1. Genetic diversity and gene flow among populations of Witheringia solanacea
Crystiana Tsujiura (’14) and Judy L. Stone
Department of Biology, Colby College, Waterville ME
Objective
To evaluate fitness consequences of population structure in the Costa Rican plant Witheringia solanacea by looking at inbreeding within populations and gene flow between populations.
Figure 4. Gel electrophoresis of a sample of Monte Verde DNA for a PCR with the CT-F2 microsatellite
Figure 3. Illustration of a microsatellite. Green region indicates the region being amplified, which varies in length among individuals.
Figure 6. Expected versus observed heterozygosity in high (left) and low (right) elevations by locus. Pairs of bars with an asterisk are the populations where Ho < He, which meant there was inbreeding within the population.
Introduction
Many plant species have self-incompatibility mechanisms, which prevent self-fertilization by recognition and rejection of self pollen. Loss-of-function mutations in the biochemical pathway that provide self-incompatibility can permit certain individuals within these species to self-fertilize. Self-fertilization in an historically outcrossing species typically causes severe inbreeding depression and can have dramatic impacts on population genetic structure.
Our study species, Witheringia solanacea, contains both self-incompatible and self-compatible individuals. It lives in a mountainous area of Costa Rica with great variation in climate and suitability for pollinating bees. At higher elevations, plant populations are smaller and there are fewer pollinators (Stone and Jenkins 2007). We expect that due to low number of pollinators and smaller population size in populations at high elevation, there will be greater levels of inbreeding in high elevation populations of Witheringia solanacea.
We are also interested in gene flow among populations. At low elevations, where there are many pollinators, it may be beneficial for plants to be self-incompatible so that their progeny will not suffer from inbreeding depression. But if seeds from these plants are carried to high elevations, they will bring the presumably non-adaptive self-incompatibility gene there. By comparing allele frequencies among populations, we can estimate how much gene flow there is between them.
Figure 5. Allele frequencies for each locus. Population 1 corresponds to high elevation while Population 2 corresponds to low elevation. We expected a bell curve for frequencies (e.g. CT-A3 graph in the top right) for all loci, but some loci’s frequencies did not exhibit this distribution pattern (CT-B11, CA-E2).
Figure 7. Genetic differentiation between the two populations as estimated by each locus. When Fst = 0, there is complete admixture. Fst = 1 means complete isolation.
Figure 2. Witheringia solanacea in the wild.
Figure 1. Topographic map of the Monte Verde region in Costa Rica. The three areas where we obtained our samples from are indicated.
Elevation: > 1,280 m
Elevation < 1,100 m
High Elevation
Low Elevation
Number of Alleles per Locus
5.60
5.20
Effective Population Size
2.52
3.06
Observed Heterozygosity
0.48
0.56
Expected Heterozygosity
0.58
0.65
Inbreeding Coefficient
0.20
0.15
Discussion
We found, as expected, that the high elevation populations had a higher inbreeding coefficient than low elevation populations. We expected this because of the lower number of pollinators and smaller population sizes at high elevations. Plants who can reproduce by self-fertilization may have higher reproductive success in high elevation populations.
Our Fst values indicate that there is substantial gene flow between the high elevation population and low elevation population. Therefore, genes for self-incompatibility will be continually re-introduced into high elevation populations, even if natural selection there would favor self-fertilization.
Table 1. Population genetic characteristics for both populations averaged across five loci.
Results
The number of alleles per locus was greater in the high elevation population, while the effective population size was greater in the low elevation population (Table 1). This indicates that allele frequencies are more evenly distributed in the low elevation population. The larger effective population size is consistent with the larger actual population size.
In both the high and low populations, observed heterozygosity (H0) was less than expected heterozygosity (He), demonstrating that there is some inbreeding (Figure 6). The average observed heterozygosity (H0) for the low elevation population was greater than for the high elevation population. Likewise, our expected heterozygosity (He) was greater for the low elevation population than for the high elevation population. Thus, the inbreeding coefficient in the high elevation population was greater than the low elevation population (Table 1).
The genetic differentiation (Fst) values varied across loci, with the greatest value at the CT-F2 locus, and the lowest value at the GATA-A6 locus (Figure 7). The mean Fst value was 0.022, with a standard error of
References
Stone, JL, and E. G. Jenkins (2007). Pollinator abundance and pollen limitation of a Solanaceous shurub at premontane and lower montane sites. Biotropica, in press.
Peakall, R., P.E. Smouse. (2006). Genalex 6: genetic analysis in Excel. Population genetic software for teaching and research. Molecular Ecology Notes 6:
Materials and Methods
We collected leaf tissue from W. solanacea at greater than 5 m spacing along roadsides and trails. DNA from 50 of these plants was extracted. Populations were differentiated by elevation, where high elevation plants were grouped as greater or equal to 1,280 meters elevation, and low elevation plants were grouped as lesser or equal to 1100 meters elevation (Figure 1). We then performed the polymerase chain reaction (PCR) with five different microsatellite primers (Figure 3) in order to amplify short segments of DNA from each plant. Successful amplification was confirmed using gel electrophoresis (Figure 4). We then sent our fluorescently-tagged PCR product to the genetic analyzer to complete the genotyping of each individual. Genalex, population genetics software for Excel, was used to analyze our data (Peakall and Smouse, 2006).
Acknowledgements
We would like to thank Patti Easton for genotyping our samples.