Abstract
Estimation of fitness is a key step in experimental evolution studies. However, no established methods currently exist to specifically estimate how successful new alleles are in invading populations. The main reason is that most assays do not accurately reflect the randomness associated with the first stages of the invasion, when invaders are rare and extinctions are frequent. In this protocol, I describe how such experiments can be done in an effective way. By using the nematode model, Caenorhabditis elegans, a large number of invasion experiments are set up, whereby invading individuals carrying a visual marker are introduced into populations in very low numbers. The number of invaders counted in consecutive generations, together with the number of extinctions, is then used in the context of individual-based computer simulations to provide likelihood (Lk) estimates for fitness. This protocol can take up to five generations of experimental invasions and a few hours of computer processing time.
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Acknowledgements
I thank I. Gordo and H. Teotónio for advice in the development and implementation of this protocol. I also thank the reviewers for suggestions and comments, which markedly improved the manuscript. This work was only possible with the support of the Foundation for Science and Technology, Portugal (FCT Investigator program and EXPL/BIA-EFV/1211/2013) to I.M.C., and financial and technical support from Henrique Teotónio, through the Human Frontiers Science Program grant (RGP0045/2010).
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I.M.C. designed the protocol and wrote the simulation, the analysis script and the manuscript.
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Integrated supplementary information
Supplementary Figure 1 Effect of generation number on fitness estimation.
Different sets were simulated for 2, 3, 4, 4 and 7 generations and selection coefficients of 0, 0.001, 0.01, 0.05, 0.1, 0.2, 0.3 and 0.5, 30 replicates were used. Maximum likelihood (ML) estimates and –2lnLk confidence intervals were obtained to show how generation number affects the accuracy (a), “statistical” power (see definition below) (b) and precision (c) of the estimation procedure. For each parameter combination, 100 simulations were carried out. Each invasion started with two individuals in a resident population of 1000 individuals. Data from every generation was used. ML estimation is based on 107 simulations for each of 61 selection coefficients (form –0.1 to 0.5 in steps of 0.01). (a) The mean (symbols) and standard deviation (error bars) of estimated s (ŝ) are shown. (b) The estimation of “statistical” power is provided by the proportion of times the 'true' selection coefficient is found in the –2lnLk confidence interval (mean standard deviations are shown) reveals how the number of generations affects precision. The increase in number of possible trajectories with the number of generations results in a lower power and downward bias in the estimation of selection coefficients.
Supplementary information
Supplementary Figure 1
Effect of generation number on fitness estimation. (PDF 2994 kb)
Supplementary Table 1
Observed counts from an experiment starting with two invading individuals where competitions experiments ran for five generations and scoring was obtained for generations 3 through 5. (TXT 0 kb)
Supplementary Data
A script file in the R language which gives the implementation in R code of the invasion simulation and estimation of selection coefficients described in steps 24 through 31. (TXT 5 kb)
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Chelo, I. Experimental determination of invasive fitness in Caenorhabditis elegans. Nat Protoc 9, 1392–1400 (2014). https://doi.org/10.1038/nprot.2014.098
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DOI: https://doi.org/10.1038/nprot.2014.098
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