Coffee leaf rust infecting a green leaf.
Home » Coffee Plants Adapt Leaf Traits in Response to Fungal Disease, Study Finds

Coffee Plants Adapt Leaf Traits in Response to Fungal Disease, Study Finds

The study provides valuable insights into plant-pathogen interactions with implications for agriculture, ecology, and climate change resilience.

A new study published in PLOS One by Gagliardi and colleagues has shed light on how coffee plants adapt their leaf traits to cope with fungal diseases, an important insight into global patterns of functional trait variation in plants. The pathogen studied, coffee leaf rust (CLR), has caused coffee farmers billions of dollars of damage.

The research focused on intraspecific functional trait variation (ITV) — which is where individuals within a species express different traits in response to environmental factors. This variation is known to underpin plant responses to environmental variability, and two of the authors of the PLOS One study have examined the leaf economic spectrum in coffee, a measure of the trade-offs plants make between resource acquisition and resource conservation. However, little research has been done about how it changes in response to plant pathogens, which can have a significant impact on plant populations and biodiversity.

Using Coffea arabica cv. Caturra, a type of coffee plant, as a model species, Gagliardi and colleagues investigated the relationship between leaf traits and the severity of coffee leaf rust. Coffee leaf rust is a widespread fungal disease that affects coffee plants, posing a significant threat to coffee production globally.

The team measured key coffee leaf functional traits in an agroforestry system under contrasting management conditions, assessing how these traits changed in response to coffee leaf rust severity. They discovered that coffee plants exhibited significant intraspecific trait variability, which was mainly related to shade tree treatment and the position of leaves within the coffee canopy. Interestingly, the researchers found that coffee leaf rust severity increased in leaves with resource-conserving traits but observed greater resource-acquiring traits in leaves with visible signs of the disease. In their article, Gagliardi and colleagues write:

Our results provide evidence that plant functional traits and their relationships shift under pathogen pressures, representing among the first lines of evidence indicating that ITV [intraspecific trait variation] plays a role in mediating disease severity, specifically coffee leaf rust in a managed agroecosystem. In doing so, our work both advances our understanding of the functional ecology of a major crop pathogen and addresses a vital gap in our understanding of global patterns of functional trait variation. 

Gagliardi et al. 2023

By identifying this previously unexplored connection between leaf traits and pathogens, the research addresses a critical gap in our understanding of global patterns of functional trait variation in plants. The authors suggest that further investigation into the potential role of pathogens within established global trait relationships and spectra is warranted, with implications for both plant ecology and agricultural practices.

READ THE ARTICLE

Gagliardi, S., Avelino, J., Martin, A.R., Cadotte, M., Virginio Filho, E. de M. and Isaac, M.E. (2023) “Leaf functional traits and pathogens: Linking coffee leaf rust with intraspecific trait variation in diversified agroecosystems,” PLOS One, 18(4), p. e0284203. Available at: https://doi.org/10.1371/journal.pone.0284203.

Dale Maylea

Dale Maylea was a system for adding value to press releases. Then he was a manual algorithm for blogging any papers that Alun Salt thinks are interesting. Now he's an AI-assisted pen name. The idea being telling people about an interesting paper NOW beats telling people about an interesting paper at some time in the future, when there's time to sit down and take things slowly. We use the pen name to keep track of what is being written and how. You can read more about our relationship with AI.

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