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Updated: Jun 9, 2025

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Annotation of Plant Gene Function via Combined Genomics, Metabolomics and Informatics

Published on: June 17, 2012

Why ecologists struggle to predict coexistence from functional traits.

Jacob I Levine¹, Ruby An¹, Nathan J B Kraft²

¹Department of Ecology and Evolutionary Biology, Princeton University, Princeton, NJ, USA.

Trends in Ecology & Evolution

|October 31, 2024

Summary

This summary is machine-generated.

Functional traits in ecology may not predict species coexistence because complex process-informed metrics (PIMs) combine multiple traits. Future research should use mechanistic models to understand competition and coexistence.

Keywords:

coexistence functional traits mechanistic models niche differentiation resource competition

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Area of Science:

Ecology
Evolutionary Biology

Background:

Trait-based ecology aims for general ecological theories by focusing on functional traits instead of species names.
Functional traits have been proposed as key predictors of species coexistence, a fundamental ecological problem.

Purpose of the Study:

To investigate why functional traits have not yet provided clear evidence for predicting species coexistence.
To propose a new theoretical framework for understanding species coexistence based on process-informed metrics.

Main Methods:

Theoretical argument based on ecological principles.
Analysis of the relationship between functional traits, demographic characteristics, and species coexistence.

Main Results:

Coexistence often depends on process-informed metrics (PIMs) that integrate multiple traits and demographic factors.
The complex, non-intuitive nature of PIMs obscures direct links between individual functional traits and coexistence.

Conclusions:

Direct prediction of species coexistence from individual functional traits is challenging due to the emergent properties of PIMs.
Future trait-based coexistence research should integrate mechanistic competition models and PIMs for greater predictive power.