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Modeling photosynthetic resource allocation connects physiology with evolutionary environments

The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to wh...

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Detalles Bibliográficos
Autores principales: Sundermann, Esther M., Lercher, Martin J., Heckmann, David
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8342476/
https://www.ncbi.nlm.nih.gov/pubmed/34354112
http://dx.doi.org/10.1038/s41598-021-94903-0
Descripción
Sumario:The regulation of resource allocation in biological systems observed today is the cumulative result of natural selection in ancestral and recent environments. To what extent are observed resource allocation patterns in different photosynthetic types optimally adapted to current conditions, and to what extent do they reflect ancestral environments? Here, we explore these questions for C(3), C(4), and C(3)–C(4) intermediate plants of the model genus Flaveria. We developed a detailed mathematical model of carbon fixation, which accounts for various environmental parameters and for energy and nitrogen partitioning across photosynthetic components. This allows us to assess environment-dependent plant physiology and performance as a function of resource allocation patterns. Models of C(4) plants optimized for conditions experienced by evolutionary ancestors perform better than models accounting for experimental growth conditions, indicating low phenotypic plasticity. Supporting this interpretation, the model predicts that C(4) species need to re-allocate more nitrogen between photosynthetic components than C(3) species to adapt to new environments. We thus hypothesize that observed resource distribution patterns in C(4) plants still reflect optimality in ancestral environments, allowing the quantitative inference of these environments from today’s plants. Our work allows us to quantify environmental effects on photosynthetic resource allocation and performance in the light of evolutionary history.