Predicting the adaptive responses of biodiverse plant communities using functional trait evolution

Joshi, J., Hofhansl, F. ORCID: https://orcid.org/0000-0003-0073-0946, Singh, S., Stocker, B., Vignal, T., Brännström, Å., Franklin, O. ORCID: https://orcid.org/0000-0002-0376-4140, Blanco, C., Aleixo, I., Lapola, D., Prentice, I., & Dieckmann, U. ORCID: https://orcid.org/0000-0001-7089-0393 (2023). Predicting the adaptive responses of biodiverse plant communities using functional trait evolution. In: Ecological Society of American 2023 Annual Meeting, 6-11 August 2023, Portland.

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Abstract

Climate change consists of synergistic changes in a wide range of environmental conditions, characterized by elevated CO2, higher mean temperatures, and higher climate variability. While elevated CO2 concentrations may potentially increase the productivity of some ecosystems, it has been argued that nutrient limitation, increased respiration, and increased mortality may dampen or even negate these productivity gains. The capacity of global forests to adjust to such synergistic environmental changes depends on their functional diversity and the ecosystem’s adaptive capacity.
The Plant-FATE eco-evolutionary model describes vegetation responses to altered environmental conditions, including CO2 concentrations, temperature, and water limitation. It represents functional diversity by modelling species as points in trait space and incorporates ecosystem adaptations at three levels: 1) to model acclimation of plastic traits of individual plants, we leverage the power of eco-evolutionary optimality principles, 2) to model shifts in species composition via demographic changes and species immigration, we implement a trait-size-structured demographic vegetation model, and 3) to model the long-term genetic evolution of species, we have developed new evolutionary theory for trait-size-structured communities.
First, we show that with just a few calibrated parameters, the Plant-FATE model accurately predicts the fluxes of CO2 and water, size distributions, and trait distributions for a tropical wet site in the Amazon Forest. Second, we show that under elevated CO2 our model predictions are broadly consistent with observations, namely: an increase in leaf area, productivity and biomass, and a decrease in stomatal conductance and photosynthetic capacity. Third, we show that CO2 and nutrient fertilization both drive changes in community composition towards fast life-histories, and that competition drives the system in a direction opposite to what is optimal for individual plants.
Our novel eco-evolutionary vegetation modelling framework combines optimality-based modelling for simulating biophysical acclimation, demographic modelling for community composition changes, and evolutionary dynamics for long-term adaptation. It thus opens a new path for predicting multi-timescale ecosystem dynamics and their responses to global change.

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