Premium
A computational‐experimental framework for mapping plant coexistence
Author(s) -
Jiang Libo,
Shi Chaozhong,
Ye Meixia,
Xi Feifei,
Cao Yige,
Wang Lina,
Zhang Miaomiao,
Sang Mengmeng,
Wu Rongling
Publication year - 2018
Publication title -
methods in ecology and evolution
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.425
H-Index - 105
ISSN - 2041-210X
DOI - 10.1111/2041-210x.12981
Subject(s) - pairwise comparison , quantitative trait locus , trait , biology , epistasis , genetic architecture , computational biology , ecology , evolutionary biology , computer science , genetics , artificial intelligence , gene , programming language
Despite its importance in understanding the emergent property of plant communities and ecosystems, the question of how genes govern species coexistence has proven very difficult to answer. In a plant community that behaves like a network game, each coexisting plant strives to maximize its fitness by pursuing a “rational self‐interest” strategy in a way that affects the decisive reaction of other plants. We integrated this principle founding game theory into a quantitative trait locus ( QTL ) mapping paradigm, on which to derive a game mapping model for the genetic landscaping of how plants coexist. The new mapping model dissolves the phenotype of each plant in a community into two components, autonomous phenotype, characteristic of the plant's intrinsic ability expected to be expressed in isolation, and social phenotype, determined by game theory‐guided interactions between the plant and other members. We implemented the new model into a competition experiment by pairwise growing 116 recombinant inbred lines of Arabidopsis. Most QTL s detected from this experiment reside within biologically meaningful genes, including SCL 6 , CAR 6 , CLPB 1 , ALDH 5F1 , and EMB 2217 , which may mediate competitive interactions in unique ways. The new model can chart more detailed genetic architecture of plant community structure and diversity by extracting the genetic effects of QTL s on social phenotypes. Our model lays the groundwork for predicting and managing dynamic relationships between biodiversity and ecosystem functioning from co‐species genotypes.