Abstract
PREMISE: Variation in seed traits is common within and among populations of plant species and often has ecological and evolutionary implications. However, due to the time-consuming nature of manual seed measurements and the level of variability in imaging techniques, quantifying and interpreting the extent of seed variation can be challenging. METHODS: We developed a standardized high-throughput technique to measure seed number, as well as individual seed area and color, using a derived empirical scale to constrain area in Arabidopsis thaliana, Brassica rapa, and Mimulus guttatus. We develop a specific rational model using seed area measured at various spatial scales relative to the pixel count, observing the asymptotic value of the seed area as the modeled number of pixels approaches infinity. RESULTS: We found that our model has high reliability in estimating seed traits and efficiently processes large numbers of images, facilitating the quantification of seed traits in studies with large sample sizes. DISCUSSION: This technique facilitates consistency between imaging sessions and standardizes the measurement of seed traits. These novel advances allow researchers to directly and reliably measure seed traits, which will enable tests of the ecological and evolutionary causes of their variation.