| Literature DB >> 23356700 |
Koen J F Verhoeven1, Arjen Biere.
Abstract
BACKGROUND: Many species with sexual and asexual variants show a pattern of geographic parthenogenesis where asexuals have broader and higher-latitude distribution than sexuals. Because sexual reproduction is often considered a costly evolutionary strategy that is advantageous in the face of selection by coevolving pests and pathogens, one possible explanation for geographic parthenogenesis is that populations at higher latitudes are exposed to fewer pests and pathogens. We tested this hypothesis in the common dandelion (Taraxacum officinale), a species with well-established geographic parthenogenesis, by screening prevalence and effects of several specialized pests and pathogens in natural dandelion populations.Entities:
Mesh:
Year: 2013 PMID: 23356700 PMCID: PMC3562243 DOI: 10.1186/1471-2148-13-23
Source DB: PubMed Journal: BMC Evol Biol ISSN: 1471-2148 Impact factor: 3.260
Characteristics and infection proportions of thepopulations
| South: | ||||||
| F1 | 46.608 | 3.826 | 256 | 18 | 0.48 | n.d. |
| F2 | 46.591 | 4.510 | 301 | 15 | 0 | 0.17 |
| F3 | 46.660 | 5.723 | 540 | 0 | 0 | 0.33 |
| F4 | 47.295 | 6.803 | 554 | 0 | 0.54 | 0.35 |
| F5 | 47.989 | 4.933 | 251 | 100 | 0.05 | n.d. |
| F6 | 48.319 | 5.704 | 302 | 48 | 0.03 | n.d. |
| F7 | 48.443 | 7.190 | 366 | 100 | 0.40 | 0.34 |
| S1 | 47.154 | 7.009 | 789 | 0 | 0 | 0.73 |
| G1 | 47.726 | 7.874 | 459 | 0 | 0.81 | 0.46 |
| G2 | 48.202 | 8.117 | 421 | 0 | 0.27 | 0.18 |
| North: | ||||||
| B1 | 50.547 | 5.822 | 226 | 100 | 0 | 0.01 |
| B2 | 50.976 | 5.7250 | 34 | 100 | 0 | n.d. |
| N1 | 50.838 | 5.853 | 117 | 100 | 0.05 | 0.08 |
| N2 | 51.958 | 5.742 | 8 | 100 | 0.49 | n.d. |
| D1 | 55.379 | 9.228 | 39 | 100 | 0 | n.d. |
| D2 | 55.570 | 11.896 | 29 | 100 | 0 | 0.37 |
| D3 | 55.666 | 11.839 | 29 | 100 | 0 | 0.19 |
| Sw1 | 55.686 | 13.482 | 30 | 100 | 0 | 0.01 |
See Figure 3 for population locations. Infection rates are the proportion of infected individuals within the population sample. Weevil infection was not determined (n.d.) in some populations.
Figure 3Locations of sampling sites. Labels correspond to Table 1.
Figure 1Population-level effects of field soil inocula on plant growth. Shown are log2 response ratios of shoot biomass obtained in inoculated soils relative to control soils, based on least squares means for each population x soil combination as obtained from the statistical model. Negative values indicate growth suppression compared to control soil and positive values indicate growth stimulation. Highlighted are suppressive effects (grey cells) and strong suppressive effects (dark grey cells, log ratio < −0.3). Highlighted diagonal cells represent plants growing in soil from their own home site.
Split-plot analysis of the soil feedback experiment, testing for soil effects on plant shoot biomass
| Pre-treatment leaf length (1) | Model residual | 687 | <0.001 |
| Block (2) | Model residual | 41.9 | <0.001 |
| Soil (7) | Soil x Rep(Block) | 2.0 | 0.061 |
| Soil x Rep(Block) | 3.9 | 0.051 | |
| Soil x Replicate(Block) (110) | Model residual | 2.6 | <0.001 |
| Plant population (7) | Model residual | 8.9 | <0.001 |
| Soil x Plant population (49) | Model residual | 1.2 | 0.131 |
Degrees of freedom are given in parentheses, the model residual has 774 degrees of freedom. The ‘south versus north soils’ contrast tests whether shoot biomass differs when plants are exposed to soils from the four southern (predominantly sexual) populations compared to soils from the four northern (apomictic) populations.
Figure 2Population-level association between the proportion of asexual plants and infection prevalence. a. Association with incidence of rust infection (18 populations). b. Association with weevil larvae infection (12 populations). Dashed lines are logistic regressions based on population proportions. Overlapping data points at x,y = 0,0 or 0,1 are jittered for the purpose of visualization.