Abstract
Organismal fitness is relevant in many contexts in biology. The most meaningful experimental measure of fitness is competitive fitness, when two or more entities (e.g., genotypes) are allowed to compete directly. In theory, competitive fitness is simple to measure: an experimental population is initiated with the different types in known proportions and allowed to evolve under experimental conditions to a predefined endpoint. In practice, there are several obstacles to obtaining robust estimates of competitive fitness in multicellular organisms, the most pervasive of which is simply the time it takes to count many individuals of different types from many replicate populations. Methods by which counting can be automated in high throughput are desirable, but for automated methods to be useful, the bias and technical variance associated with the method must be (a) known, and (b) sufficiently small relative to other sources of bias and variance to make the effort worthwhile. The nematode Caenorhabditis elegans is an important model organism, and the fitness effects of genotype and environmental conditions are often of interest. We report a comparison of three experimental methods of quantifying competitive fitness, in which wild-type strains are competed against GFP-marked competitors under standard laboratory conditions. Population samples were split into three replicates and counted (1) "by eye" from a saved image, (2) from the same image using CellProfiler image analysis software, and (3) with a large particle flow cytometer (a "worm sorter"). From 720 replicate samples, neither the frequency of wild-type worms nor the among-sample variance differed significantly between the three methods. CellProfiler and the worm sorter provide at least a tenfold increase in sample handling speed with little (if any) bias or increase in variance.