Modelling belowground plant acclimation to low soil nitrogen - An eco-evolutionary approach
Chakrawal, A.; Mooney, S. J.; Colombi, T.
Show abstract
Increased root growth to access greater soil mineral nitrogen resources and increased root exudation to stimulate microbial mineralisation of soil organic nitrogen are widely observed plant acclimations to nitrogen limitation. However, their quantitative contribution to plant growth and ecosystem productivity remains largely elusive. Here, we present a novel optimality-based eco-evolutionary model in which plants dynamically regulate carbon partitioning between root growth and exudation to maximise their aboveground growth. Our simulations indicated that the dynamic availability of soil mineral and organic nitrogen as well as plant nitrogen demand and nitrogen uptake capacity shape optimal carbon partitioning between root growth and exudation. The simulated carbon allocation patterns aligned with empirical studies on belowground plant responses to varying soil nitrogen resources. Our eco-evolutionary approach represents a paradigmatic change in modelling plant nitrogen foraging, which is essential to generate hypotheses on optimal plant acclimation in future soil environments characterised by more erratic nitrogen availability.
Matching journals
The top 8 journals account for 50% of the predicted probability mass.
Similar papers in this journal
Similar papers in this journal
Similar papers in this journal
- Microbial community regulation of extracellular enzyme production can mediate patterns of particulate and mineral-associated organic matter accumulation in undersaturated soils 94%
- Microbial extracellular polysaccharide production and aggregate stability controlled by Switchgrass (Panicum virgatum) root biomass and soil water potential 93%
- Competition within low-density bacterial populations as an unexpected factor regulating carbon decomposition in bulk soil 93%
Similar papers in this journal
"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.