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Inferring ecological selection from multidimensional community trait distributions along environmental gradients
Elina Kaarlejärvi1, Malcolm Itter2, Tiina Tonteri3
1Research Centre for Ecological Change, Organismal and Evolutionary Biology Research Programme, Faculty of Biological and Environmental Sciences, University of Helsinki, Helsinki, Finland.
Ecology
|July 26, 2024
Summary
Ecological selection shapes plant communities by favoring specific traits. This study introduces a new framework to detect selection types, revealing how directional, stabilizing, and divergent selection influence boreal forest plant communities over time.
Area of Science:
- Ecology
- Evolutionary Biology
- Plant Science
Background:
- Community assembly is driven by ecological selection, influencing biodiversity and ecosystem services.
- Understanding trait-based selection is key to predicting community dynamics and responses to environmental change.
Purpose of the Study:
- To develop and apply a predictive framework for quantifying multidimensional community trait spaces.
- To identify and characterize ecological selection types acting on plant communities along environmental gradients.
Main Methods:
- Developed a model-based predictive framework to analyze community trait distributions.
- Applied the framework to over 3600 boreal forest understory plant communities.
- Quantified multidimensional trait distributions to identify ecological selection types.
Main Results:
- Directional, stabilizing, and divergent selection were identified as significant drivers of community trait distributions.
- Empirical evidence for divergent selection in a natural system was provided.
- Demonstrated that selection types on traits can change over time.
Conclusions:
- The developed framework effectively identifies key traits under ecological selection.
- The findings enhance our understanding of the abiotic and biotic drivers shaping community dynamics.
- Provides a novel approach for detecting ecological selection processes in natural systems.
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