus vs. 1D/2D and Relative Direction  Alleles per locus vs. 1D/2D and Taxa  Expected Heterozygosity vs. Taxa and 1D/2D  Expected Heterozygosity vs. Taxa and Relative Direction  Expected Heterozygosity vs. 1D/2D and Map Source

Discussion Our results partially support our hypothesis that coastal populations' distance to the center of the range is positively correlated with genetic diversity. In particular, the result indicating the increase in alleles per locus as the distance from the center increased in one dimensional ranges supports the findings in Sagarin et al. (2006). Throughout our project, we learned that in order to accurately correlate genetic diversity and location of a population, we needed to take into account multiple factors. Factors such as taxa, ocean(s) where ranges are located, range coverage of samples, sample distributions within ranges, range shapes, and sources of range maps have effects on how genetic variables respond to geographic locations of samples

Future Work    

Include more studies and species Include studies from Southern Hemisphere Test more genetic variables Test more taxa

Literature cited Antoro, S., U. Na-Nakorn, and W. Koedprang. 2006. Study of genetic diversity of orange-spotted grouper, Epinephelus coioides, from Thailand and Indonesia using microsatellite markers. Mar Biotechnol (NY) 8:17-26. Eckert, C. G., K. E. Samis, and S. C. Lougheed. 2008. Genetic variation across species' geographical ranges: the central-marginal hypothesis and beyond. Mol Ecol 17:1170-1188. Harrison, F. 2011. Getting started with meta-analysis. Methods in Ecology and Evolution 2:1-10. Hauswaldt, J. S., and T. C. Glenn. 2005. Population genetics of the diamondback terrapin (Malaclemys terrapin). Mol Ecol 14:723-732. Pumitinsee, P., W. Senanan, U. Na-Nakorn, W. Kamonrat, and W. Koedprang. 2009. Temporal genetic heterogeneity of juvenile orange-spotted grouper (Epinephelus coioides, Pisces: Serranidae). Aquaculture Research 40:1111-1122. Sagarin, R. D., S. D. Gaines, and B. Gaylord. 2006. Moving beyond assumptions to understand abundance distributions across the ranges of species. Trends Ecol Evol 21:524-530.

For further information contact: Brittany Boribong ([email protected]), Michelle Cruz ([email protected]), Fangyuan Hong ([email protected]),

Acknowledgments: This work was conducted with funding from the National Institute for Mathematical and Biological Synthesis, an Institute sponsored by the National Science Foundation, the U.S. Department of Homeland Security, and the U.S. Department of Agriculture through NSF Award #EF-0832858, with additional support from The University of Tennessee, Knoxville.