Pollen interference emerges as a property from agent-based modelling of pollen competition in Arabidopsis thaliana. Issue 2 (25th August 2022)

• Record Type: Journal Article
• Authors: Beckford, Charlotte; Ferita, Montana; Fucarino, Julie; Elzinga, David C; Bassett, Katherine; Carlson, Ann L; Swanson, Robert; Capaldi, Alex
• Editors: Long, Steve
• Is Part Of: In silico plants. Volume 4:Issue 2(2022)
• Publication Date: 2022-08-25
• Subjects: Agent-based modelling -- Arabidopsis -- interference competition -- mate choice -- non-random mating -- pollen competition; Plant physiology -- Periodicals; Botany -- Periodicals; Botany -- Mathematical models -- Periodicals; Crop science -- Periodicals; 580
• DOI: 10.1093/insilicoplants/diac016
• Languages: English
• ISSNs: 2517-5025

Abstract

Abstract: Differences in pollen performance, often revealed during pollen competition, have long been recognized as evolutionarily significant and agriculturally important. Though we have sophisticated models for the growth of individual pollen tubes, we have few robust models for larger-scale pollen competition, a process that has been linked with inbreeding avoidance, sexual selection, reproductive barrier reinforcement and speciation. Here we use existing data on pollen performance traits to develop an agent-based model of pollen competition. We calibrate our model parameters to empirical data found in the literature of seed siring proportions from mixed pollinations and pollen tube length distributions from single-accession pollinations. In this model, parameters that influence pollen tube movement and sensing of ovules were found to be primary factors in competition. Our model also demonstrates that interference competition emerges as a property of pollen competition, and suggests a potential mechanism for this phenomenon. This study integrates pollen performance measures with mathematical modelling conducted on a simplified and accessible system. This represents the first mechanistic agent-based model for pollen competition. Our model may be extended to predict seed siring proportions for other accessions of Arabidopsis thaliana given data on their pollen performance traits.