Plant Functional Acclimation and Trait Evolution (PlantFATE)

Unveiling the Mysteries of Forest Resilience

Tropical forests play a vital role in mitigating climate change. However, their ability to withstand the impacts of a changing climate relies on complex interactions operating at various temporal and organizational scales. To delve into the resilience of these magnificent ecosystems, PlantFATE combines three crucial aspects: short-term physiological acclimation, mid-term demographic changes, and long-term evolution of genetic traits.

Key Features of PlantFATE

1. Eco-evolutionary Optimality: PlantFATE employs eco-evolutionary optimality principles to model short-term acclimation of physiological processes. This approach captures trade-offs and simplifies representations of photosynthesis, leaf economics, and hydraulics.
2. Trait-based Functional Diversity: Departing from fixed plant functional types, PlantFATE embraces a multidimensional representation of functional diversity using a trait-based approach. Each species is represented as a narrow distribution centered around a unique combination of genetic traits, allowing for a more realistic representation of both intraspecific and interspecific variation.
3. Size-structured Competition: PlantFATE incorporates size structure, recognizing the importance of size-dependent competition, particularly for resources like light. This enables the modeling of continuous acclimation of physiology as trees grow from the understorey to the canopy.
4. Community-level Trait Evolution: Unlike traditional models, PlantFATE predicts long-term evolution of species' genetic traits using an evolutionary dynamics algorithm. Species interactions, competition, and feedbacks with the environment inform the prediction of evolutionarily stable community-level properties.

First Use: Exploring Elevated CO2 Effects in the Amazon Forest 

PlantFATE's maiden voyage took place in the heart of the Amazon. By calibrating the model using data from a hyperdiverse terra-firme forest, we investigated the ecosystem's response to elevated atmospheric CO2 concentrations.

The model revealed fascinating insights: elevated CO2 led to increased productivity, leaf area, and aboveground biomass. However, nutrient-deprived soils dampened these effects, as trees allocated more carbon to the rhizosphere to overcome nutrient limitations.

An unexpected finding emerged as increased productivity intensified competition for light. This drove a significant shift in community composition towards fast-growing but short-lived species characterized by lower wood densities. The transition reduced the carbon residence time of woody biomass, potentially rendering the Amazon Forest more vulnerable to future climatic extreme events.

PlantFATE's predictions aligned seamlessly with observed forest structure and function, validating its accuracy. The model successfully captured size and trait distributions, offering a comprehensive understanding of the intricate dynamics of the Amazon ecosystem.

Next steps

In an ongoing project, we are coupling PlantFATE with CWATM to build a coupled model of vegetation and soil-water dynamics. The coupled model will be applied to predict ecosystem services in India, Israel, and Brazil. For further information on how the model will be applied to different case study regions please see the RESIST project.

Acknowledgements

The development of Plant-FATE started with a project funded by the European Union's Horizon 2020 Research and Innovation program via a Marie Skłodowska-Curie Actions Individual Fellowship awarded to Jaideep Joshi under the supervision of Ulf Dieckmann and in collaboration with Florian Hofhansl. Initial model development, particularly on the population dynamics component, was inspired by the eco-evolutionary model "Plant" developed by Daniel Falster, Ulf Dieckmann, and colleagues.