Abstract
High levels of intraspecific variation are commonly observed in natural microbial populations, yet the consequences of this variation for ecological and evolutionary processes remain poorly understood. Protists are excellent experimental models for investigating fundamental and applied questions in ecology and evolution, but studying intraspecific variation remains a challenge due to a lack of molecular resources to aid in quantifying and distinguishing strains during experiments. Here we present a molecular method, quantitative microsatellite genotyping, to accurately quantify strain-specific frequencies from microcosm experiments of the marine flagellate Oxyrrhis marina, both between many pairs of strains and between strains in a multistrain mixture. We find that for pairs of strains, the method is effective for relative frequencies as low as 0·02 and with around 99% accuracy. The method is able to quantify four strains reasonably well, though less accurate than for pairs (range 92-97% accuracy). This makes accessible a cheap and easy-to-implement method for quantifying strain (or allele) frequencies and is suitable for use in a broad range of single-celled eukaryotes (protists) where copy number should correlate well with number of individuals (i.e. cells). This opens up the possibility of examining the role of intraspecific variation using experimental protist microcosms.
Original language | English |
---|---|
Pages (from-to) | 315-323 |
Number of pages | 9 |
Journal | Methods in ecology and evolution |
Volume | 6 |
Issue number | 3 |
Early online date | 26 Dec 2014 |
DOIs | |
Publication status | Published - Mar 2015 |
Keywords
- Oxyrrhis
- Competition
- Experiments
- Frequency
- Intraspecific
- Microsatellites
- Molecular assay
- Selection
Datasets
-
Data from: A rapid and cost-effective quantitative microsatellite genotyping protocol to estimate intraspecific competition in protist microcosm experiments
Minter, E. (Creator), Lowe, C. D. (Creator), Brockhurst, M. (Creator) & Watts, P. C. (Creator), Dryad, 25 Nov 2015
DOI: 10.5061/dryad.dc7s2
Dataset